Meta-analytic evidence that sexual selection improves population fitness
Supplementary Information
Justin G. Cally1, Devi Stuart-Fox1 and Luke Holman1
1The University of Melbourne
library(knitr)
library(pander)
library(tidyr)
library(compute.es)
library(metafor)
library(plyr)
library(dplyr)
library(lme4)
library(car)
library(forestplot)
library(ggplot2)
library(ggthemes)
library(kableExtra)
library(ggrepel)
library(reshape2)
library(RColorBrewer)
library(ggridges)
library(rstan) #Note that installation requires some effort: dependency for brms
#devtools::install_github("paul-buerkner/brms")
library(brms)
library(backports) #seems to be a dependency
library(bayesplot)
#devtools::install_github("mvuorre/brmstools")
library(brmstools)
library(metaAidR) # install.packages("metaAidR")
library(cowplot)
#devtools::install_github("eclarke/ggbeeswarm")
library(ggbeeswarm)
library(gridExtra)
source("I2_function.R") #Adapted function for obtaining I2 with CIs
source("Vdodge_function.R") # nice function for ggplot
source("Tidy_functions_for_brms.R") #Tidy functions for making model tables for brmsSupplementary Methods
Our aim was to investigate the effects of sexual selection on population fitness by conducting a meta-analysis on studies that measured fitness related outcomes after experimentally evolving a population under varying levels of opportunity for sexual selection. Here we describe the process of the literature search, data extraction, effect size calculation, formulation of multilevel models and assessing publication bias. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) as a guide during this meta-analysis. The repository used to formulate this document can be found here: https://github.com/JustinCally/SexualSelection
Literature Search
The literature search was conducted under the following conditions:
We searched ISI Web of Science and Scopus on 9th June 2017. The two search engines produded a somewhat different set of papers (see PRISMA Figure in manuscript).
Studies were restricted to those from peer-reviewed and in the English language.
We devised a search strategy that sought to find studies which manipulated the presence or strength of sexual selection using experimental evolution, and then measured some proxy of population fitness. As such the search terms were as follows:
ISI Web of Science
We used the following search on ISI Web of Science:
Topic (TS) = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”
AND
Topic (TS) = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* OR | “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR “Novel | Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”
AND
Topic (TS) = Generations OR “Experimental evolution” OR “mutation load”
AND
Research Area (SU) = “Evolutionary Biology”
Scopus
We used the following search on Scopus:
TITLE-ABS-KEY = “Sexual Selection” OR Promisc* OR Monogam* OR Polygam* OR Polyandr* OR Polygyn* OR “Mate choice”
AND
TITLE-ABS-KEY = Fitness OR “Population Fitness” OR Deleterious OR “Male Strength” OR Fecund* OR Viability OR Productiv* OR “Reproductive Success” OR “Reproductive Rate” OR Surviv* | OR “Development Rate” OR Extinct* OR “Competitive Success” OR Mortality OR Mass OR “Body Size” OR “Wing Size” OR Emergence OR Mating Rate OR “Mating Propensity” OR Adapt* OR | “Novel Environment” OR “Sexual Conflict” OR “Sexual Antagonis*”
AND
TITLE-ABS-KEY = Generations OR “Experimental evolution” OR “mutation load”
Additions to the Literature Search
In addition to studies found from the literature search we also included three relevant studies that we found, which were not picked up in the subsequent formal searches (Partridge 1980a; Price, Hurst, and Wedell 2010a; Savic Veselinovic et al. 2013a) see PRISMA Figure in manuscript.
Inclusion/Exclusion criteria
After removing duplicates papers recovered from both Web of Science and Scopus, we read the titles and abstracts of the remaining 1015 papers, and removed papers that were not relevant (typically because they were not an empirical study using experimental evolution). This left 130 papers, for which we read the full text and applied the following selection criteria:
- (1: Study Design) The study was an experimental evolution study lasting >1 generation
- (2: Population) a) The study was conducted using an animal species that was b) dioecious
- (3: Intervention and Control) The study experimentally manipulated the strength of sexual selection for at least one generation (e.g. via enforced monogamy or an altered sex ratio)
- (4: Outcomes) The study measured a trait that we judged to be a potential correlate of population fitness.
Criterion 4 is somewhat subjective, because there is rarely enough data justify the assumption that a particular trait is (or is not) correlated with population fitness. We therefore relied on our best judgement when deciding which studies to exclude (see Supplementary Table 1). The inclusion/exlusion critera as applied to each study are detailed in Supplementary Table 2.
Supplementary Table 1: We classified each of the twenty fitness outcomes into three broad groups – direct, indirect and ambiguous – based on the established link with population fitness, the directionality of the measure. Here we detailed how these outcomes were measured in the primary studies.
read.csv('data/outcome.descriptions.csv', fileEncoding="UTF-8") %>% select(-Examples) %>%
kable("html") %>% kable_styling() %>%
scroll_box(width = "100%", height = "500px")| Outcome | Classification | Explanation | Citation |
|---|---|---|---|
| Behavioural Plasticity | Ambiguous | Female kicking against male harassment in different sociosexual contexts for the beetle Callosobruchus maculatus. | (Lieshout, McNamara, and Simmons 2014) |
| Body Size | Ambiguous | Body size was often recorded to correct for other morphometric traits (e.g. body condition, strength or testes weight). It was measured as either length or dry mass. | (Simmons and Garcia-Gonzalez 2008; Almbro and Simmons 2014) |
| Development Rate | Ambiguous | Egg-to adult development time was recorded in several studies and often alongside traits other life-history traits suspected to impact fitness. | (Fricke and Arnqvist 2007; Hollis and Kawecki 2014; McKean and Nunney 2008) |
| Early Fecundity | Ambiguous | Early fecundity was measured (alongside lifetime fecundity) as a life-history trait that may impact lifetime reproductive success. It was defined as either the total or proportional reproductive output in earlier stages of maturity (e.g. within the first 7 days). | (Crudgington, Fellows, and Snook 2010; Tilszer et al. 2006) |
| Immunity | Ambiguous | Phenoloxidase (PO) activity or parasite load. | (McKean and Nunney 2008; Hangartner et al. 2015; Hangartner et al. 2013; McNamara, Lieshout, and Simmons 2014) |
| Male Attractiveness | Ambiguous | Inferred from female preference tests in mice and male ornament size (coloration) in guppies. | (Firman 2014; Nelson et al. 2013; Pélabon et al. 2014) |
| Male Reproductive Success | Ambiguous | Measured as the total progeny sired in males. | (Hollis and Kawecki 2014) |
| Mating Duration | Ambiguous | Mating duration may have variable fitness impacts based on the soiciosexual conditions and extent of sexual conflict. It may be beneficial to have longer mating bouts for a male in a competitive environment however it may be damaging for a female under benign conditions. | (Lieshout, McNamara, and Simmons 2014; Edward, Fricke, and Chapman 2010; L. Michalczyk, Millard, Martin, Lumley, Emerson, and Gage 2011; Nandy et al. 2013) |
| Pesticide Resistance | Ambiguous | Pesticide resistance was measured both in the presence and absence of pesticides for the insect Tribolium castaneum, it was a binary measure of resistance to knockdown that was incorporated into generalized linear mixed models. | (Jacomb, Marsh, and Holman 2016) |
| Mutant Frequency | Indirect | Allele and mutant frequency measured at the population level. | (Arbuthnott and Rundle 2012; Hollis, Fierst, and Houle 2009) |
| Body Condition | Indirect | Mean body weight of Onthophagus Taurus adjusted for body size (thorax width). | (Simmons and Garcia-Gonzalez 2008) |
| Fitness Senescence | Indirect | Rate of decline in survival probability across lifespan. | (Hollis and Kawecki 2014; Archer et al. 2015) |
| Lifespan | Indirect | Longevity or survival across the entire lifespan or from a given point once under stressful conditions, such as starvation or after females mated in different operational sex ratios. | (Wigby and Chapman 2004; Martin and Hosken 2003) |
| Mating Frequency | Indirect | Number of mounts by males on females in Tribolium castaneum and Drosophila melanogaster. | (Hangartner et al. 2015; L. Michalczyk, Millard, Martin, Lumley, Emerson, and Gage 2011) |
| Mating Latency | Indirect | Time taken for a male to undertake their first copulatory mount from the time of being first put together with female/s. | (Lieshout, McNamara, and Simmons 2014; Hollis and Kawecki 2014; Edward, Fricke, and Chapman 2010; L. Michalczyk, Millard, Martin, Lumley, Emerson, and Gage 2011; Nandy et al. 2013) |
| Mating Success | Indirect | Male mating success measured males ability to successfully mate with females. Often in the presence of other males. Mating success of a male against a rival male can be determined via competing a focal male against an irradiated (infertile) competitor, the resulting proportion of eggs hatching are then determined to be a measure of the focal males success. Mating success also included measurements of mating capacity where males were continually presented with females until exhausted, the number of sequential matings were then recorded and mating offence and defence ability. The mating offence and defence capability was estimated via paternity share of a male when in the first mating position (P1) or the second (P2). | (Tilszer et al. 2006; Nandy et al. 2013; Debelle, Ritchie, and Snook 2016; McGuigan, Petfield, and Blows 2011; Crudgington et al. 2009) |
| Strength | Indirect | Male pulling strength in the dung beetle, Onthophagus Taurus, measured by attaching weights and measuring the weight the beetle was able to pull. | (Almbro and Simmons 2014) |
| Ejaculate Quality and Production | Indirect | Sperm quality and production grouped multiple measured outcomes together, both within a study (28) and during the meta-analysis. This includes sperm size, plug size, testes size, soporific effect, ejaculate weight, accessory gland size, motility, path velocity, sperm longevity. | (Lieshout, McNamara, and Simmons 2014; Firman and Simmons 2010; Fritzsche et al. 2014; L. Gay, Hosken, et al. 2009; McNamara et al. 2016) |
| Extinction Rate | Direct | Extinction rate was measured at the population level, either via recording the proportion of extinct lines after a given number of generations or via analysis of extinction rate over consecutive generations via the Weibull baseline hazard distribution. | (Jarzebowska and Radwan 2010; Plesnar-Bielak et al. 2012; Lumley et al. 2015) |
| Offspring Viability | Direct | Offspring viability, also recorded as egg-to-adult viability or embryonic viability, was measured as survival to a certain age (e.g. 1 year or life stage and hatching). | (Pélabon et al. 2014; Plesnar, Konior, and Radwan 2011) |
| Female Reproductive Success | Direct | A measure of the number of offspring produced by an individual female. Reproductive success was also described as fecundity, number of offspring produced, fertility in females and proportion. | (Edward, Fricke, and Chapman 2010; Firman 2011; Bernasconi and Keller 2001) |
| Both Reproductive Success | Direct | Similar to Female Reproductive Success, however measurements of offspring produced are sourced from a focal male-female pair, of which either male or female may limit the number of offspring produced. | (Lumley et al. 2015) |
Supplementary Table 2: A study was deemed eligible for inclusion in the meta-analysis if it met all four criteria discussed above (referred to by their numbers, 1-4, in this table). We went through these four criteria in a step-wise fashion, for each of the 130 studies for which we read the full text, and noted the first criterion that was failed (if any). The table provides notes on our inclusion/exclusion decisions.
read.csv('data/Eligibility Workbook.csv', fileEncoding="UTF-8") %>%
mutate(Citation = paste0('[@',Citation,']')) %>%
kable("html") %>% kable_styling() %>%
scroll_box(width = "100%", height = "500px")| Citation | Authors | Year | Title | Study.Design | Population | Intervention.and.Control | Outcomes | Included | Exclusion.Reason | Notes |
|---|---|---|---|---|---|---|---|---|---|---|
| (Aguirre and Marshall 2012) | Aguirre, J. D. and D. J. Marshall | 2012 | Does Genetic Diversity Reduce Sibling Competition? | No | No | 1 | Not an experimental evolution study: full-sib/half-sib breeding design | |||
| (Ahuja and Singh 2008) | Ahuja, A. and R. S. Singh | 2008 | Variation and evolution of male sex combs in Drosophila: Nature of selection response and theories of genetic variation for sexual traits | No | No | 1 | Artificial selection was conducted | |||
| (Almbro and Simmons 2014) | Almbro, M. and L. W. Simmons | 2014 | Sexual Selection Can Remove an Experimentally Induced Mutation Load | Yes | Yes | Yes | Yes | Yes | Male strength is important in male-male competition | |
| (Amitin and Pitnick 2007) | Amitin, E. G. and S. Pitnick | 2007 | Influence of developmental environment on male- and female-mediated sperm precedence in Drosophila melanogaster | Yes | Yes | No | No | 3 | Larval density was the intervention: not strength of sexual selection | |
| (Antolin et al. 2003) | Antolin, M. F., P. J. Ode, G. E. Heimpel, R. B. O’Hara and M. R. Strand | 2003 | Population structure, mating system, and sex-determining allele diversity of the parasitoid wasp Habrobracon hebetor | No | No | 1 | Not experimental evolution: Lab rearing of wild populations with eventual genetic analysis | |||
| (Arbuthnott et al. 2014) | Arbuthnott, D., E. M. Dutton, A. F. Agrawal and H. D. Rundle | 2014 | The ecology of sexual conflict: ecologically dependent parallel evolution of male harm and female resistance in Drosophila melanogaster | Yes | Yes | No | No | 3 | Intervention was either ethanol or cadmium mixture | |
| (Arbuthnott and Rundle 2012) | Arbuthnott, D. and H. D. Rundle | 2012 | Sexual Selection Is Ineffectual or Inhibits the Purging of Deleterious Mutations in Drosophila Melanogaster | Yes | Yes | Yes | Yes | Yes | Natural selection acted against known deleterious alleles, thus indicate fitness aspect | |
| (Arbuthnott and Rundle 2014) | Arbuthnott, D. and H. D. Rundle | 2014 | Misalignment of natural and sexual selection among divergently adapted Drosophila melanogaster populations | Yes | Yes | No | No | 3 | Intervention was either ethanol or cadmium mixture | |
| (Archer et al. 2015) | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Sex-specific effects of natural and sexual selection on the evolution of life span and ageing in Drosophila simulans | Yes | Yes | Yes | Yes | Natural selection was measured simultanous and thus provides measurement of suitability of phenotype to environment | ||
| (Artieri et al. 2008) | Artieri, C. G., W. Haerty, B. P. Gupta and R. S. Singh | 2008 | Sexual selection and maintenance of sex: Evidence from comparisons of rates of genomic accumulation of mutations and divergence of sex-related genes in sexual and hermaphroditic species of Caenorhabditis | No | No | 1 | Comparative genomic approach | |||
| (Bacigalupe et al. 2007) | Bacigalupe, L. D., H. S. Crudgington, F. Hunter, A. J. Moore and R. R. Snook | 2007 | Sexual conflict does not drive reproductive isolation in experimental populations of Drosophila pseudoobscura | Yes | Yes | Yes | No | No | 4 | Viability and sterility were measured as well as mating speed, however these were in crosses, refer to 2008 study for beater outcomes |
| (Bacigalupe et al. 2008) | Bacigalupe, L. D., H. S. Crudgington, J. Slate, A. J. Moore and R. R. Snook | 2008 | Sexual selection and interacting phenotypes in experimental evolution: A study of Drosophila pseudoobscura mating behavior | Yes | Yes | Yes | Yes | No | Data not suitable | Mating speed cited as a measure of fitness. Because of the crossses the data is not able to be extracted to an effect size that is comprable to other studies |
| (Barbosa et al. 2012) | Barbosa, M., S. R. Connolly, M. Hisano, M. Dornelas and A. E. Magurran | 2012 | Fitness consequences of female multiple mating: A direct test of indirect benefits | No | No | 1 | Measures multiple mating not experimental evolution with sexual selection treatments | |||
| (Bernasconi and Keller 2001) | Bernasconi, G. and L. Keller | 2001 | Female polyandry affects their sons’ reproductive success in the red flour beetle Tribolium castaneum | Yes | Yes | Yes | Yes | Yes | Polyandry was done sequentially with postcop mate choice. | |
| (Bielak et al. 2014) | Bielak, A. P., A. M. Skrzynecka, K. Miler and J. Radwan | 2014 | Selection for alternative male reproductive tactics alters intralocus sexual conflict | No | No | 1 | Artificial selection was conducted | |||
| (Blows 2002) | Blows, M. W. | 2002 | Interaction between natural and sexual selection during the evolution of mate recognition | Yes | Yes | Yes | No | No | 4 | Hybrid Drosophilia used, indirect fitness was measured (mate recognition system) |
| (Brommer et al. 2012) | Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman | 2012 | Interactions between Genotype and Sexual Conflict Environment Influence Transgenerational Fitness in Drosophila Melanogaster | Yes | Yes | Yes | Yes | Yes | Multiple males but only one at a time: still is post copulatory SS, so included | |
| (Castillo et al. 2015) | Castillo, D. M., M. K. Burger, C. M. Lively and L. F. Delph | 2015 | Experimental evolution: Assortative mating and sexual selection, independent of local adaptation, lead to reproductive isolation in the nematode Caenorhabditis remanei | Yes | Yes | No | No | 3 | No SS lines | |
| (Cayetano et al. 2011) | Cayetano, L., A. A. Maklakov, R. C. Brooks and R. Bonduriansky | 2011 | Evolution of Male and Female Genitalia Following Release from Sexual Selection | Yes | Yes | Yes | No | No | 4 | Conflict / burdensome and defensive / offensive traits have fitness costs and benefits: Removing as too difficult to see clear fitness of measurements |
| (Chandler, Ofria, and Dworkin 2013) | Chandler, C. H., C. Ofria and I. Dworkin | 2013 | Runaway Sexual Selection Leads to Good Genes | Yes | No | No | 2a | Digital organisms used | ||
| (Chenoweth et al. 2015) | Chenoweth, S. F., N. C. Appleton, S. L. Allen and H. D. Rundle | 2015 | Genomic Evidence that Sexual Selection Impedes Adaptation to a Novel Environment | Yes | Yes | Yes | No | No | 4 | Alongside direct fitness, SNPs also used. This paper reports SNPs while Rundle (2006) reports fitness measures. Thus data is extracted from that paper, not this one |
| (Chenoweth et al. 2007) | Chenoweth, S. F., D. Petfield, P. Doughty and M. W. Blows | 2007 | Male choice generates stabilizing sexual selection on a female fecundity correlate | No | No | 1 | Behavioural mate choice experiment | |||
| (Chenoweth, Rundle, and Blows 2008) | Chenoweth, S. F., H. D. Rundle and M. W. Blows | 2008 | Genetic constraints and the evolution of display trait sexual dimorphism by natural and sexual selection | Yes | Yes | Yes | No | No | 4 | Natural selection was also measured and CHCs provide an indirect fitness aspect but too difficult to compare (CHCs were not used as outcome in this meta-analysis) |
| (Chenoweth, Rundle, and Blows 2010) | Chenoweth, S. F., H. D. Rundle and M. W. Blows | 2010 | Experimental evidence for the evolution of indirect genetic effects: changes in the interaction effect coefficient, psi (_), due to sexual selection | Yes | Yes | Yes | No | No | 4 | CHCs may provide indirect fitness aspect but are very difficult measures to compare or turn into effect sizes |
| (Crudgington et al. 2005) | Crudgington, H. S., A. P. Beckerman, L. Brustle, K. Green and R. R. Snook | 2005 | Experimental removal and elevation of sexual selection: Does sexual selection generate manipulative males and resistant females? | Yes | Yes | Yes | Yes | Yes | ||
| (Crudgington et al. 2009) | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Experimental Manipulation of Sexual Selection Promotes Greater Male Mating Capacity but Does Not Alter Sperm Investment | Yes | Yes | Yes | Yes | Yes | Appears to measure more direct and indirect outcomes | |
| (Crudgington, Fellows, and Snook 2010) | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Increased opportunity for sexual conflict promotes harmful males with elevated courtship frequencies | Yes | Yes | Yes | Yes | Yes | ||
| (Debelle, Ritchie, and Snook 2016) | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Sexual selection and assortative mating: an experimental test | Yes | Yes | Yes | Yes | Yes | ||
| (Demont et al. 2014) | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Experimental Removal of Sexual Selection Reveals Adaptations to Polyandry in Both Sexes | Yes | Yes | Yes | Yes | Yes | ||
| (Edward, Fricke, and Chapman 2010) | Edward, D. A., C. Fricke and T. Chapman | 2010 | Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection | Yes | Yes | Yes | Yes | Yes | ||
| (Fava 1975) | Fava, G. | 1975 | Studies on the selective agents operating in experimental populations of Tisbe clodiensis (Copepoda, Harpacticoida) | Yes | Yes | No | No | 3 | No difference in SS between treatments: Instead different genotype frequencies. | |
| (Firman 2011) | Firman, R. C. | 2011 | Polyandrous females benefit by producing sons that achieve high reproductive success in a competitive environment | Yes | Yes | Yes | Yes | Yes | It looks like post copulatory selection was used here | |
| (Firman 2014) | Firman, R. C. | 2014 | Female social preference for males that have evolved via monogamy: evidence of a trade-off between pre- and post-copulatory sexually selected traits? | Yes | Yes | Yes | Yes | Yes | The outcome measured was female preference and male scent marking rate. Likely to have a role in fitness but not explicitly stated | |
| (Firman, Cheam, and Simmons 2011) | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Sperm competition does not influence sperm hook morphology in selection lines of house mice | Yes | Yes | Yes | Yes | Yes | Sperm quality was measured | |
| (Firman et al. 2015) | Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons | 2015 | Evolutionary change in testes tissue composition among experimental populations of house mice | Yes | Yes | Yes | Yes | Yes | Amount of sperm producing tissue was measured as it provides an advantage in sperm competition | |
| (Firman et al. 2014) | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | The Coevolution of Ova Defensiveness with Sperm Competitiveness in House Mice | Yes | Yes | Yes | Yes | Yes | Ova defensivenenss can bias fertilization to a more specific type of sperm and thus be a fitness adavantage | |
| (Firman and Simmons 2010) | Firman, R. C. and L. W. Simmons | 2010 | Experimental Evolution of Sperm Quality Via Postcopulatory Sexual Selection in House Mice | Yes | Yes | Yes | Yes | Yes | Polygamous lines have only post-copulatory selection | |
| (Firman and Simmons 2011) | Firman, R. C. and L. W. Simmons | 2011 | Experimental evolution of sperm competitiveness in a mammal | Yes | Yes | Yes | Yes | Yes | Sperm competition is a fitness advantage | |
| (Firman and Simmons 2012) | Firman, R. C. and L. W. Simmons | 2012 | Male house mice evolving with post-copulatory sexual selection sire embryos with increased viability | Yes | Yes | Yes | Yes | Yes | Post cop SS used | |
| (Fricke, Andersson, and Arnqvist 2010) | Fricke, C., C. Andersson and G. Arnqvist | 2010 | Natural selection hampers divergence of reproductive traits in a seed beetle | Yes | Yes | Yes | No | No | 4 | Could not use the broad outcome of reproductive characteristics as it is not directional |
| (Fricke and Arnqvist 2007) | Fricke, C. and G. Arnqvist | 2007 | Rapid adaptation to a novel host in a seed beetle (Callosobruchus maculatus): The role of sexual selection | Yes | Yes | Yes | Yes | Yes | Post cop SS used | |
| (Fritzsche, Booksmythe, and Arnqvist 2016) | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Sex Ratio Bias Leads to the Evolution of Sex Role Reversal in Honey Locust Beetles | Yes | Yes | Yes | Yes | Yes | Male bias and female bias setups without monogamus/lack of SS | |
| (Fritzsche et al. 2014) | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Female, but not male, nematodes evolve under experimental sexual coevolution | Yes | Yes | Yes | Yes | Yes | Male bias and female bias setups without monogamus/lack of SS | |
| (Garcia-Gonzalez, Yasui, and Evans 2015) | Garcia-Gonzalez, F., Y. Yasui and J. P. Evans | 2015 | Mating portfolios: bet-hedging, sexual selection and female multiple mating | Yes | Yes | Yes | Yes | No | Data not suitable | Experiments run alongside bet-hedging, perhaps confounding and not able to be placed alongside other studies in this meta-analysis |
| (L. Gay, Eady, et al. 2009) | Gay, L., P. E. Eady, R. Vasudev, D. J. Hosken and T. Tregenza | 2009 | Does reproductive isolation evolve faster in larger populations via sexually antagonistic coevolution? | Yes | Yes | No | No | 3 | Generations of monoandry were replaced by polyandry (not done simultaneously ), Not sure whether the monogamous lines were maintained. This experiment was focussed on reproductive isolation anyway | |
| (Gay et al. 2011) | Gay, L., D. J. Hosken, P. Eady, R. Vasudev and T. Tregenza | 2011 | The Evolution of Harm-Effect of Sexual Conflicts and Population Size | Yes | Yes | No | No | 3 | Generations of monoandry were replaced by polyandry (not done simultaneously ), Not sure whether the monogamous lines were maintained. Also, did not directly look at SS+ vs SS- | |
| (L. Gay, Hosken, et al. 2009) | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Sperm competition and maternal effects differentially influence testis and sperm size in Callosobruchus maculatus | Yes | Yes | Yes | Yes | Yes | Appears to be direct comparison bw monogamous and polygamous structures | |
| (Grazer et al. 2014) | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Environmental quality alters female costs and benefits of evolving under enforced monogamy | Yes | Yes | Yes | Yes | Yes | Direct Measures of fitness in environments that had standard and sub-standard food quality | |
| (Grieshop et al. 2016) | Grieshop, K., J. Stangberg, I. Martinossi-Allibert, G. Arnqvist and D. Berger | 2016 | Strong sexual selection in males against a mutation load that reduces offspring production in seed beetles | Yes | Yes | No | No | 3 | Different mating systems/ opportunity for SS were not imposed | |
| (Hall, Bussiere, and Brooks 2009) | Hall, M. D., L. F. Bussiere and R. Brooks | 2009 | Diet-dependent female evolution influences male lifespan in a nuptial feeding insect | Yes | Yes | No | No | 3 | Different mating systems/ opportunity for SS were not imposed | |
| (Hangartner et al. 2015) | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Experimental removal of sexual selection leads to decreased investment in an immune component in female Tribolium castaneum | Yes | Yes | Yes | Yes | Yes | ||
| (Hangartner et al. 2013) | Hangartner, S., S. H. Sbilordo, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Are there genetic trade-offs between immune and reproductive investments in Tribolium castaneum? | Yes | Yes | Yes | Yes | Yes | Different levels of SS, but none with enforced monogamy (no choice) | |
| (Hicks, Hagenbuch, and Meffert 2004) | Hicks, S. K., K. L. Hagenbuch and L. M. Meffert | 2004 | Variable costs of mating, longevity, and starvation resistance in Musca domestica (Diptera: Muscidae) | Yes | Yes | No | No | 3 | Study on environmental conditions not SS treatment | |
| (Holland 2002) | Holland, B. | 2002 | Sexual selection fails to promote adaptation to a new environment | Yes | Yes | Yes | Yes | Yes | Also looks at thermal stress | |
| (Holland and Rice 1999) | Holland, B. and W. R. Rice | 1999 | Experimental removal of sexual selection reverses intersexual antagonistic coevolution and removes a reproductive load | Yes | Yes | Yes | Yes | Yes | ||
| (Hollis, Fierst, and Houle 2009) | Hollis, B., J. L. Fierst and D. Houle | 2009 | Sexual Selection Accelerates the Elimination of a Deleterious Mutant in Drosophila Melanogaster | Yes | Yes | Yes | Yes | Yes | looked at the purging of a deleterious allele | |
| (Hollis and Houle 2011) | Hollis, B. and D. Houle | 2011 | Populations with elevated mutation load do not benefit from the operation of sexual selection | Yes | Yes | Yes | Yes | Yes | Mutagenesis took place and direct fitness measurements were made | |
| (Hollis, Houle, and Kawecki 2016) | Hollis, B., D. Houle and T. J. Kawecki | 2016 | Evolution of reduced post-copulatory molecular interactions in Drosophila populations lacking sperm competition | Yes | Yes | Yes | No | No | 4 | Seminal fluid proteins have a fitness advantage in a polygamous setting, thus is favoured; perhaps this was a bit too ambiguous. |
| (Hollis et al. 2014) | Hollis, B., D. Houle, Z. Yan, T. J. Kawecki and L. Keller | 2014 | Evolution under monogamy feminizes gene expression in Drosophila melanogaster | Yes | Yes | Yes | No | No | 4 | Sex biased gene expression was measured, showing sexual antagonism. Would be stretched to consider it as a fitness measure. |
| (Hollis and Kawecki 2014) | Hollis, B. and T. J. Kawecki | 2014 | Male cognitive performance declines in the absence of sexual selection | Yes | Yes | Yes | Yes | Yes | Cognitive ability measured in both male and female | |
| (Hollis, Keller, and Kawecki 2017) | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Sexual selection shapes development and maturation rates in Drosophila | Yes | Yes | Yes | Yes | Yes | Development and fitness measured | |
| (Hosken et al. 2009) | Hosken, D. J., O. Y. Martin, S. Wigby, T. Chapman and D. J. Hodgson | 2009 | Sexual conflict and reproductive isolation in flies | Yes | Yes | Yes | No | No | 4 | Reproductive isolation measured without fitness components |
| (House et al. 2013) | House, C. M., Z. Lewis, D. J. Hodgson, N. Wedell, M. D. Sharma, J. Hunt and D. J. Hosken | 2013 | Sexual and Natural Selection Both Influence Male Genital Evolution | Yes | Yes | Yes | No | No | 4 | Genitalia too complicated and hard to extract effect size |
| (Hunt et al. 2012) | Hunt, J., R. R. Snook, C. Mitchell, H. S. Crudgington and A. J. Moore | 2012 | Sexual selection and experimental evolution of chemical signals in Drosophila pseudoobscura | Yes | Yes | Yes | No | No | 4 | Body size measured as well as CHC, like other studies may confer fitness advantage |
| (Immonen, Snook, and Ritchie 2014) | Immonen, E., R. R. Snook and M. G. Ritchie | 2014 | Mating system variation drives rapid evolution of the female transcriptome in Drosophila pseudoobscura | Yes | Yes | Yes | Yes | Yes | While transcriptome outcomes not exclusively measuring fitness they also measures aspects of fecundity | |
| (Innocenti, Flis, and Morrow 2014) | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Female responses to experimental removal of sexual selection components in Drosophila melanogaster | Yes | Yes | Yes | Yes | Yes | To some extent the nature of SS treatment is unclear. Gene expression and fecundity are measured | |
| (Jacomb, Marsh, and Holman 2016) | Jacomb, F., J. Marsh and L. Holman | 2016 | Sexual selection expedites the evolution of pesticide resistance | Yes | Yes | Yes | Yes | Yes | Pesticide Resistance as an environmental condition that needs to be adapted to | |
| (Janicke et al. 2016) | Janicke, T., P. Sandner, S. A. Ramm, D. B. Vizoso and L. Schaerer | 2016 | Experimentally evolved and phenotypically plastic responses to enforced monogamy in a hermaphroditic flatworm | Yes | No | No | 2b | Hermaphroditic | ||
| (Jarzebowska and Radwan 2010) | Jarzebowska, M. and J. Radwan | 2010 | Sexual Selection Counteracts Extinction of Small Populations of the Bulb Mites | Yes | Yes | Yes | Yes | Yes | Direct fitness measurements over several generations | |
| (Klemme and Firman 2013) | Klemme, I. and R. C. Firman | 2013 | Male house mice that have evolved with sperm competition have increased mating duration and paternity success | Yes | Yes | Yes | Yes | Yes | Paternity Success measured | |
| (Long, Agrawal, and Rowe 2012) | Long, T. A. F., A. F. Agrawal and L. Rowe | 2012 | The Effect of Sexual Selection on Offspring Fitness Depends on the Nature of Genetic Variation | Yes | Yes | No | No | 3 | No enforced SS regimes | |
| (Lumley et al. 2015) | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Sexual selection protects against extinction | Yes | Yes | Yes | Yes | Yes | Reproductive fitness and time to extinction measured | |
| (MacLellan et al. 2012) | MacLellan, K., L. Kwan, M. C. Whitlock and H. D. Rundle | 2012 | Dietary stress does not strengthen selection against single deleterious mutations in Drosophila melanogaster | No | No | 1 | Selection based experiment rather than experimental evolution | |||
| (MacLellan, Whitlock, and Rundle 2009) | MacLellan, K., M. C. Whitlock and H. D. Rundle | 2009 | Sexual selection against deleterious mutations via variable male search success | No | No | 1 | Selection based experiment rather than experimental evolution | |||
| (Maklakov, Bonduriansky, and Brooks 2009) | Maklakov, A. A., R. Bonduriansky and R. C. Brooks | 2009 | Sex Differences, Sexual Selection, and Ageing: An Experimental Evolution Approach | Yes | Yes | Yes | Yes | Yes | Life History traits of ageing were measured | |
| (Maklakov and Fricke 2009) | Maklakov, A. A. and C. Fricke | 2009 | Sexual selection did not contribute to the evolution of male lifespan under curtailed age at reproduction in a seed beetle | Yes | Yes | Yes | No | No | 4 | Pseudoreplication to the above studies mut outcome metrics align less with the meta-analysis so we discard |
| (Maklakov, Fricke, and Arnqvist 2007) | Maklakov, A. A., C. Fricke and G. Arnqvist | 2007 | Sexual selection affects lifespan and aging in the seed beetle | Yes | Yes | Yes | No | No | 4 | Pseudoreplication to the above studies mut outcome metrics align less with the meta-analysis so we discard |
| (Mallet et al. 2011) | Mallet, M. A., J. M. Bouchard, C. M. Kimber and A. K. Chippindale | 2011 | Experimental mutation-accumulation on the X chromosome of Drosophila melanogaster reveals stronger selection on males than females | Yes | Yes | No | No | 3 | No SS+ and SS- treatments | |
| (Mallet and Chippindale 2011) | Mallet, M. A. and A. K. Chippindale | 2011 | Inbreeding reveals stronger net selection on Drosophila melanogaster males: implications for mutation load and the fitness of sexual females | No | No | 1 | Mutation levels analysed | |||
| (Martin and Hosken 2003) | Martin, O. Y. and D. J. Hosken | 2003 | Costs and benefits of evolving under experimentally enforced polyandry or monogamy | Yes | Yes | Yes | Yes | Yes | Crossing took place after Gen 29, results still contain fitness components though | |
| (Martin and Hosken 2004) | Martin, O. Y. and D. J. Hosken | 2004 | Reproductive consequences of population divergence through sexual conflict | Yes | Yes | Yes | Yes | Yes | Crossing also took place, it should still be fine as they some populations were not crossed | |
| (Matsuyama and Kuba 2009) | Matsuyama, T. and H. Kuba | 2009 | Mating time and call frequency of males between mass-reared and wild strains of melon fly, Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) | Yes | Yes | No | No | 3 | Mate choice in different populations | |
| (McGuigan, Petfield, and Blows 2011) | McGuigan, K., D. Petfield and M. W. Blows | 2011 | REDUCING MUTATION LOAD THROUGH SEXUAL SELECTION ON MALES | Yes | Yes | Yes | Yes | Yes | The control line was not enforced monomagous (did not remove SS)., it was just a control where the population was mutagenised. No clear SS treatment as level of selection varied across the generations. | |
| (McKean and Nunney 2008) | McKean, K. A. and L. Nunney | 2008 | Sexual selection and immune function in Drosophila melanogaster | Yes | Yes | Yes | Yes | Yes | The control line was a 1:1 SR but not enforced monogamy | |
| (McLain 1992) | McLain, D. K. | 1992 | Population density and the intensity of sexual selection on body length in spatially or temporally restricted natural populations of a seed bug | No | No | 1 | Field study | |||
| (McNamara et al. 2016) | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Male-biased sex ratio does not promote increased sperm competitiveness in the seed beetle, Callosobruchus maculatus | Yes | Yes | Yes | Yes | Yes | No SS- (enforced monogamy) just altered SR | |
| (McNamara, Lieshout, and Simmons 2014) | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | A test of the sexy-sperm and good-sperm hypotheses for the evolution of polyandry | Yes | Yes | Yes | Yes | Yes | Polygamy was still randomly done meaning post-cop SS is only available. Numorous measures of fitness conducted | |
| (Meffert et al. 2006) | Meffert, L. M., J. L. Regan, S. K. Hicks, N. Mukana and S. B. Day | 2006 | Testing alternative methods for purging genetic load using the housefly (Musca domestica L.) | Yes | Yes | No | No | 3 | No tsts of SS | |
| (L. Michalczyk, Millard, Martin, Lumley, Emerson, Chapman, et al. 2011) | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson, T. Chapman and M. J. G. Gage | 2011 | Inbreeding Promotes Female Promiscuity | Yes | Yes | No | No | 3 | It does not appear the SS regimes were enforced (fig 1) | |
| (L. Michalczyk, Millard, Martin, Lumley, Emerson, and Gage 2011) | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Experimental Evolution Exposes Female and Male Responses to Sexual Selection and Conflict in Tribolium Castaneum | Yes | Yes | Yes | Yes | Yes | No enforced monogamy (no SS-), but different OSR | |
| (Morrow, Stewart, and Rice 2008) | Morrow, E. H., A. D. Stewart and W. R. Rice | 2008 | Assessing the extent of genome-wide intralocus sexual conflict via experimentally enforced gender-limited selection | Yes | Yes | No | No | 3 | Not using different SS treatment lines | |
| (Nandy et al. 2013) | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Sperm Competitive Ability Evolves in Response to Experimental Alteration of Operational Sex Ratio | Yes | Yes | Yes | Yes | Yes | Use an OSR of male and female bias | |
| (Nandy et al. 2014) | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Experimental Evolution of Female Traits under Different Levels of Intersexual Conflict in Drosophila Melanogaster | Yes | Yes | No | Yes | Yes | Use an OSR of male and female bias | |
| (Nelson et al. 2013) | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Rapid adaptation to mammalian sociality via sexually selected traits | Yes | Yes | Yes | Yes | Yes | 3 generations in mice with direct fitness outcomes | |
| (Nie and Kaneshiro 2016) | Nie, H. and K. Kaneshiro | 2016 | Sexual selection and incipient speciation in Hawaiian Drosophila | No | No | 1 | Artificial selection was conducted alongside mate choice | |||
| (Palopoli et al. 2015) | Palopoli, M. F., C. Peden, C. Woo, K. Akiha, M. Ary, L. Cruze, J. L. Anderson and P. C. Phillips | 2015 | Natural and experimental evolution of sexual conflict within Caenorhabditis nematodes | Yes | No | No | 2b | Hermaphroditic, also competition not SS was modulated | ||
| (Partridge 1980b) | Partridge, L. | 1980 | Mate Choice Increases a Component of Offspring Fitness in Fruit-Flies | Yes | Yes | Yes | Yes | Yes | Competitive success from 1 generation of populations with and without mate choice | |
| (Pélabon et al. 2014) | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | The effects of sexual selection on life-history traits: An experimental study on guppies | Yes | Yes | Yes | Yes | Yes | Direct and indirect outcomes | |
| (Perry et al. 2016) | Perry, J. C., R. Joag, D. J. Hosken, N. Wedell, J. Radwan and S. Wigby | 2016 | Experimental evolution under hyper-promiscuity in Drosophila melanogaster | Yes | Yes | No | No | 3 | SS was manipulated with sex peptide receptor (SPR) not enforced selection conditions | |
| (Pischedda and Chippindale 2005) | Pischedda, A. and A. Chippindale | 2005 | Sex, mutation and fitness: asymmetric costs and routes to recovery through compensatory evolution | No | No | 1 | Measures the effect of mutation in different populations | |||
| (Pischedda and Chippindale 2006) | Pischedda, A. and A. K. Chippindale | 2006 | Intralocus sexual conflict diminishes the benefits of sexual selection | No | No | 1 | Focussed on fitness effects of conflict, not experimental evolution | |||
| (Pitnick, Brown, and Miller 2001) | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Evolution of female remating behaviour following experimental removal of sexual selection | Yes | Yes | Yes | Yes | Yes | Body size and number of progeny measured. Not purpose of study though | |
| (Pitnick et al. 2001) | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Males’ evolutionary responses to experimental removal of sexual selection | Yes | Yes | Yes | Yes | Yes | Male and population fitness outcomes measured | |
| (Plesnar, Konior, and Radwan 2011) | Plesnar, A., M. Konior and J. Radwan | 2011 | The role of sexual selection in purging the genome of induced mutations in the bulb mite (Rizoglyphus robini) | Yes | Yes | Yes | Yes | Yes | ||
| (Plesnar-Bielak et al. 2013) | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop, M. Kolasa, M. Dzialo and J. Radwan | 2013 | No Evidence for Reproductive Isolation through Sexual Conflict in the Bulb Mite Rhizoglyphus robini | Yes | Yes | Yes | No | No | 4 | Reproductive isolation measured without fitness components |
| (Plesnar-Bielak et al. 2012) | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Mating system affects population performance and extinction risk under environmental challenge | Yes | Yes | Yes | Yes | Yes | ||
| (Power and Holman 2014) | Power, D. J. and L. Holman | 2014 | Polyandrous females found fitter populations | Yes | Yes | Yes | Yes | Yes | Remating was presented to the females 72 hours after first mating. Measuring effects of polyandry, thus multiple mating has more of an effect. Post copulatory selection will take place though. | |
| (Power and Holman 2015) | Power, D. J. and L. Holman | 2015 | Assessing the alignment of sexual and natural selection using radiomutagenized seed beetles | Yes | Yes | Yes | Yes | Yes | Experiment 2 Measures affect of SS | |
| (Price, Hurst, and Wedell 2010b) | Price, T. A. R., G. D. D. Hurst and N. Wedell | 2010 | Polyandry Prevents Extinction | Yes | Yes | No | No | 3 | Appears that individuals that only mated once still had a choice, post cop SS would be enacted then. Interested in mating freq over choice | |
| (Prokop et al. 2017) | Prokop, Z. M., M. A. Prus, T. S. Gaczorek, K. Sychta, J. K. Palka, A. Plesnar-Bielak and M. Skarbon | 2017 | Do males pay for sex? Sex-specific selection coefficients suggest not | No | No | 1 | SS was estimated using models: not enforced in experimental evolution | |||
| (Promislow, Smith, and Pearse 1998) | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Adult fitness consequences of sexual selection in Drosophila melanogaster | Yes | Yes | Yes | Yes | Yes | ||
| (Radwan 2004) | Radwan, J. | 2004 | Effectiveness of sexual selection in removing mutations induced with ionizing radiation | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| (Radwan et al. 2004) | Radwan, J., J. Unrug, K. Snigorska and K. Gawronska | 2004 | Effectiveness of sexual selection in preventing fitness deterioration in bulb mite populations under relaxed natural selection | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| (Rundle, Chenoweth, and Blows 2006) | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | The roles of natural and sexual selection during adaptation to a novel environment | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| (Rundle, Chenoweth, and Blows 2009) | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2009 | The diversification of mate preferences by natural and sexual selection | Yes | Yes | Yes | No | No | 4 | CHCs / mate preference outcome measured alongside natural selection. CHCs not used in this meta-analysis |
| (Rundle, Odeen, and Mooers 2007) | Rundle, H. D., A. Odeen and A. O. Mooers | 2007 | An experimental test for indirect benefits in Drosophila melanogaster | Yes | Yes | No | No | 3 | Between studs and duds not SS+ / SS- | |
| (Savic Veselinovic et al. 2013b) | Savic Veselinovic, M., S. Pavkovic-Lucic, Z. Kurbalija Novicic, M. Jelic and M. Andelkovic | 2013 | Sexual Selection Can Reduce Mutational Load in Drosophila Subobscura | Yes | Yes | Yes | Yes | No | Data not suitable | Irradiated and direct fitness outcomes measured: However when extracting data there were no sample sizes presented so we excluded the study as author did not respond to email |
| (Seslija, Marecko, and Tucic 2008) | Seslija, D., I. Marecko and N. Tucic | 2008 | Sexual selection and senescence: Do seed beetle males (Acanthoscelides obtectus, Bruchidae, Coleoptera) shape the longevity of their mates? | Yes | Yes | No | No | 3 | While there is monoandrous lines, these lines were not enforced and choice still existed. Put post-cop choice may be stronger in other lines. This is a strange setup and may be hard to compare with other studies | |
| (Sharma, Hunt, and Hosken 2012) | Sharma, M. D., J. Hunt and D. J. Hosken | 2012 | Antagonistic Responses to Natural and Sexual Selection and the Sex-Specific Evolution of Cuticular Hydrocarbons in Drosophila Simulans | Yes | Yes | Yes | No | No | 4 | CHCs / mate preference outcome measured alongside natural selection |
| (Sharp and Agrawal 2008) | Sharp, N. P. and A. F. Agrawal | 2008 | Mating density and the strength of sexual selection against deleterious alleles in Drosophila melanogaster | No | No | 1 | One generation w/ gene freq. Also no enforced monogamy | |||
| (Sharp and Agrawal 2009) | Sharp, N. P. and A. F. Agrawal | 2009 | Sexual Selection and the Random Union of Gametes: Testing for a Correlation in Fitness between Mates in Drosophila melanogaster | No | No | 1 | Assortive mating study | |||
| (Simmons and Firman 2014) | Simmons, L. W. and R. C. Firman | 2014 | Experimental Evidence for the Evolution of the Mammalian Baculum by Sexual Selection | Yes | Yes | Yes | No | No | 4 | States that “Far less is known of the fitness consequences of variation in baculum morphology for mammals.” - No direct link with fitness advantage. Genital morphology not used in this meta-analysis |
| (Simmons and Garcia-Gonzalez 2008) | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Evolutionary Reduction in Testes Size and Competitive Fertilization Success in Response to the Experimental Removal of Sexual Selection in Dung Beetles | Yes | Yes | Yes | Yes | Yes | Fitness outcomes measured | |
| (Simmons and Garcia-Gonzalez 2011) | Simmons, L. W. and F. Garcia-Gonzalez | 2011 | Experimental coevolution of male and female genital morphology | Yes | Yes | Yes | No | No | 4 | Genital morphology has conflicting fitness outcomes for males and females, not used in this meta-analysis |
| (Simmons et al. 2009) | Simmons, L. W., C. M. House, J. Hunt and F. Garcia-Gonzalez | 2009 | Evolutionary Response to Sexual Selection in Male Genital Morphology | Yes | Yes | Yes | No | No | 4 | Genital Morphology not used in this meta-analysis |
| (Snook et al. 2013) | Snook, R. R., N. A. Gidaszewski, T. Chapman and L. W. Simmons | 2013 | Sexual selection and the evolution of secondary sexual traits: sex comb evolution in Drosophila | Yes | Yes | Yes | No | No | 4 | In D. pseudo monogamy was enforced. Sex combs are cited as having positive fitness effects at high and low numbers. Would not give an accurate representation of a fitness comparison |
| (Tilszer et al. 2006) | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Evolution under relaxed sexual conflict in the bulb mite Rhizoglyphus robini | Yes | Yes | Yes | Yes | Yes | ||
| (Lieshout, McNamara, and Simmons 2014) | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Rapid Loss of Behavioral Plasticity and Immunocompetence under Intense Sexual Selection | Yes | Yes | Yes | Yes | Yes | Did not use enforced monogamy but had different operational sex ratio | |
| (Whitlock and Bourguet 2000) | Whitlock, M. C. and D. Bourguet | 2000 | Factors affecting the genetic load in Drosophila: Synergistic epistasis and correlations among fitness components | Yes | Yes | No | No | 3 | No manipulation of sexual selection | |
| (Wigby and Chapman 2004) | Wigby, S. and T. Chapman | 2004 | Female resistance to male harm evolves in response to manipulation of sexual conflict | Yes | Yes | Yes | Yes | Yes | Did not use enforced monogamy but had different sex ratio |
Data Extraction and Effect Size Calculation
The rules utilised during the data extraction and effect size calculation were as follows:
Arithmatic means, standard deviations/errors and sample sizes were extracted from a paper, supplementary material or a linked data repository (e.g. Data Dryad). This was possible when means and SD were reported in text or in a table. We would preferentially extract data for each experimental evolution line/replicat/family if possible and only extract data for the final reported generation (which was noted down).
If we could not find the means and SD in text format we used web-plot digitizer (v.3.12) to extract data from graphs.
If means were not reported then we extracted a summary statistic or proportion value, which we could later convert to Hedges g’ using the compute.es package (Re 2013). Summary statistics included F, z, t and chi2. These conversions still required providing sample sizes for each treatment so these needed to be extractable from the study. Some summary statistics were obtained from generalized linear model summary tabels, others from straight forward ANOVAs and then some from more complex analysis such as proportional hazards statistical tests.
We also collected various covariates for some of the studies (see source data), which are discussed later.
The Effect Size Dataset
Table of Effect Sizes
Source Data Table of effect sizes included in our meta-analysis. See the text following the table for an explanation of each column.
# Load the data and format the variables
full_dataset <- read.csv('data/meta_analysis_dataset.csv') %>%
mutate(Study.ID = factor(Study.ID),
Group.ID = factor(Group.ID),
Environment = relevel(factor(Environment), ref = "Unstressed"),
Outcome.Class = relevel(factor(Outcome.Class), ref = "Indirect"),
Pre.cop = relevel(factor(Pre.cop), ref = "0"),
Post.cop = relevel(factor(Post.cop), ref = "0"),
Blinding = factor(Blinding))
kable(full_dataset, "html") %>%
kable_styling() %>%
scroll_box(width = "100%", height = "500px")| Study.ID | Group.ID | AuthorYear | Outcome | Environment | Group.ID.1 | Authors | Year | Species | Taxon | SS.density.high.to.low | SS.ratio.high | SS.density.high | Pre.cop | Post.cop | Blinding | Generations | Enforced.Monogamy | n | Sex | Ambiguous | Outcome.Class | g | var.g | Positive.Fitness | mean.low | sd.low | n.low | mean.high | sd.high | n.high | JIF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 37 | Almbro 2014 | Strength | Stressed | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 182 | M | NO | Indirect | 0.385 | 0.022 | 1 | 0.0470000 | 0.0572364 | 91 | 0.0940000 | 0.1621697 | 91 | 4.612 |
| 1 | 37 | Almbro 2014 | Strength | Unstressed | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 182 | M | NO | Indirect | 0.000 | 0.022 | 1 | 0.1170000 | 0.1717091 | 91 | 0.1170000 | 0.1717091 | 91 | 4.612 |
| 1 | 37 | Almbro 2014 | Ejaculate Quality and Production | Stressed | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 222 | M | NO | Indirect | 0.172 | 0.018 | 1 | 1.8920000 | 0.9060662 | 111 | 2.0510000 | 0.9376732 | 111 | 4.612 |
| 1 | 37 | Almbro 2014 | Ejaculate Quality and Production | Unstressed | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 3 | YES | 222 | M | NO | Indirect | 0.204 | 0.018 | 1 | 2.1900000 | 0.9692801 | 111 | 2.3820000 | 0.9060662 | 111 | 4.612 |
| 1 | 37 | Almbro 2014 | Female Reproductive Success | Not Stated | 37 | Almbro, M. and L. W. Simmons | 2014 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 2 | YES | 414 | F | NO | Direct | 0.258 | 0.010 | 1 | 15.4000000 | 10.0712462 | 207 | 18.0000000 | 10.0712462 | 207 | 4.612 |
| 2 | 14 | Arbuthnott 2012 | Mutant Frequency | Stressed | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | B | NO | Indirect | -0.011 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott 2012 | Mutant Frequency | Stressed | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | B | NO | Indirect | 0.434 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott 2012 | Mutant Frequency | Stressed | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | B | NO | Indirect | -0.064 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott 2012 | Mutant Frequency | Stressed | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | B | NO | Indirect | -0.037 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott 2012 | Mutant Frequency | Stressed | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | B | NO | Indirect | -0.129 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 2 | 14 | Arbuthnott 2012 | Mutant Frequency | Stressed | 14 | Arbuthnott, D. and H. D. Rundle | 2012 | Drosophila melanogaster | Fly | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 7 | YES | 400 | B | NO | Indirect | 0.032 | 0.010 | -1 | NA | NA | NA | NA | NA | NA | 4.864 |
| 3 | 35 | Archer 2015 | Lifespan | Stressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | M | NO | Indirect | -0.971 | 0.005 | 1 | 30.5200000 | 5.9396970 | 450 | 24.2100000 | 7.0003571 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Lifespan | Stressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | F | NO | Indirect | -0.154 | 0.004 | 1 | 34.2800000 | 26.9407684 | 450 | 31.3200000 | 4.0305087 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Fitness Senescence | Stressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | M | NO | Indirect | 0.074 | 0.004 | -1 | 3.6300000 | 1.2727922 | 450 | 3.4300000 | 3.6062446 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Fitness Senescence | Stressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | F | NO | Indirect | -0.087 | 0.004 | -1 | 3.9200000 | 1.4849242 | 450 | 4.3500000 | 6.7882251 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Offspring Viability | Stressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | M | NO | Direct | -0.868 | 0.005 | -1 | 0.0295858 | 0.0063640 | 450 | 0.0372000 | 0.0106066 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Offspring Viability | Stressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | F | NO | Direct | -0.148 | 0.004 | -1 | 0.0264000 | 0.0254558 | 450 | 0.0291000 | 0.0042426 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Lifespan | Unstressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | M | NO | Indirect | -0.780 | 0.005 | 1 | 35.5500000 | 11.0308658 | 450 | 26.9400000 | 11.0308658 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Lifespan | Unstressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | F | NO | Indirect | -0.146 | 0.004 | 1 | 34.2800000 | 26.9407684 | 450 | 30.1900000 | 28.8499567 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Fitness Senescence | Unstressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | M | NO | Indirect | 0.021 | 0.004 | -1 | 4.5000000 | 6.1518290 | 450 | 4.3300000 | 9.9702056 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Fitness Senescence | Unstressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | F | NO | Indirect | -0.038 | 0.004 | -1 | 4.8200000 | 5.9396970 | 450 | 5.1500000 | 10.8187337 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Offspring Viability | Unstressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | M | NO | Direct | -0.259 | 0.004 | -1 | 0.0258000 | 0.0424264 | 450 | 0.0339000 | 0.0127279 | 450 | 5.210 |
| 3 | 35 | Archer 2015 | Offspring Viability | Unstressed | 35 | Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt | 2015 | Drosophila simulans | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 45 | YES | 900 | F | NO | Direct | -0.176 | 0.004 | -1 | 0.0267000 | 0.0169706 | 450 | 0.0305000 | 0.0254558 | 450 | 5.210 |
| 5 | 1 | Bernasconi 2001 | Male Reproductive Success | Unstressed | 1 | Bernasconi, G. and L. Keller | 2001 | Tribolium castaneum | Beetle | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 3 | YES | 20 | M | YES | Ambiguous | 1.533 | 0.242 | 1 | 0.5600000 | 0.3600000 | 10 | 0.9700000 | 0.0400000 | 10 | 2.673 |
| 5 | 1 | Bernasconi 2001 | Female Reproductive Success | Unstressed | 1 | Bernasconi, G. and L. Keller | 2001 | Tribolium castaneum | Beetle | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 3 | YES | 20 | F | NO | Direct | -0.123 | 0.184 | 1 | 63.0000000 | 27.0000000 | 10 | 60.0000000 | 19.0000000 | 10 | 2.673 |
| 6 | 15 | Brommer 2012 | Both Reproductive Success | Unstressed | 15 | Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman | 2012 | Drosophila melanogaster | Fly | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 4 | YES | 93 | B | NO | Direct | -0.378 | 0.043 | 1 | 1.0000000 | 0.3316625 | 44 | 0.8700000 | 0.3500000 | 49 | 4.864 |
| 7 | 29 | Crudgington 2005 | Female Reproductive Success | Stressed | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 21 | YES | 200 | F | NO | Direct | -0.216 | 0.020 | 1 | 76.9000000 | 47.0000000 | 100 | 66.4000000 | 50.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington 2005 | Female Reproductive Success | Stressed | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | F | NO | Direct | 0.280 | 0.020 | 1 | 120.6000000 | 119.0000000 | 100 | 153.6000000 | 116.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington 2005 | Offspring Viability | Stressed | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | F | NO | Direct | 0.365 | 0.045 | 1 | NA | NA | NA | NA | NA | NA | 4.464 |
| 7 | 29 | Crudgington 2005 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 21 | YES | 200 | F | NO | Direct | -0.244 | 0.020 | 1 | 108.5000000 | 44.0000000 | 100 | 97.9000000 | 43.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington 2005 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | F | NO | Direct | 0.281 | 0.020 | 1 | 164.1000000 | 119.0000000 | 100 | 197.5000000 | 119.0000000 | 100 | 4.464 |
| 7 | 29 | Crudgington 2005 | Offspring Viability | Unstressed | 29 | Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook | 2005 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 28 | YES | 200 | F | NO | Direct | -0.311 | 0.155 | 1 | NA | NA | NA | NA | NA | NA | 4.464 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 62 | YES | 10 | M | NO | Indirect | -0.168 | 0.184 | 1 | 15.7249071 | 1.9984654 | 10 | 15.3903346 | 1.7633519 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 61 | YES | 10 | M | NO | Indirect | -0.576 | 0.192 | 1 | 15.3903346 | 2.1160222 | 10 | 14.3122677 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 60 | YES | 10 | M | NO | Indirect | 1.311 | 0.226 | 1 | 15.0185874 | 1.0580111 | 10 | 16.3940520 | 0.9404543 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 58 | YES | 10 | M | NO | Indirect | 0.512 | 0.190 | 1 | 15.7992565 | 1.6457951 | 10 | 16.6542751 | 1.5282383 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 62 | YES | 10 | M | NO | Indirect | 1.373 | 0.231 | 1 | 15.7249071 | 1.9984654 | 10 | 18.0669145 | 1.1755679 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 61 | YES | 10 | M | NO | Indirect | 1.190 | 0.219 | 1 | 15.3903346 | 2.1160222 | 10 | 17.6208178 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | YES | 10 | M | NO | Indirect | 1.305 | 0.226 | 1 | 15.0185874 | 1.0580111 | 10 | 17.1003718 | 1.8809086 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 58 | YES | 10 | M | NO | Indirect | 1.928 | 0.276 | 1 | 15.7992565 | 1.6457951 | 10 | 18.5873606 | 1.0580111 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 62 | NO | 10 | M | NO | Indirect | 1.713 | 0.257 | 1 | 15.3903346 | 1.7633519 | 10 | 18.0700000 | 1.1755679 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 61 | NO | 10 | M | NO | Indirect | 2.248 | 0.310 | 1 | 14.3122677 | 1.4106815 | 10 | 17.6200000 | 1.4106815 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | NO | 10 | M | NO | Indirect | 0.458 | 0.189 | 1 | 16.3940520 | 0.9404543 | 10 | 17.1000000 | 1.8809086 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Mating Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 58 | NO | 10 | M | NO | Indirect | 1.414 | 0.233 | 1 | 16.6542751 | 1.5282383 | 10 | 18.5900000 | 1.0580111 | 10 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 60 | YES | 20 | M | YES | Ambiguous | 0.060 | 0.096 | 1 | 622.3853211 | 367.6177813 | 20 | 642.9357798 | 301.9717489 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 59 | YES | 20 | M | YES | Ambiguous | -0.089 | 0.096 | 1 | 760.3669725 | 407.0054007 | 20 | 733.9449541 | 354.4885748 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 57 | YES | 20 | M | YES | Ambiguous | -0.214 | 0.097 | 1 | 728.0733945 | 407.0054007 | 20 | 648.8073394 | 315.1009554 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | YES | 20 | M | YES | Ambiguous | 0.515 | 0.099 | 1 | 622.4000000 | 367.6177800 | 20 | 819.0825688 | 380.7469877 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 59 | YES | 20 | M | YES | Ambiguous | 0.768 | 0.103 | 1 | 760.4000000 | 407.0054000 | 20 | 1200.7339450 | 682.7187366 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 57 | YES | 20 | M | YES | Ambiguous | 1.068 | 0.110 | 1 | 728.1000000 | 407.0054000 | 20 | 1150.8256880 | 367.6177813 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 60 | NO | 20 | M | YES | Ambiguous | 0.503 | 0.099 | 1 | 642.9357798 | 301.9717489 | 20 | 819.0825688 | 380.7469877 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 59 | NO | 20 | M | YES | Ambiguous | 0.841 | 0.105 | 1 | 733.9449541 | 354.4885748 | 20 | 1200.7339450 | 682.7187366 | 20 | 5.429 |
| 8 | 29 | Crudgington 2009 | Male Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook | 2009 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 57 | NO | 20 | M | YES | Ambiguous | 1.437 | 0.122 | 1 | 648.8073394 | 315.1009554 | 20 | 1150.8256880 | 367.6177813 | 20 | 5.429 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 55 | YES | 18 | F | YES | Ambiguous | -0.861 | 0.111 | 1 | 237.3000000 | 55.0072700 | 20 | 169.5000000 | 95.8836795 | 18 | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 54 | YES | 18 | F | YES | Ambiguous | -0.655 | 0.118 | 1 | 210.6000000 | 67.6189300 | 17 | 170.5000000 | 50.7141992 | 17 | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 55 | YES | 18 | F | YES | Ambiguous | 0.026 | 0.123 | 1 | NA | NA | NA | NA | NA | NA | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 54 | YES | 18 | F | YES | Ambiguous | 0.360 | 0.140 | 1 | NA | NA | NA | NA | NA | NA | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | YES | 18 | F | YES | Ambiguous | -1.447 | 0.132 | 1 | 237.3000000 | 55.0072700 | 20 | 150.0000000 | 63.4958266 | 17 | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | YES | 18 | F | YES | Ambiguous | -0.739 | 0.114 | 1 | 210.6000000 | 67.6189300 | 17 | 154.1000000 | 80.6396305 | 19 | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | YES | 18 | F | YES | Ambiguous | 0.620 | 0.160 | 1 | NA | NA | NA | NA | NA | NA | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | YES | 18 | F | YES | Ambiguous | 0.450 | 0.140 | 1 | NA | NA | NA | NA | NA | NA | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | NO | 18 | F | YES | Ambiguous | -0.233 | 0.110 | 1 | 169.5000000 | 95.8836795 | 18 | 150.0000000 | 63.4958266 | 17 | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | NO | 18 | F | YES | Ambiguous | -0.235 | 0.107 | 1 | 170.5000000 | 50.7141992 | 17 | 154.1000000 | 80.6396305 | 19 | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | NO | 18 | F | YES | Ambiguous | 0.590 | 0.160 | 1 | NA | NA | NA | NA | NA | NA | 3.636 |
| 9 | 29 | Crudgington 2010 | Early Fecundity | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | NO | 18 | F | YES | Ambiguous | 0.080 | 0.150 | 1 | NA | NA | NA | NA | NA | NA | 3.636 |
| 9 | 29 | Crudgington 2010 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 55 | YES | 18 | F | NO | Direct | -0.520 | 0.105 | 1 | 500.3000000 | 174.4133000 | 20 | 403.8000000 | 261.3466663 | 18 | 3.636 |
| 9 | 29 | Crudgington 2010 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 54 | YES | 18 | F | NO | Direct | -0.843 | 0.123 | 1 | 474.7000000 | 195.0229000 | 17 | 315.4000000 | 173.5827468 | 17 | 3.636 |
| 9 | 29 | Crudgington 2010 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | YES | 18 | F | NO | Direct | -0.796 | 0.188 | 1 | 403.8000000 | 261.3466663 | 18 | 228.1000000 | 152.5549081 | 17 | 3.636 |
| 9 | 29 | Crudgington 2010 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | YES | 18 | F | NO | Direct | -1.065 | 0.122 | 1 | 474.7000000 | 195.0229000 | 17 | 266.1000000 | 188.3044344 | 19 | 3.636 |
| 9 | 29 | Crudgington 2010 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 55 | NO | 18 | F | NO | Direct | -1.616 | 0.139 | 1 | 500.3000000 | 174.4133000 | 20 | 228.1000000 | 152.5549081 | 17 | 3.636 |
| 9 | 29 | Crudgington 2010 | Female Reproductive Success | Unstressed | 29 | Crudgington, H. S., S. Fellows and R. R. Snook | 2010 | Drosophila pseudoobscura | Fly | 1.750 | 6.00 | 7.00 | 1 | 1 | Not Blind | 54 | NO | 18 | F | NO | Direct | -0.266 | 0.108 | 1 | 315.4000000 | 173.5827468 | 17 | 266.1000000 | 188.3044344 | 19 | 3.636 |
| 10 | 29 | Debelle 2016 | Body Size | Unstressed | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | M | YES | Ambiguous | 0.555 | 0.002 | 1 | 2.2200000 | 0.0730000 | 1019 | 2.2600000 | 0.0710000 | 1019 | 2.792 |
| 10 | 29 | Debelle 2016 | Body Size | Unstressed | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | F | YES | Ambiguous | 0.111 | 0.002 | 1 | 2.4500000 | 0.0820000 | 1019 | 2.4600000 | 0.0980000 | 1019 | 2.792 |
| 10 | 29 | Debelle 2016 | Mating Success | Unstressed | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | M | NO | Indirect | -0.663 | 0.004 | 1 | NA | NA | NA | NA | NA | NA | 2.792 |
| 10 | 29 | Debelle 2016 | Mating Success | Unstressed | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | M | NO | Indirect | -0.655 | 0.004 | 1 | NA | NA | NA | NA | NA | NA | 2.792 |
| 10 | 29 | Debelle 2016 | Mating Latency | Unstressed | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | M | YES | Indirect | -0.197 | 0.002 | -1 | 126.5000000 | 15.8000000 | 1019 | 129.4000000 | 13.5000000 | 1019 | 2.792 |
| 10 | 29 | Debelle 2016 | Mating Latency | Unstressed | 29 | Debelle, A., M. G. Ritchie and R. R. Snook | 2016 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 98 | YES | 2038 | M | YES | Indirect | 2.486 | 0.003 | -1 | 153.8000000 | 19.7000000 | 1019 | 113.6000000 | 11.6000000 | 1019 | 2.792 |
| 11 | 2 | Demont 2014 | Female Reproductive Success | Stressed | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 38 | F | NO | Direct | 1.810 | 0.144 | 1 | 91.7000000 | 9.4400000 | 19 | 105.7700000 | 5.1700000 | 19 | 2.606 |
| 11 | 2 | Demont 2014 | Female Reproductive Success | Unstressed | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 38 | F | NO | Direct | 0.299 | 0.102 | 1 | 93.9700000 | 21.3500000 | 19 | 101.2400000 | 26.0600000 | 19 | 2.606 |
| 11 | 2 | Demont 2014 | Male Reproductive Success | Unstressed | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 24 | M | YES | Ambiguous | 0.222 | 0.156 | 1 | 106.8500000 | 6.2000000 | 12 | 108.6500000 | 9.2000000 | 12 | 2.606 |
| 11 | 2 | Demont 2014 | Male Reproductive Success | Unstressed | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 24 | M | YES | Ambiguous | 0.279 | 0.209 | 1 | NA | NA | NA | NA | NA | NA | 2.606 |
| 11 | 2 | Demont 2014 | Offspring Viability | Unstressed | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 44 | F | NO | Direct | 0.415 | 0.090 | 1 | 24.0000000 | 8.9442719 | 20 | 27.0000000 | 4.8989795 | 24 | 2.606 |
| 11 | 2 | Demont 2014 | Offspring Viability | Unstressed | 2 | Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 36 | YES | 45 | M | NO | Direct | 0.407 | 0.088 | 1 | 23.0000000 | 9.3808315 | 22 | 26.0000000 | 4.7958315 | 23 | 2.606 |
| 12 | 16 | Edward 2010 | Mating Latency | Stressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | M | NO | Indirect | 0.324 | 0.020 | -1 | 6.5230000 | 5.4190000 | 102 | 5.0170000 | 3.6600000 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Mating Duration | Stressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | M | NO | Ambiguous | 0.219 | 0.020 | 1 | 11.9500000 | 2.9810000 | 102 | 12.6440000 | 3.3310000 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Mating Latency | Unstressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | M | NO | Indirect | -0.099 | 0.019 | -1 | 5.5121951 | 3.5893711 | 102 | 5.8885017 | 3.9412702 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Mating Duration | Unstressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | M | NO | Ambiguous | 0.393 | 0.020 | 1 | 9.1892361 | 2.5424101 | 102 | 10.4565972 | 3.7697805 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Female Reproductive Success | Stressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | F | NO | Direct | 0.070 | 0.019 | 1 | 72.3810000 | 35.0550000 | 102 | 74.8857645 | 35.9428779 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Female Reproductive Success | Stressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | F | NO | Direct | 0.015 | 0.019 | 1 | 0.6410000 | 0.5090000 | 102 | 0.6491071 | 0.5545710 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Male Reproductive Success | Stressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | M | YES | Ambiguous | 0.001 | 0.019 | 1 | 0.7750000 | 0.6600000 | 102 | 0.7759516 | 0.7391160 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Female Reproductive Success | Unstressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | F | NO | Direct | -0.312 | 0.020 | 1 | 81.9595782 | 34.6116602 | 102 | 71.0632689 | 35.0553994 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Female Reproductive Success | Unstressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | F | NO | Direct | 0.099 | 0.019 | 1 | 0.5946429 | 0.5004665 | 102 | 0.6446429 | 0.5049752 | 102 | 8.090 |
| 12 | 16 | Edward 2010 | Male Reproductive Success | Unstressed | 16 | Edward, D. A., C. Fricke and T. Chapman | 2010 | Drosophila melanogaster | Fly | 0.760 | 75.00 | 76.00 | 1 | 1 | Not Blind | 70 | NO | 204 | M | YES | Ambiguous | 0.049 | 0.019 | 1 | 0.7157439 | 0.6919384 | 102 | 0.7510381 | 0.7495999 | 102 | 8.090 |
| 13 | 6 | Firman 2011a | Female Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | F | NO | Direct | 0.396 | 0.080 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Female Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | F | NO | Direct | -1.258 | 0.114 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Female Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | F | NO | Direct | -0.352 | 0.076 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Female Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | F | NO | Direct | 1.316 | 0.146 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Male Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | M | YES | Ambiguous | 1.196 | 0.132 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Male Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | M | YES | Ambiguous | 1.142 | 0.104 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Male Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | M | YES | Ambiguous | 0.131 | 0.072 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 13 | 6 | Firman 2011a | Male Reproductive Success | Stressed | 6 | Firman, R. C. | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 16 | YES | 63 | M | YES | Ambiguous | 1.747 | 0.360 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 14 | 6 | Firman 2011a | Male Attractiveness | Unstressed | 6 | Firman, R. C. | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 25 | YES | 30 | M | YES | Ambiguous | -1.177 | 0.149 | 1 | NA | NA | NA | NA | NA | NA | 3.248 |
| 15 | 6 | Firman 2011b | Ejaculate Quality and Production | Not Stated | 6 | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 54 | M | NO | Indirect | 0.303 | 0.073 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 15 | 6 | Firman 2011b | Ejaculate Quality and Production | Not Stated | 6 | Firman, R. C., L. Y. Cheam and L. W. Simmons | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 54 | M | NO | Indirect | 1.844 | 0.105 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 16 | 6 | Firman 2015 | Ejaculate Quality and Production | Not Stated | 6 | Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons | 2015 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 18 | YES | 60 | M | NO | Indirect | 1.003 | 0.073 | 1 | 0.7010000 | 0.0492950 | 30 | 0.7470000 | 0.0438178 | 30 | 4.007 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 88 | F | NO | Direct | -0.963 | 0.068 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 41 | F | NO | Direct | -1.733 | 0.349 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 78 | F | NO | Direct | -1.717 | 0.111 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 55 | F | NO | Direct | -0.974 | 0.115 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 86 | F | NO | Direct | -0.599 | 0.102 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 55 | F | NO | Direct | -0.904 | 0.159 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 17 | 6 | Firman 2014b | Female Reproductive Success | Not Stated | 6 | Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons | 2014 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 24 | YES | 36 | F | NO | Direct | -0.504 | 0.199 | 1 | NA | NA | NA | NA | NA | NA | 3.832 |
| 18 | 6 | Firman 2010 | Ejaculate Quality and Production | Not Stated | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 144 | M | NO | Indirect | 0.399 | 0.026 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 18 | 6 | Firman 2010 | Female Reproductive Success | Stressed | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 7 | YES | 40 | F | NO | Direct | -0.564 | 0.100 | 1 | 17.5500000 | 5.4112845 | 20 | 14.4500000 | 5.3665631 | 20 | 3.521 |
| 18 | 6 | Firman 2010 | Female Reproductive Success | Unstressed | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 7 | YES | 40 | F | NO | Direct | -0.328 | 0.097 | 1 | 16.1500000 | 4.1143651 | 20 | 14.5500000 | 5.3665631 | 20 | 3.521 |
| 18 | 6 | Firman 2010 | Female Reproductive Success | Not Stated | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 10 | YES | 144 | F | NO | Direct | 0.668 | 0.029 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 18 | 6 | Firman 2010 | Body Size | Not Stated | 6 | Firman, R. C. and L. W. Simmons | 2010 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 8 | YES | 128 | B | NO | Ambiguous | -0.364 | 0.031 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 19 | 6 | Firman 2010 | Male Reproductive Success | Stressed | 6 | Firman, R. C. and L. W. Simmons | 2011 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 12 | YES | 128 | M | YES | Ambiguous | -1.008 | 0.035 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman 2010 | Female Reproductive Success | Unstressed | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 144 | F | NO | Direct | 0.784 | 0.030 | 1 | 4.9400000 | 2.2910260 | 72 | 6.6500000 | 2.0364675 | 72 | 3.521 |
| 20 | 6 | Firman 2010 | Female Reproductive Success | Unstressed | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | F | NO | Direct | -0.213 | 0.031 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman 2010 | Female Reproductive Success | Unstressed | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | F | NO | Direct | 0.416 | 0.032 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman 2010 | Offspring Viability | Unstressed | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | B | NO | Direct | 0.014 | 0.031 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 20 | 6 | Firman 2010 | Offspring Viability | Unstressed | 6 | Firman, R. C. and L. W. Simmons | 2012 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 15 | YES | 128 | B | NO | Direct | 0.408 | 0.032 | 1 | NA | NA | NA | NA | NA | NA | 3.521 |
| 22 | 9 | Fricke 2007 | Body Size | Not Stated | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 155 | F | YES | Ambiguous | 0.080 | 0.026 | 1 | 0.0011635 | 0.0001053 | 77 | 0.0011720 | 0.0001073 | 78 | 4.502 |
| 22 | 9 | Fricke 2007 | Body Size | Not Stated | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 155 | M | YES | Ambiguous | 0.102 | 0.026 | 1 | 0.0009178 | 0.0000963 | 77 | 0.0009283 | 0.0001084 | 77 | 4.502 |
| 22 | 9 | Fricke 2007 | Development Rate | Stressed | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 76 | B | NO | Ambiguous | -0.453 | 0.053 | 1 | 0.8289099 | 0.0286151 | 38 | 0.8135570 | 0.0377825 | 38 | 4.502 |
| 22 | 9 | Fricke 2007 | Development Rate | Unstressed | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 79 | B | NO | Ambiguous | 0.772 | 0.053 | 1 | 0.8251609 | 0.0378719 | 39 | 0.8534363 | 0.0346177 | 40 | 4.502 |
| 22 | 9 | Fricke 2007 | Female Reproductive Success | Stressed | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 76 | F | NO | Direct | -0.579 | 0.054 | 1 | 419.3947368 | 34.3546995 | 38 | 397.4210526 | 40.5573545 | 38 | 4.502 |
| 22 | 9 | Fricke 2007 | Female Reproductive Success | Unstressed | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 79 | F | NO | Direct | 0.185 | 0.050 | 1 | 292.2051282 | 55.6900159 | 39 | 301.0500000 | 37.3678526 | 40 | 4.502 |
| 22 | 9 | Fricke 2007 | Offspring Viability | Stressed | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 76 | F | NO | Direct | -0.476 | 0.053 | 1 | 0.5462428 | 0.0780889 | 38 | 0.5107408 | 0.0691831 | 38 | 4.502 |
| 22 | 9 | Fricke 2007 | Offspring Viability | Unstressed | 9 | Fricke, C. and G. Arnqvist | 2007 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 35 | YES | 79 | F | NO | Direct | 0.543 | 0.052 | 1 | 0.4180605 | 0.0784570 | 39 | 0.4575765 | 0.0652579 | 40 | 4.502 |
| 23 | 7 | Fritzsche 2016 | Male Reproductive Success | Not Stated | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | M | YES | Ambiguous | -0.056 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche 2016 | Female Reproductive Success | Not Stated | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | F | NO | Direct | -0.031 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche 2016 | Lifespan | Not Stated | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | M | NO | Indirect | -0.066 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 23 | 7 | Fritzsche 2016 | Lifespan | Not Stated | 7 | Fritzsche, K., I. Booksmythe and G. Arnqvist | 2016 | Megabruchidius dorsalis | Beetle | 1.000 | 5.00 | 150.00 | 1 | 1 | Blind | 20 | NO | 1200 | F | NO | Indirect | -0.083 | 0.003 | 1 | NA | NA | NA | NA | NA | NA | 8.851 |
| 24 | 30 | Fritzsche 2014 | Ejaculate Quality and Production | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 90 | M | NO | Indirect | -0.197 | 0.044 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Mating Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | M | NO | Indirect | -0.041 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Mating Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | M | NO | Indirect | -0.065 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Mating Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | M | NO | Indirect | -0.078 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Mating Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 256 | M | NO | Indirect | -0.267 | 0.016 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Both Reproductive Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 184 | B | NO | Direct | 0.095 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Both Reproductive Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 184 | B | NO | Direct | 0.407 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Both Reproductive Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 392 | B | NO | Direct | 0.059 | 0.010 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 24 | 30 | Fritzsche 2014 | Both Reproductive Success | Not Stated | 30 | Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels | 2014 | Caenorhabditis remanei | Nematode | 1.000 | 5.00 | 60.00 | 0 | 0 | Not Blind | 20 | NO | 392 | B | NO | Direct | 0.219 | 0.010 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 26 | 10 | Gay 2009 | Body Size | Unstressed | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 90 | YES | 80 | M | YES | Ambiguous | 1.971 | 0.073 | 1 | 1.8700000 | 0.0822192 | 40 | 2.0400000 | 0.0885438 | 40 | 3.816 |
| 26 | 10 | Gay 2009 | Ejaculate Quality and Production | Unstressed | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 90 | YES | 80 | M | NO | Indirect | 1.385 | 0.061 | 1 | 0.4500000 | 0.1201666 | 40 | 0.6400000 | 0.1517893 | 40 | 3.816 |
| 26 | 10 | Gay 2009 | Ejaculate Quality and Production | Unstressed | 10 | Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady | 2009 | Callosobruchus maculatus | Beetle | 60.000 | 1.00 | 120.00 | 1 | 1 | Not Blind | 90 | YES | 80 | M | NO | Indirect | 0.661 | 0.052 | 1 | 0.1571000 | 0.0059000 | 40 | 0.1626000 | 0.0103000 | 40 | 3.816 |
| 27 | 2 | Grazer 2014 | Both Reproductive Success | Stressed | 2 | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 39 | YES | 228 | B | NO | Direct | 0.211 | 0.018 | 1 | 149.9000000 | 174.9000000 | 114 | 181.6000000 | 119.5000000 | 114 | 3.368 |
| 27 | 2 | Grazer 2014 | Both Reproductive Success | Unstressed | 2 | Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2014 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 39 | YES | 240 | B | NO | Direct | 0.214 | 0.017 | 1 | 240.6000000 | 189.7000000 | 120 | 291.5000000 | 275.6000000 | 120 | 3.368 |
| 28 | 2 | Hangartner 2015 | Immunity | Unstressed | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 66 | M | NO | Ambiguous | -0.141 | 0.059 | 1 | 6.9700000 | 1.7400000 | 33 | 6.7000000 | 2.0300000 | 33 | 2.591 |
| 28 | 2 | Hangartner 2015 | Immunity | Unstressed | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 66 | F | NO | Ambiguous | 0.848 | 0.065 | 1 | 6.3300000 | 1.3400000 | 33 | 7.7900000 | 2.0000000 | 33 | 2.591 |
| 28 | 2 | Hangartner 2015 | Immunity | Stressed | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | M | NO | Ambiguous | 0.175 | 0.014 | 1 | 80.8600000 | 41.5400000 | 144 | 87.9200000 | 39.1000000 | 144 | 2.591 |
| 28 | 2 | Hangartner 2015 | Immunity | Stressed | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | F | NO | Ambiguous | 0.089 | 0.014 | 1 | 85.0000000 | 41.5400000 | 144 | 88.9400000 | 46.4300000 | 144 | 2.591 |
| 28 | 2 | Hangartner 2015 | Immunity | Unstressed | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | M | NO | Ambiguous | -0.097 | 0.014 | 1 | 92.8100000 | 35.8400000 | 144 | 89.2100000 | 38.2800000 | 144 | 2.591 |
| 28 | 2 | Hangartner 2015 | Immunity | Unstressed | 2 | Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 49 | YES | 288 | F | NO | Ambiguous | 0.070 | 0.014 | 1 | 87.9900000 | 35.8400000 | 144 | 90.2900000 | 29.3200000 | 144 | 2.591 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | M | NO | Ambiguous | -0.107 | 0.054 | 1 | 6.0300000 | 2.5400000 | 36 | 5.7500000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | F | NO | Ambiguous | 0.121 | 0.054 | 1 | 6.8100000 | 2.6200000 | 36 | 7.1300000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | B | NO | Ambiguous | -0.281 | 0.055 | 1 | 6.8400000 | 3.6700000 | 36 | 5.8200000 | 3.5000000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 72 | M | NO | Ambiguous | -0.150 | 0.055 | 1 | 6.1400000 | 2.5400000 | 36 | 5.7500000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 72 | F | NO | Ambiguous | -0.081 | 0.054 | 1 | 7.3400000 | 2.5400000 | 36 | 7.1300000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 72 | B | NO | Ambiguous | -0.361 | 0.394 | 1 | 7.1100000 | 3.5000000 | 36 | 5.8200000 | 3.5000000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | M | NO | Ambiguous | -0.043 | 0.054 | 1 | 6.1400000 | 2.5400000 | 36 | 6.0300000 | 2.5400000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | F | NO | Ambiguous | -0.203 | 0.055 | 1 | 7.3400000 | 2.5400000 | 36 | 6.8100000 | 2.6200000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 72 | B | NO | Ambiguous | -0.074 | 0.054 | 1 | 7.1100000 | 3.5000000 | 36 | 6.8400000 | 3.6700000 | 36 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Stressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | M | NO | Ambiguous | -0.073 | 0.014 | 1 | 1.7100000 | 3.0500000 | 144 | 1.5000000 | 2.6800000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Stressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | F | NO | Ambiguous | 0.035 | 0.014 | 1 | 1.3600000 | 2.7800000 | 144 | 1.4600000 | 2.9600000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | M | NO | Ambiguous | -0.164 | 0.014 | 1 | 2.1100000 | 3.3300000 | 144 | 1.6200000 | 2.5900000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | F | NO | Ambiguous | 0.022 | 0.014 | 1 | 2.6900000 | 4.3500000 | 144 | 2.7900000 | 4.8100000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Stressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | M | NO | Ambiguous | 0.013 | 0.014 | 1 | 1.6700000 | 2.9600000 | 144 | 1.7100000 | 3.0500000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Stressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | F | NO | Ambiguous | -0.025 | 0.014 | 1 | 1.4300000 | 2.7800000 | 144 | 1.3600000 | 2.7800000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | M | NO | Ambiguous | -0.190 | 0.014 | 1 | 2.2100000 | 3.5200000 | 144 | 1.6200000 | 2.5900000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 56 | NO | 288 | F | NO | Ambiguous | 0.087 | 0.014 | 1 | 2.4100000 | 3.8900000 | 144 | 2.7900000 | 4.8100000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Stressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | M | NO | Ambiguous | -0.060 | 0.014 | 1 | 1.6700000 | 2.9600000 | 144 | 1.5000000 | 2.6800000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Stressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | F | NO | Ambiguous | 0.010 | 0.014 | 1 | 1.4300000 | 2.7800000 | 144 | 1.4600000 | 2.9600000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 288 | M | NO | Ambiguous | -0.029 | 0.014 | 1 | 2.2100000 | 3.5200000 | 144 | 2.1100000 | 3.3300000 | 144 | 3.264 |
| 29 | 2 | Hangartner 2013 | Immunity | Unstressed | 2 | Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin | 2013 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 56 | NO | 144 | F | NO | Ambiguous | 0.068 | 0.014 | 1 | 2.4100000 | 3.8900000 | 144 | 2.6900000 | 4.3500000 | 144 | 3.264 |
| 30 | 17 | Holland 2002 | Female Reproductive Success | Stressed | 17 | Holland, B. | 2002 | Drosophila melanogaster | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 38 | YES | 89 | F | NO | Direct | -0.116 | 0.015 | 1 | 11.5900000 | 10.1800000 | 133 | 10.6600000 | 4.9500000 | 133 | 3.516 |
| 30 | 17 | Holland 2002 | Female Reproductive Success | Stressed | 17 | Holland, B. | 2002 | Drosophila melanogaster | Fly | 2.500 | 4.00 | 5.00 | 1 | 1 | Not Blind | 51 | YES | 89 | F | NO | Direct | 0.070 | 0.015 | 1 | 14.4300000 | 3.2800000 | 133 | 14.8100000 | 6.9300000 | 133 | 3.516 |
| 31 | 18 | Holland 1999 | Female Reproductive Success | Stressed | 18 | Holland, B. and W. R. Rice | 1999 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 47 | YES | 76 | F | NO | Direct | -0.305 | 0.018 | 1 | 11.2400000 | 10.6600000 | 114 | 8.9300000 | 3.6000000 | 114 | 10.260 |
| 32 | 19 | Hollis 2009 | Mutant Frequency | Stressed | 19 | Hollis, B., J. L. Fierst and D. Houle | 2009 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 27 | M | NO | Indirect | 0.807 | 0.053 | -1 | NA | NA | NA | NA | NA | NA | 5.429 |
| 32 | 19 | Hollis 2009 | Mutant Frequency | Unstressed | 19 | Hollis, B., J. L. Fierst and D. Houle | 2009 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 27 | M | NO | Indirect | 0.237 | 0.049 | -1 | 0.9410000 | 1.7760000 | 40 | 0.3990000 | 2.6590000 | 40 | 5.429 |
| 33 | 19 | Hollis 2011 | Both Reproductive Success | Stressed | 19 | Hollis, B. and D. Houle | 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 60 | YES | 120 | B | NO | Direct | -0.304 | 0.011 | 1 | 126.5900000 | 28.9794410 | 180 | 117.6000000 | 29.1136051 | 180 | 3.276 |
| 33 | 19 | Hollis 2011 | Female Reproductive Success | Stressed | 19 | Hollis, B. and D. Houle | 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 60 | YES | 164 | F | NO | Direct | 0.031 | 0.008 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 33 | 19 | Hollis 2011 | Offspring Viability | Stressed | 19 | Hollis, B. and D. Houle | 2011 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 60 | YES | 164 | F | NO | Direct | -0.064 | 0.008 | 1 | NA | NA | NA | NA | NA | NA | 3.276 |
| 34 | 19 | Hollis 2014 | Mating Latency | Stressed | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 38 | M | YES | Indirect | 0.038 | 0.062 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 34 | 19 | Hollis 2014 | Mating Latency | Stressed | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 90 | M | YES | Indirect | 0.194 | 0.043 | 1 | NA | NA | NA | NA | NA | NA | 5.051 |
| 34 | 19 | Hollis 2014 | Male Reproductive Success | Stressed | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 17 | M | YES | Ambiguous | 1.216 | 0.091 | 1 | 0.6010000 | 0.2950000 | 23 | 0.8760000 | 0.1380000 | 28 | 5.051 |
| 34 | 19 | Hollis 2014 | Male Reproductive Success | Stressed | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 21 | M | YES | Ambiguous | 0.659 | 0.066 | 1 | 0.5530000 | 0.3660000 | 30 | 0.7710000 | 0.2860000 | 33 | 5.051 |
| 34 | 19 | Hollis 2014 | Male Reproductive Success | Stressed | 19 | Hollis, B. and T. J. Kawecki | 2014 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 100 | YES | 15 | M | YES | Ambiguous | 0.830 | 0.090 | 1 | 0.6100000 | 0.3400000 | 22 | 0.8530000 | 0.2300000 | 23 | 5.051 |
| 35 | 19 | Hollis 2017 | Development Rate | Stressed | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 139 | YES | 48 | M | NO | Ambiguous | -0.482 | 0.028 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis 2017 | Development Rate | Stressed | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 139 | YES | 48 | F | NO | Ambiguous | 0.414 | 0.028 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis 2017 | Body Size | Stressed | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 162 | YES | 60 | M | YES | Ambiguous | 0.000 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis 2017 | Body Size | Stressed | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 162 | YES | 60 | F | YES | Ambiguous | -0.238 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis 2017 | Fitness Senescence | Stressed | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 117 | YES | 44 | M | YES | Indirect | 0.500 | 0.031 | -1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 35 | 19 | Hollis 2017 | Fitness Senescence | Stressed | 19 | Hollis, B., L. Keller and T. J. Kawecki | 2017 | Drosophila melanogaster | Fly | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 117 | YES | 45 | F | YES | Indirect | 0.017 | 0.030 | -1 | NA | NA | NA | NA | NA | NA | 4.201 |
| 36 | 29 | Immonen 2014 | Female Reproductive Success | Unstressed | 29 | Immonen, E., R. R. Snook and M. G. Ritchie | 2014 | Drosophila pseudoobscura | Fly | 3.500 | 6.00 | 7.00 | 1 | 1 | Not Blind | 100 | YES | 30 | F | NO | Direct | 0.636 | 0.046 | 1 | NA | NA | NA | NA | NA | NA | 2.320 |
| 37 | 20 | Innocenti 2014 | Body Size | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 110 | M | YES | Ambiguous | -0.306 | 0.120 | 1 | 780.1295325 | 24.5249313 | 169 | 773.1405125 | 20.8806168 | 160 | 3.368 |
| 37 | 20 | Innocenti 2014 | Body Size | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 107 | F | YES | Ambiguous | -0.290 | 0.013 | 1 | 879.0553188 | 25.2349182 | 160 | 870.6142500 | 32.5085570 | 160 | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 27 | F | NO | Direct | 0.745 | 0.053 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 31 | NO | 27 | F | NO | Direct | 0.490 | 0.051 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 50 | NO | 27 | F | NO | Direct | 0.545 | 0.051 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 58 | NO | 27 | F | NO | Direct | 0.379 | 0.050 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 30 | NO | 27 | F | NO | Direct | -0.228 | 0.049 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 31 | NO | 27 | F | NO | Direct | 0.300 | 0.050 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 50 | NO | 27 | F | NO | Direct | -0.108 | 0.049 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 37 | 20 | Innocenti 2014 | Female Reproductive Success | Unstressed | 20 | Innocenti, P., I. Flis and E. H. Morrow | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 96.00 | 0 | 1 | Not Blind | 58 | NO | 27 | F | NO | Direct | 0.080 | 0.049 | 1 | NA | NA | NA | NA | NA | NA | 3.368 |
| 38 | 3 | Jacomb 2016 | Pesticide Resistance | Stressed | 3 | Jacomb, F., J. Marsh and L. Holman | 2016 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Blind | 5 | YES | 320 | B | NO | Ambiguous | 1.246 | 0.005 | 1 | 0.8560000 | 0.0210000 | 480 | 0.8920000 | 0.0350000 | 480 | 4.201 |
| 38 | 3 | Jacomb 2016 | Pesticide Resistance | Unstressed | 3 | Jacomb, F., J. Marsh and L. Holman | 2016 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Blind | 5 | YES | 176 | B | NO | Ambiguous | 1.001 | 0.005 | -1 | 0.0880000 | 0.0850000 | 480 | 0.0270000 | 0.0140000 | 48 | 4.201 |
| 39 | 32 | Jarzebowska 2010 | Female Reproductive Success | Stressed | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | F | NO | Direct | 0.390 | 0.019 | 1 | 37.9100000 | 27.9900000 | 96 | 51.3200000 | 38.5200000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska 2010 | Female Reproductive Success | Unstressed | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | F | NO | Direct | -0.190 | 0.020 | 1 | 70.4400000 | 32.3000000 | 96 | 61.8700000 | 54.1700000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska 2010 | Extinction Rate | Stressed | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 11 | B | NO | Direct | 0.752 | 0.133 | 1 | NA | NA | NA | NA | NA | NA | 5.659 |
| 39 | 32 | Jarzebowska 2010 | Offspring Viability | Stressed | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | B | NO | Direct | 0.150 | 0.019 | 1 | 0.7390000 | 0.5240000 | 96 | 0.8080000 | 0.4010000 | 120 | 5.659 |
| 39 | 32 | Jarzebowska 2010 | Offspring Viability | Unstressed | 32 | Jarzebowska, M. and J. Radwan | 2010 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 8 | YES | 72 | B | NO | Direct | -0.088 | 0.019 | 1 | 0.8350000 | 0.4730000 | 96 | 0.7880000 | 0.5730000 | 120 | 5.659 |
| 40 | 6 | Klemme 2013 | Male Reproductive Success | Stressed | 6 | Klemme, I. and R. C. Firman | 2013 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 18 | YES | 12 | M | YES | Ambiguous | 0.769 | 0.114 | 1 | 0.2800000 | 0.4200000 | 18 | 0.7200000 | 0.6700000 | 18 | 3.068 |
| 40 | 6 | Klemme 2013 | Male Reproductive Success | Unstressed | 6 | Klemme, I. and R. C. Firman | 2013 | Mus domesticus | Mouse | 2.000 | 3.00 | 4.00 | 0 | 1 | Not Blind | 18 | YES | 12 | M | YES | Ambiguous | 0.946 | 0.119 | 1 | 0.3400000 | 0.3900000 | 18 | 0.7900000 | 0.5300000 | 18 | 3.068 |
| 41 | 4 | Lumley 2015 | Both Reproductive Success | Stressed | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 1.000 | 9.00 | 100.00 | 1 | 1 | Not Blind | 20 | NO | 56 | B | NO | Direct | 0.576 | 0.025 | -1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley 2015 | Both Reproductive Success | Stressed | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 20 | YES | 16 | B | NO | Direct | 0.559 | 0.084 | -1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley 2015 | Extinction Rate | Stressed | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 1.000 | 9.00 | 100.00 | 1 | 1 | Not Blind | 20 | NO | 56 | B | NO | Direct | 0.522 | 0.024 | 1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 41 | 4 | Lumley 2015 | Extinction Rate | Stressed | 4 | Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage | 2015 | Tribolium castaneum | Beetle | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 20 | YES | 16 | B | NO | Direct | 0.798 | 0.087 | 1 | NA | NA | NA | NA | NA | NA | 38.138 |
| 42 | 11 | Maklakov 2009 | Female Reproductive Success | Not Stated | 11 | Maklakov, A. A., R. Bonduriansky and R. C. Brooks | 2009 | Callosobruchus maculatus | Beetle | 50.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 11 | YES | 11 | F | NO | Direct | -0.958 | 0.133 | 1 | 155.1200000 | 37.7600000 | 16 | 118.0000000 | 37.7600000 | 16 | 5.429 |
| 43 | 5 | Martin 2003 | Lifespan | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | F | YES | Indirect | 0.841 | 0.138 | 1 | NA | NA | NA | NA | NA | NA | 3.833 |
| 43 | 5 | Martin 2003 | Mating Success | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | M | NO | Indirect | 0.920 | 0.140 | 1 | NA | NA | NA | NA | NA | NA | 3.833 |
| 43 | 5 | Martin 2003 | Female Reproductive Success | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | F | NO | Direct | 1.038 | 0.144 | 1 | 28.2000000 | 15.4532035 | 15 | 49.2000000 | 23.1604404 | 15 | 3.833 |
| 43 | 5 | Martin 2003 | Lifespan | Stressed | 5 | Martin, O. Y. and D. J. Hosken | 2003 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 29 | YES | 10 | F | NO | Indirect | -1.314 | 0.155 | 1 | 2.2130508 | 0.0600641 | 15 | 2.1161864 | 0.0817265 | 15 | 3.833 |
| 44 | 5 | Martin 2003 | Female Reproductive Success | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 42 | YES | 12 | F | NO | Direct | 0.421 | 0.159 | 1 | 34.9043478 | 21.5075526 | 12 | 42.9391304 | 14.7600851 | 12 | 3.833 |
| 44 | 5 | Martin 2003 | Female Reproductive Success | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 250.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | YES | 12 | F | NO | Direct | -0.075 | 0.155 | 1 | 34.9043478 | 21.5075526 | 12 | 33.4434783 | 15.6035186 | 12 | 3.833 |
| 44 | 5 | Martin 2003 | Female Reproductive Success | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 10.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | NO | 12 | F | NO | Direct | -0.603 | 0.163 | 1 | 42.9391304 | 14.7600851 | 12 | 33.4434783 | 15.6035186 | 12 | 3.833 |
| 44 | 5 | Martin 2003 | Lifespan | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 25.000 | 1.00 | 50.00 | 1 | 1 | Not Blind | 42 | YES | 24 | F | NO | Indirect | -0.405 | 0.082 | 1 | 17.3460898 | 2.4943223 | 24 | 16.3327787 | 2.4698682 | 24 | 3.833 |
| 44 | 5 | Martin 2003 | Lifespan | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 250.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | YES | 24 | F | NO | Indirect | -0.638 | 0.085 | 1 | 17.3460898 | 2.4943223 | 24 | 15.8086522 | 2.2497809 | 24 | 3.833 |
| 44 | 5 | Martin 2003 | Lifespan | Unstressed | 5 | Martin, O. Y. and D. J. Hosken | 2004 | Sepsis cynipsea | Fly | 10.000 | 1.00 | 500.00 | 1 | 1 | Not Blind | 42 | NO | 24 | F | NO | Indirect | -0.216 | 0.081 | 1 | 16.3327787 | 2.4698682 | 24 | 15.8086522 | 2.2497809 | 24 | 3.833 |
| 45 | 33 | McGuigan 2011 | Mating Success | Stressed | 33 | McGuigan, K., D. Petfield and M. W. Blows | 2011 | Drosophila serrata | Fly | 2.405 | 3.81 | 4.81 | 1 | 0 | Not Blind | 23 | YES | 292 | M | NO | Indirect | 0.034 | 0.014 | 1 | 0.4997000 | 0.3400000 | 146 | 0.5097000 | 0.2460000 | 146 | 5.146 |
| 45 | 33 | McGuigan 2011 | Female Reproductive Success | Stressed | 33 | McGuigan, K., D. Petfield and M. W. Blows | 2011 | Drosophila serrata | Fly | 2.405 | 3.81 | 4.81 | 1 | 0 | Not Blind | 26 | YES | 208 | F | NO | Direct | 0.114 | 0.019 | 1 | 49.9300000 | 22.7000000 | 104 | 52.1740000 | 16.1000000 | 104 | 5.146 |
| 46 | 21 | McKean 2008 | Body Size | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | B | YES | Ambiguous | 1.528 | 0.242 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Development Rate | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | B | NO | Ambiguous | 0.853 | 0.105 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Development Rate | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | B | NO | Ambiguous | 3.124 | 0.218 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Development Rate | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 40 | B | NO | Ambiguous | 2.655 | 0.184 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Mating Success | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 52 | M | NO | Indirect | 0.839 | 0.081 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Mating Success | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 52 | M | NO | Indirect | 1.598 | 0.099 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Mating Success | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 52 | M | NO | Indirect | 1.907 | 0.110 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 46 | 21 | McKean 2008 | Immunity | Unstressed | 21 | McKean, K. A. and L. Nunney | 2008 | Drosophila melanogaster | Fly | 1.700 | 2.40 | 170.00 | 1 | 1 | Not Blind | 58 | NO | 80 | B | NO | Ambiguous | -0.911 | 0.054 | -1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 47 | 28 | McNamara 2016 | Ejaculate Quality and Production | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 153 | M | YES | Indirect | -0.106 | 0.027 | 1 | 2.7000000 | 0.8442748 | 88 | 2.6000000 | 1.0480935 | 65 | 4.259 |
| 47 | 28 | McNamara 2016 | Ejaculate Quality and Production | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 145 | M | YES | Indirect | -0.157 | 0.028 | 1 | 0.1600000 | 0.0728835 | 83 | 0.1500000 | 0.0472440 | 62 | 4.259 |
| 47 | 28 | McNamara 2016 | Ejaculate Quality and Production | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 202 | M | NO | Indirect | 0.100 | 0.020 | 1 | 0.5700000 | 0.1014889 | 103 | 0.5800000 | 0.0994987 | 99 | 4.259 |
| 47 | 28 | McNamara 2016 | Ejaculate Quality and Production | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 101 | M | YES | Indirect | -0.280 | 0.039 | 1 | 0.8600000 | 0.1428286 | 51 | 0.8200000 | 0.1414214 | 50 | 4.259 |
| 47 | 28 | McNamara 2016 | Mating Duration | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 127 | M | YES | Ambiguous | -0.371 | 0.032 | 1 | 534.6200000 | 204.1600000 | 64 | 466.0200000 | 160.0944118 | 63 | 4.259 |
| 47 | 28 | McNamara 2016 | Female Reproductive Success | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 127 | F | NO | Direct | 0.156 | 0.031 | 1 | 34.2600000 | 18.7200000 | 64 | 37.1700000 | 18.2556840 | 63 | 4.259 |
| 47 | 28 | McNamara 2016 | Male Reproductive Success | Unstressed | 28 | McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons | 2016 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 32 | NO | 125 | M | YES | Ambiguous | 0.315 | 0.032 | 1 | 0.6700000 | 0.3149603 | 62 | 0.7700000 | 0.3174902 | 63 | 4.259 |
| 48 | 12 | McNamara 2014 | Ejaculate Quality and Production | Unstressed | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 351 | M | NO | Indirect | 0.568 | 0.012 | 1 | 0.9400000 | 0.3200000 | 179 | 1.0800000 | 0.1300000 | 172 | 3.177 |
| 48 | 12 | McNamara 2014 | Immunity | Unstressed | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 336 | M | NO | Ambiguous | 0.000 | 0.012 | 1 | 1.6500000 | 3.4000000 | 175 | 1.6500000 | 3.2000000 | 161 | 3.177 |
| 48 | 12 | McNamara 2014 | Immunity | Unstressed | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 413 | F | NO | Ambiguous | -0.050 | 0.010 | 1 | 80.2000000 | 21.8000000 | 203 | 79.0500000 | 20.3000000 | 210 | 3.177 |
| 48 | 12 | McNamara 2014 | Immunity | Unstressed | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 788 | B | NO | Ambiguous | -0.106 | 0.005 | -1 | NA | NA | NA | NA | NA | 401 | 3.177 |
| 48 | 12 | McNamara 2014 | Immunity | Unstressed | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 335 | M | NO | Ambiguous | -0.108 | 0.012 | 1 | 0.5300000 | 0.1650000 | 173 | 0.5100000 | 0.2050000 | 162 | 3.177 |
| 48 | 12 | McNamara 2014 | Immunity | Unstressed | 12 | McNamara, K. B., E. van Lieshout and L. W. Simmons | 2014 | Teleogryllus oceanicus | Cricket | 2.000 | 3.00 | 4.00 | 0 | 1 | Blind | 1 | YES | 406 | F | NO | Ambiguous | -0.098 | 0.010 | 1 | 0.5500000 | 0.2040000 | 202 | 0.5300000 | 0.2050000 | 204 | 3.177 |
| 49 | 4 | Michalczyk 2011 | Mating Latency | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 47 | M | YES | Indirect | 0.556 | 0.086 | -1 | 358.9000000 | 494.4000000 | 24 | 143.4000000 | 203.6000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Mating Latency | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 57 | M | YES | Indirect | 0.470 | 0.070 | -1 | 294.7000000 | 313.6000000 | 28 | 158.0000000 | 259.1000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Mating Duration | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 53 | M | YES | Ambiguous | 1.987 | 0.112 | 1 | 73.5000000 | 67.7000000 | 30 | 483.4000000 | 299.5000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Mating Duration | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 58 | M | YES | Ambiguous | 0.551 | 0.070 | 1 | 181.8000000 | 198.5000000 | 29 | 323.3000000 | 298.6000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Mating Frequency | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 53 | M | YES | Indirect | 1.982 | 0.112 | 1 | 2.1000000 | 2.2000000 | 30 | 22.2000000 | 15.0000000 | 23 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Mating Frequency | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 58 | M | YES | Indirect | 0.929 | 0.075 | 1 | 4.2000000 | 4.1000000 | 29 | 15.0000000 | 15.7000000 | 29 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Female Reproductive Success | Stressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 30 | F | NO | Direct | 1.852 | 0.183 | 1 | 183.8000000 | 80.6000000 | 15 | 409.5000000 | 147.0000000 | 15 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Female Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 30 | F | NO | Direct | 0.061 | 0.126 | 1 | 346.1000000 | 255.8000000 | 15 | 366.3000000 | 378.7000000 | 15 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 20 | M | YES | Ambiguous | 0.614 | 0.193 | 1 | 0.4570000 | 0.3580000 | 10 | 0.6320000 | 0.1450000 | 10 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 20 | M | YES | Ambiguous | 0.931 | 0.205 | 1 | 0.5290000 | 0.2500000 | 10 | 0.7200000 | 0.1210000 | 10 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 20 | M | YES | Ambiguous | 0.319 | 0.186 | 1 | 0.5700000 | 0.0640000 | 10 | 0.6210000 | 0.2070000 | 10 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | M | YES | Ambiguous | -0.219 | 0.156 | 1 | 0.4530000 | 0.3920000 | 12 | 0.3610000 | 0.4180000 | 12 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | M | YES | Ambiguous | 0.178 | 0.156 | 1 | 0.4450000 | 0.4240000 | 12 | 0.5140000 | 0.3180000 | 12 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | M | YES | Ambiguous | 0.025 | 0.155 | 1 | 0.4210000 | 0.3560000 | 12 | 0.4300000 | 0.3340000 | 12 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | M | YES | Ambiguous | 0.110 | 0.156 | 1 | 0.7970000 | 0.3520000 | 12 | 0.8330000 | 0.2730000 | 12 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | M | YES | Ambiguous | 0.724 | 0.166 | 1 | 0.6940000 | 0.3350000 | 12 | 0.9010000 | 0.2000000 | 12 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Male Reproductive Success | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 24 | M | YES | Ambiguous | -0.389 | 0.159 | 1 | 0.8390000 | 0.2080000 | 12 | 0.7280000 | 0.3300000 | 12 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Lifespan | Stressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 30 | F | NO | Indirect | 0.211 | 0.127 | 1 | 8.3000000 | 2.5500000 | 15 | 8.9000000 | 2.9700000 | 15 | 5.146 |
| 49 | 4 | Michalczyk 2011 | Lifespan | Unstressed | 4 | Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage | 2011 | Tribolium castaneum | Beetle | 1.050 | 6.00 | 105.00 | 1 | 1 | Not Blind | 20 | NO | 29 | F | NO | Indirect | 0.677 | 0.138 | 1 | 8.8000000 | 2.7200000 | 14 | 10.3000000 | 1.4400000 | 15 | 5.146 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 27 | M | NO | Indirect | 0.471 | 0.074 | 1 | 0.2000000 | 0.1120000 | 27 | 0.2540000 | 0.1140000 | 27 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 29 | M | NO | Indirect | 0.041 | 0.069 | 1 | 0.8890000 | 0.0740000 | 28 | 0.8920000 | 0.0700000 | 30 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 31 | M | NO | Indirect | 0.211 | 0.063 | 1 | 0.8760000 | 0.1030000 | 31 | 0.9010000 | 0.1290000 | 31 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Latency | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | M | YES | Indirect | -0.062 | 0.014 | -1 | 2.9400000 | 1.8800000 | 149 | 3.1200000 | 3.6900000 | 143 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Duration | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | M | YES | Ambiguous | 0.918 | 0.015 | 1 | 11.7400000 | 2.3700000 | 149 | 14.0500000 | 2.6500000 | 143 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 122 | M | NO | Indirect | 0.153 | 0.016 | 1 | 0.0771000 | 0.1630000 | 122 | 0.1023000 | 0.1650000 | 121 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 27 | M | NO | Indirect | 0.314 | 0.073 | 1 | 0.1700000 | 0.0720000 | 27 | 0.2000000 | 0.1120000 | 27 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 29 | M | NO | Indirect | 0.613 | 0.073 | 1 | 0.8350000 | 0.0980000 | 28 | 0.8890000 | 0.0740000 | 28 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 31 | M | NO | Indirect | -0.178 | 0.064 | 1 | 0.8970000 | 0.1290000 | 30 | 0.8760000 | 0.1030000 | 31 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Latency | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | M | YES | Indirect | 0.159 | 0.014 | -1 | 3.5600000 | 5.2200000 | 142 | 2.9400000 | 1.8800000 | 149 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Duration | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | M | YES | Ambiguous | -0.471 | 0.014 | 1 | 12.8800000 | 2.4600000 | 142 | 11.7400000 | 2.3700000 | 149 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 122 | M | NO | Indirect | 0.170 | 0.016 | 1 | 0.0543000 | 0.0961000 | 122 | 0.0771000 | 0.1630000 | 122 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 27 | M | NO | Indirect | 0.868 | 0.079 | 1 | 0.1700000 | 0.0720000 | 27 | 0.2540000 | 0.1140000 | 27 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 29 | M | NO | Indirect | 0.660 | 0.073 | 1 | 0.8350000 | 0.0980000 | 28 | 0.8920000 | 0.0700000 | 30 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 31 | M | NO | Indirect | 0.031 | 0.064 | 1 | 0.8970000 | 0.1290000 | 30 | 0.9010000 | 0.1290000 | 31 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Latency | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | M | YES | Indirect | 0.097 | 0.014 | -1 | 3.5600000 | 5.2200000 | 142 | 3.1200000 | 3.6900000 | 143 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Duration | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 145 | M | YES | Ambiguous | 0.456 | 0.014 | 1 | 12.8800000 | 2.4600000 | 142 | 14.0500000 | 2.6500000 | 143 | 4.659 |
| 50 | 22 | Nandy 2013 | Mating Success | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 122 | M | NO | Indirect | 0.355 | 0.017 | 1 | 0.0543000 | 0.0961000 | 122 | 0.1023000 | 0.1650000 | 121 | 4.659 |
| 50 | 22 | Nandy 2013 | Offspring Viability | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 1440 | B | NO | Direct | 0.088 | 0.001 | 1 | 0.8700000 | 0.3415260 | 1440 | 0.9000000 | 0.3415260 | 1440 | 4.659 |
| 50 | 22 | Nandy 2013 | Offspring Viability | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 1440 | B | NO | Direct | -0.088 | 0.001 | 1 | 0.9000000 | 0.3415260 | 1440 | 0.8700000 | 0.3415260 | 1440 | 4.659 |
| 50 | 22 | Nandy 2013 | Offspring Viability | Unstressed | 22 | Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad | 2013 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Not Blind | 50 | NO | 1440 | B | NO | Direct | 0.000 | 0.001 | 1 | 0.9000000 | 0.3415260 | 1440 | 0.9000000 | 0.3415260 | 1440 | 4.659 |
| 51 | 22 | Nandy 2014 | Body Size | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | F | YES | Ambiguous | -0.089 | 0.072 | 1 | 0.2830000 | 0.0124900 | 27 | 0.2820000 | 0.0101274 | 27 | 4.612 |
| 51 | 22 | Nandy 2014 | Body Size | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | F | YES | Ambiguous | -0.981 | 0.081 | 1 | 0.2943519 | 0.0103532 | 27 | 0.2826667 | 0.0124900 | 27 | 4.612 |
| 51 | 22 | Nandy 2014 | Body Size | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | F | YES | Ambiguous | -1.183 | 0.085 | 1 | 0.2943519 | 0.0103532 | 27 | 0.2822222 | 0.0101274 | 27 | 4.612 |
| 51 | 22 | Nandy 2014 | Mating Frequency | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 30 | F | YES | Indirect | 0.090 | 0.065 | 1 | 6.7439524 | 3.1492291 | 30 | 7.0200794 | 2.9816484 | 30 | 4.612 |
| 51 | 22 | Nandy 2014 | Mating Frequency | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 30 | F | YES | Indirect | -0.284 | 0.066 | 1 | 7.6940238 | 3.4353720 | 30 | 6.7439524 | 3.1492291 | 30 | 4.612 |
| 51 | 22 | Nandy 2014 | Mating Frequency | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 30 | F | YES | Indirect | -0.205 | 0.065 | 1 | 7.6940238 | 3.4353720 | 30 | 7.0200794 | 2.9816484 | 30 | 4.612 |
| 51 | 22 | Nandy 2014 | Female Reproductive Success | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | F | NO | Direct | 0.185 | 0.067 | 1 | 50.3663793 | 7.6774347 | 29 | 51.5985906 | 5.1763110 | 29 | 4.612 |
| 51 | 22 | Nandy 2014 | Female Reproductive Success | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | F | NO | Direct | -0.890 | 0.075 | 1 | 56.1414116 | 4.7002918 | 28 | 50.3663793 | 7.6774347 | 29 | 4.612 |
| 51 | 22 | Nandy 2014 | Female Reproductive Success | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | F | NO | Direct | -0.905 | 0.075 | 1 | 56.1414116 | 4.7002918 | 28 | 51.5985906 | 5.1763110 | 29 | 4.612 |
| 51 | 22 | Nandy 2014 | Female Reproductive Success | Stressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 28 | F | NO | Direct | 0.771 | 0.076 | 1 | 47.9508929 | 7.0792749 | 28 | 52.6177249 | 4.5419731 | 27 | 4.612 |
| 51 | 22 | Nandy 2014 | Female Reproductive Success | Stressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 28 | F | NO | Direct | 0.675 | 0.071 | 1 | 42.6208333 | 8.3991535 | 30 | 47.9508929 | 7.0792749 | 28 | 4.612 |
| 51 | 22 | Nandy 2014 | Female Reproductive Success | Stressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 28 | F | NO | Direct | 1.439 | 0.087 | 1 | 42.6208333 | 8.3991535 | 30 | 52.6177249 | 4.5419731 | 27 | 4.612 |
| 51 | 22 | Nandy 2014 | Lifespan | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | F | NO | Indirect | 1.315 | 0.081 | 1 | 33.4558333 | 4.1586802 | 30 | 38.8756979 | 3.9717452 | 29 | 4.612 |
| 51 | 22 | Nandy 2014 | Lifespan | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | F | NO | Indirect | 0.041 | 0.067 | 1 | 33.2781463 | 4.5549330 | 28 | 33.4558333 | 4.1586802 | 30 | 4.612 |
| 51 | 22 | Nandy 2014 | Lifespan | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | F | NO | Indirect | 1.295 | 0.083 | 1 | 33.2781463 | 4.5549330 | 28 | 38.8756979 | 3.9717452 | 29 | 4.612 |
| 51 | 22 | Nandy 2014 | Lifespan | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | F | NO | Indirect | 0.189 | 0.074 | 1 | 56.2509143 | 5.8375189 | 25 | 57.2156463 | 4.2069037 | 28 | 4.612 |
| 51 | 22 | Nandy 2014 | Lifespan | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 29 | F | NO | Indirect | -0.699 | 0.078 | 1 | 60.1348639 | 5.1204446 | 28 | 56.2509143 | 5.8375189 | 25 | 4.612 |
| 51 | 22 | Nandy 2014 | Lifespan | Unstressed | 22 | Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad | 2014 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 32.00 | 1 | 1 | Blind | 45 | NO | 27 | F | NO | Indirect | -0.612 | 0.073 | 1 | 60.1348639 | 5.1204446 | 28 | 57.2156463 | 4.2069037 | 28 | 4.612 |
| 52 | 27 | Nelson 2013 | Body Size | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 3 | YES | 20 | M | YES | Ambiguous | -0.831 | 0.201 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson 2013 | Body Size | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 3 | YES | 20 | F | YES | Ambiguous | -0.831 | 0.201 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson 2013 | Male Attractiveness | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 3 | YES | 20 | M | YES | Ambiguous | 1.999 | 0.283 | 1 | 0.3850000 | 0.1090000 | 10 | 0.6210000 | 0.1170000 | 10 | 3.407 |
| 52 | 27 | Nelson 2013 | Male Reproductive Success | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 100 | M | YES | Ambiguous | 0.415 | 0.040 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson 2013 | Female Reproductive Success | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 200 | F | NO | Direct | -0.118 | 0.020 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 52 | 27 | Nelson 2013 | Male Reproductive Success | Stressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 12 | M | YES | Ambiguous | 0.835 | 0.313 | 1 | 4.5300000 | 3.5000000 | 6 | 9.5400000 | 7.0100000 | 6 | 3.407 |
| 52 | 27 | Nelson 2013 | Male Reproductive Success | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 12 | M | YES | Ambiguous | 0.849 | 0.314 | 1 | 13.5000000 | 11.5700000 | 6 | 23.1800000 | 9.3500000 | 6 | 3.407 |
| 52 | 27 | Nelson 2013 | Offspring Viability | Unstressed | 27 | Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts | 2013 | Mus musculus | Mouse | 15.000 | 0.50 | 30.00 | 1 | 1 | Blind | 2 | YES | 100 | M | NO | Direct | -0.304 | 0.041 | 1 | NA | NA | NA | NA | NA | NA | 3.407 |
| 53 | 23 | Partridge 1980 | Offspring Viability | Unstressed | 23 | Partridge, L. | 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | 1 | 1 | Not Blind | 1 | YES | 41 | B | NO | Direct | 0.773 | 0.103 | 1 | 48.9000000 | 2.9495762 | 18 | 51.1000000 | 2.6645825 | 23 | NA |
| 53 | 23 | Partridge 1980 | Offspring Viability | Unstressed | 23 | Partridge, L. | 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | 1 | 1 | Not Blind | 1 | YES | 35 | B | NO | Direct | 0.874 | 0.125 | 1 | 48.1000000 | 2.4083189 | 14 | 49.8000000 | 1.4832397 | 21 | NA |
| 53 | 23 | Partridge 1980 | Offspring Viability | Unstressed | 23 | Partridge, L. | 1980 | Drosophila melanogaster | Fly | 100.000 | 1.00 | 200.00 | 1 | 1 | Not Blind | 1 | YES | 60 | B | NO | Direct | 0.707 | 0.069 | 1 | 49.4400000 | 1.4142136 | 32 | 50.4500000 | 1.4142136 | 28 | NA |
| 54 | 31 | Pelabon 2014 | Body Size | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 171 | F | YES | Ambiguous | 0.080 | 0.023 | 1 | 25.0000000 | 4.3826932 | 80 | 25.3600000 | 4.5789082 | 91 | 3.232 |
| 54 | 31 | Pelabon 2014 | Body Size | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | M | YES | Ambiguous | 0.019 | 0.014 | 1 | 16.1800000 | 1.6099182 | 127 | 16.2100000 | 1.5982097 | 157 | 3.232 |
| 54 | 31 | Pelabon 2014 | Male Attractiveness | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | M | YES | Ambiguous | 0.120 | 0.014 | 1 | 1.5900000 | 0.8624562 | 127 | 1.7000000 | 0.9589258 | 157 | 3.232 |
| 54 | 31 | Pelabon 2014 | Male Attractiveness | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | M | YES | Ambiguous | 0.000 | 0.014 | 1 | 3.1300000 | 0.1437427 | 127 | 3.1300000 | 0.1278568 | 157 | 3.232 |
| 54 | 31 | Pelabon 2014 | Male Attractiveness | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | M | YES | Ambiguous | 0.193 | 0.014 | 1 | 0.1600000 | 0.8624562 | 127 | 0.3300000 | 0.8949974 | 157 | 3.232 |
| 54 | 31 | Pelabon 2014 | Male Attractiveness | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 284 | M | YES | Ambiguous | 0.055 | 0.014 | 1 | 150.8900000 | 7.9058485 | 127 | 151.3400000 | 8.2900267 | 157 | 3.232 |
| 54 | 31 | Pelabon 2014 | Female Reproductive Success | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 174 | F | NO | Direct | -0.277 | 0.023 | 1 | 1.5900000 | 0.7244860 | 80 | 1.3820000 | 0.7659334 | 94 | 3.232 |
| 54 | 31 | Pelabon 2014 | Offspring Viability | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 173 | F | YES | Direct | 0.621 | 0.024 | 1 | 6.9400000 | 0.5992662 | 80 | 7.3200000 | 0.6171936 | 93 | 3.232 |
| 54 | 31 | Pelabon 2014 | Offspring Viability | Unstressed | 31 | Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist | 2014 | Poecilia reticulata | Guppy | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 9 | YES | 145 | F | NO | Direct | 0.010 | 0.027 | 1 | 3.3200000 | 2.8195212 | 73 | 3.3500000 | 2.9698485 | 72 | 3.232 |
| 55 | 24 | Pitnick 2001a | Body Size | Unstressed | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 228 | F | YES | Ambiguous | 0.973 | 0.020 | 1 | 0.8790000 | 0.0427083 | 114 | 0.9210000 | 0.0427083 | 114 | NA |
| 55 | 24 | Pitnick 2001a | Body Size | Unstressed | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 234 | F | YES | Ambiguous | 0.763 | 0.018 | 1 | 0.8950000 | 0.0432666 | 117 | 0.9240000 | 0.0324500 | 117 | NA |
| 55 | 24 | Pitnick 2001a | Female Reproductive Success | Unstressed | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 230 | F | NO | Direct | -0.363 | 0.018 | 1 | 129.1000000 | 80.4285397 | 115 | 99.0000000 | 84.7180618 | 115 | NA |
| 55 | 24 | Pitnick 2001a | Female Reproductive Success | Unstressed | 24 | Pitnick, S., W. D. Brown and G. T. Miller | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Not Blind | 84 | YES | 236 | F | NO | Direct | -0.246 | 0.017 | 1 | 122.0000000 | 86.9022439 | 118 | 101.2000000 | 81.4708537 | 118 | NA |
| 56 | 24 | Pitnick 2001b | Body Size | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | M | YES | Ambiguous | 2.115 | 0.062 | 1 | 233.1300000 | 16.9400000 | 50 | 270.8300000 | 18.4100000 | 50 | NA |
| 56 | 24 | Pitnick 2001b | Body Size | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | M | YES | Ambiguous | 1.346 | 0.048 | 1 | 211.6700000 | 19.8900000 | 50 | 237.1900000 | 17.6800000 | 50 | NA |
| 56 | 24 | Pitnick 2001b | Ejaculate Quality and Production | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | M | NO | Indirect | 2.886 | 0.081 | 1 | 8.7307692 | 1.5410000 | 50 | 13.7564103 | 1.9037490 | 50 | NA |
| 56 | 24 | Pitnick 2001b | Ejaculate Quality and Production | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 100 | M | NO | Indirect | 0.596 | 0.041 | 1 | 7.7820513 | 2.2663679 | 50 | 9.0897436 | 2.0850585 | 50 | NA |
| 56 | 24 | Pitnick 2001b | Ejaculate Quality and Production | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 30 | M | NO | Indirect | 1.069 | 0.145 | 1 | 25.5723951 | 4.4651987 | 15 | 30.4600812 | 4.4023085 | 15 | NA |
| 56 | 24 | Pitnick 2001b | Ejaculate Quality and Production | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 61 | YES | 30 | M | NO | Indirect | 1.484 | 0.163 | 1 | 27.4722598 | 3.3331765 | 15 | 32.9769959 | 3.8991876 | 15 | NA |
| 56 | 24 | Pitnick 2001b | Ejaculate Quality and Production | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 30 | M | NO | Indirect | 0.175 | 0.127 | 1 | 177.4228571 | 4.2492160 | 15 | 178.1600000 | 3.9836400 | 15 | NA |
| 56 | 24 | Pitnick 2001b | Ejaculate Quality and Production | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 30 | M | NO | Indirect | -1.448 | 0.161 | 1 | 179.7885714 | 3.1869120 | 15 | 174.8857143 | 3.3860940 | 15 | NA |
| 56 | 24 | Pitnick 2001b | Mating Success | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 178 | M | NO | Indirect | 0.015 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick 2001b | Mating Success | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 81 | YES | 180 | M | NO | Indirect | 0.148 | 0.022 | 1 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick 2001b | Male Reproductive Success | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 66 | YES | 140 | M | YES | Ambiguous | -0.436 | 0.029 | 1 | NA | NA | NA | NA | NA | NA | NA |
| 56 | 24 | Pitnick 2001b | Male Reproductive Success | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 38 | YES | 315 | M | YES | Ambiguous | 0.022 | 0.014 | 1 | 0.5878581 | 0.3673826 | 112 | 0.5976808 | 0.4837226 | 203 | NA |
| 56 | 24 | Pitnick 2001b | Male Reproductive Success | Unstressed | 24 | Pitnick, S., G. T. Miller, J. Reagan and B. Holland | 2001 | Drosophila melanogaster | Fly | 2.000 | 3.00 | 4.00 | 1 | 1 | Blind | 38 | YES | 344 | M | YES | Ambiguous | 0.327 | 0.012 | 1 | 0.4503411 | 0.4170165 | 162 | 0.5968622 | 0.4754863 | 182 | NA |
| 57 | 32 | Plesnar-Bielak 2011 | Offspring Viability | Stressed | 32 | Plesnar, A., M. Konior and J. Radwan | 2011 | Rhizoglyphus robini | Mite | 1.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 2 | NO | 80 | M | NO | Direct | 0.060 | 0.049 | 1 | 0.7700000 | 0.1700000 | 40 | 0.7800000 | 0.1600000 | 40 | 1.029 |
| 57 | 32 | Plesnar-Bielak 2011 | Offspring Viability | Unstressed | 32 | Plesnar, A., M. Konior and J. Radwan | 2011 | Rhizoglyphus robini | Mite | 1.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 2 | NO | 80 | M | NO | Direct | -0.094 | 0.049 | 1 | 0.9500000 | 0.1100000 | 40 | 0.9400000 | 0.1000000 | 40 | 1.029 |
| 58 | 32 | Plesnar-Bielak 2012 | Female Reproductive Success | Stressed | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 60 | F | NO | Direct | 1.504 | 0.127 | 1 | 31.0909091 | 15.1950949 | 11 | 92.8571429 | 43.4161068 | 49 | 5.683 |
| 58 | 32 | Plesnar-Bielak 2012 | Female Reproductive Success | Unstressed | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 95 | F | NO | Direct | 0.174 | 0.038 | 1 | 134.5000000 | 48.3000374 | 48 | 143.1428571 | 49.8409939 | 56 | 5.683 |
| 58 | 32 | Plesnar-Bielak 2012 | Female Reproductive Success | Stressed | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 104 | F | NO | Direct | 1.171 | 0.071 | 1 | NA | NA | NA | NA | NA | NA | 5.683 |
| 58 | 32 | Plesnar-Bielak 2012 | Female Reproductive Success | Unstressed | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 117 | F | NO | Direct | 0.526 | 0.120 | 1 | NA | NA | NA | NA | NA | NA | 5.683 |
| 58 | 32 | Plesnar-Bielak 2012 | Extinction Rate | Stressed | 32 | Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan | 2012 | Rhizoglyphus robini | Mite | 20.000 | 1.00 | 40.00 | 1 | 1 | Not Blind | 14 | YES | 11 | B | NO | Direct | 1.510 | 0.740 | 1 | NA | NA | NA | NA | NA | NA | 5.683 |
| 59 | 13 | Power 2014 | Female Reproductive Success | Stressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | F | NO | Direct | 1.331 | 0.148 | 1 | 741.0000000 | 154.4321210 | 18 | 948.0000000 | 147.7954668 | 14 | 2.747 |
| 59 | 13 | Power 2014 | Female Reproductive Success | Stressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | F | NO | Direct | 1.339 | 0.149 | 1 | 37.0000000 | 7.6367532 | 18 | 47.4000000 | 7.4833148 | 14 | 2.747 |
| 59 | 13 | Power 2014 | Female Reproductive Success | Unstressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | F | NO | Direct | 1.242 | 0.128 | 1 | 602.0000000 | 143.8255193 | 18 | 752.0000000 | 84.8528137 | 18 | 2.747 |
| 59 | 13 | Power 2014 | Female Reproductive Success | Unstressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | F | NO | Direct | 1.240 | 0.128 | 1 | 30.1000000 | 7.2124892 | 18 | 37.6000000 | 4.2426407 | 18 | 2.747 |
| 59 | 13 | Power 2014 | Offspring Viability | Stressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | F | NO | Direct | 1.465 | 0.154 | 1 | 765.0000000 | 156.9777054 | 18 | 978.0000000 | 118.9847049 | 14 | 2.747 |
| 59 | 13 | Power 2014 | Offspring Viability | Stressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | F | NO | Direct | 1.428 | 0.153 | 1 | 38.3000000 | 8.0610173 | 18 | 48.9000000 | 5.9866518 | 14 | 2.747 |
| 59 | 13 | Power 2014 | Offspring Viability | Stressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 32 | B | NO | Direct | 1.017 | 0.137 | 1 | 70.4000000 | 9.7580736 | 18 | 79.1000000 | 5.9866518 | 14 | 2.747 |
| 59 | 13 | Power 2014 | Offspring Viability | Unstressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | F | NO | Direct | 1.194 | 0.126 | 1 | 674.0000000 | 181.5850214 | 18 | 852.0000000 | 97.5807358 | 18 | 2.747 |
| 59 | 13 | Power 2014 | Offspring Viability | Unstressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | F | NO | Direct | 1.223 | 0.127 | 1 | 33.7000000 | 8.9095454 | 18 | 42.6000000 | 4.6669048 | 18 | 2.747 |
| 59 | 13 | Power 2014 | Offspring Viability | Unstressed | 13 | Power, D. J. and L. Holman | 2014 | Callosobruchus maculatus | Beetle | 1.500 | 2.00 | 3.00 | 0 | 1 | Not Blind | 5 | YES | 36 | B | NO | Direct | 1.050 | 0.122 | 1 | 73.0000000 | 7.6367532 | 18 | 79.8000000 | 4.6669048 | 18 | 2.747 |
| 60 | 13 | Power 2015 | Female Reproductive Success | Unstressed | 13 | Power, D. J. and L. Holman | 2015 | Callosobruchus maculatus | Beetle | 2.000 | 3.00 | 4.00 | 1 | 0 | Blind | 3 | YES | 39 | F | NO | Direct | 0.160 | 0.099 | 1 | 39.4500000 | 15.0559483 | 20 | 41.7368421 | 12.7446813 | 19 | 2.747 |
| 60 | 13 | Power 2015 | Offspring Viability | Unstressed | 13 | Power, D. J. and L. Holman | 2015 | Callosobruchus maculatus | Beetle | 2.000 | 3.00 | 4.00 | 1 | 0 | Blind | 3 | YES | 39 | F | NO | Direct | -0.396 | 0.100 | 1 | 0.6091667 | 0.1700941 | 20 | 0.5425014 | 0.1557092 | 19 | 2.747 |
| 61 | 25 | Promislow 1998 | Body Size | Unstressed | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 13 | YES | 150 | M | YES | Ambiguous | 0.100 | 0.026 | 1 | -0.0125000 | 0.2600000 | 75 | 0.0168000 | 0.3190000 | 75 | 9.821 |
| 61 | 25 | Promislow 1998 | Body Size | Unstressed | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 13 | YES | 150 | F | YES | Ambiguous | -0.449 | 0.027 | 1 | 0.0950000 | 0.1750000 | 75 | -0.0870000 | 0.5430000 | 75 | 9.821 |
| 61 | 25 | Promislow 1998 | Offspring Viability | Unstressed | 25 | Promislow, D. E. L., E. A. Smith and L. Pearse | 1998 | Drosophila melanogaster | Fly | 3.000 | 5.00 | 6.00 | 1 | 1 | Not Blind | 17 | YES | 10182 | B | NO | Direct | 0.006 | 0.001 | 1 | NA | NA | NA | NA | NA | NA | 9.821 |
| 62 | 32 | Radwan 2004a | Offspring Viability | Stressed | 32 | Radwan, J. | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 2 | YES | 50 | B | NO | Direct | 0.739 | 0.118 | 1 | 42.1100000 | 32.8700000 | 39 | 65.3900000 | 22.6900000 | 11 | 3.914 |
| 63 | 32 | Radwan 2004b | Female Reproductive Success | Unstressed | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 92 | F | NO | Direct | -0.142 | 0.043 | 1 | 112.7000000 | 25.1624442 | 46 | 108.7000000 | 30.3170150 | 46 | 2.893 |
| 63 | 32 | Radwan 2004b | Male Reproductive Success | Unstressed | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 66 | M | YES | Ambiguous | -0.123 | 0.059 | 1 | 0.6170000 | 0.7180703 | 33 | 0.5430000 | 0.4423313 | 33 | 2.893 |
| 63 | 32 | Radwan 2004b | Offspring Viability | Unstressed | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 106 | B | NO | Direct | 0.106 | 0.037 | 1 | 0.7030000 | 0.1965630 | 53 | 0.7610000 | 0.7425712 | 53 | 2.893 |
| 63 | 32 | Radwan 2004b | Lifespan | Unstressed | 32 | Radwan, J., J. Unrug, K. Sigorska and K. Gawronska | 2004 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 11 | YES | 90 | F | NO | Indirect | -0.085 | 0.044 | 1 | 25.3700000 | 21.1979244 | 45 | 23.7400000 | 16.4350996 | 45 | 2.893 |
| 64 | 34 | Rundle 2006 | Both Reproductive Success | Stressed | 34 | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | Drosophila serrata | Fly | 55.000 | 1.00 | 110.00 | 1 | 1 | Not Blind | 16 | YES | 552 | B | NO | Direct | -0.067 | 0.007 | 1 | 30.4100000 | 40.5200000 | 276 | 27.6800000 | 40.5200000 | 276 | 4.292 |
| 64 | 34 | Rundle 2006 | Both Reproductive Success | Unstressed | 34 | Rundle, H. D., S. F. Chenoweth and M. W. Blows | 2006 | Drosophila serrata | Fly | 55.000 | 1.00 | 110.00 | 1 | 1 | Not Blind | 16 | YES | 552 | B | NO | Direct | -0.028 | 0.007 | 1 | 19.5700000 | 23.5600000 | 276 | 18.8300000 | 28.2700000 | 276 | 4.292 |
| 66 | 37 | Simmons 2008 | Ejaculate Quality and Production | Unstressed | 37 | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 20 | YES | 88 | M | NO | Indirect | 0.918 | 0.049 | 1 | 2.1300000 | 0.5969925 | 44 | 2.6000000 | 0.3979950 | 44 | 4.737 |
| 66 | 37 | Simmons 2008 | Body Condition | Unstressed | 37 | Simmons, L. W. and F. Garcia-Gonzalez | 2008 | Onthophagus taurus | Beetle | 10.000 | 1.00 | 20.00 | 1 | 1 | Not Blind | 20 | YES | 88 | M | NO | Indirect | -0.727 | 0.048 | 1 | NA | NA | NA | NA | NA | NA | 4.737 |
| 67 | 32 | Tilszer 2006 | Early Fecundity | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | F | YES | Ambiguous | 0.205 | 0.033 | 1 | 86.7000000 | 40.2790268 | 60 | 95.5000000 | 44.9266068 | 60 | 4.292 |
| 67 | 32 | Tilszer 2006 | Early Fecundity | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | F | YES | Ambiguous | 0.259 | 0.033 | 1 | 90.7000000 | 52.6725735 | 60 | 102.4000000 | 35.6314468 | 60 | 4.292 |
| 67 | 32 | Tilszer 2006 | Mating Success | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | M | NO | Indirect | 1.768 | 0.046 | 1 | 0.4310000 | 0.1006976 | 60 | 0.6170000 | 0.1084435 | 60 | 4.292 |
| 67 | 32 | Tilszer 2006 | Mating Success | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | M | NO | Indirect | 0.282 | 0.033 | 1 | 0.4760000 | 0.5654556 | 60 | 0.6340000 | 0.5499636 | 60 | 4.292 |
| 67 | 32 | Tilszer 2006 | Female Reproductive Success | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | F | NO | Direct | 0.022 | 0.033 | 1 | 284.3000000 | 105.3451470 | 60 | 286.8000000 | 120.8370804 | 60 | 4.292 |
| 67 | 32 | Tilszer 2006 | Female Reproductive Success | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 120 | F | NO | Direct | 0.123 | 0.033 | 1 | 278.0000000 | 61.1931369 | 60 | 284.4000000 | 39.5044301 | 60 | 4.292 |
| 67 | 32 | Tilszer 2006 | Offspring Viability | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 42 | F | NO | Direct | -0.287 | 0.093 | 1 | 97.8000000 | 0.4582576 | 21 | 97.5000000 | 1.3747727 | 21 | 4.292 |
| 67 | 32 | Tilszer 2006 | Offspring Viability | Unstressed | 32 | Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan | 2006 | Rhizoglyphus robini | Mite | 5.000 | 1.00 | 10.00 | 1 | 1 | Not Blind | 37 | YES | 42 | F | NO | Direct | -0.199 | 0.092 | 1 | 97.4000000 | 0.9165151 | 21 | 96.6000000 | 5.4990908 | 21 | 4.292 |
| 68 | 12 | van Lieshout 2014 | Behavioural Plasticity | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | F | YES | Ambiguous | -0.018 | 0.040 | 1 | 285.4200000 | 196.1144075 | 50 | 282.2448980 | 155.8838417 | 49 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Behavioural Plasticity | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | F | YES | Ambiguous | -0.132 | 0.040 | 1 | 393.4166667 | 153.0849901 | 48 | 371.1800000 | 179.6165633 | 50 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Body Size | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | M | YES | Ambiguous | 0.155 | 0.040 | 1 | 3.4253300 | 0.5533225 | 50 | 3.5132898 | 0.4702925 | 49 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Body Size | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | F | YES | Ambiguous | 0.259 | 0.041 | 1 | 4.4623021 | 0.6760828 | 48 | 4.6295060 | 0.6208700 | 50 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Ejaculate Quality and Production | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | M | NO | Indirect | 0.116 | 0.040 | 1 | 0.2007420 | 0.0585906 | 50 | 0.2075663 | 0.0648678 | 49 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Ejaculate Quality and Production | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | M | NO | Indirect | -0.022 | 0.040 | 1 | 0.1668542 | 0.0523804 | 48 | 0.1663250 | 0.0433648 | 50 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Mating Latency | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | M | YES | Indirect | 0.084 | 0.040 | -1 | 49.2000000 | 73.2039365 | 50 | 44.1836735 | 40.4874844 | 49 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Mating Latency | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | F | YES | Indirect | -0.105 | 0.040 | 1 | 69.6458333 | 86.1992964 | 48 | 61.4200000 | 68.2764758 | 50 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Immunity | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 96 | M | NO | Ambiguous | -0.373 | 0.042 | 1 | 12.7920000 | 0.3350000 | 49 | 12.6780000 | 0.2580000 | 47 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Immunity | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 94 | F | NO | Ambiguous | -0.564 | 0.044 | 1 | 12.9760000 | 0.2400000 | 47 | 12.8530000 | 0.1880000 | 47 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Mating Duration | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 99 | M | YES | Ambiguous | 0.029 | 0.040 | 1 | 565.1000000 | 277.6167708 | 50 | 572.7551020 | 244.4586307 | 49 | 4.612 |
| 68 | 12 | van Lieshout 2014 | Mating Duration | Unstressed | 12 | van Lieshout, E., K. B. McNamara and L. W. Simmons | 2014 | Callosobruchus maculatus | Beetle | 1.000 | 2.00 | 120.00 | 1 | 1 | Not Blind | 11 | NO | 98 | F | YES | Ambiguous | -0.354 | 0.041 | 1 | 616.3958333 | 261.4579206 | 48 | 530.8400000 | 217.4206268 | 50 | 4.612 |
| 69 | 26 | Wigby 2004 | Mating Frequency | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 31 | NO | 180 | F | YES | Indirect | 0.236 | 0.011 | 1 | 0.3000000 | 0.5366563 | 180 | 0.6700000 | 2.1466253 | 180 | 3.719 |
| 69 | 26 | Wigby 2004 | Mating Frequency | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 900 | M | YES | Indirect | 0.161 | 0.002 | 1 | 0.0390000 | 0.0900000 | 900 | 0.0650000 | 0.2100000 | 900 | 3.719 |
| 69 | 26 | Wigby 2004 | Mating Frequency | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 31 | NO | 180 | F | YES | Indirect | 0.178 | 0.011 | 1 | 0.2300000 | 0.1341641 | 180 | 0.3000000 | 0.5366563 | 180 | 3.719 |
| 69 | 26 | Wigby 2004 | Mating Frequency | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 900 | M | YES | Indirect | 0.783 | 0.007 | 1 | 0.0530000 | 0.2400000 | 900 | 0.2300000 | 0.1341641 | 180 | 3.719 |
| 69 | 26 | Wigby 2004 | Mating Frequency | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 31 | NO | 180 | F | YES | Indirect | 0.288 | 0.011 | 1 | 0.2300000 | 0.1341641 | 180 | 0.6700000 | 2.1466253 | 180 | 3.719 |
| 69 | 26 | Wigby 2004 | Mating Frequency | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 900 | M | YES | Indirect | 0.053 | 0.002 | 1 | 0.0530000 | 0.2400000 | 900 | 0.0650000 | 0.2100000 | 900 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.009 | 0.126 | 1 | 91.0000000 | 68.9050989 | 15 | 91.5000000 | 35.8880723 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.235 | 0.127 | 1 | 83.0000000 | 54.5498700 | 15 | 68.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.259 | 0.127 | 1 | 98.0000000 | 54.5498700 | 15 | 81.0000000 | 71.7761447 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.094 | 0.126 | 1 | 90.5000000 | 49.5255398 | 15 | 86.0000000 | 43.0656868 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.119 | 0.126 | 1 | 76.0000000 | 85.4136122 | 15 | 84.5000000 | 48.8077784 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.397 | 0.129 | 1 | 96.5000000 | 68.9050989 | 15 | 73.5000000 | 40.1946410 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.057 | 0.126 | 1 | 88.0000000 | 22.9683663 | 15 | 91.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.201 | 0.127 | 1 | 92.0000000 | 28.7104579 | 15 | 83.0000000 | 54.5498700 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.132 | 0.127 | 1 | 88.0000000 | 89.0024194 | 15 | 98.0000000 | 54.5498700 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.006 | 0.126 | 1 | 91.0000000 | 114.8418315 | 15 | 90.5000000 | 49.5255398 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.171 | 0.127 | 1 | 89.0000000 | 60.2919615 | 15 | 76.0000000 | 85.4136122 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.118 | 0.126 | 1 | 88.5000000 | 63.1630073 | 15 | 96.5000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.113 | 0.126 | 1 | 88.0000000 | 22.9683663 | 15 | 91.5000000 | 35.8880723 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.442 | 0.129 | 1 | 92.0000000 | 28.7104579 | 15 | 68.0000000 | 68.9050989 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.084 | 0.126 | 1 | 88.0000000 | 89.0024194 | 15 | 81.0000000 | 71.7761447 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.056 | 0.126 | 1 | 91.0000000 | 114.8418315 | 15 | 86.0000000 | 43.0656868 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.080 | 0.126 | 1 | 89.0000000 | 60.2919615 | 15 | 84.5000000 | 48.8077784 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Female Reproductive Success | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.276 | 0.127 | 1 | 88.5000000 | 63.1630073 | 15 | 73.5000000 | 40.1946410 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.174 | 0.007 | 1 | 25.5400000 | 9.6994845 | 300 | 27.2600000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.091 | 0.007 | 1 | 24.0500000 | 10.3923049 | 300 | 25.0300000 | 11.0851252 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.251 | 0.007 | 1 | 24.9300000 | 10.3923049 | 300 | 27.5900000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | -0.390 | 0.007 | 1 | 42.3600000 | 16.8008928 | 300 | 36.3500000 | 13.8564065 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | 0.485 | 0.007 | 1 | 33.3700000 | 18.5329436 | 300 | 43.3200000 | 22.3434554 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | 0.227 | 0.007 | 1 | 38.4700000 | 23.2094808 | 300 | 43.8100000 | 23.7290961 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.164 | 0.127 | 1 | 0.8200000 | 0.4880778 | 15 | 0.9000000 | 0.4593673 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.548 | 0.131 | 1 | 0.9100000 | 0.2583941 | 15 | 0.7200000 | 0.4019464 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.298 | 0.128 | 1 | 0.8800000 | 0.2583941 | 15 | 0.7600000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | 0.007 | 0.007 | 1 | 22.2400000 | 10.0458947 | 300 | 22.3100000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | -0.251 | 0.007 | 1 | 23.9000000 | 11.9511506 | 300 | 21.1700000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | -0.070 | 0.007 | 1 | 24.2800000 | 10.7387150 | 300 | 23.5500000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.124 | 0.007 | 1 | 24.2700000 | 10.2190998 | 300 | 25.5400000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.140 | 0.007 | 1 | 22.7300000 | 8.8334591 | 300 | 24.0500000 | 10.3923049 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.095 | 0.007 | 1 | 24.0100000 | 9.1798693 | 300 | 24.9300000 | 10.3923049 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | 0.210 | 0.007 | 1 | 38.2400000 | 22.3434554 | 300 | 42.3600000 | 16.8008928 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | -0.457 | 0.007 | 1 | 41.2900000 | 16.1080725 | 300 | 33.3700000 | 18.5329436 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | -0.202 | 0.007 | 1 | 42.9300000 | 20.6114046 | 300 | 38.4700000 | 23.2094808 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.069 | 0.126 | 1 | 0.8600000 | 0.6316301 | 15 | 0.8200000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.040 | 0.126 | 1 | 0.9000000 | 0.2296837 | 15 | 0.9100000 | 0.2583941 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.058 | 0.126 | 1 | 0.9200000 | 0.9187347 | 15 | 0.8800000 | 0.2583941 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | 0.061 | 0.007 | 1 | 21.6300000 | 10.0458947 | 300 | 22.2400000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | 0.159 | 0.007 | 1 | 22.1700000 | 9.6994845 | 300 | 23.9000000 | 11.9511506 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 1.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | 0.141 | 0.007 | 1 | 22.7800000 | 10.5655099 | 300 | 24.2800000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.292 | 0.007 | 1 | 24.2700000 | 10.2190998 | 300 | 27.2600000 | 10.0458947 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.232 | 0.007 | 1 | 22.7300000 | 8.8334591 | 300 | 25.0300000 | 11.0851252 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 300 | F | NO | Indirect | 0.359 | 0.007 | 1 | 24.0100000 | 9.1798693 | 300 | 27.5900000 | 10.7387150 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | -0.100 | 0.007 | 1 | 38.2400000 | 22.3434554 | 300 | 36.3500000 | 13.8564065 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | 0.104 | 0.007 | 1 | 41.2900000 | 16.1080725 | 300 | 43.3200000 | 22.3434554 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Lifespan | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 26 | NO | 300 | F | NO | Indirect | 0.041 | 0.007 | 1 | 42.9300000 | 20.6114046 | 300 | 43.8100000 | 23.7290961 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | 0.071 | 0.126 | 1 | 0.8600000 | 0.6316301 | 15 | 0.9000000 | 0.4593673 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.537 | 0.131 | 1 | 0.9000000 | 0.2296837 | 15 | 0.7200000 | 0.4019464 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 22 | NO | 15 | F | NO | Direct | -0.211 | 0.127 | 1 | 0.9200000 | 0.9187347 | 15 | 0.7600000 | 0.4880778 | 15 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | 0.067 | 0.007 | 1 | 21.6300000 | 10.0458947 | 300 | 22.3100000 | 10.2190998 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | -0.103 | 0.007 | 1 | 22.1700000 | 9.6994845 | 300 | 21.1700000 | 9.6994845 | 300 | 3.719 |
| 69 | 26 | Wigby 2004 | Offspring Viability | Unstressed | 26 | Wigby, S. and T. Chapman | 2004 | Drosophila melanogaster | Fly | 1.000 | 3.00 | 100.00 | 1 | 1 | Not Blind | 33 | NO | 300 | M | NO | Direct | 0.074 | 0.007 | 1 | 22.7800000 | 10.5655099 | 300 | 23.5500000 | 10.2190998 | 300 | 3.719 |
Study ID: An ID given to the published paper the effect size is sourced from (n = 69).
Group ID: An ID given to the research group that may have published several papers on the same species usuing the same or very similar experimental setup. [Was not used in analysis]
Species: The species used in the experimental evolution procedure (n = 15).
Taxon: The taxon to which the species belongs. One of the following: Beetle, fly, mouse, nematode, guppy, mite and cricket (taxa were selected arbitrarily based on the available data).
SS Strength, Ratios and SS Density’s (Column 7-9): Various ratios of the number of males to females and the total number of individuals kept together in an experiment [Was not used in any analysis]
Post cop and Pre cop: Whether a study allowed Pre/Post-copulatory sexual selection (1) or not (0).
Blinding: A binary classification, describing whether blind protocols were used during the experiment. Papers were assumed to be not blind unless declared otherwise.
Generations: The number of generations that the species was subject to differing levels of sexual selection, ranging from 1 to 162.
Enforced Monogamy: Whether the study had the low sexual selection treatment as enforced monogamy (YES) or not (NO). Not all studies compared enforced monogamy and SS+ treatments. Some used FB vs MB, where FB is the SS (low intensity).
n: Pooled sample size of the paired treatments.
Outcome: The fitness related outcome that was measured, e.g. fecundity, survival, or mating success (see Supplementary Table 1 for all 20 categories). We applied our own classifications rather than relying on those provided by the authors, because different papers sometimes used different names for the same trait.
Outcome Class: To help guide analysis the outcomes were classed into three categories; ambiguous, indirect and direct (see Supplementary Table 1).
Sex: A moderator variable with three levels, describing whether the effect size in question comes from a measurement of males (M), females (F), or individuals of both sexes (B).
Ambiguous: Is the fitness outcome ambiguous (YES) or not ambigous (NO). Ambiguous outcomes may be those that may not necessarily be directional, that is to say they may be a life history trait.
Environment: In the methods of the papers included in this study it was usually stated whether additional modifications to the experimental lines were made. Briefly, this was usually a modification that made conditions more stressful such as using a novel food source or elevated mutation load, the effect sizes from these experimental lines are labelled as ‘Stressed’. If it was clearly stated that there was no such modification it is labelled ‘Unstressed’. However, sometimes the paper was ambiguous in what lines had added stress or the results from stressed and unstressed lines were pooled together, in this case we label it as ‘Not Stated’.
g: Hedge’s g calculated using the compute.es package.
var.g: The within study variance associated with the effect size, g.
Positive Fitness: Whether the measurment used in the study is beneficial for fitness (1) or not (0). Note that g has already been multiplied by this column. We inverted all of the effect sizes pertaining to fitness outcomes that are expected to be negatively related to fitness by multiplying the effect size by -1.
mean/sd/n.low/high: The means, standard deviation and sample size for the low or high sexual selection treatments, used to calculate lnCVR (meta-analysis of variance). Rows without these values (NA) had hedges g’ derived from summary statistics (F, z, chi-square etc.).
JIF: Journal Impact factor at year of publication. Several impact factors were unable to be determined/found and are NA.We obtained the journal impact factor for each effect size at the time of publication using InCites Journal Citation Reports.
Tables of Sample Sizes
Here we present the number of effect sizes, publications, blind experiments, effect sizes in stresful conditions, male, female and both measures and different species used.
Supplementary Table 3: Table of effect sizes included in our meta-analysis. See the text following the data table for an explanation of each column.
n.blind.ones <- (sum(full_dataset$Blind == "Blind"))
full_dataset %>%
summarise(
Effect_sizes_.Totalq = n(),
Publications = Study.ID %>% unique() %>% length(),
Blind_experiments = n.blind.ones,
Effect_sizes_.Enforced_monogamyq = (sum(Enforced.Monogamy == "YES")),
Effect_sizes_.Ambiguousq = (sum(Outcome.Class == "Ambiguous")),
Effect_sizes_.Indirectq = (sum(Outcome.Class == "Indirect")),
Effect_sizes_.Directq = (sum(Outcome.Class == "Direct")),
Effect_sizes_.Stressfulq = (sum(Environment == "Stressed")),
Effect_sizes_.Benignq = (sum(Environment == "Unstressed")),
Effect_sizes_.Maleq = (sum(Sex == "M")),
Effect_sizes_.Femaleq = (sum(Sex == "F")),
Effect_sizes_.Both_sexesq = (sum(Sex == "B")),
Different_species = Species %>% unique() %>% length(),
Effect_sizes_.Beetleq = sum(Taxon == "Beetle"),
Effect_sizes_.Flyq = sum(Taxon == "Fly"),
Effect_sizes_.Mouseq = sum(Taxon == "Mouse"),
Effect_sizes_.Nematodeq = sum(Taxon == "Nematode"),
Effect_sizes_.Miteq = sum(Taxon == "Mite"),
Effect_sizes_.Cricketq = sum(Taxon == "Cricket"),
Effect_sizes_.Guppyq = sum(Taxon == "Guppy")) %>% melt() %>%
mutate(variable = gsub("_", " ", variable),
variable = gsub("[.]", "(", variable),
variable = gsub("q", ")", variable)) %>%
rename_("n" = "value", " " = "variable") %>%
pander(split.cell = 40, split.table = Inf)| n | |
|---|---|
| Effect sizes (Total) | 459 |
| Publications | 65 |
| Blind experiments | 54 |
| Effect sizes (Enforced monogamy) | 241 |
| Effect sizes (Ambiguous) | 144 |
| Effect sizes (Indirect) | 141 |
| Effect sizes (Direct) | 174 |
| Effect sizes (Stressful) | 92 |
| Effect sizes (Benign) | 337 |
| Effect sizes (Male) | 189 |
| Effect sizes (Female) | 219 |
| Effect sizes (Both sexes) | 51 |
| Different species | 15 |
| Effect sizes (Beetle) | 116 |
| Effect sizes (Fly) | 254 |
| Effect sizes (Mouse) | 40 |
| Effect sizes (Nematode) | 9 |
| Effect sizes (Mite) | 25 |
| Effect sizes (Cricket) | 6 |
| Effect sizes (Guppy) | 9 |
Supplementary Table 4: Table of fitness outcomes included in our meta-analysis by sex.
Outcome_and_sex <- as.data.frame.matrix(table(full_dataset$Outcome, full_dataset$Sex))
colnames(Outcome_and_sex) <- cbind("Both", "Female", "Male")
Outcome_and_sex %>% tibble::rownames_to_column("Parameters") %>% mutate(Total = Both+Female+Male) %>% pander(split.cell = 40, split.table = Inf)| Parameters | Both | Female | Male | Total |
|---|---|---|---|---|
| Behavioural Plasticity | 0 | 2 | 0 | 2 |
| Body Condition | 0 | 0 | 1 | 1 |
| Body Size | 2 | 13 | 11 | 26 |
| Both Reproductive Success | 12 | 0 | 0 | 12 |
| Development Rate | 5 | 1 | 1 | 7 |
| Early Fecundity | 0 | 14 | 0 | 14 |
| Ejaculate Quality and Production | 0 | 0 | 23 | 23 |
| Extinction Rate | 4 | 0 | 0 | 4 |
| Female Reproductive Success | 0 | 102 | 0 | 102 |
| Fitness Senescence | 0 | 3 | 3 | 6 |
| Immunity | 5 | 15 | 15 | 35 |
| Lifespan | 0 | 35 | 3 | 38 |
| Male Attractiveness | 0 | 0 | 6 | 6 |
| Male Reproductive Success | 0 | 0 | 42 | 42 |
| Mating Duration | 0 | 1 | 9 | 10 |
| Mating Frequency | 0 | 6 | 5 | 11 |
| Mating Latency | 0 | 1 | 12 | 13 |
| Mating Success | 0 | 0 | 39 | 39 |
| Mutant Frequency | 6 | 0 | 2 | 8 |
| Offspring Viability | 15 | 26 | 15 | 56 |
| Pesticide Resistance | 2 | 0 | 0 | 2 |
| Strength | 0 | 0 | 2 | 2 |
Meta-Analysis
Overall effect of sexual selection on fitness
We can obtain an overall weghted grand mean and confidence intervals with a simple intercept only for both Bayesian and REML models. Notably, in both models the estimates are approximately the same, with Bayesian estimates being marginally wider. The priors for brms are set from a weakly non-informative student t-distribution: student_t(3, 0, 10). We also tested that using a stronger prior (e.g. standard normal distribution: normal(0, 1)) has negligible effects on the model results.
Run the Bayesian meta-analysis to get the overall effect
if(!file.exists("data/grand.mean.bayes.rds")){
grand.mean.bayes <- brm(g | se(SE) ~ 1 # Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID)
+ (1|Outcome)
+ (1|Taxon),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = full_dataset %>% mutate(SE = sqrt(var.g)))
saveRDS(grand.mean.bayes, "data/grand.mean.bayes.rds") # Save to avoid re-running during knit
}
grand.mean.bayes <- readRDS("data/grand.mean.bayes.rds")Run the REML meta-analysis to get the overall effect
forest.model <- rma.mv(g, var.g,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = full_dataset)Inspect the effect sizes esimates from these two models
Supplementary Table 5: These estimates are presented in the text of the Results section. The test statistic is either the p-value (REML) or Bayes factor (BF) comparing the effect size to zero (Bayesian).
pander(data.frame(
Method = c("REML", "Bayesian"),
Grand_mean_effect_size_g = c(forest.model$b, summary(grand.mean.bayes)$fixed[,"Estimate"]),
Lower_95_CI = c(forest.model$ci.lb, summary(grand.mean.bayes)$fixed[,"l-95% CI"]),
Upper_95_CI = c(forest.model$ci.ub, summary(grand.mean.bayes)$fixed[,"u-95% CI"]),
Test_statistic = c(forest.model$pval, hypothesis(grand.mean.bayes, "Intercept > 0")$hypothesis$Evid.Ratio)), digits = 2) | Method | Grand_mean_effect_size_g | Lower_95_CI | Upper_95_CI | Test_statistic |
|---|---|---|---|---|
| REML | 0.24 | 0.055 | 0.43 | 0.011 |
| Bayesian | 0.25 | -0.0074 | 0.51 | 35 |
Effect of sexual selection on direct, indirect and ambiguous measures of fitness
Supplementary Table 6: The predicted effect size for each of the three fitness trait classes (Ambiguous, Indirect and Direct) that are presented in Figure 1 in the manuscript. This table presents both Bayesian and REML predictions with some discrepencies in the estimated error margins. Figure 1 within the manuscript uses REML predictions.
if(!file.exists("data/grand.mean.class.bayes.rds")){
grand.mean.class.bayes <- brm(g | se(SE) ~ Outcome.Class # Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID)
+ (1|Taxon),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.9999, max_treedepth = 15),
data = full_dataset %>% mutate(SE = sqrt(var.g)))
saveRDS(grand.mean.class.bayes, "data/grand.mean.class.bayes.rds") # Save to avoid re-running during knit
}
grand.mean.class.bayes <- readRDS("data/grand.mean.class.bayes.rds")
# Define new data for prediction
brms.newdata.class <- as.data.frame(expand.grid(Outcome.Class = unique(full_dataset$Outcome.Class)))
# Get average SE: useful if using predict, but not fitted
av.se.g.class <- full_dataset %>% group_by(Outcome.Class) %>% summarise(mean = mean(sqrt(var.g)))
brms.newdata.class <- left_join(av.se.g.class %>% rename(SE = mean), brms.newdata.class)
# Find the fitted values (i.e. predictions from the linear mixed model fit by brms)
brms.predict.class <- fitted(grand.mean.class.bayes, newdata = brms.newdata.class, re_formula = NA) %>% as.data.frame()
brms.predictions.class <- data.frame(brms.newdata.class$Outcome.Class, brms.predict.class$Estimate, brms.predict.class$Est.Error, brms.predict.class$Q2.5, brms.predict.class$Q97.5)
#Name columns
colnames(brms.predictions.class) <- c("Relationship to Fitness", "Bayes Prediction", "Bayes SE", "Bayes LCI", "Bayes UCI")
outcome.list.factor.class <- c('Indirect', 'Ambiguous', 'Direct')
brms.predictions.class <- brms.predictions.class[match(outcome.list.factor.class, brms.predictions.class$`Relationship to Fitness`),]
rownames(brms.predictions.class) <- NULL
sample.sizes.outcome.class <- as.data.frame(table(full_dataset$Outcome.Class))
colnames(sample.sizes.outcome.class) <- c("Relationship to Fitness", "n")
fitness.class.predictions <- left_join(brms.predictions.class, sample.sizes.outcome.class, by = "Relationship to Fitness")
# Obtain Bayes Factor of likelihood that the outcome is greater than 0
BF.outcome.class.list <- c('Ambiguous', 'Direct')
# Obtain BF for all components
Hypotheses <- list()
BFs <- list()
Hypotheses[["Indirect"]] <- hypothesis(grand.mean.class.bayes, "Intercept > 0") #Intercept in model, need to do it by itself
BFs[["Indirect"]] <- Hypotheses[["Indirect"]][["hypothesis"]][["Evid.Ratio"]]
for (i in BF.outcome.class.list){
Hypotheses[[i]] <- hypothesis(grand.mean.class.bayes, paste("Intercept + Outcome.Class", i," > 0", sep = ""))
BFs[[i]] <- Hypotheses[[i]][["hypothesis"]][["Evid.Ratio"]]
} #Loop over all fitness compoinents
# Now for REML. Not easy because the predict function for rma.mv has an odd interface
model.fitness.class.REML <- rma.mv(g, var.g, mods = ~ 1 + Outcome.Class,
random = list(~ 1 | Study.ID,
~ 1 | Taxon),
method = "REML",
data = full_dataset)
get.predictions.class <- function(newdata){
Indirect <- 0; Ambiguous <- 0; Direct <- 0
if(newdata[1] == 'Ambiguous') Ambiguous<-1
if(newdata[1] == 'Direct') Direct<-1
predict(model.fitness.class.REML, newmods=c(Ambiguous, Direct))
}
# Get the predictions for each combination of moderators
predictions.class <- as.data.frame(expand.grid(Outcome.Class = outcome.list.factor.class))
predictions.class <- cbind(predictions.class, do.call("rbind", apply(predictions.class, 1, get.predictions.class))) %>%
select(Outcome.Class, pred, se, ci.lb, ci.ub)
for(i in 2:5) predictions.class[,i] <- unlist(predictions.class[,i])
colnames(predictions.class) <- c("Relationship to Fitness", "REML Prediction", "REML SE", "REML LCI", "REML UCI")
predictions.class <- format(predictions.class, digits = 2)
fitness.class.predictions <- fitness.class.predictions %>% cbind.data.frame(as.data.frame(BFs) %>% t() %>% as.data.frame() %>% `colnames<-`("BF")) %>% `row.names<-`(NULL) %>% format(digits = 2)
left_join(fitness.class.predictions, predictions.class, by = "Relationship to Fitness") %>% pander(split.table = Inf, digits = 2)| Relationship to Fitness | Bayes Prediction | Bayes SE | Bayes LCI | Bayes UCI | n | BF | REML Prediction | REML SE | REML LCI | REML UCI |
|---|---|---|---|---|---|---|---|---|---|---|
| Indirect | 0.24 | 0.098 | 0.0326 | 0.43 | 141 | 59 | 0.24 | 0.057 | 0.132 | 0.36 |
| Ambiguous | 0.20 | 0.098 | -0.0016 | 0.39 | 144 | 38 | 0.21 | 0.058 | 0.093 | 0.32 |
| Direct | 0.13 | 0.097 | -0.0790 | 0.31 | 174 | 11 | 0.13 | 0.057 | 0.019 | 0.24 |
pd <- position_dodge(0.3)
predictions.class[,2:5] <- sapply(predictions.class[,2:5], as.numeric)
forest.plot.class <- predictions.class %>%
rename(Outcome.Class = "Relationship to Fitness",
Pred = 'REML Prediction',
LCI = 'REML LCI',
UCI = 'REML UCI') %>%
mutate(Outcome.Class = factor(Outcome.Class, levels = c("Direct", "Indirect", "Ambiguous"))) %>%
ggplot(aes(x = Outcome.Class, y = Pred, colour = Outcome.Class, fill = Outcome.Class)) +
geom_hline(yintercept = 0, linetype = 2) +
geom_hline(yintercept = 0.23, linetype = 2, colour = "steelblue", size = 1) +
geom_quasirandom(data = full_dataset %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Outcome.Class = factor(Outcome.Class, levels = c("Direct", "Indirect", "Ambiguous"))),
aes(x = Outcome.Class, y = g), alpha=0.4) +
geom_errorbar(mapping = aes(ymin = LCI, ymax = UCI), width = 0, position = pd, size=1, colour = "grey10") +
geom_point(position = pd, size=3.25, shape = 23, stroke = .75, color = "grey10") +
ylab("Standardized Mean Difference (g) \n[positive values indicate sexual selection improves fitness components]") +
theme_minimal(14) +
theme(panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
legend.position = "none",
axis.text.y = element_text(size = 13, hjust = 1)) +
scale_color_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"),
name = "Relationship\nto fitness")+
scale_fill_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"),
name = "Relationship\nto fitness")+
xlab("Relationship to Fitness\n")+
coord_flip()
forest.plot.class
Figure 1: The effect sizes used in this meta-analysis (\(n\) = 459) were grouped into either direct, indirect or ambiguous measures of fitness. Overall, effect sizes were more often positive than negative. Predicted average values are presented as a diamond for each fitness-relationship category. The estimates presented here are from REML models with the grand mean across all effect sizes (\(\beta\) = 0.25) shown as the blue dotted line. Predictions from both Bayesian and REML models can be found in Supplementary Table 6.
Effect of Sexual Selection for different fitness components
Forest Plot
# Create new factor to order factors in a way where Ambig, Indirect and Direct are Grouped
full_dataset$Outcome_f = factor(full_dataset$Outcome, levels = c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Male Attractiveness', 'Male Reproductive Success', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Both Reproductive Success', 'Extinction Rate', 'Female Reproductive Success', 'Offspring Viability'))
# define upper and lower bounds
full_dataset$lowerci <- full_dataset$g - 1.96*(sqrt(full_dataset$var.g))
full_dataset$upperci <- full_dataset$g + 1.96*(sqrt(full_dataset$var.g))
p.meta <- full_dataset %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Outcome.Class = factor(Outcome.Class, levels = c("Ambiguous", "Indirect", "Direct"))) %>%
ggplot(aes(y=reorder(AuthorYear, -g), x = g)) +
scale_color_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"),
name = "Relationship\nto fitness")+
scale_shape_manual(values=c(21,22,24))+
scale_fill_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"),
name = "Relationship\nto fitness")+
geom_errorbarh(aes(xmin = lowerci,
xmax = upperci,
color = Outcome.Class), height = 0.1, show.legend = FALSE) +
geom_point(aes(shape = Sex,
fill = Outcome.Class),
size = 1.75,
color = "grey20") +
scale_x_continuous(limits=c(-3.35, 10),
breaks = c(-3, -2, -1, 0, 1, 2, 3),
name='Standardized Mean Difference (g) \n[positive values indicate sexual selection improves fitness components]') +
ylab('Reference') +
geom_vline(xintercept=0,
color='black',
linetype='dashed')+
facet_grid(Outcome_f~.,
labeller = label_wrap_gen(width=23),
scales= 'free',
space='free')+
guides(fill = guide_legend(override.aes = list(shape = 21, colour = "grey20", size = 6)),
shape = guide_legend(override.aes = list(size = 4.5)))+
#Add theme specifying text size, margins, lines etc.
theme_bw()+
theme(strip.text.y = element_text(angle = 0, size = 8, margin = margin(t=15, r=15, b=15, l=15)),
strip.background = element_rect(colour = NULL,
linetype = "blank",
fill = "gray90"),
text = element_text(size=11),
panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14))
knitr::include_graphics(path = "figures/ForestPlot_large.png") 
Supplementary Figure 1: Forest plot of raw effect sizes and their 95% confidence intervals, grouped according to measured fitness components and the sex of the individuals whose fitness trait was measured (male, female, or both sexes mixed together). Rows with multiple data points denote studies that provided multiple effect sizes. Positive values indicate fitness benefits of sexual selection.
Instead of running individual models for each fitness component we can run a model with the fitness components as predictors. In this case we maintain all of our fitness components and include study.id as a group level effect (to account for within study correlations in effect size). Using the brms package we can run a Bayesian model and generate fitted values for each fitness component.
if(!file.exists("data/components.brms.rds")){
components.brms <-
brm(g | se(SE) ~ Outcome #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID)
+ (1|Taxon),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = full_dataset %>% mutate(SE = sqrt(var.g)))
saveRDS(components.brms, "data/components.brms.rds")
}
components.brms <- readRDS(file = "data/components.brms.rds") Supplementary Table 7: Summary of model predictions for 22 fitness components. In Supplementary Figure 1 these values are presented as a text overlay using the Bayesian values. Additionally, Bayes Factors (BF) are presented as the likelihood ratio that the effect size is greater than 0. Where values greater than 1 correspond to higher likelihood of the effect size being positive and values less than 1 suggest that the effect size is more likely to be negative. The right side of the table provides the REML estimates with SE and 95 % CIs.
# Define new data for prediction
brms.newdata <- as.data.frame(expand.grid(Outcome = unique(full_dataset$Outcome)))
# Get average SE: useful if using predict, but not fitted
av.se.g <- full_dataset %>% group_by(Outcome) %>% summarise(mean = mean(sqrt(var.g)))
brms.newdata$SE <- av.se.g$mean
# Find the fitted values (i.e. predictions from the linear mixed model fit by brms)
brms.predict <- fitted(components.brms, newdata = brms.newdata, re_formula = NA) %>% as.data.frame()
brms.predictions <- data.frame(brms.newdata$Outcome, brms.predict$Estimate, brms.predict$Est.Error, brms.predict$Q2.5, brms.predict$Q97.5)
#Name columns
colnames(brms.predictions) <- c("Fitness Component", "Bayes Prediction", "Bayes SE", "Bayes LCI", "Bayes UCI")
outcome.list.factor <- c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Male Attractiveness', 'Male Reproductive Success', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Both Reproductive Success', 'Extinction Rate', 'Female Reproductive Success', 'Offspring Viability')
brms.predictions <- brms.predictions[match(outcome.list.factor, brms.predictions$`Fitness Component`),]
rownames(brms.predictions) <- NULL
sample.sizes.outcomes <- as.data.frame(table(full_dataset$Outcome))
colnames(sample.sizes.outcomes) <- c("Fitness Component", "n")
fitness.component.predictions <- left_join(brms.predictions, sample.sizes.outcomes, by = "Fitness Component")
# Obtain Bayes Factor of likelihood that the outcome is greater than 0
BF.outcome.list <- c('Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Male Attractiveness', 'Male Reproductive Success', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Both Reproductive Success', 'Extinction Rate', 'Female Reproductive Success', 'Offspring Viability')
# Obtain BF for all components
Hypotheses <- list()
BFs <- list()
Hypotheses[["Behavioural Plasticity"]] <- hypothesis(components.brms, "Intercept > 0") #Intercept in model, need to do it by itself
BFs[["Behavioural Plasticity"]] <- Hypotheses[["Behavioural Plasticity"]][["hypothesis"]][["Evid.Ratio"]]
for (i in BF.outcome.list){
Hypotheses[[i]] <- hypothesis(components.brms, paste("Intercept + Outcome", i," > 0", sep = ""))
BFs[[i]] <- Hypotheses[[i]][["hypothesis"]][["Evid.Ratio"]]
} #Loop over all fitness compoinents
# Now for REML. Not easy because the predict function for rma.mv has an odd interface
model.fitness.components.REML <- rma.mv(g, var.g, mods = ~ 1 + Outcome,
random = list(~ 1 | Study.ID,
~ 1 | Taxon),
method = "REML",
data = full_dataset)
get.predictions.outcomes <- function(newdata){
`Body Condition`<-0; `Body Size`<-0; `Development Rate`<-0; `Early Fecundity`<-0; `Ejaculate Quality and Production`<-0; `Extinction Rate`<-0; `Fitness Senescence`<-0; Immunity<-0; Lifespan<-0; `Male Attractiveness`<-0; `Male Reproductive Success`<-0; `Mating Duration`<-0; `Mating Frequency`<-0; `Mating Latency`<-0; `Mating Success`<-0; `Mutant Frequency`<-0; `Offspring Viability`<-0; `Pesticide Resistance`<-0; `Female Reproductive Success`<-0; `Both Reproductive Success`<-0; Strength<-0
if(newdata[1] == 'Body Condition')`Body Condition`<-1
if(newdata[1] == 'Body Size') `Body Size`<-1
if(newdata[1] == 'Both Reproductive Success') `Both Reproductive Success`<-1
if(newdata[1] == 'Development Rate') `Development Rate`<-1
if(newdata[1] == 'Early Fecundity') `Early Fecundity`<-1
if(newdata[1] == 'Ejaculate Quality and Production')`Ejaculate Quality and Production`<-1
if(newdata[1] == 'Extinction Rate')`Extinction Rate`<-1
if(newdata[1] == 'Female Reproductive Success')`Female Reproductive Success`<-1
if(newdata[1] == 'Fitness Senescence')`Fitness Senescence`<-1
if(newdata[1] == 'Immunity')Immunity<-1
if(newdata[1] == 'Lifespan')Lifespan<-1
if(newdata[1] == 'Male Attractiveness')`Male Attractiveness`<-1
if(newdata[1] == 'Male Reproductive Success')`Male Reproductive Success`<-1
if(newdata[1] == 'Mating Duration')`Mating Duration`<-1
if(newdata[1] == 'Mating Frequency')`Mating Frequency`<-1
if(newdata[1] == 'Mating Latency')`Mating Latency`<-1
if(newdata[1] == 'Mating Success')`Mating Success`<-1
if(newdata[1] == 'Mutant Frequency')`Mutant Frequency`<-1
if(newdata[1] == 'Offspring Viability')`Offspring Viability`<-1
if(newdata[1] == 'Pesticide Resistance')`Pesticide Resistance`<-1
if(newdata[1] == 'Strength')Strength<-1
predict(model.fitness.components.REML, newmods=c(`Body Condition`,`Body Size`,`Both Reproductive Success`,`Development Rate`,`Early Fecundity`,`Ejaculate Quality and Production`,`Extinction Rate`,`Female Reproductive Success`,`Fitness Senescence`,`Immunity`,`Lifespan`,`Male Attractiveness`,`Male Reproductive Success`,`Mating Duration`,`Mating Frequency`,`Mating Latency`,`Mating Success`,`Mutant Frequency`,`Offspring Viability`,`Pesticide Resistance`,`Strength`))
}
outcome.list.model.levels <- c('Behavioural Plasticity' ,'Body Condition','Body Size','Both Reproductive Success','Development Rate','Early Fecundity','Ejaculate Quality and Production','Extinction Rate','Female Reproductive Success','Fitness Senescence','Immunity','Lifespan','Male Attractiveness','Male Reproductive Success','Mating Duration','Mating Frequency','Mating Latency','Mating Success','Mutant Frequency','Offspring Viability','Pesticide Resistance','Strength')
# Get the predictions for each combination of moderators
predictions.outcomes <- as.data.frame(expand.grid(Outcome = outcome.list.model.levels))
predictions.outcomes <- cbind(predictions.outcomes, do.call("rbind", apply(predictions.outcomes, 1, get.predictions.outcomes))) %>%
select(Outcome, pred, se, ci.lb, ci.ub)
for(i in 2:5) predictions.outcomes[,i] <- unlist(predictions.outcomes[,i])
colnames(predictions.outcomes) <- c("Fitness Component", "REML Prediction", "REML SE", "REML LCI", "REML UCI")
predictions.outcomes <- format(predictions.outcomes, digits = 2)
fitness.component.predictions <- fitness.component.predictions %>% cbind.data.frame(as.data.frame(BFs) %>% t() %>% as.data.frame() %>% `colnames<-`("BF")) %>% `row.names<-`(NULL) %>% format(digits = 2)
left_join(fitness.component.predictions, predictions.outcomes, by = "Fitness Component") %>% pander(split.table = Inf, digits = 2)| Fitness Component | Bayes Prediction | Bayes SE | Bayes LCI | Bayes UCI | n | BF | REML Prediction | REML SE | REML LCI | REML UCI |
|---|---|---|---|---|---|---|---|---|---|---|
| Behavioural Plasticity | 0.282 | 0.19 | -0.090 | 0.66 | 2 | 1.5e+01 | 0.279 | 0.172 | -0.057 | 0.616 |
| Body Size | 0.380 | 0.11 | 0.159 | 0.62 | 26 | 2.2e+02 | 0.378 | 0.078 | 0.225 | 0.532 |
| Development Rate | 0.517 | 0.15 | 0.223 | 0.82 | 7 | 6.7e+02 | 0.515 | 0.125 | 0.270 | 0.761 |
| Early Fecundity | 0.281 | 0.16 | -0.027 | 0.59 | 14 | 2.7e+01 | 0.280 | 0.134 | 0.017 | 0.543 |
| Immunity | -0.422 | 0.14 | -0.702 | -0.15 | 35 | 2.6e-03 | -0.419 | 0.111 | -0.636 | -0.201 |
| Male Attractiveness | 0.302 | 0.14 | 0.031 | 0.59 | 6 | 5.8e+01 | 0.298 | 0.111 | 0.081 | 0.515 |
| Male Reproductive Success | 0.155 | 0.12 | -0.072 | 0.39 | 42 | 1.4e+01 | 0.152 | 0.080 | -0.005 | 0.310 |
| Mating Duration | 0.422 | 0.12 | 0.186 | 0.67 | 10 | 3.1e+02 | 0.420 | 0.089 | 0.247 | 0.594 |
| Pesticide Resistance | 1.051 | 0.49 | 0.095 | 2.01 | 2 | 6.4e+01 | 1.076 | 0.457 | 0.180 | 1.973 |
| Mutant Frequency | 0.319 | 0.36 | -0.387 | 1.02 | 8 | 4.5e+00 | 0.294 | 0.334 | -0.361 | 0.949 |
| Body Condition | -1.235 | 0.32 | -1.871 | -0.63 | 1 | 0.0e+00 | -1.227 | 0.305 | -1.825 | -0.629 |
| Fitness Senescence | 0.587 | 0.12 | 0.363 | 0.83 | 6 | 1.1e+03 | 0.585 | 0.083 | 0.423 | 0.748 |
| Lifespan | 0.190 | 0.11 | -0.025 | 0.42 | 38 | 2.5e+01 | 0.188 | 0.076 | 0.038 | 0.337 |
| Mating Frequency | 0.333 | 0.12 | 0.113 | 0.57 | 11 | 1.4e+02 | 0.330 | 0.080 | 0.173 | 0.487 |
| Mating Latency | 0.722 | 0.12 | 0.499 | 0.96 | 13 | 8.0e+03 | 0.720 | 0.080 | 0.563 | 0.877 |
| Mating Success | -0.083 | 0.11 | -0.303 | 0.15 | 39 | 2.4e-01 | -0.085 | 0.078 | -0.238 | 0.068 |
| Strength | 0.212 | 0.17 | -0.111 | 0.53 | 2 | 1.0e+01 | 0.211 | 0.145 | -0.074 | 0.496 |
| Ejaculate Quality and Production | 0.308 | 0.12 | 0.066 | 0.55 | 23 | 9.4e+01 | 0.308 | 0.088 | 0.137 | 0.480 |
| Both Reproductive Success | 0.141 | 0.13 | -0.101 | 0.40 | 12 | 8.0e+00 | 0.140 | 0.095 | -0.046 | 0.327 |
| Extinction Rate | 0.348 | 0.20 | -0.053 | 0.73 | 4 | 2.3e+01 | 0.350 | 0.185 | -0.012 | 0.712 |
| Female Reproductive Success | 0.170 | 0.11 | -0.044 | 0.40 | 102 | 1.8e+01 | 0.168 | 0.074 | 0.022 | 0.314 |
| Offspring Viability | 0.173 | 0.11 | -0.041 | 0.40 | 56 | 1.9e+01 | 0.171 | 0.075 | 0.024 | 0.318 |
# saveRDS(left_join(fitness.component.predictions, predictions.outcomes, by = "Fitness Component"), "PDF_RDS_files/ST7.rds")Supplementary Table 8: In some instances it may be beneficial to the reader to obtain average effect sizes of each fitness trait entirely independently of other traits. For this reason we present a summary of independent model estimates for 16 fitness components with a sample size greater than 3 effect sizes (n>3). Unlike above where estimates were generated based on predictions from a single model, here we run individual meta-analyses for each fitness related trait. Independent models generally reduce the power and significance of some of the estimates with ‘Extinction rate’ and ‘Ejaculate quality and production’ the only two traits with p-values < 0.05.
#Excluding those with 3 or less effect sizes.
outcome.list <- as.list(c('Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Male Attractiveness', 'Male Reproductive Success', 'Mating Duration', 'Mutant Frequency', 'Fitness Senescence', 'Lifespan', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Ejaculate Quality and Production', 'Both Reproductive Success', 'Extinction Rate', 'Female Reproductive Success', 'Offspring Viability'))
names(outcome.list) <- c('Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Male Attractiveness', 'Male Reproductive Success', 'Mating Duration', 'Mutant Frequency', 'Fitness Senescence', 'Lifespan', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Ejaculate Quality and Production', 'Both Reproductive Success', 'Extinction Rate', 'Female Reproductive Success', 'Offspring Viability')
outcome.models <- llply(outcome.list, function(x) rma.mv(g, var.g,
mods = ~ 1,
method = "REML",
random = list(~ 1 | Study.ID,
~ 1 | Taxon),
subset = (Outcome_f == x),
data = full_dataset))
df.list <- as.data.frame(do.call("rbind", outcome.models)) # data frame of model results
simple.frame <- subset(df.list, select=c("b", "zval", "ci.lb", "ci.ub", "k", "pval"))
# simple.frame$vi <- as.matrix(simple.frame$vi)
# simple.frame$W <- diag(1/(simple.frame$vi))
#
# XO <- model.matrix(model.complete2)
# PO <- W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
# 100 * sum(model.complete2$sigma2) / (sum(model.complete2$sigma2) + (model.complete2$k-model.complete2$p)/sum(diag(P)))
#I couldn't get mapply to work when calculating I2 so I calculated them manually.
#Body Size
restricted.dataBS <- full_dataset %>% filter(full_dataset$Outcome == "Body Size")
#Run estimate of heterogeneity
WBS = diag(1/restricted.dataBS$var.g)
XBS = model.matrix(outcome.models[["Body Size"]])
PBS = WBS - WBS %*% XBS %*% solve(t(XBS) %*% WBS %*% XBS) %*% t(XBS) %*% WBS
BodySizeI2 <- 100 * sum(outcome.models[["Body Size"]]$sigma2) / (sum(outcome.models[["Body Size"]]$sigma2) + (outcome.models[["Body Size"]]$k-outcome.models[["Body Size"]]$p)/sum(diag(PBS)))
#Development Rate
restricted.dataDR <- full_dataset %>% filter(full_dataset$Outcome == "Development Rate")
#Run estimate of heterogeneity
W = diag(1/restricted.dataDR$var.g)
X = model.matrix(outcome.models[["Development Rate"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
DevelopmentRateI2 <- 100 * sum(outcome.models[["Development Rate"]]$sigma2) / (sum(outcome.models[["Development Rate"]]$sigma2) + (outcome.models[["Development Rate"]]$k-outcome.models[["Development Rate"]]$p)/sum(diag(P)))
#Early Fecundity
restricted.dataEF <- full_dataset %>% filter(full_dataset$Outcome == "Early Fecundity")
#Run estimate of heterogeneity
W = diag(1/restricted.dataEF$var.g)
X = model.matrix(outcome.models[["Early Fecundity"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
EarlyFecundityI2 <- 100 * sum(outcome.models[["Early Fecundity"]]$sigma2) / (sum(outcome.models[["Early Fecundity"]]$sigma2) + (outcome.models[["Early Fecundity"]]$k-outcome.models[["Early Fecundity"]]$p)/sum(diag(P)))
#Immunity
restricted.dataI <- full_dataset %>% filter(full_dataset$Outcome == "Immunity")
#Run estimate of heterogeneity
W = diag(1/restricted.dataI$var.g)
X = model.matrix(outcome.models[["Immunity"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
ImmunityI2 <- 100 * sum(outcome.models[["Immunity"]]$sigma2) / (sum(outcome.models[["Immunity"]]$sigma2) + (outcome.models[["Immunity"]]$k-outcome.models[["Immunity"]]$p)/sum(diag(P)))
#Mating Duration
restricted.dataMD <- full_dataset %>% filter(full_dataset$Outcome == "Mating Duration")
#Run estimate of heterogeneity
W = diag(1/restricted.dataMD$var.g)
X = model.matrix(outcome.models[["Mating Duration"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MatingDurationI2 <- 100 * sum(outcome.models[["Mating Duration"]]$sigma2) / (sum(outcome.models[["Mating Duration"]]$sigma2) + (outcome.models[["Mating Duration"]]$k-outcome.models[["Mating Duration"]]$p)/sum(diag(P)))
#Mutant Frequency
restricted.dataMF <- full_dataset %>% filter(full_dataset$Outcome == "Mutant Frequency")
#Run estimate of heterogeneity
W = diag(1/restricted.dataMF$var.g)
X = model.matrix(outcome.models[["Mutant Frequency"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MutantFrequencyI2 <- 100 * sum(outcome.models[["Mutant Frequency"]]$sigma2) / (sum(outcome.models[["Mutant Frequency"]]$sigma2) + (outcome.models[["Mutant Frequency"]]$k-outcome.models[["Mutant Frequency"]]$p)/sum(diag(P)))
#Fitness Senescence
restricted.dataFS <- full_dataset %>% filter(full_dataset$Outcome == "Fitness Senescence")
#Run estimate of heterogeneity
W = diag(1/restricted.dataFS$var.g)
X = model.matrix(outcome.models[["Fitness Senescence"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
FitnessSenescenceI2 <- 100 * sum(outcome.models[["Fitness Senescence"]]$sigma2) / (sum(outcome.models[["Fitness Senescence"]]$sigma2) + (outcome.models[["Fitness Senescence"]]$k-outcome.models[["Fitness Senescence"]]$p)/sum(diag(P)))
#Lifespan
restricted.dataL <- full_dataset %>% filter(full_dataset$Outcome == "Lifespan")
#Run estimate of heterogeneity
W = diag(1/restricted.dataL$var.g)
X = model.matrix(outcome.models[["Lifespan"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
LifespanI2 <- 100 * sum(outcome.models[["Lifespan"]]$sigma2) / (sum(outcome.models[["Lifespan"]]$sigma2) + (outcome.models[["Lifespan"]]$k-outcome.models[["Lifespan"]]$p)/sum(diag(P)))
#Male Attractiveness
restricted.dataMA <- full_dataset %>% filter(full_dataset$Outcome == "Male Attractiveness")
#Run estimate of heterogeneity
W = diag(1/restricted.dataMA$var.g)
X = model.matrix(outcome.models[["Male Attractiveness"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MaleAttractivenessI2 <- 100 * sum(outcome.models[["Male Attractiveness"]]$sigma2) / (sum(outcome.models[["Male Attractiveness"]]$sigma2) + (outcome.models[["Male Attractiveness"]]$k-outcome.models[["Male Attractiveness"]]$p)/sum(diag(P)))
#Male Reproductive Success
restricted.dataMRS <- full_dataset %>% filter(full_dataset$Outcome == "Male Reproductive Success")
#Run estimate of heterogeneity
W = diag(1/restricted.dataMRS$var.g)
X = model.matrix(outcome.models[["Male Reproductive Success"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MaleReproductiveSuccessI2 <- 100 * sum(outcome.models[["Male Reproductive Success"]]$sigma2) / (sum(outcome.models[["Male Reproductive Success"]]$sigma2) + (outcome.models[["Male Reproductive Success"]]$k-outcome.models[["Male Reproductive Success"]]$p)/sum(diag(P)))
#Mating Frequency
restricted.dataMF <- full_dataset %>% filter(full_dataset$Outcome == "Mating Frequency")
#Run estimate of heterogeneity
W = diag(1/restricted.dataMF$var.g)
X = model.matrix(outcome.models[["Mating Frequency"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MatingFrequencyI2 <- 100 * sum(outcome.models[["Mating Frequency"]]$sigma2) / (sum(outcome.models[["Mating Frequency"]]$sigma2) + (outcome.models[["Mating Frequency"]]$k-outcome.models[["Mating Frequency"]]$p)/sum(diag(P)))
#Mating Latency
restricted.dataML <- full_dataset %>% filter(full_dataset$Outcome == "Mating Latency")
#Run estimate of heterogeneity
W = diag(1/restricted.dataML$var.g)
X = model.matrix(outcome.models[["Mating Latency"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MatingLatencyI2 <- 100 * sum(outcome.models[["Mating Latency"]]$sigma2) / (sum(outcome.models[["Mating Latency"]]$sigma2) + (outcome.models[["Mating Latency"]]$k-outcome.models[["Mating Latency"]]$p)/sum(diag(P)))
#Mating Success
restricted.dataMS <- full_dataset %>% filter(full_dataset$Outcome == "Mating Success")
#Run estimate of heterogeneity
W = diag(1/restricted.dataMS$var.g)
X = model.matrix(outcome.models[["Mating Success"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
MatingSuccessI2 <- 100 * sum(outcome.models[["Mating Success"]]$sigma2) / (sum(outcome.models[["Mating Success"]]$sigma2) + (outcome.models[["Mating Success"]]$k-outcome.models[["Mating Success"]]$p)/sum(diag(P)))
#Ejaculate Quality and Production
restricted.dataEQ <- full_dataset %>% filter(full_dataset$Outcome == "Ejaculate Quality and Production")
#Run estimate of heterogeneity
W = diag(1/restricted.dataEQ$var.g)
X = model.matrix(outcome.models[["Ejaculate Quality and Production"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
EjaculateQualityI2 <- 100 * sum(outcome.models[["Ejaculate Quality and Production"]]$sigma2) / (sum(outcome.models[["Ejaculate Quality and Production"]]$sigma2) + (outcome.models[["Ejaculate Quality and Production"]]$k-outcome.models[["Ejaculate Quality and Production"]]$p)/sum(diag(P)))
#Extinction Rate
restricted.dataER <- full_dataset %>% filter(full_dataset$Outcome == "Extinction Rate")
#Run estimate of heterogeneity
W = diag(1/restricted.dataER$var.g)
X = model.matrix(outcome.models[["Extinction Rate"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
ExtinctionRateI2 <- 100 * sum(outcome.models[["Extinction Rate"]]$sigma2) / (sum(outcome.models[["Extinction Rate"]]$sigma2) + (outcome.models[["Extinction Rate"]]$k-outcome.models[["Extinction Rate"]]$p)/sum(diag(P)))
#Offspring Viability
restricted.dataOV <- full_dataset %>% filter(full_dataset$Outcome == "Offspring Viability")
#Run estimate of heterogeneity
W = diag(1/restricted.dataOV$var.g)
X = model.matrix(outcome.models[["Offspring Viability"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
OffspringViabilityI2 <- 100 * sum(outcome.models[["Offspring Viability"]]$sigma2) / (sum(outcome.models[["Offspring Viability"]]$sigma2) + (outcome.models[["Offspring Viability"]]$k-outcome.models[["Offspring Viability"]]$p)/sum(diag(P)))
#Both Reproductive Success
restricted.dataBRS <- full_dataset %>% filter(full_dataset$Outcome == "Both Reproductive Success")
#Run estimate of heterogeneity
W = diag(1/restricted.dataBRS$var.g)
X = model.matrix(outcome.models[["Both Reproductive Success"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
BothReproductiveSuccessI2 <- 100 * sum(outcome.models[["Both Reproductive Success"]]$sigma2) / (sum(outcome.models[["Both Reproductive Success"]]$sigma2) + (outcome.models[["Both Reproductive Success"]]$k-outcome.models[["Both Reproductive Success"]]$p)/sum(diag(P)))
#Female Reproductive Success
restricted.dataFRS <- full_dataset %>% filter(full_dataset$Outcome == "Female Reproductive Success")
#Run estimate of heterogeneity
W = diag(1/restricted.dataFRS$var.g)
X = model.matrix(outcome.models[["Female Reproductive Success"]])
P = W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
FemaleReproductiveSuccessI2 <- 100 * sum(outcome.models[["Female Reproductive Success"]]$sigma2) / (sum(outcome.models[["Female Reproductive Success"]]$sigma2) + (outcome.models[["Female Reproductive Success"]]$k-outcome.models[["Female Reproductive Success"]]$p)/sum(diag(P)))
simple.frame$I2 <- c(BodySizeI2, DevelopmentRateI2, EarlyFecundityI2, ImmunityI2, MaleAttractivenessI2, MaleReproductiveSuccessI2, MatingDurationI2, MutantFrequencyI2, FitnessSenescenceI2, LifespanI2, MatingFrequencyI2, MatingLatencyI2, MatingSuccessI2, EjaculateQualityI2, BothReproductiveSuccessI2, ExtinctionRateI2, FemaleReproductiveSuccessI2, OffspringViabilityI2)
outcome.frame <- format(simple.frame, digits = 2)
outcome.frame <- add_rownames(outcome.frame, "Outcome")
outcome.frame$b <- as.numeric(outcome.frame$b)
outcome.frame$k <- as.numeric(outcome.frame$k)
outcome.frame$ci.lb <- as.numeric(outcome.frame$ci.lb)
outcome.frame$ci.ub <- as.numeric(outcome.frame$ci.ub)
outcome.frame$I2 <- as.numeric(outcome.frame$I2)
outcome.frame$Class <- c("Ambiguous", "Ambiguous", "Ambiguous", "Ambiguous", "Ambiguous", "Ambiguous", "Ambiguous", "Indirect", "Indirect", "Indirect", "Indirect", "Indirect", "Indirect", "Indirect", "Direct", "Direct", "Direct", "Direct")
outcome.frame %>% rename(beta = b, n = k) %>% filter(Outcome != "Behavioural Plasticity" & Outcome != "Pesticide Resistance" & Outcome != "Strength") %>% pander(split.cell = 40, split.table = Inf)| Outcome | beta | zval | ci.lb | ci.ub | n | pval | I2 | Class |
|---|---|---|---|---|---|---|---|---|
| Body Size | 0.28 | 1.2 | -0.16 | 0.72 | 26 | 0.22 | 97 | Ambiguous |
| Development Rate | 0.66 | 1.1 | -0.53 | 1.8 | 7 | 0.28 | 95 | Ambiguous |
| Early Fecundity | -0.0022 | -0.0095 | -0.45 | 0.44 | 14 | 0.99 | 49 | Ambiguous |
| Immunity | -0.29 | -1.3 | -0.73 | 0.15 | 35 | 0.19 | 90 | Ambiguous |
| Male Attractiveness | 0.27 | 0.3 | -1.5 | 2 | 6 | 0.76 | 99 | Ambiguous |
| Male Reproductive Success | 0.3 | 2.1 | 0.018 | 0.58 | 42 | 0.037 | 84 | Ambiguous |
| Mating Duration | 0.23 | 0.94 | -0.25 | 0.7 | 10 | 0.35 | 91 | Ambiguous |
| Mutant Frequency | 0.25 | 1.1 | -0.21 | 0.71 | 8 | 0.29 | 88 | Indirect |
| Fitness Senescence | 0.096 | 0.75 | -0.15 | 0.35 | 6 | 0.45 | 81 | Indirect |
| Lifespan | -0.076 | -0.71 | -0.29 | 0.13 | 38 | 0.48 | 89 | Indirect |
| Mating Frequency | 0.69 | 1.1 | -0.57 | 1.9 | 11 | 0.29 | 99 | Indirect |
| Mating Latency | 0.28 | 1.9 | -0.0083 | 0.58 | 13 | 0.057 | 90 | Indirect |
| Mating Success | 0.39 | 1.8 | -0.037 | 0.82 | 39 | 0.073 | 93 | Indirect |
| Ejaculate Quality and Production | 0.5 | 3.6 | 0.23 | 0.77 | 23 | 0.00031 | 83 | Indirect |
| Both Reproductive Success | 0.1 | 0.6 | -0.24 | 0.45 | 12 | 0.55 | 87 | Direct |
| Extinction Rate | 0.62 | 4.9 | 0.37 | 0.87 | 4 | 9.4e-07 | 3e-08 | Direct |
| Female Reproductive Success | 0.071 | 0.9 | -0.084 | 0.23 | 102 | 0.37 | 82 | Direct |
| Offspring Viability | 0.13 | 1.5 | -0.042 | 0.31 | 56 | 0.14 | 94 | Direct |
# saveRDS(outcome.frame %>% rename(beta = b, n = k) %>% filter(Outcome != "Behavioural Plasticity" & Outcome != "Pesticide Resistance" & Outcome != "Strength"), "PDF_RDS_files/ST8.rds")Meta-analyses including many moderator variables
We collected data from fitness components that were deemed ambiguous as well as unambiguous. The ambiguous outcomes are likely to add heterogeneity to the models and may not help us in answering questions of the fitness effects of sexual selection. A REML model utilising our complete dataset with many moderator variables would thus be:
model.preliminary <- rma.mv(g, var.g,
mods = ~ 1 + Sex * Environment + Taxon + Outcome.Class + log(Generations) + Blinding + Enforced.Monogamy,
random = list(~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML",
data = full_dataset)
summary(model.preliminary, digits = 2)##
## Multivariate Meta-Analysis Model (k = 459; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1676.23 3352.46 3396.46 3486.32 3398.89
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.24 0.49 65 no Study.ID
## sigma^2.2 0.11 0.33 22 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 439) = 5469.94, p-val < .01
##
## Test of Moderators (coefficient(s) 2:20):
## QM(df = 19) = 58.44, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.45 0.29 1.57 0.12 -0.11 1.01
## SexF 0.12 0.05 2.70 <.01 0.03 0.21 **
## SexM 0.11 0.04 2.56 0.01 0.03 0.20 *
## EnvironmentNot Stated 0.04 0.15 0.27 0.79 -0.25 0.34
## EnvironmentStressed 0.03 0.06 0.49 0.62 -0.09 0.15
## TaxonCricket 0.11 0.56 0.20 0.85 -0.99 1.21
## TaxonFly -0.17 0.16 -1.04 0.30 -0.49 0.15
## TaxonGuppy -0.29 0.51 -0.57 0.57 -1.30 0.71
## TaxonMite 0.01 0.25 0.03 0.98 -0.49 0.50
## TaxonMouse -0.29 0.21 -1.37 0.17 -0.70 0.13
## TaxonNematode -0.32 0.52 -0.61 0.54 -1.34 0.70
## Outcome.ClassAmbiguous 0.04 0.17 0.22 0.83 -0.29 0.36
## Outcome.ClassDirect 0.03 0.21 0.15 0.88 -0.38 0.45
## log(Generations) -0.02 0.05 -0.45 0.66 -0.13 0.08
## BlindingNot Blind -0.05 0.22 -0.24 0.81 -0.47 0.37
## Enforced.MonogamyYES -0.13 0.09 -1.47 0.14 -0.30 0.04
## SexF:EnvironmentNot Stated 0.12 0.13 0.96 0.34 -0.13 0.38
## SexM:EnvironmentNot Stated 0.08 0.12 0.67 0.50 -0.16 0.32
## SexF:EnvironmentStressed 0.09 0.07 1.38 0.17 -0.04 0.22
## SexM:EnvironmentStressed -0.13 0.07 -1.88 0.06 -0.26 0.01 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Here we can also run a Bayesian model alongside the REML model (metafor). The R2 for this model is 0.36 (95% CIs = 0.33-0.4).
if(!file.exists("data/brms.preliminary.rds")){
brms.preliminary <- brm(g | se(SE) ~ 1 + Sex * Environment + log(Generations) + Blinding + Enforced.Monogamy #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
+ (1|Study.ID) #group level effects
+ (1|Outcome)
+ (1|Taxon),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = full_dataset %>% mutate(SE = sqrt(var.g)))
saveRDS(brms.preliminary, file = "data/brms.preliminary.rds")
}
brms.preliminary <- readRDS(file = "data/brms.preliminary.rds") # Avoid re-running model aboveSupplementary Table 9: Bayesian model results for a preliminary model that explores many covariates collected in the dataset.
#Plot model results
prelim.results.bayesplot <- bayesplot::mcmc_areas(posterior_samples(brms.preliminary)[,1:11]) +
geom_vline(xintercept = 0, linetype = 2) +
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
make_text_summary(brms.preliminary) %>%
add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()| Model Parameter | Estimate | Est.Error | Q2.5 | Q97.5 | |
|---|---|---|---|---|---|
| b_Intercept | 0.395 | 0.251 | -0.095 | 0.899 | |
| b_SexF | 0.122 | 0.046 | 0.031 | 0.211 | * |
| b_SexM | 0.112 | 0.044 | 0.025 | 0.198 | * |
| b_EnvironmentNotStated | 0.028 | 0.146 | -0.262 | 0.315 | |
| b_EnvironmentStressed | 0.03 | 0.06 | -0.088 | 0.146 | |
| b_logGenerations | -0.034 | 0.048 | -0.127 | 0.061 | |
| b_BlindingNotBlind | -0.065 | 0.201 | -0.463 | 0.327 | |
| b_Enforced.MonogamyYES | -0.133 | 0.086 | -0.301 | 0.034 | |
| b_SexF:EnvironmentNotStated | 0.122 | 0.127 | -0.13 | 0.374 | |
| b_SexM:EnvironmentNotStated | 0.076 | 0.119 | -0.16 | 0.316 | |
| b_SexF:EnvironmentStressed | 0.09 | 0.067 | -0.039 | 0.223 | |
| b_SexM:EnvironmentStressed | -0.132 | 0.068 | -0.264 | 0.002 | |
| sd_Outcome__Intercept | 0.327 | 0.074 | 0.214 | 0.499 | * |
| sd_Study.ID__Intercept | 0.485 | 0.053 | 0.393 | 0.6 | * |
| sd_Taxon__Intercept | 0.134 | 0.121 | 0.005 | 0.439 | * |
From these models we can see that the moderators Blinding and Generations have little effect on effect size, and they are also tangential to our research question (unlike e.g. sex and environment).
Restricted analysis of just the higher-quality data
The models and plots above all use the full dataset (called full_dataset in the R code). However, some of the variables included in that dataset are not clearly related to population fitness (these were scored as “Ambiguous” in the “Outcome.Class” column), or it was unclear whether the environmental conditions could be termed “Stressful” or “Benign”. To check whether our findings are robust to the inclusion of the ambiguous results, and to properly evaluate the effects of environmental stress, we next restricted the dataset to exclude these unclear cases (called strict_dataset in the R code). We also focus on the model containing the fixed effects Sex, Environment, Taxon and the interaction between sex and environment, as these are key to our research question.
strict_dataset <- full_dataset %>%
filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated") %>%
mutate(Sex = relevel(Sex, ref = "M"),
Environment = relevel(factor(Environment), ref = "Unstressed"),
Taxon = relevel(factor(Taxon), ref = "Beetle"))
# strict_dataset <- full_dataset %>%
# filter(Outcome.Class == "Direct" & Environment != "Not Stated") %>%
# mutate(Sex = relevel(Sex, ref = "M"),
# Environment = relevel(factor(Environment), ref = "Unstressed"),
# Taxon = relevel(factor(Taxon), ref = "Beetle"))
model.complete <- rma.mv(g, V = var.g,
mods = ~ 1 + Sex * Environment + Taxon,
random = list(~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML",
data = strict_dataset)
summary(model.complete, digits = 2)##
## Multivariate Meta-Analysis Model (k = 289; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1316.74 2633.48 2659.48 2706.64 2660.86
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.21 0.45 53 no Study.ID
## sigma^2.2 0.13 0.36 13 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 278) = 4120.24, p-val < .01
##
## Test of Moderators (coefficient(s) 2:11):
## QM(df = 10) = 77.87, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.27 0.17 1.57 0.12 -0.07 0.60
## SexB 0.00 0.07 0.03 0.98 -0.14 0.15
## SexF 0.11 0.03 3.72 <.01 0.05 0.17 ***
## EnvironmentStressed -0.16 0.04 -3.61 <.01 -0.24 -0.07 ***
## TaxonCricket -0.02 0.49 -0.05 0.96 -0.99 0.94
## TaxonFly -0.12 0.16 -0.75 0.45 -0.44 0.20
## TaxonGuppy -0.13 0.48 -0.27 0.79 -1.08 0.82
## TaxonMite 0.10 0.24 0.42 0.67 -0.37 0.57
## TaxonMouse -0.31 0.28 -1.14 0.26 -0.86 0.23
## SexB:EnvironmentStressed 0.18 0.09 2.00 0.05 0.00 0.35 *
## SexF:EnvironmentStressed 0.26 0.05 5.11 <.01 0.16 0.37 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The result is a model with estimates for various taxa, species, sexes and environments. We again write a function to predict the average effect size for each sub-group in the data.
# function that makes predict.rma work like a normal predict() function, instead of the idiosyncratic way that it works by default.
get.predictions.complete <- function(newdata){
B<-0; F<-0; Stressed<-0; Cricket<-0; Fly<-0; Guppy<-0; Mite<-0; Mouse<-0; interaction1<-0; interaction2<-0;
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[2] == "Cricket") Cricket<-1
if(newdata[3] == "Fly") Fly<-1
if(newdata[3] == "Guppy") Guppy<-1
if(newdata[3] == "Mite") Mite<-1
if(newdata[3] == "Mouse") Mouse<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete, newmods=c(B, F, Stressed, Cricket, Fly, Guppy, Mite, Mouse, interaction1, interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed"),
Taxon = c("Beetle", "Cricket", "Fly", "Guppy", "Mite", "Mouse")))
predictions.complete <- cbind(predictions.complete, do.call("rbind", apply(predictions.complete, 1, get.predictions.complete))) %>%
select(Sex, Environment, Taxon, pred, se, ci.lb, ci.ub)
for(i in 4:7) predictions.complete[,i] <- unlist(predictions.complete[,i])
countpred <- count_(strict_dataset, c("Sex", "Environment", "Taxon"))
predictions.complete <- left_join(predictions.complete, countpred, by = c("Sex", "Environment", "Taxon"))
countpred <- count_(strict_dataset, c("Sex", "Environment", "Taxon"))
predictions.complete <- left_join(predictions.complete, countpred, by = c("Sex", "Environment", "Taxon"))
# plot the model predictions for effect size (Hedges' g) for male, female and both sexes under both stressed and unstressed condition and faceted for each taxon.
pd <- position_dodgev(0.6)
Taxon.metaanlysis <- predictions.complete %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"),
Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>%
ggplot(aes(x = pred, y= Environment, fill = Sex)) +
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
geom_errorbarh(aes(xmin = predictions.complete$ci.lb,
xmax = predictions.complete$ci.ub,
color= Sex),
height = 0, position = pd, show.legend = F) +
geom_point(position = pd, size=2, shape=21, color = "grey20") +
facet_grid(Taxon ~.)+
ylab("Environment \n")+
xlab("\nModel Prediction (Hedges g)")+
xlim(-1, 2)+
ggtitle('Effects of Sex and Stress on \nPopulation Fitness for Each Taxon')+
scale_fill_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
guides(fill = guide_legend(reverse=T, override.aes = list(size = 4.5)))+
theme_bw()+
theme(strip.text.y = element_text(angle = 0, size = 14, margin = margin(r=20, l=20)),
strip.background = element_rect(colour = NULL,
linetype = "blank",
fill = "gray90"),
text = element_text(size=14),
panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=14),
legend.title=element_text(size=14,
face = "bold"),
axis.title.x = element_text(hjust = 0.3, size = 14),
axis.title.y = element_text(size = 14),
plot.title = element_text(size = 14))
#ggsave(plot = Taxon.metaanlysis, filename = "PDF_RDS_files/SF2.pdf", height = 8, width = 8)
Taxon.metaanlysis
#saveRDS(Taxon.metaanlysis, "PDF_RDS_files/SF2.rds")
Supplementary Figure 2: The predictions from this model indicate some heterogeneity between taxon. However, the most apparent difference between taxa is that confidence bands increase for taxa with low sample size. As previously shown, the beetle and fly taxa are the most heavily sampled and in the above figure have the narrowest confidence bands. Importantly, the overall direction of effect does not change between taxon, although guppies and mice show near zero effect sizes. Here we see that under stressed environments, females from all taxa appear to have greater fitness increase than males or ‘both’.
Supplementary Table 10: The predictions for the above figure looking at the effect of sexual selection amongst taxa uses the following dataframe.
colnames(predictions.complete) <- c("Sex", "Environment", "Taxon", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.complete <- format(predictions.complete, digits = 2)
predictions.complete[[9]] <- NULL
predictions.complete %>% pander()| Sex | Environment | Taxon | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|---|
| M | Unstressed | Beetle | 0.2680 | 0.17 | -0.066 | 0.60 | 18 |
| B | Unstressed | Beetle | 0.2702 | 0.18 | -0.081 | 0.62 | 2 |
| F | Unstressed | Beetle | 0.3810 | 0.17 | 0.047 | 0.71 | 15 |
| M | Stressed | Beetle | 0.1128 | 0.17 | -0.228 | 0.45 | 2 |
| B | Stressed | Beetle | 0.2934 | 0.19 | -0.072 | 0.66 | 6 |
| F | Stressed | Beetle | 0.4905 | 0.17 | 0.153 | 0.83 | 9 |
| M | Unstressed | Cricket | 0.2680 | 0.17 | -0.066 | 0.60 | 1 |
| B | Unstressed | Cricket | 0.2702 | 0.18 | -0.081 | 0.62 | NA |
| F | Unstressed | Cricket | 0.3810 | 0.17 | 0.047 | 0.71 | NA |
| M | Stressed | Cricket | 0.1128 | 0.17 | -0.228 | 0.45 | NA |
| B | Stressed | Cricket | 0.2934 | 0.19 | -0.072 | 0.66 | NA |
| F | Stressed | Cricket | 0.4905 | 0.17 | 0.153 | 0.83 | NA |
| M | Unstressed | Fly | 0.1453 | 0.14 | -0.128 | 0.42 | 60 |
| B | Unstressed | Fly | 0.1475 | 0.15 | -0.142 | 0.44 | 9 |
| F | Unstressed | Fly | 0.2583 | 0.14 | -0.017 | 0.53 | 93 |
| M | Stressed | Fly | -0.0099 | 0.14 | -0.290 | 0.27 | 9 |
| B | Stressed | Fly | 0.1707 | 0.16 | -0.138 | 0.48 | 8 |
| F | Stressed | Fly | 0.3678 | 0.14 | 0.090 | 0.65 | 19 |
| M | Unstressed | Guppy | 0.1374 | 0.48 | -0.796 | 1.07 | NA |
| B | Unstressed | Guppy | 0.1395 | 0.48 | -0.806 | 1.08 | NA |
| F | Unstressed | Guppy | 0.2503 | 0.48 | -0.682 | 1.18 | 3 |
| M | Stressed | Guppy | -0.0178 | 0.48 | -0.953 | 0.92 | NA |
| B | Stressed | Guppy | 0.1627 | 0.48 | -0.786 | 1.11 | NA |
| F | Stressed | Guppy | 0.3599 | 0.48 | -0.575 | 1.30 | NA |
| M | Unstressed | Mite | 0.3690 | 0.23 | -0.075 | 0.81 | 3 |
| B | Unstressed | Mite | 0.3711 | 0.23 | -0.085 | 0.83 | 2 |
| F | Unstressed | Mite | 0.4820 | 0.23 | 0.037 | 0.93 | 9 |
| M | Stressed | Mite | 0.2138 | 0.23 | -0.235 | 0.66 | 1 |
| B | Stressed | Mite | 0.3943 | 0.23 | -0.064 | 0.85 | 4 |
| F | Stressed | Mite | 0.5915 | 0.23 | 0.143 | 1.04 | 3 |
| M | Unstressed | Mouse | -0.0463 | 0.26 | -0.564 | 0.47 | 1 |
| B | Unstressed | Mouse | -0.0442 | 0.27 | -0.574 | 0.49 | 2 |
| F | Unstressed | Mouse | 0.0666 | 0.26 | -0.452 | 0.58 | 5 |
| M | Stressed | Mouse | -0.2016 | 0.27 | -0.723 | 0.32 | NA |
| B | Stressed | Mouse | -0.0210 | 0.27 | -0.558 | 0.52 | NA |
| F | Stressed | Mouse | 0.1761 | 0.27 | -0.344 | 0.70 | 5 |
#saveRDS(predictions.complete, "PDF_RDS_files/ST10.rds")Given that none of the levels of Taxon have a particularly strong impact on effect size, and many categories are incompletely sampled with regards to Sex and Environment (Supplementary Table 10), we elected to treat Taxon as a random/group level effect in all subsequent models.
REML: Effects of Environment and Sex
Here we ask two key questions: Does sexual selection benefit populations in stressful environments more than benign environments? AND Do the benefits of sexual selection differ between the sexes? The only difference to the previous model is that Taxon is now treated as a random effect. Note that this analysis again uses the strict dataset, containing only “direct” fitness measures and those where we were able to score the presence/absence of environmental stress.
model.complete2 <- rma.mv(g, V = var.g,
mods = ~ 1 + Sex * Environment,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = strict_dataset)
saveRDS(model.complete2, 'data/model.complete2.rds')
summary(model.complete2, digits = 2)##
## Multivariate Meta-Analysis Model (k = 289; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1321.34 2642.68 2660.68 2693.49 2661.34
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.19 0.44 53 no Study.ID
## sigma^2.2 0.13 0.36 13 no Outcome
## sigma^2.3 0.00 0.00 6 no Taxon
##
## Test for Residual Heterogeneity:
## QE(df = 283) = 4226.68, p-val < .01
##
## Test of Moderators (coefficient(s) 2:6):
## QM(df = 5) = 75.51, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.19 0.12 1.53 0.13 -0.05 0.43
## SexB 0.00 0.07 0.04 0.97 -0.14 0.15
## SexF 0.11 0.03 3.73 <.01 0.05 0.17 ***
## EnvironmentStressed -0.16 0.04 -3.63 <.01 -0.24 -0.07 ***
## SexB:EnvironmentStressed 0.18 0.09 2.07 0.04 0.01 0.36 *
## SexF:EnvironmentStressed 0.26 0.05 5.11 <.01 0.16 0.37 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Next, we conducted planned contrasts on model.complete2 to investigate the difference in effect size between groups, using the anova method from metafor.
Supplementary Table 11: Using the anova.rma function we can conduct hypothesis tests between two categorical groups in the model. Here we conduct 5 tests comparing the relative effect of sexual selection between the sexes, and in different environments.
#anova where you specify the values based on the list of moderators
anova.1 = anova(model.complete2, L=c(0, 0, -1, 0, 0, 0))
anova.2 = anova(model.complete2, L=c(0, 0, -1, 0, 0, -1))
anova.3 = anova(model.complete2, L=c(0, 0, 0, -1, 0, -1))
anova.4 = anova(model.complete2, L=c(0, 0, 0, -1, 0, 0))
anova.5 = anova(model.complete2, L=c(0, 0, 0, -1, -1, 0))
anova.list <- list(anova.1, anova.2, anova.3, anova.4, anova.5)
anova.frame <- t(data.frame(lapply(anova.list, function(x) {
data.frame(x[["hyp"]],
x[["Lb"]],
x[["se"]],
x[["Lb"]] - 1.96*x[["se"]],
x[["Lb"]] + 1.96*x[["se"]],
x[["pval"]])
})))
anova.frame <- as.data.frame(split(anova.frame, rep(1:6)))
colnames(anova.frame) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", "pval")
anova.frame$Estimate <- as.numeric(levels(anova.frame$Estimate))[anova.frame$Estimate]
anova.frame$Est.Error <- as.numeric(levels(anova.frame$Est.Error))[anova.frame$Est.Error]
anova.frame$CI.Lower <- as.numeric(levels(anova.frame$CI.Lower))[anova.frame$CI.Lower]
anova.frame$CI.Upper <- as.numeric(levels(anova.frame$CI.Upper))[anova.frame$CI.Upper]
anova.frame$pval <- as.numeric(levels(anova.frame$pval))[anova.frame$pval]
anova.frame <- format(anova.frame, digits = 2)
anova.frame$star <- c("", "*", "*", "*", "")
colnames(anova.frame)[colnames(anova.frame)=="star"] <- " "
anova.frame$pval <- NULL
rownames(anova.frame) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
anova.frame %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | -SexF = 0 | -0.113 | 0.030 | -0.173 | -0.054 | |
| M vs F, Stressful | -SexF - SexF:EnvironmentStressed = 0 | -0.377 | 0.046 | -0.468 | -0.287 | * |
| Benign vs Stressful, Female | -EnvironmentStressed - SexF:EnvironmentStressed = 0 | -0.108 | 0.037 | -0.181 | -0.036 | * |
| Benign vs Stressful, Male | -EnvironmentStressed = 0 | 0.156 | 0.043 | 0.072 | 0.240 | * |
| Benign vs Stressful, Both | -EnvironmentStressed - SexB:EnvironmentStressed = 0 | -0.028 | 0.080 | -0.184 | 0.128 |
#saveRDS(anova.frame, "PDF_RDS_files/ST11.rds")Bayesian model: Effects of Environment and Sex
if(!file.exists("data/brms.complete2.rds")){
brms.complete2 <- brm(g | se(SE) ~ 1 + Sex * Environment
+ (1|Taxon)
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000,
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = strict_dataset %>% mutate(SE = sqrt(var.g)))
saveRDS(brms.complete2, file = "data/brms.complete2.rds")
}
brms.complete2 <- readRDS(file = "data/brms.complete2.rds") Supplementary Table 12: Model estimate summary table for the Bayesian model investigating the effect of environment and sex (alongside sexual selection) on fitness.
# lternatively you can obtain posterior samples manually.
post <- (posterior_samples(brms.complete2,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
# Add columns for Environment and Sex
post <- as.data.frame(t(post))
post$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
post <- melt(post, id = c("Sex", "Environment"))
post$variable <- NULL
make_text_summary(brms.complete2) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()| Model Parameter | Estimate | Est.Error | Q2.5 | Q97.5 | |
|---|---|---|---|---|---|
| b_Intercept | 0.188 | 0.175 | -0.167 | 0.522 | |
| b_SexB | 0.003 | 0.073 | -0.143 | 0.144 | |
| b_SexF | 0.113 | 0.03 | 0.053 | 0.171 | * |
| b_EnvironmentStressed | -0.156 | 0.043 | -0.241 | -0.073 | * |
| b_SexB:EnvironmentStressed | 0.182 | 0.089 | 0.006 | 0.354 | * |
| b_SexF:EnvironmentStressed | 0.264 | 0.052 | 0.163 | 0.369 | * |
| sd_Outcome__Intercept | 0.413 | 0.119 | 0.241 | 0.703 | * |
| sd_Study.ID__Intercept | 0.452 | 0.053 | 0.36 | 0.57 | * |
| sd_Taxon__Intercept | 0.147 | 0.152 | 0.004 | 0.545 | * |
Supplementary Table 13: Hypothesis tests for the Bayesian model are similar to the REML model, with slight differences to CIs.
#Obtain hypothesis estimates
brms.hypothesis <- hypothesis(brms.complete2, c("0 = SexF",
"0 = SexF + SexF:EnvironmentStressed",
"0 = SexF:EnvironmentStressed + EnvironmentStressed",
"0 = EnvironmentStressed",
"0 = SexB:EnvironmentStressed + EnvironmentStressed"))
#Format into dataframe
brms.hypothesis.table <-
data.frame(brms.hypothesis[["hypothesis"]][["Hypothesis"]],
brms.hypothesis[["hypothesis"]][["Estimate"]],
brms.hypothesis[["hypothesis"]][["Est.Error"]],
brms.hypothesis[["hypothesis"]][["CI.Lower"]],
brms.hypothesis[["hypothesis"]][["CI.Upper"]],
brms.hypothesis[["hypothesis"]][["Star"]])
colnames(brms.hypothesis.table) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", " ")
brms.hypothesis.table <- format(brms.hypothesis.table, digits = 2)
rownames(brms.hypothesis.table) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
brms.hypothesis.table %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | (0)-(SexF) = 0 | -0.113 | 0.030 | -0.171 | -0.053 | * |
| M vs F, Stressful | (0)-(SexF+SexF:EnvironmentStressed) = 0 | -0.377 | 0.047 | -0.471 | -0.286 | * |
| Benign vs Stressful, Female | (0)-(SexF:EnvironmentStressed+EnvironmentStressed) = 0 | -0.109 | 0.037 | -0.182 | -0.035 | * |
| Benign vs Stressful, Male | (0)-(EnvironmentStressed) = 0 | 0.156 | 0.043 | 0.073 | 0.241 | * |
| Benign vs Stressful, Both | (0)-(SexB:EnvironmentStressed+EnvironmentStressed) = 0 | -0.026 | 0.080 | -0.182 | 0.133 |
Using predictions from both REML and Bayesian models we can obtain a figure that plots the mean/median predictions as well as distribution density (Bayesian) and 95 % CI (REML).
#Generate predictions without taxon utilising the previously described function
get.predictions.complete2 <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.complete2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])
countpred <- count_(strict_dataset, c("Sex", "Environment"))
predictions.complete2 <- left_join(predictions.complete2, countpred, by = c("Sex", "Environment"))
colnames(predictions.complete2) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.complete2 <- predictions.complete2 %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"),
Sex = factor(Sex, levels = c("Male", "Both", "Female")))
#Plot the posterior values from the Bayesian model as density ridges
pd <- position_dodgev(height = 0.3)
posterior.plot <- post %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment")+
xlab("\nEffect Size (Hedges' g)")+
# scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
# scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_fill_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_x_continuous(limits = c(-0.75, 1.5), breaks = c(-1, -.5, 0, 0.5, 1, 1.5))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16, face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
#Add the REML predictions as circles with error bars
both.plots <- posterior.plot +
geom_errorbarh(data = predictions.complete2 %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(xmin = predictions.complete2$CI.lb,
xmax = predictions.complete2$CI.ub, y = Environment,
color = Sex),
height = 0, show.legend = F, position = pd)+
geom_point(data = predictions.complete2 %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075, 0))
both.plots
Figure 2a: Sexual selection generally increases population fitness, especially for females under stressful conditions. The benefits of sexual selection on fitness for females under stressful conditions are small-medium according to Cohen’s interperetation of effect sizes. Circle size is proportional to sample size (shown below). The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. This figure can also be found in the main manuscript.
Supplementary Table 14: The REML predictions in the plot above use the following dataframe
predictions.complete2 <- format(predictions.complete2, digits = 2)
predictions.complete2$Prediction = as.numeric(predictions.complete2$Prediction)
predictions.complete2$CI.lb = as.numeric(predictions.complete2$CI.lb)
predictions.complete2$CI.ub = as.numeric(predictions.complete2$CI.ub)
predictions.complete2$n = as.numeric(predictions.complete2$n)
predictions.complete2 %>% pander()| Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.188 | 0.12 | -0.053 | 0.43 | 83 |
| Both | Benign | 0.191 | 0.13 | -0.071 | 0.45 | 15 |
| Female | Benign | 0.301 | 0.12 | 0.058 | 0.54 | 125 |
| Male | Stressful | 0.032 | 0.13 | -0.218 | 0.28 | 12 |
| Both | Stressful | 0.219 | 0.14 | -0.061 | 0.5 | 18 |
| Female | Stressful | 0.409 | 0.13 | 0.162 | 0.66 | 36 |
#saveRDS(predictions.complete2, "PDF_RDS_files/ST14.rds")Effects of environment and sex on direct fitness components
model.direct.only <- rma.mv(g, V = var.g,
mods = ~ 1 + Sex * Environment,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = strict_dataset %>% filter(Outcome.Class == "Direct"))
#Generate predictions without taxon utilising the previously described function
get.predictions.direct <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(model.direct.only, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.direct <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.direct <- cbind(predictions.direct, do.call("rbind", apply(predictions.direct, 1, get.predictions.direct))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.direct[,i] <- unlist(predictions.direct[,i])
countpred <- count_(strict_dataset %>% filter(Outcome.Class == "Direct"), c("Sex", "Environment"))
predictions.direct <- left_join(predictions.direct, countpred, by = c("Sex", "Environment"))
colnames(predictions.direct) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.direct <- predictions.direct %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"),
Sex = factor(Sex, levels = c("Male", "Both", "Female")))
predictions.direct <- format(predictions.direct, digits = 2)
predictions.direct$Prediction = as.numeric(predictions.direct$Prediction)
predictions.direct$CI.lb = as.numeric(predictions.direct$CI.lb)
predictions.direct$CI.ub = as.numeric(predictions.direct$CI.ub)
predictions.direct$n = as.numeric(predictions.direct$n)
if(!file.exists("data/brms.direct.rds")){
brms.direct <- brm(g | se(SE) ~ 1 + Sex * Environment
+ (1|Taxon)
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000,
control = list(adapt_delta = 0.9999, max_treedepth = 15),
data = strict_dataset %>%
filter(Outcome.Class == "Direct") %>%
mutate(SE = sqrt(var.g)))
saveRDS(brms.direct, file = "data/brms.direct.rds")
}
brms.direct <- readRDS(file = "data/brms.direct.rds")
# lternatively you can obtain posterior samples manually.
post.direct <- (posterior_samples(brms.direct,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
# Add columns for Environment and Sex
post.direct <- as.data.frame(t(post.direct))
post.direct$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post.direct$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
post.direct <- melt(post.direct, id = c("Sex", "Environment"))
post.direct$variable <- NULL
#Plot the posterior values from the Bayesian model as density ridges
pd <- position_dodgev(height = 0.3)
posterior.direct.plot <- post.direct %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment\n")+
xlab("\nEffect Size (Hedges' g)")+
# scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
# scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_fill_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_x_continuous(limits = c(-1.25, 1.25), breaks = c(-1, -.5, 0, 0.5, 1))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16, face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
#Add the REML predictions as circles with error bars
both.direct.plots <- posterior.direct.plot +
geom_errorbarh(data = predictions.direct %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(xmin = predictions.direct$CI.lb,
xmax = predictions.direct$CI.ub, y = Environment,
color = Sex),
height = 0, show.legend = F, position = pd)+
geom_point(data = predictions.direct %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075, 0))
#pdf("PDF_RDS_files/SF3.pdf", width = 10, height = 6)
grid.arrange(both.plots +
guides(fill = FALSE)+
ggtitle("Indirect And Direct\n")+ theme(plot.title = element_text(size = 18, face = "bold", colour = "Red")),
both.direct.plots + ggtitle("Direct Only\n")+
theme(axis.title.y = element_blank(),
plot.title = element_text(size = 18, face = "bold", colour = "Red")),
nrow = 1,
widths = c(3,3.5))
#dev.offSupplementary Figure 3: The comparison of model predictions between models that use a dataset compiled from direct and indirect fitness components against a model that only uses direct fitness components. Notably, in both cases there is a significant positive effect of sexual selection on fitness for females evolving in stressful conditions. Bayesian predictions are depicted by a density curve while REML predictions are depicted with a circle (size corresponds to n) and 95 % CIs.
Supplementary Table 15: The REML predictions for the ‘direct model’ in the plot above use the following dataframe.
predictions.direct %>% pander()| Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.131 | 0.12 | -0.098 | 0.36 | 13 |
| Both | Benign | 0.104 | 0.12 | -0.137 | 0.34 | 15 |
| Female | Benign | 0.091 | 0.11 | -0.121 | 0.3 | 86 |
| Male | Stressful | -0.45 | 0.13 | -0.707 | -0.19 | 2 |
| Both | Stressful | 0.135 | 0.12 | -0.108 | 0.38 | 12 |
| Female | Stressful | 0.312 | 0.11 | 0.093 | 0.53 | 31 |
#saveRDS(predictions.direct, "PDF_RDS_files/ST15.rds")Estimating Hedges’ g heterogeneity using I2
Using a function from https://github.com/daniel1noble/metaAidR we can obtain confidence intervals for total I2 and the individual components of the random effects. There are different methods to obtain estimates of I2. Here we obtain an overall value of I2 that is weighted based on variance and where estimates of heterogeneity are sourced from sigma2 of the respective models. The values are based on the REML models.
I2.model.g <- rma.mv(g, V = var.g,
mods = ~ 1 + Sex * Environment,
random = list(~ 1 | Observation.level,
~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = strict_dataset %>% mutate(Observation.level = 1:n()))
I2(I2.model.g, strict_dataset$var.g) %>% pander(digits = 3)| I2_Est. | 2.5% CI | 97.5% CI | |
|---|---|---|---|
| Observation.level | 57.4 | 48.7 | 66.2 |
| Study.ID | 36 | 26.5 | 45.4 |
| Outcome | 0.428 | 0.157 | 0.852 |
| Taxon | 1.39 | 0.223 | 3.51 |
| total | 95.2 | 94.4 | 95.9 |
These values indicate that 36 % of total heterogeneity is due to the between study. The total I2 is 95.2 %, a reasonably high I2 value. However this is relatively common in Ecology and Evolution total (Senior, Grueber, et al. 2016).
Meta-analysis using lnRR
As per reviewers comments, we can use the log-response ratio (lnRR) instead of Hedges’ g as the effect size (response variable in meta-analysis models). To calculate lnRR we use the metafor function escalc.
lnRR.data <-
escalc(measure = "ROM",
m1i = mean.high,
m2i = mean.low,
sd1i = sd.high,
sd2i = sd.low,
n1i = n.high,
n2i = n.low,
vtype = "LS",
data = full_dataset, var.names=c("lnRR","lnRR_var"), digits=4) %>%
filter(lnRR != "NA") %>%
mutate(lnRR = round(lnRR, 3)*Positive.Fitness,
Sex = relevel(Sex, ref = "M"),
Environment = relevel(Environment, ref = "Unstressed"),
Taxon = relevel(Taxon, ref = "Beetle"),
Outcome.Class = relevel(factor(Outcome.Class), ref = "Indirect"))
SMD.data <-
escalc(measure = "SMD",
m1i = mean.high,
m2i = mean.low,
sd1i = sd.high,
sd2i = sd.low,
n1i = n.high,
n2i = n.low,
vtype = "UB",
data = full_dataset, var.names=c("SMD","SMD_var"), digits=4) %>%
filter(SMD != "NA") %>%
mutate(SMD = round(SMD, 3)*Positive.Fitness,
Sex = relevel(Sex, ref = "M"),
Environment = relevel(Environment, ref = "Unstressed"),
Taxon = relevel(Taxon, ref = "Beetle"),
Outcome.Class = relevel(factor(Outcome.Class), ref = "Indirect"))
SMDH.data <-
escalc(measure = "SMDH",
m1i = mean.high,
m2i = mean.low,
sd1i = sd.high,
sd2i = sd.low,
n1i = n.high,
n2i = n.low,
data = full_dataset, var.names=c("SMDH","SMDH_var"), digits=4) %>%
filter(SMDH != "NA") %>%
mutate(SMDH = round(SMDH, 3)*Positive.Fitness,
Sex = relevel(Sex, ref = "M"),
Environment = relevel(Environment, ref = "Unstressed"),
Taxon = relevel(Taxon, ref = "Beetle"),
Outcome.Class = relevel(factor(Outcome.Class), ref = "Indirect"))
lnRR.grandmean <- rma.mv(lnRR, lnRR_var,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = lnRR.data)
SMD.grandmean <- rma.mv(SMD, SMD_var,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = SMD.data)
SMDH.grandmean <- rma.mv(SMDH, SMDH_var,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = SMDH.data)
if(!file.exists("data/lnRR.brms.env.sex.rds")){
lnRR.brms.env.sex <- brm(lnRR | se(SE) ~ 1 + Sex * Environment
+ (1|Taxon)
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000,
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = lnRR.data %>% mutate(SE = sqrt(lnRR_var)) %>%
filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated"))
saveRDS(lnRR.brms.env.sex, file = "data/lnRR.brms.env.sex.rds")
}
lnRR.brms.env.sex <- readRDS(file = "data/lnRR.brms.env.sex.rds")
# lternatively you can obtain posterior samples manually.
lnRR.post <- (posterior_samples(lnRR.brms.env.sex,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
# Add columns for Environment and Sex
lnRR.post <- as.data.frame(t(lnRR.post))
lnRR.post$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
lnRR.post$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
lnRR.post <- melt(lnRR.post, id = c("Sex", "Environment"))
lnRR.post$variable <- NULL
lnRR.env.sex.model <- rma.mv(lnRR, V = lnRR_var,
mods = ~ 1 + Sex * Environment,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = lnRR.data %>% filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated"))
#Generate predictions without taxon utilising the previously described function
get.predictions.lnRR <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(lnRR.env.sex.model, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.lnRR <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.lnRR <- cbind(predictions.lnRR, do.call("rbind", apply(predictions.lnRR, 1, get.predictions.lnRR))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.lnRR[,i] <- unlist(predictions.lnRR[,i])
countpred <- count_(lnRR.data %>% filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated"), c("Sex", "Environment"))
predictions.lnRR <- left_join(predictions.lnRR, countpred, by = c("Sex", "Environment"))
colnames(predictions.lnRR) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.lnRR <- predictions.lnRR %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female"),
Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"),
Sex = factor(Sex, levels = c("Male", "Both", "Female")))
predictions.lnRR <- format(predictions.lnRR, digits = 2)
predictions.lnRR$Prediction = as.numeric(predictions.lnRR$Prediction)
predictions.lnRR$CI.lb = as.numeric(predictions.lnRR$CI.lb)
predictions.lnRR$CI.ub = as.numeric(predictions.lnRR$CI.ub)
predictions.lnRR$n = as.numeric(predictions.lnRR$n)
#Plot the posterior values from the Bayesian model as density ridges
pd <- position_dodgev(height = 0.3)
posterior.lnRR.plot <- lnRR.post %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment")+
xlab("\nEffect Size (lnRR)")+
# scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
# scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
scale_fill_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_x_continuous(limits = c(-0.5, 1), breaks = c(-1, -.5, 0, 0.5, 1, 1.5))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16, face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
#Add the REML predictions as circles with error bars
lnRR.plots <- posterior.lnRR.plot +
geom_errorbarh(data = predictions.lnRR %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(xmin = predictions.lnRR$CI.lb,
xmax = predictions.lnRR$CI.ub, y = Environment,
color = Sex),
height = 0, show.legend = F, position = pd)+
geom_point(data = predictions.lnRR %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075, 0))
#pdf("PDF_RDS_files/SF4.pdf", width = 6, height = 6)
lnRR.plots
#dev.off()Supplementary Figure 4: Using an alternative effect size (lnRR) sexual selection also increases population fitness. Circle size is proportional to sample size (shown below). The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. Note that the magnitude of the effect sizes presented here should not be directly compared with those using Hedges’ g as lnRR is a log-transformed value.
Supplementary Table 16: The REML predictions for the meta-analysis using lnRR (plotted above) are formulated from the following model and predictions.
summary(lnRR.env.sex.model)##
## Multivariate Meta-Analysis Model (k = 236; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1247.9877 2495.9754 2513.9754 2544.9181 2514.7936
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0117 0.1081 43 no Study.ID
## sigma^2.2 0.0160 0.1264 11 no Outcome
## sigma^2.3 0.0057 0.0754 6 no Taxon
##
## Test for Residual Heterogeneity:
## QE(df = 230) = 4484.8873, p-val < .0001
##
## Test of Moderators (coefficient(s) 2:6):
## QM(df = 5) = 46.6937, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1162 0.0593 1.9611 0.0499 0.0001
## SexB 0.0937 0.0261 3.5915 0.0003 0.0426
## SexF 0.0185 0.0158 1.1721 0.2412 -0.0125
## EnvironmentStressed 0.0125 0.0184 0.6828 0.4947 -0.0235
## SexB:EnvironmentStressed -0.0604 0.0414 -1.4598 0.1444 -0.1415
## SexF:EnvironmentStressed 0.0705 0.0215 3.2785 0.0010 0.0283
## ci.ub
## intrcpt 0.2324 *
## SexB 0.1448 ***
## SexF 0.0495
## EnvironmentStressed 0.0485
## SexB:EnvironmentStressed 0.0207
## SexF:EnvironmentStressed 0.1126 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1predictions.lnRR %>% pander()| Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.12 | 0.059 | 7e-05 | 0.23 | 73 |
| Both | Benign | 0.21 | 0.063 | 0.08705 | 0.33 | 12 |
| Female | Benign | 0.13 | 0.059 | 0.0193 | 0.25 | 110 |
| Male | Stressful | 0.13 | 0.061 | 0.00907 | 0.25 | 8 |
| Both | Stressful | 0.16 | 0.068 | 0.02938 | 0.29 | 6 |
| Female | Stressful | 0.22 | 0.060 | 0.1002 | 0.34 | 27 |
#saveRDS(predictions.lnRR, "PDF_RDS_files/ST16.rds")Meta-Analysis on Variance
Obtaining lnCVR and Meta-Analysis Models
This meta-analysis on variation utilises previously described and utilised methods devoleped (Nakagawa et al. 2015; Senior, Gosby, et al. 2016). Our goal is to determine whether the phenotypic variance in fitness related traits is impacted by sexual selection. We would assume that if selection is occuring not only would the trait mean shift in a certain direction but the variance associated with those changes to the mean would also decrease. In this case we use an effect size statistic known as the natural log of the coefficient of variation ratio (lnCVR).
# Firstly, we setup our calculation by creating a a restricted dataset with only unabmiguous fitness outcomes and running the functions developed by Nakagawa et al. 2015:
Calc.lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN){
ES <- log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - log(CMean) + 1 / (2*(CN - 1)))
return(ES)
}
# Function to find the variance of lnCVR
# Equal.E.C.Corr = T assumes that the correlaiton between mean and sd (Taylor's Law) is equal for the mean and control groups
Calc.var.lnCVR <- function(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr = TRUE){
if(Equal.E.C.Corr==T){
mvcorr <- cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate
S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
}
else{
Cmvcorr<-cor.test(log(CMean), log(CSD))$estimate
Emvcorr<-cor.test(log(EMean), (ESD))$estimate
S2 <- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
}
return(S2)
}
# Secondly, we utilise those formulas to obtain lnCVR and var.CVR for all applicable effect sizes. Noting that not all of the dataset has means, SD and n; some were calculated from summary statistics and are not able to have lnCVR calculated:
#Calculate lnCVr and var.lnCVr
strict_dataset$lnCVr <- with(strict_dataset, Calc.lnCVR (mean.low, sd.low, n.low, mean.high, sd.high, n.high))
strict_dataset$var.lnCVr <- with(strict_dataset, Calc.var.lnCVR (mean.low, sd.low, n.low, mean.high, sd.high, n.high))Meta-analysis of lnCVR using REML
variance.model <- rma.mv(lnCVr, V = var.lnCVr, mods = ~ 1 + Sex*Environment,
random = list(~ 1 | Study.ID,
~ 1 | Taxon,
~ 1 | Outcome),
method = "REML", data = strict_dataset)
summary(variance.model, digits = 2)##
## Multivariate Meta-Analysis Model (k = 236; method: REML)
##
## logLik Deviance AIC BIC AICc
## -3757.34 7514.69 7532.69 7563.63 7533.50
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.07 0.26 43 no Study.ID
## sigma^2.2 0.15 0.38 6 no Taxon
## sigma^2.3 0.23 0.48 11 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 230) = 12270.31, p-val < .01
##
## Test of Moderators (coefficient(s) 2:6):
## QM(df = 5) = 2804.84, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.07 0.23 0.30 0.76 -0.38 0.51
## SexB -0.26 0.04 -6.67 <.01 -0.33 -0.18 ***
## SexF -0.03 0.01 -2.13 0.03 -0.06 -0.00 *
## EnvironmentStressed 0.16 0.02 7.29 <.01 0.11 0.20 ***
## SexB:EnvironmentStressed -0.73 0.04 -16.65 <.01 -0.82 -0.65 ***
## SexF:EnvironmentStressed -0.98 0.03 -38.24 <.01 -1.03 -0.93 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Bayesian meta-analysis of lnCVR
Again, we use brms to obtain Bayesian model estimates. For this model the R2 is 0.34 (95% CIs = 0.32-0.36).
if(!file.exists("data/variance.brms.rds")){
variance.brms <- brm(lnCVr| se(SE.v) ~ 1 + Sex * Environment
+ (1|Taxon)
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000,
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = strict_dataset %>% mutate(SE.v = sqrt(var.lnCVr)))
saveRDS(variance.brms, "data/variance.brms.rds")
}
var.brms <- readRDS(file = "data/variance.brms.rds") #Avoid re-running model aboveSupplementary Table 17: Model estimates, including random effect sigma value for the model of phenotypic variance (lnCVR)
post.variance <- (posterior_samples(var.brms,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
#Add columns for Environment and Sex
post.variance <- as.data.frame(t(post.variance))
post.variance$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post.variance$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
post.variance <- melt(post.variance, id = c("Sex", "Environment"))
post.variance$variable <- NULL
make_text_summary(var.brms) %>%
add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()| Model Parameter | Estimate | Est.Error | Q2.5 | Q97.5 | |
|---|---|---|---|---|---|
| b_Intercept | 0.067 | 0.304 | -0.537 | 0.671 | |
| b_SexB | -0.257 | 0.039 | -0.333 | -0.181 | * |
| b_SexF | -0.031 | 0.015 | -0.06 | -0.002 | * |
| b_EnvironmentStressed | 0.156 | 0.022 | 0.115 | 0.199 | * |
| b_SexB:EnvironmentStressed | -0.733 | 0.044 | -0.82 | -0.645 | * |
| b_SexF:EnvironmentStressed | -0.975 | 0.026 | -1.027 | -0.925 | * |
| sd_Outcome__Intercept | 0.557 | 0.16 | 0.342 | 0.963 | * |
| sd_Study.ID__Intercept | 0.267 | 0.038 | 0.203 | 0.352 | * |
| sd_Taxon__Intercept | 0.521 | 0.32 | 0.137 | 1.319 | * |
Predictions based on the REML and Bayesian model can then be generated in the same way as for Hedges’g. Here, negative values of lnCVR indicate a narrowing (decrease) in phenotypic variance as a result of sexual selection.
#Generate predictions
get.predictions.variance <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(variance.model, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.variance <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.variance <- cbind(predictions.variance, do.call("rbind", apply(predictions.variance, 1, get.predictions.variance))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.variance[,i] <- unlist(predictions.variance[,i])
countpred <- count_(strict_dataset %>% filter(lnCVr != "NA" ), c("Sex", "Environment"))
predictions.variance <- left_join(predictions.variance, countpred, by = c("Sex", "Environment"))
#Change names to make them more clear
predictions.variance <- predictions.variance %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female")) %>%
mutate(Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"))
colnames(predictions.variance) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
#And plot the results, first for the posterior results of the brms model then for the metafor predictions
pd <- position_dodgev(height = 0.3)
var.plot.posterior <- post.variance %>%
mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>%
ggplot() +
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment\n")+
xlab("\nPhenotypic Variance (lnCVR)")+
scale_x_continuous(limits = c(-2.1, 1.2), breaks = c(-2, -1.5, -1, -0.5, 0, 0.5, 1))+
scale_fill_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16, face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
both.var.plots <- var.plot.posterior +
geom_errorbarh(data = predictions.variance %>%
mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(xmin = predictions.variance$CI.lb,
xmax = predictions.variance$CI.ub, y = Environment,
color = Sex),
height = 0, position = pd, show.legend = F) +
geom_point(data = predictions.variance %>%
mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075,0))
both.var.plots
Figure 2b: Phenotypic variation changes under sexual selection in stressful environments. For females under stressful conditions phenotypic variation decreases (narrows). While for males in stressful environments it increases. For outcomes that measured a mix of both males and females (pooled samples) in stressful environments phenotypic variation decreased slightly. The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. Circle size is proportional to sample size.
Supplementary Table 18: The REML predictions in the plot above use the following dataframe.
predictions.variance <- format(predictions.variance, digits = 2)
predictions.variance %>% pander(digits = 2)| Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.068 | 0.23 | -0.38 | 0.51 | 73 |
| Both | Benign | -0.187 | 0.23 | -0.64 | 0.26 | 12 |
| Female | Benign | 0.037 | 0.23 | -0.41 | 0.48 | 110 |
| Male | Stressful | 0.225 | 0.23 | -0.22 | 0.67 | 8 |
| Both | Stressful | -0.764 | 0.23 | -1.22 | -0.31 | 6 |
| Female | Stressful | -0.781 | 0.23 | -1.23 | -0.34 | 27 |
#saveRDS(predictions.variance, "PDF_RDS_files/ST18.rds")Hypothesis tests For models of lnCVR
As we did for Hedges’ \(g\), we here conduct hypothesis tests between categorical groups, to identify groups that differ significantly in how much sexual selection affects the phenotypic variance. Positive values indivate the first term in the hypothesis is larger (which would be male for rows 1-2 or benign for rows 3-5).
Supplementary Table 19: Bayesian hypothesis tests between categorical groups for phenotypic variation (lnCVR)
#Obtain hypothesis estimates
brms.hypothesis.var <- hypothesis(var.brms, c("0 = SexF",
"0 = SexF + SexF:EnvironmentStressed",
"0 = SexF:EnvironmentStressed + EnvironmentStressed",
"0 = EnvironmentStressed",
"0 = SexB:EnvironmentStressed + EnvironmentStressed"))
#Format into dataframe
brms.hypothesis.table.var <-
data.frame(brms.hypothesis.var[["hypothesis"]][["Hypothesis"]],
brms.hypothesis.var[["hypothesis"]][["Estimate"]],
brms.hypothesis.var[["hypothesis"]][["Est.Error"]],
brms.hypothesis.var[["hypothesis"]][["CI.Lower"]],
brms.hypothesis.var[["hypothesis"]][["CI.Upper"]],
brms.hypothesis.var[["hypothesis"]][["Star"]])
colnames(brms.hypothesis.table.var) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", " ")
row.names(brms.hypothesis.table.var) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
brms.hypothesis.table.var <- format(brms.hypothesis.table.var, digits = 2)
brms.hypothesis.table.var %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | (0)-(SexF) = 0 | 0.031 | 0.015 | 0.0016 | 0.06 | * |
| M vs F, Stressful | (0)-(SexF+SexF:EnvironmentStressed) = 0 | 1.006 | 0.024 | 0.9600 | 1.05 | * |
| Benign vs Stressful, Female | (0)-(SexF:EnvironmentStressed+EnvironmentStressed) = 0 | 0.819 | 0.018 | 0.7828 | 0.85 | * |
| Benign vs Stressful, Male | (0)-(EnvironmentStressed) = 0 | -0.156 | 0.022 | -0.1990 | -0.11 | * |
| Benign vs Stressful, Both | (0)-(SexB:EnvironmentStressed+EnvironmentStressed) = 0 | 0.577 | 0.040 | 0.4978 | 0.65 | * |
Supplementary Table 20: REML hypothesis tests between categorical groups for phenotypic variation (lnCVR)
#anova where you specify the values based on the list of moderators
anova.1 = anova(variance.model, L=c(0, 0, -1, 0, 0, 0))
anova.2 = anova(variance.model, L=c(0, 0, -1, 0, 0, -1))
anova.3 = anova(variance.model, L=c(0, 0, 0, -1, 0, -1))
anova.4 = anova(variance.model, L=c(0, 0, 0, -1, 0, 0))
anova.5 = anova(variance.model, L=c(0, 0, 0, -1, -1, 0))
anova.list.var <- list(anova.1, anova.2, anova.3, anova.4, anova.5)
anova.frame.var <- t(data.frame(lapply(anova.list.var, function(x) {
data.frame(x[["hyp"]],
x[["Lb"]],
x[["se"]],
x[["Lb"]] - 1.96*x[["se"]],
x[["Lb"]] + 1.96*x[["se"]],
x[["pval"]])
})))
anova.frame.var <- as.data.frame(split(anova.frame.var, rep(1:6)))
colnames(anova.frame.var) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", "pval")
anova.frame.var$Estimate <- as.numeric(levels(anova.frame.var$Estimate))[anova.frame.var$Estimate]
anova.frame.var$Est.Error <- as.numeric(levels(anova.frame.var$Est.Error))[anova.frame.var$Est.Error]
anova.frame.var$CI.Lower <- as.numeric(levels(anova.frame.var$CI.Lower))[anova.frame.var$CI.Lower]
anova.frame.var$CI.Upper <- as.numeric(levels(anova.frame.var$CI.Upper))[anova.frame.var$CI.Upper]
anova.frame.var$pval <- as.numeric(levels(anova.frame.var$pval))[anova.frame.var$pval]
anova.frame.var <- format(anova.frame.var, digits = 2)
anova.frame.var$star <- c("*", "*", "*", "*", "*")
colnames(anova.frame.var)[colnames(anova.frame.var)=="star"] <- " "
anova.frame.var$pval <- NULL
row.names(anova.frame.var) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
anova.frame.var %>% pander(split.table = Inf)| Hypothesis | Estimate | Est.Error | CI.Lower | CI.Upper | ||
|---|---|---|---|---|---|---|
| M vs F, Benign | -SexF = 0 | 0.031 | 0.015 | 0.0025 | 0.06 | * |
| M vs F, Stressful | -SexF - SexF:EnvironmentStressed = 0 | 1.006 | 0.024 | 0.9598 | 1.05 | * |
| Benign vs Stressful, Female | -EnvironmentStressed - SexF:EnvironmentStressed = 0 | 0.818 | 0.018 | 0.7831 | 0.85 | * |
| Benign vs Stressful, Male | -EnvironmentStressed = 0 | -0.157 | 0.022 | -0.1990 | -0.11 | * |
| Benign vs Stressful, Both | -EnvironmentStressed - SexB:EnvironmentStressed = 0 | 0.577 | 0.040 | 0.4995 | 0.65 | * |
#saveRDS(anova.frame.var, "PDF_RDS_files/ST20.rds")Estimating lnCVR Heterogeneity Using I2
Similar to the meta-analysis on Hedges’ g we can obtain I2 for lnCVR REML model. In this case (compared to Hedges’ g) we see Taxon has more of a variable effect on overall I2 estimates and Study.ID has the variance component (\(\sigma^2\)) esgtimated at zero.
I2.model.lnCVr <- rma.mv(lnCVr, V = var.lnCVr,
mods = ~ 1 + Sex * Environment,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon,
~ 1 | Observation.level),
method = "REML",
data = strict_dataset %>% mutate(Observation.level = 1:n()))
I2(I2.model.lnCVr, na.omit(strict_dataset$var.lnCVr)) %>% pander(digits = 3)| I2_Est. | 2.5% CI | 97.5% CI | |
|---|---|---|---|
| Study.ID | 0 | 0 | 0 |
| Outcome | 12.7 | 4.54 | 23.1 |
| Taxon | 7.68 | 1.29 | 18.4 |
| Observation.level | 78.6 | 65.9 | 89 |
| total | 98.9 | 98.7 | 99.1 |
Disclaimer: Noticeabley, the Study.ID \(I^2\) is estimated at zero with no CIs. This result is sourced from the variance component for Study.ID (\(\sigma_2\)) being estimated at zero with a standard error of zero. This result is questionable, thus we run the above model using brms and find the variance of the Study.ID group-level effect to be non-zero with confidence intervals including zero (see below). This is potential advantage of the Bayesian approach.
if(!file.exists("data/I2.variance.brms.rds")){
I2.variance.brms <- brm(lnCVr| se(SE.v) ~ 1 + Sex * Environment
+ (1|Taxon)
+ (1|Study.ID)
+ (1|Outcome)
+ (1|Observation.level),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000,
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = strict_dataset %>% mutate(SE.v = sqrt(var.lnCVr)) %>% mutate(Observation.level = 1:n()))
saveRDS(I2.variance.brms, "data/I2.variance.brms.rds")
}
I2.variance.brms <- readRDS(file = "data/I2.variance.brms.rds") #Avoid re-running model above
I2.variance.brms## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: lnCVr | se(SE.v) ~ 1 + Sex * Environment + (1 | Taxon) + (1 | Study.ID) + (1 | Outcome) + (1 | Observation.level)
## Data: strict_dataset %>% mutate(SE.v = sqrt(var.lnCVr)) (Number of observations: 236)
## Samples: 4 chains, each with iter = 4000; warmup = 2000; thin = 1;
## total post-warmup samples = 8000
##
## Group-Level Effects:
## ~Observation.level (Number of levels: 236)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.46 0.03 0.42 0.51 1639 1.00
##
## ~Outcome (Number of levels: 11)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.24 0.14 0.03 0.55 1120 1.00
##
## ~Study.ID (Number of levels: 43)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.07 0.05 0.00 0.19 665 1.01
##
## ~Taxon (Number of levels: 6)
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sd(Intercept) 0.26 0.24 0.02 0.84 1523 1.00
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample
## Intercept -0.10 0.19 -0.49 0.28 2816
## SexB 0.25 0.18 -0.10 0.60 1511
## SexF 0.23 0.11 0.01 0.46 1346
## EnvironmentStressed 0.31 0.19 -0.06 0.68 1791
## SexB:EnvironmentStressed -0.79 0.32 -1.41 -0.16 2105
## SexF:EnvironmentStressed -0.58 0.22 -1.00 -0.15 1664
## Rhat
## Intercept 1.00
## SexB 1.00
## SexF 1.00
## EnvironmentStressed 1.00
## SexB:EnvironmentStressed 1.00
## SexF:EnvironmentStressed 1.00
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).lnVr Effect Size of Variance
In this meta-analysis we used the the log-coefficient of variaince ratio (lnCVR) as the response variable when investigating the impact of sexual selection on the variation in the distribution of traits associated with fitness. The lnCVR was used over the log-variance ration (lnVR) because lnVR does not account for the mean-variance relationship (Nakagawa et al. 2015; Senior, Gosby, et al. 2016) seen in this meta-analysis (see below). However, for completeness we present a similar style meta-analysis for lnVR to investigate the effects of environment
full_dataset %>% ggplot(aes(x = mean.high, y = sd.high))+
geom_point()+
scale_x_log10()+
scale_y_log10()+
xlab("Mean of Treatment Group")+
ylab("Standard Deviation of Treatment Group") +
geom_smooth(method = "lm", colour = "red")
Supplementary Figure 5: The use of lnCVR (as opposed to lnVR) is justified in this meta-analysis due to the strong mean-variance relationship. In this case the standard deviation from the treatment group is compared to the means of the treatment group on a log-scale.
lnVR.data <-
escalc(measure = "VR",
m1i = mean.high,
m2i = mean.low,
sd1i = sd.high,
sd2i = sd.low,
n1i = n.high,
n2i = n.low,
vtype = "LS",
data = full_dataset, var.names=c("lnVR","lnVR_var"), digits=4) %>%
filter(lnVR != "NA") %>%
mutate(lnVR = round(lnVR, 3),
Sex = relevel(Sex, ref = "M"),
Environment = relevel(Environment, ref = "Unstressed"),
Taxon = relevel(Taxon, ref = "Beetle"),
Outcome.Class = relevel(factor(Outcome.Class), ref = "Indirect"))
lnCVR.data <-
escalc(measure = "CVR",
m1i = mean.high,
m2i = mean.low,
sd1i = sd.high,
sd2i = sd.low,
n1i = n.high,
n2i = n.low,
vtype = "LS",
data = full_dataset, var.names=c("lnCVR","lnCVR_var"), digits=4) %>%
filter(lnCVR != "NA") %>%
mutate(lnCVR = round(lnCVR, 3),
Sex = relevel(Sex, ref = "M"),
Environment = relevel(Environment, ref = "Unstressed"),
Taxon = relevel(Taxon, ref = "Beetle"),
Outcome.Class = relevel(factor(Outcome.Class), ref = "Indirect"))
#MA on lnVR
lnVR.model <- rma.mv(lnVR, V = lnVR_var, mods = ~ 1 + Sex*Environment,
random = list(~ 1 | Taxon,
~ 1 | Study.ID,
~ 1 | Outcome),
method = "REML", data = lnVR.data %>% filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated"))
#Generate predictions
get.predictions.lnVR <- function(newdata){
B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
if(newdata[1] == "B") B<-1
if(newdata[1] == "F") F<-1
if(newdata[2] == "Stressed") Stressed<-1
if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1
predict(lnVR.model, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.lnVR <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
Environment = c("Unstressed", "Stressed")))
predictions.lnVR <- cbind(predictions.lnVR, do.call("rbind", apply(predictions.lnVR, 1, get.predictions.lnVR))) %>%
select(Sex, Environment, pred, se, ci.lb, ci.ub)
for(i in 3:6) predictions.lnVR[,i] <- unlist(predictions.lnVR[,i])
countpred <- count_(lnVR.data, c("Sex", "Environment"))
predictions.lnVR <- left_join(predictions.lnVR, countpred, by = c("Sex", "Environment"))
#Change names to make them more clear
predictions.lnVR <- predictions.lnVR %>%
mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
Sex = replace(Sex, Sex == "M", "Male"),
Sex = replace(Sex, Sex == "F", "Female")) %>%
mutate(Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
Environment = replace(Environment, Environment == "Unstressed", "Benign"))
colnames(predictions.lnVR) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
if(!file.exists("data/lnVR.brms.rds")){
lnVR.brms <- brm(lnVR| se(SE.lnVR) ~ 1 + Sex * Environment
+ (1|Taxon)
+ (1|Study.ID)
+ (1|Outcome),
family = "gaussian",
seed = 1,
cores = 4, chains = 4, iter = 4000,
control = list(adapt_delta = 0.999, max_treedepth = 15),
data = lnVR.data %>% mutate(SE.lnVR = sqrt(lnVR_var)) %>% filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated"))
saveRDS(lnVR.brms, "data/lnVR.brms.rds")
}
lnVR.brms <- readRDS(file = "data/lnVR.brms.rds") #Avoid re-running model abovepost.lnVR <- (posterior_samples(lnVR.brms,
pars = c("b_Intercept", "b_SexB", "b_SexF",
"b_EnvironmentStressed", "b_SexB:EnvironmentStressed",
"b_SexF:EnvironmentStressed")) %>%
mutate(both_benign = b_Intercept + b_SexB,
both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
male_benign = b_Intercept,
male_stressful = b_Intercept + b_EnvironmentStressed,
female_benign = b_Intercept + b_SexF,
female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]
#Add columns for Environment and Sex
post.lnVR <- as.data.frame(t(post.lnVR))
post.lnVR$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post.lnVR$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")
#Clean up dataframe
post.lnVR <- melt(post.lnVR, id = c("Sex", "Environment"))
post.lnVR$variable <- NULL
#And plot the results, first for the posterior results of the brms model then for the metafor predictions
lnVR.plot.posterior <- post.lnVR %>%
mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>%
ggplot() +
stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
geom_vline(xintercept = 0, linetype = 2, colour = "black") +
ylab("Environment\n")+
xlab("\nPhenotypic Variance (lnVR)")+
scale_x_continuous(limits = c(-1.6, 1.75), breaks = c(-2, -1.5, -1, -0.5, 0, 0.5, 1))+
scale_fill_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
scale_color_manual(values = c("Male" = "#ff7f00", "Female" = "#984ea3", "Both" = "#4daf4a"))+
theme_bw()+
theme(panel.spacing = unit(0.1, "lines"),
text = element_text(size=16),
panel.border= element_blank(),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=16),
legend.title=element_text(size=16, face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 14),
axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
axis.text.y = element_text(angle = 0),
plot.title = element_text(size = 16))
both.lnVR.plots <- lnVR.plot.posterior +
geom_errorbarh(data = predictions.lnVR %>%
mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(xmin = predictions.lnVR$CI.lb,
xmax = predictions.lnVR$CI.ub, y = Environment,
color = Sex),
height = 0, position = pd, show.legend = F) +
geom_point(data = predictions.lnVR %>%
mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))),
aes(x = Prediction, y = Environment, size=n, fill = Sex),
shape=21, color = "grey20", position = pd) +
guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
scale_size(guide = 'none')+
scale_y_discrete(expand=c(0.075,0))
#pdf("PDF_RDS_files/SF6.pdf", width =6, height = 6)
both.lnVR.plots
#dev.off()Supplementary Figure 6: The effects of sexual selection on the log-variance ratio (lnVR). Without accounting for the mean variance relationship (lnCVR) sexual selection has different effects on the variance measure.
Supplementary Table 21: The points in the above plot are based on the following REML model and predictions. While the predicted effects of sexual selection on lnVR in stressful conditions is non-significantly negative, there is still a negative significant interaction between stressful environments and the female sex.
summary(lnVR.model, digits = 2)##
## Multivariate Meta-Analysis Model (k = 236; method: REML)
##
## logLik Deviance AIC BIC AICc
## -2608.67 5217.34 5235.34 5266.28 5236.16
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.04 0.20 6 no Taxon
## sigma^2.2 0.10 0.31 43 no Study.ID
## sigma^2.3 0.26 0.51 11 no Outcome
##
## Test for Residual Heterogeneity:
## QE(df = 230) = 9664.58, p-val < .01
##
## Test of Moderators (coefficient(s) 2:6):
## QM(df = 5) = 1287.82, p-val < .01
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.05 0.19 0.24 0.81 -0.33 0.42
## SexB -0.19 0.06 -3.08 <.01 -0.31 -0.07 **
## SexF 0.42 0.02 18.97 <.01 0.37 0.46 ***
## EnvironmentStressed 0.69 0.03 22.09 <.01 0.62 0.75 ***
## SexB:EnvironmentStressed -1.04 0.07 -15.80 <.01 -1.17 -0.91 ***
## SexF:EnvironmentStressed -1.33 0.04 -34.88 <.01 -1.40 -1.25 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1predictions.lnVR %>% pander(digits = 2) | Sex | Environment | Prediction | SE | CI.lb | CI.ub | n |
|---|---|---|---|---|---|---|
| Male | Benign | 0.047 | 0.19 | -0.33 | 0.42 | 132 |
| Both | Benign | -0.14 | 0.2 | -0.53 | 0.25 | 17 |
| Female | Benign | 0.46 | 0.19 | 0.09 | 0.84 | 142 |
| Male | Stressful | 0.73 | 0.19 | 0.35 | 1.1 | 19 |
| Both | Stressful | -0.49 | 0.2 | -0.89 | -0.098 | 8 |
| Female | Stressful | -0.18 | 0.19 | -0.55 | 0.2 | 31 |
#saveRDS(predictions.lnVR, "PDF_RDS_files/ST21.rds")Publication Bias
Funnel plots and Egger’s Test
Here we check for publication bias with a funnel plot. Note that the trim and fill or Eggers test method does not work with rma.mv objects. We can perform Eggers test using the regtest() function. This tests for asymmetry via assessing relationships between effect size and a specified predictor. Because the Eggers test does not work for rma.mv objects we remove the random effects and run with Sex * Environment as moderators.
standard.model <- rma(g, var.g,
mods = ~ Sex * Environment,
data = full_dataset)
regtest(standard.model)##
## Regression Test for Funnel Plot Asymmetry
##
## model: mixed-effects meta-regression model
## predictor: standard error
##
## test for funnel plot asymmetry: z = 5.9109, p < .0001We can use ggplot to create a nice funnel plot. The following code takes inspiration from John K. Sakaluk.
#Using residuals for the funnel plot means that we need to generate residuals (intercept only)
forest.model <- rma.mv(g, var.g,
mods = ~ 1,
random = list(~ 1 | Study.ID,
~ 1 | Outcome,
~ 1 | Taxon),
method = "REML",
data = full_dataset)
# Obtain residuals
resstandards <- rstandard.rma.mv(forest.model, type = "response")
# Obtain grand mean effect size
grand.mean <- as.numeric(forest.model$b)
# Create new df with residuals replacing raw
df.forest.model <- full_dataset
df.forest.model$g <- resstandards$resid + grand.mean
df.forest.model$sei <- resstandards$se
# Funnel plot for all outcome classes
make.funnel <- function(dataset, model){
apatheme <- theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line = element_line(),
text = element_text(family = 'Times'),
legend.position = 'none')
estimate <- model$b %>% as.numeric()
SE <- model$se
se.seq <- seq(0, max(sqrt(dataset$var.g)), 0.001)
dfCI <- data.frame(ll95 = estimate - (1.96 * se.seq),
ul95 = estimate + (1.96 * se.seq),
ll99 = estimate - (3.29 * se.seq),
ul99 = estimate + (3.29 * se.seq),
se.seq = se.seq,
meanll95 = estimate - (1.96 * SE),
meanul95 = estimate + (1.96 * SE))
ggplot(dataset, aes(x = sqrt(var.g), y = g)) +
geom_point(size=1.5, shape = 21, color= "grey20") +
xlab("Standard Error") + ylab("Effect Size (Hedges' g)") +
geom_line(aes(x = se.seq, y = ll95), linetype = 'dotted', data = dfCI) + # confidence lines
geom_line(aes(x = se.seq, y = ul95), linetype = 'dotted', data = dfCI) +
geom_line(aes(x = se.seq, y = ll99), linetype = 'dashed', data = dfCI) +
geom_line(aes(x = se.seq, y = ul99), linetype = 'dashed', data = dfCI) +
geom_segment(aes(x = min(se.seq), y = meanll95, xend = max(se.seq), yend = meanll95), linetype='dotdash', data=dfCI, colour = "tomato", size =0.75) +
geom_segment(aes(x = min(se.seq), y = meanul95, xend = max(se.seq), yend = meanul95), linetype='dotdash', data=dfCI, colour = "tomato",size=0.75) +
scale_x_reverse() +
coord_flip() +
scale_fill_brewer(palette = "Set1")+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
text = element_text(size=14),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(hjust = 0.5, size = 12),
axis.title.y = element_text(size = 12))
}
funnel.plot <- make.funnel(df.forest.model, forest.model)
#ggsave(plot = funnel.plot, filename = "figures/funnel_plot.eps", height = 7.5, width = 10)
funnel.plot
Figure 3a: A funnel plot of 459 effect sizes shows asymmetry, indicating potential publication bias, egger’s regression test for funnel plot asymmetry also suggests the plot is asymmetrical (z = 6.22, p < 0.0001). The asymmetry appears to come from a spread of positive effect sizes outside the funnel and of varying degrees of precision. Counter to expectations of publication bias these positive studies are not just ‘low precision, large effect’ results. Funnel plot asymmetry may also be due to genuine heterogeneity in effect sizes between studies, which is high in the present meta-analysis because it covers many species, outcome measurements, and experimental designs.
Journal Impact Factor
If we see a positive trend with effect size and Journal Impact Factor (JIF) it may represent publication bias whereby significant (positive) results are published more readily and in more circulated journals and non-confirmitory or negative results are not published or publiushed in lower impact journals. Our journal impact factor dataset is not evenly distributed as several publications in Nature (JIF ~ 40) are much larger than the next highest JIF (~11).
JIF.plot <- ggplot(data = full_dataset, aes(x=JIF, y=g, size = 1 / var.g))+
geom_point(fill='darkgreen', shape = 21, colour = 'grey20', alpha = 0.75)+
geom_hline(yintercept=0, linetype = 'dotted')+
geom_smooth(method='lm', color='darkgreen', linetype="solid")+
scale_x_log10(limits = c(-5, 40), breaks = c(0, 1, 2, 5, 10, 20, 40))+
labs(size = 'Weight (%)', y="Effect size (Hedges' g)", x= 'Journal Impact Factor (log-scale)')+
guides(size = F)+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
text = element_text(size=14),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
JIF.plot
Figure 3b: Journal impact factor is not correlated with effect size. Point size is proportional to the precision of the effect size.
Testing the effect of JIF on effect size with a simple linear model (slope = +0.1, p = 0.25):
JIFlm <- lm(g ~ log(JIF), data = full_dataset)
summary(JIFlm)##
## Call:
## lm(formula = g ~ log(JIF), data = full_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9062 -0.3472 -0.1088 0.2535 2.9273
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02453 0.13646 0.180 0.857
## log(JIF) 0.11069 0.09272 1.194 0.233
##
## Residual standard error: 0.6669 on 437 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.003251, Adjusted R-squared: 0.0009696
## F-statistic: 1.425 on 1 and 437 DF, p-value: 0.2332Time-lag Bias
We can also look at the time-lag bias, which suggests effect size decreases over time. Again, because one publication from 1980 is well before the next publication in the late 1990s we see a very uneven distribution of data points.
time.plot <- full_dataset %>%
ggplot(aes(x=Year, y=g, size = 1/(var.g)))+
geom_jitter(fill='darkorange', alpha=.75, shape = 21, colour ='grey20')+
geom_hline(yintercept=0, linetype = 'dotted')+
guides(size = F)+
geom_smooth(method='lm', color='darkorange')+
labs(y="Effect size (Hedges' g)", x= 'Year')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
text = element_text(size=14),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
time.plot
Figure 3c: The effect size dataset shows little to no signs of the time-lag bias: the average effect size from published studies has remained consistent across the two previous decades. Point size is proportional to the precision of the effect size.
Again, a linear model for the regression line in the plot shows no effect (slope = -0.007, p = 0.3):
Yearlm <- lm(g ~ Year, data = full_dataset)
summary(Yearlm)##
## Call:
## lm(formula = g ~ Year, data = full_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9042 -0.3505 -0.1305 0.2938 2.9088
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.930289 12.628117 1.182 0.238
## Year -0.007328 0.006283 -1.166 0.244
##
## Residual standard error: 0.6857 on 457 degrees of freedom
## Multiple R-squared: 0.002968, Adjusted R-squared: 0.0007866
## F-statistic: 1.361 on 1 and 457 DF, p-value: 0.2441Other Moderators
Blinding
In addition to publication bias, other forms of bias may exist within studies. We initially collected data on whether studies were blind or not. Although not many studies (n=8) used blinding there was multiple effect sizes reported in these studies, thus we can visualise whether blinding affects the effect sizes from the model. Blinding was regarded as a redundant predictor in the model (estimate = 0.0287, p = 0.8974) and was dropped.
blind.plot <- df.forest.model %>% ggplot(aes(x=Blinding, y=g))+
geom_jitter(aes(fill=Blinding, size = ((1/var.g)/27708.14)*100), shape=21, color='grey20')+ #total 1/var.g
geom_boxplot(outlier.shape = NA, fill = NA)+
geom_hline(yintercept=0, linetype = 'dotted') +
scale_fill_brewer(palette = "Set2")+
labs(y="Effect size (Hedges' g)", x= 'Blinding', size = 'Weight (%)')+
guides(fill=FALSE, size = guide_legend(override.aes = list(fill = "#66c2a5")))+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
text = element_text(size=14),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12, face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
#ggsave(plot = blind.plot, filename = "PDF_RDS_files/blind_plot.pdf", height = 6, width = 8)
blind.plot
Supplementary Figure 7: Blinding does not appear to alter the magnitude or direction of effect sizes for the studies used in this meta-analysis. However, this should not be viewed as evidence against the validity of blinding as a research method.
Generations
We recorded the number of generations of experimental exolution each study used. The number of generations proved a negligible predictor in the meta-analytic models (estimate = 0.0019, p = 0.2341). The effect sizes are plotted against the generation at which the effect size was extracted.
generations.plot <- strict_dataset %>% ggplot(aes(x=Generations, y=g))+
geom_jitter(shape=21, color = "grey20", size=2, aes(fill=Taxon))+
ylim(-3.5,3.5)+
geom_hline(yintercept=0, linetype="dashed") +
scale_fill_brewer(palette = "Set3")+
geom_smooth(method = 'lm', color='black')+
labs(y="Effect size (Hedges' g)", x= 'Generations', size= 'Weight (%)')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
axis.line=element_line(),
text = element_text(size=14),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12, face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
#ggsave(plot = generations.plot, filename = "PDF_RDS_files/generations_plot.pdf", height = 7.5, width = 10)
generations.plot
Supplementary Figure 8: The number of generations an experimental evolution procedure is run for does not appear to affect the magnitude or direction of the effect size from the fitness related outcome measured at that point.
A linear model shows next to no effect of generations on effect size:
summary(lm(g ~ Generations, data = strict_dataset))##
## Call:
## lm(formula = g ~ Generations, data = strict_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8295 -0.3640 -0.1260 0.2705 2.6740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.227210 0.068643 3.310 0.00105 **
## Generations -0.000250 0.001655 -0.151 0.88005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.654 on 287 degrees of freedom
## Multiple R-squared: 7.949e-05, Adjusted R-squared: -0.003405
## F-statistic: 0.02281 on 1 and 287 DF, p-value: 0.88Kawecki et al. (2012) reviewed the field of experimental evolution and noted that changes to variation may need longer generations to become apparent. The following graph looks at the relationship between number of generations and lnCVr:
generations.plot.var <- strict_dataset %>% ggplot(aes(x=Generations, y=lnCVr))+
geom_jitter(shape=21, color = "grey20", size=2, aes(fill=Taxon))+
ylim(-3.5,3.5)+
geom_hline(yintercept=0, linetype="dashed") +
scale_fill_brewer(palette = "Set3")+
geom_smooth(method = 'lm', color='black')+
labs(y='Effect size (lnCVR)', x= 'Generations', size= 'Weight (%)')+
theme_bw()+
theme(panel.spacing = unit(0.5, "lines"),
panel.border= element_blank(),
text = element_text(size=14),
axis.line=element_line(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
legend.text = element_text(size=12),
legend.title=element_text(size=12,
face = "bold"),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))
#ggsave(plot = generations.plot.var, filename = "PDF_RDS_files/generations_plot_var.pdf", height = 7.5, width = 10)
generations.plot.var
Supplementary Figure 9: Phenotypic variation (lnCVR) is not affected by the number of generations an experiment is ran for.
summary(lm(lnCVr ~ Generations, data = strict_dataset))##
## Call:
## lm(formula = lnCVr ~ Generations, data = strict_dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.99065 -0.25090 0.00081 0.23828 1.85152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.097351 0.065168 -1.494 0.137
## Generations 0.001239 0.001654 0.749 0.455
##
## Residual standard error: 0.52 on 234 degrees of freedom
## (53 observations deleted due to missingness)
## Multiple R-squared: 0.002391, Adjusted R-squared: -0.001872
## F-statistic: 0.5608 on 1 and 234 DF, p-value: 0.4547R Session Information
This section shows the operating system and R packages attached during the production of this document
sessionInfo() %>% panderR version 3.3.1 (2016-06-21)
**Platform:** x86_64-apple-darwin13.4.0 (64-bit)
locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8
attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: bindrcpp(v.0.2.2), gridExtra(v.2.3), ggbeeswarm(v.0.6.0), cowplot(v.0.9.3), metaAidR(v.0.0.0.9000), brmstools(v.0.5.1), bayesplot(v.1.6.0), backports(v.1.1.2), brms(v.2.6.1), Rcpp(v.0.12.18), rstan(v.2.17.3), StanHeaders(v.2.17.2), ggridges(v.0.5.0), RColorBrewer(v.1.1-2), reshape2(v.1.4.3), ggrepel(v.0.8.0), kableExtra(v.1.0.1), ggthemes(v.4.0.1), ggplot2(v.3.1.0.9000), forestplot(v.1.7.2), checkmate(v.1.8.5), magrittr(v.1.5), car(v.3.0-2), carData(v.3.0-1), lme4(v.1.1-18-1), dplyr(v.0.7.6), plyr(v.1.8.4), metafor(v.2.0-0), Matrix(v.1.2-14), compute.es(v.0.2-4), tidyr(v.0.7.2), pander(v.0.6.2) and knitr(v.1.22.1)
loaded via a namespace (and not attached): minqa(v.1.2.4), colorspace(v.1.3-2), rio(v.0.5.10), rsconnect(v.0.8.8), rprojroot(v.1.3-2), markdown(v.0.8), base64enc(v.0.1-3), rstudioapi(v.0.7), DT(v.0.4), mvtnorm(v.1.0-6), xml2(v.1.2.0), codetools(v.0.2-14), bridgesampling(v.0.5-2), splines(v.3.3.1), shinythemes(v.1.1.1), nloptr(v.1.0.4), png(v.0.1-7), shiny(v.1.1.0), readr(v.1.1.1), httr(v.1.3.1), assertthat(v.0.2.0), lazyeval(v.0.2.1), later(v.0.7.3), htmltools(v.0.3.6), tools(v.3.3.1), igraph(v.1.1.2), coda(v.0.19-1), gtable(v.0.2.0), glue(v.1.3.0), cellranger(v.1.1.0), nlme(v.3.1-128), crosstalk(v.1.0.0), xfun(v.0.3), stringr(v.1.2.0), openxlsx(v.4.1.0), rvest(v.0.3.2), miniUI(v.0.1.1.1), mime(v.0.5), gtools(v.3.8.1), MASS(v.7.3-50), zoo(v.1.8-3), scales(v.1.0.0), colourpicker(v.1.0), hms(v.0.4.2), promises(v.1.0.1), Brobdingnag(v.1.2-6), parallel(v.3.3.1), inline(v.0.3.15), shinystan(v.2.4.0), yaml(v.2.2.0), curl(v.3.2), loo(v.2.0.0), stringi(v.1.1.6), highr(v.0.6), dygraphs(v.1.1.1.6), zip(v.1.0.0), rlang(v.0.2.2), pkgconfig(v.2.0.2), matrixStats(v.0.54.0), evaluate(v.0.10.1), lattice(v.0.20-33), purrr(v.0.2.4), bindr(v.0.1.1), labeling(v.0.3), rstantools(v.1.5.1), htmlwidgets(v.1.2), tidyselect(v.0.2.4), R6(v.2.2.2), haven(v.1.1.2), foreign(v.0.8-66), withr(v.2.1.2), xts(v.0.11-0), abind(v.1.4-5), tibble(v.1.3.4), rmarkdown(v.1.10), readxl(v.1.0.0), data.table(v.1.11.4), forcats(v.0.3.0), threejs(v.0.3.1), digest(v.0.6.16), webshot(v.0.5.0), xtable(v.1.8-2), httpuv(v.1.4.5), stats4(v.3.3.1), munsell(v.0.5.0), beeswarm(v.0.2.3), viridisLite(v.0.3.0), vipor(v.0.4.5) and shinyjs(v.1.0)
References
Aguirre, J. David, and Dustin J. Marshall. 2012. “Does Genetic Diversity Reduce Sibling Competition?” Journal Article. Evolution 66 (1): 94–102. doi:10.1111/j.1558-5646.2011.01413.x.
Ahuja, Abha, and Rama S. Singh. 2008. “Variation and Evolution of Male Sex Combs in Drosophila: Nature of Selection Response and Theories of Genetic Variation for Sexual Traits.” Journal Article. Genetics 179 (1): 503–9. doi:10.1534/genetics.107.08063.
Almbro, Maria, and Leigh W. Simmons. 2014. “Sexual Selection Can Remove an Experimentally Induced Mutation Load.” Journal Article. Evolution 68 (1): 295–300. doi:10.1111/evo.12238.
Amitin, E. G., and S. Pitnick. 2007. “Influence of Developmental Environment on Male- and Female-Mediated Sperm Precedence in Drosophila Melanogaster.” Journal Article. Journal of Evolutionary Biology 20 (1): 381–91. doi:10.1111/j.1420-9101.2006.01184.x.
Antolin, M. F., P. J. Ode, G. E. Heimpel, R. B. O’Hara, and M. R. Strand. 2003. “Population Structure, Mating System, and Sex-Determining Allele Diversity of the Parasitoid Wasp Habrobracon Hebetor.” Journal Article. Heredity 91 (4): 373–81. doi:10.1038/sj.hdy.6800337.
Arbuthnott, Devin, and Howard D. Rundle. 2012. “Sexual Selection Is Ineffectual or Inhibits the Purging of Deleterious Mutations in Drosophila Melanogaster.” Journal Article. Evolution 66 (7): 2127–37. doi:10.1111/j.1558-5646.2012.01584.x.
———. 2014. “Misalignment of Natural and Sexual Selection Among Divergently Adapted Drosophila Melanogaster Populations.” Journal Article. Animal Behaviour 87: 45–51. doi:10.1016/j.anbehav.2013.10.005.
Arbuthnott, Devin, Emily M. Dutton, Aneil F. Agrawal, and Howard D. Rundle. 2014. “The Ecology of Sexual Conflict: Ecologically Dependent Parallel Evolution of Male Harm and Female Resistance in Drosophila Melanogaster.” Journal Article. Ecology Letters 17 (2): 221–28. doi:10.1111/ele.12222.
Archer, C. Ruth, Eoin Duffy, David J. Hosken, Mikael Mokkonen, Kensuke Okada, Keiko Oku, Manmohan D. Sharma, and John Hunt. 2015. “Sex-Specific Effects of Natural and Sexual Selection on the Evolution of Life Span and Ageing in Drosophila Simulans.” Journal Article. Functional Ecology 29 (4): 562–69. doi:10.1111/1365-2435.12369.
Artieri, Carlo G., Wilfried Haerty, Bhagwati P. Gupta, and Rama S. Singh. 2008. “Sexual Selection and Maintenance of Sex: Evidence from Comparisons of Rates of Genomic Accumulation of Mutations and Divergence of Sex-Related Genes in Sexual and Hermaphroditic Species of Caenorhabditis.” Journal Article. Molecular Biology and Evolution 25 (5): 972–79. doi:10.1093/molbev/msn046.
Bacigalupe, L. D., H. S. Crudgington, F. Hunter, A. J. Moore, and R. R. Snook. 2007. “Sexual Conflict Does Not Drive Reproductive Isolation in Experimental Populations of Drosophila Pseudoobscura.” Journal Article. Journal of Evolutionary Biology 20 (5): 1763–71. doi:10.1111/j.1420-9101.2007.01389.x.
Bacigalupe, Leonardo D., Helen S. Crudgington, Jon Slate, Allen J. Moore, and Rhonda R. Snook. 2008. “Sexual Selection and Interacting Phenotypes in Experimental Evolution: A Study of Drosophila Pseudoobscura Mating Behavior.” Journal Article. Evolution 62 (7): 1804–12. doi:10.1111/j.1558-5646.2008.00402.x.
Barbosa, Miguel, Sean R. Connolly, Mizue Hisano, Maria Dornelas, and Anne E. Magurran. 2012. “Fitness Consequences of Female Multiple Mating: A Direct Test of Indirect Benefits.” Journal Article. Bmc Evolutionary Biology 12. doi:10.1186/1471-2148-12-185.
Bernasconi, G., and L. Keller. 2001. “Female Polyandry Affects Their Sons’ Reproductive Success in the Red Flour Beetle Tribolium Castaneum.” Journal Article. Journal of Evolutionary Biology 14 (1): 186–93. doi:10.1046/j.1420-9101.2001.00247.x.
Bielak, A. P., A. M. Skrzynecka, K. Miler, and J. Radwan. 2014. “Selection for Alternative Male Reproductive Tactics Alters Intralocus Sexual Conflict.” Journal Article. Evolution 68 (7): 2137–44. doi:10.1111/evo.12409.
Blows, M. W. 2002. “Interaction Between Natural and Sexual Selection During the Evolution of Mate Recognition.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 269 (1496): 1113–8. doi:10.1098/rspb.2002.2002.
Brommer, Jon E., Claudia Fricke, Dominic A. Edward, and Tracey Chapman. 2012. “Interactions Between Genotype and Sexual Conflict Environment Influence Transgenerational Fitness in Drosophila Melanogaster.” Journal Article. Evolution 66 (2): 517–31. doi:10.1111/j.1558-5646.2011.01449.x.
Castillo, Dean M., Melissa K. Burger, Curtis M. Lively, and Lynda F. Delph. 2015. “Experimental Evolution: Assortative Mating and Sexual Selection, Independent of Local Adaptation, Lead to Reproductive Isolation in the Nematode Caenorhabditis Remanei.” Journal Article. Evolution 69 (12): 3141–55. doi:10.1111/evo.12815.
Cayetano, Luis, Alexei A. Maklakov, Robert C. Brooks, and Russell Bonduriansky. 2011. “Evolution of Male and Female Genitalia Following Release from Sexual Selection.” Journal Article. Evolution 65 (8): 2171–83. doi:10.1111/j.1558-5646.2011.01309.x.
Chandler, Christopher H., Charles Ofria, and Ian Dworkin. 2013. “Runaway Sexual Selection Leads to Good Genes.” Journal Article. Evolution 67 (1): 110–19. doi:10.1111/j.1558-5646.2012.01750.x.
Chenoweth, S. F., D. Petfield, P. Doughty, and M. W. Blows. 2007. “Male Choice Generates Stabilizing Sexual Selection on a Female Fecundity Correlate.” Journal Article. Journal of Evolutionary Biology 20 (5): 1745–50. doi:10.1111/j.1420-9101.2007.01390.x.
Chenoweth, S. F., H. D. Rundle, and M. W. Blows. 2010. “Experimental Evidence for the Evolution of Indirect Genetic Effects: Changes in the Interaction Effect Coefficient, Psi, Due to Sexual Selection.” Journal Article. Evolution 64 (6): 1849–56. doi:10.1111/j.1558-5646.2010.00952.x.
Chenoweth, Stephen F., Nicholas C. Appleton, Scott L. Allen, and Howard D. Rundle. 2015. “Genomic Evidence That Sexual Selection Impedes Adaptation to a Novel Environment.” Journal Article. Current Biology 25 (14): 1860–6. doi:10.1016/j.cub.2015.05.034.
Chenoweth, Stephen F., Howard D. Rundle, and Mark W. Blows. 2008. “Genetic Constraints and the Evolution of Display Trait Sexual Dimorphism by Natural and Sexual Selection.” Journal Article. American Naturalist 171 (1): 22–34. doi:10.1086/523946.
Crudgington, H. S., A. P. Beckerman, L. Brüstle, K. Green, and R. R. Snook. 2005. “Experimental Removal and Elevation of Sexual Selection: Does Sexual Selection Generate Manipulative Males and Resistant Females?” Journal Article. American Naturalist 165 (SUPPL.): S72–S87. doi:10.1086/429353.
Crudgington, H. S., S. Fellows, and R. R. Snook. 2010. “Increased Opportunity for Sexual Conflict Promotes Harmful Males with Elevated Courtship Frequencies.” Journal Article. Journal of Evolutionary Biology 23 (2): 440–46. doi:10.1111/j.1420-9101.2009.01907.x.
Crudgington, Helen S., Sarah Fellows, Nichola S. Badcock, and Rhonda R. Snook. 2009. “Experimental Manipulation of Sexual Selection Promotes Greater Male Mating Capacity but Does Not Alter Sperm Investment.” Journal Article. Evolution 63 (4): 926–38. doi:10.1111/j.1558-5646.2008.00601.x.
Debelle, A., M. G. Ritchie, and R. R. Snook. 2016. “Sexual Selection and Assortative Mating: An Experimental Test.” Journal Article. Journal of Evolutionary Biology 29 (7): 1307–16. doi:10.1111/jeb.12855.
Demont, Marco, Vera M. Grazer, Lukasz Michalczyk, Anna L. Millard, Sonja H. Sbilordo, Brent C. Emerson, Matthew J. G. Gage, and Oliver Y. Martin. 2014. “Experimental Removal of Sexual Selection Reveals Adaptations to Polyandry in Both Sexes.” Journal Article. Evolutionary Biology 41 (1): 62–70. doi:10.1007/s11692-013-9246-3.
Edward, Dominic A., Claudia Fricke, and Tracey Chapman. 2010. “Adaptations to Sexual Selection and Sexual Conflict: Insights from Experimental Evolution and Artificial Selection.” Journal Article. Philosophical Transactions of the Royal Society B-Biological Sciences 365 (1552): 2541–8. doi:10.1098/rstb.2010.0027.
Fava, G. 1975. “Studies on the Selective Agents Operating in Experimental Populations of Tisbe Clodiensis (Copepoda, Harpacticoida).” Journal Article. Genetica 45 (3): 289–305. doi:10.1007/BF01508304.
Firman, R. C., and L. W. Simmons. 2012. “Male House Mice Evolving with Post-Copulatory Sexual Selection Sire Embryos with Increased Viability.” Journal Article. Ecology Letters 15 (1): 42–46. doi:10.1111/j.1461-0248.2011.01706.x.
Firman, R. C., L. Y. Cheam, and L. W. Simmons. 2011. “Sperm Competition Does Not Influence Sperm Hook Morphology in Selection Lines of House Mice.” Journal Article. Journal of Evolutionary Biology 24 (4): 856–62. doi:10.1111/j.1420-9101.2010.02219.x.
Firman, Renee C. 2011. “Polyandrous Females Benefit by Producing Sons That Achieve High Reproductive Success in a Competitive Environment.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 278 (1719): 2823–31. doi:10.1098/rspb.2010.2791.
———. 2014. “Female Social Preference for Males That Have Evolved via Monogamy: Evidence of a Trade-Off Between Pre- and Post-Copulatory Sexually Selected Traits?” Journal Article. Biology Letters 10 (10). doi:10.1098/rsbl.2014.0659.
Firman, Renee C., and Leigh W. Simmons. 2010. “Experimental Evolution of Sperm Quality via Postcopulatory Sexual Selection in House Mice.” Journal Article. Evolution 64 (5): 1245–56. doi:10.1111/j.1558-5646.2009.00894.x.
———. 2011. “Experimental Evolution of Sperm Competitiveness in a Mammal.” Journal Article. Bmc Evolutionary Biology 11. doi:10.1186/1471-2148-11-19.
Firman, Renee C., Francisco Garcia-Gonzalez, Evan Thyer, Samantha Wheeler, Zayaputeri Yamin, Michael Yuan, and Leigh W. Simmons. 2015. “Evolutionary Change in Testes Tissue Composition Among Experimental Populations of House Mice.” Journal Article. Evolution 69 (3): 848–55. doi:10.1111/evo.12603.
Firman, Renee C., Montserrat Gomendio, Eduardo R. S. Roldan, and Leigh W. Simmons. 2014. “The Coevolution of Ova Defensiveness with Sperm Competitiveness in House Mice.” Journal Article. American Naturalist 183 (4): 565–72. doi:10.1086/675395.
Fricke, C., C. Andersson, and G. Arnqvist. 2010. “Natural Selection Hampers Divergence of Reproductive Traits in a Seed Beetle.” Journal Article. Journal of Evolutionary Biology 23 (9): 1857–67. doi:10.1111/j.1420-9101.2010.02050.x.
Fricke, Claudia, and Goran Arnqvist. 2007. “Rapid Adaptation to a Novel Host in a Seed Beetle (Callosobruchus Maculatus): The Role of Sexual Selection.” Journal Article. Evolution 61 (2): 440–54. doi:10.1111/j.1558-5646.2007.00038.x.
Fritzsche, K., N. Timmermeyer, M. Wolter, and N. K. Michiels. 2014. “Female, but Not Male, Nematodes Evolve Under Experimental Sexual Coevolution.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 281 (1796). doi:10.1098/rspb.2014.0942.
Fritzsche, Karoline, Isobel Booksmythe, and Goran Arnqvist. 2016. “Sex Ratio Bias Leads to the Evolution of Sex Role Reversal in Honey Locust Beetles.” Journal Article. Current Biology 26 (18): 2522–6. doi:10.1016/j.cub.2016.07.018.
Garcia-Gonzalez, Francisco, Yukio Yasui, and Jonathan P. Evans. 2015. “Mating Portfolios: Bet-Hedging, Sexual Selection and Female Multiple Mating.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 282 (1798). doi:10.1098/rspb.2014.1525.
Gay, L., P. E. Eady, R. Vasudev, D. J. Hosken, and T. Tregenza. 2009. “Does Reproductive Isolation Evolve Faster in Larger Populations via Sexually Antagonistic Coevolution?” Journal Article. Biology Letters 5 (5): 693–96. doi:10.1098/rsbl.2009.0072.
Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza, and P. E. Eady. 2009. “Sperm Competition and Maternal Effects Differentially Influence Testis and Sperm Size in Callosobruchus Maculatus.” Journal Article. Journal of Evolutionary Biology 22 (5): 1143–50. doi:10.1111/j.1420-9101.2009.01724.x.
Gay, Laurene, David J. Hosken, Paul Eady, Ram Vasudev, and Tom Tregenza. 2011. “The Evolution of Harm-Effect of Sexual Conflicts and Population Size.” Journal Article. Evolution 65 (3): 725–37. doi:10.1111/j.1558-5646.2010.01181.x.
Grazer, Vera M., Marco Demont, Lukasz Michalczyk, Matthew J. G. Gage, and Oliver Y. Martin. 2014. “Environmental Quality Alters Female Costs and Benefits of Evolving Under Enforced Monogamy.” Journal Article. Bmc Evolutionary Biology 14. doi:10.1186/1471-2148-14-21.
Grieshop, K., J. Stangberg, I. Martinossi-Allibert, G. Arnqvist, and D. Berger. 2016. “Strong Sexual Selection in Males Against a Mutation Load That Reduces Offspring Production in Seed Beetles.” Journal Article. Journal of Evolutionary Biology 29 (6): 1201–10. doi:10.1111/jeb.12862.
Hall, M. D., L. F. Bussiere, and R. Brooks. 2009. “Diet-Dependent Female Evolution Influences Male Lifespan in a Nuptial Feeding Insect.” Journal Article. Journal of Evolutionary Biology 22 (4): 873–81. doi:10.1111/j.1420-9101.2009.01687.x.
Hangartner, S., L. Michalczyk, M. J. G. Gage, and O. Y. Martin. 2015. “Experimental Removal of Sexual Selection Leads to Decreased Investment in an Immune Component in Female Tribolium Castaneum.” Journal Article. Infection, Genetics and Evolution 33: 212–18. doi:10.1016/j.meegid.2015.05.005.
Hangartner, S., S. H. Sbilordo, L Michalczyk, M. J. G. Gage, and O. Y. Martin. 2013. “Are There Genetic Trade-Offs Between Immune and Reproductive Investments in Tribolium Castaneum?” Journal Article. Infection, Genetics and Evolution 19: 45–50. doi:10.1016/j.meegid.2013.06.007.
Hicks, S. K., K. L. Hagenbuch, and L. M. Meffert. 2004. “Variable Costs of Mating, Longevity, and Starvation Resistance in Musca Domestica (Diptera: Muscidae).” Journal Article. Environmental Entomology 33 (3): 779–86. https://www.scopus.com/inward/record.uri?eid=2-s2.0-10644256548&partnerID=40&md5=1c7238ee84f7772d815135692978af9b.
Holland, B. 2002. “Sexual Selection Fails to Promote Adaptation to a New Environment.” Journal Article. Evolution 56 (4): 721–30. doi:10.1554/0014-3820(2002)056[0721:SSFTPA]2.0.CO;2.
Holland, B., and W. R. Rice. 1999. “Experimental Removal of Sexual Selection Reverses Intersexual Antagonistic Coevolution and Removes a Reproductive Load.” Journal Article. Proceedings of the National Academy of Sciences of the United States of America 96 (9): 5083–8. doi:10.1073/pnas.96.9.5083.
Hollis, B., and D. Houle. 2011. “Populations with Elevated Mutation Load Do Not Benefit from the Operation of Sexual Selection.” Journal Article. Journal of Evolutionary Biology 24 (9): 1918–26. doi:10.1111/j.1420-9101.2011.02323.x.
Hollis, B., D. Houle, and T. J. Kawecki. 2016. “Evolution of Reduced Post-Copulatory Molecular Interactions in Drosophila Populations Lacking Sperm Competition.” Journal Article. Journal of Evolutionary Biology 29 (1): 77–85. doi:10.1111/jeb.12763.
Hollis, Brian, and Tadeusz J. Kawecki. 2014. “Male Cognitive Performance Declines in the Absence of Sexual Selection.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 281 (1781). doi:10.1098/rspb.2013.2873.
Hollis, Brian, Janna L. Fierst, and David Houle. 2009. “Sexual Selection Accelerates the Elimination of a Deleterious Mutant in Drosophila Melanogaster.” Journal Article. Evolution 63 (2): 324–33. doi:10.1111/j.1558-5646.2008.00551.x.
Hollis, Brian, David Houle, Zheng Yan, Tadeusz J. Kawecki, and Laurent Keller. 2014. “Evolution Under Monogamy Feminizes Gene Expression in Drosophila Melanogaster.” Journal Article. Nature Communications 5. doi:10.1038/ncomms4482.
Hollis, Brian, Laurent Keller, and Tadeusz J. Kawecki. 2017. “Sexual Selection Shapes Development and Maturation Rates in Drosophila.” Journal Article. Evolution 71 (2): 304–14. doi:10.1111/evo.13115.
Hosken, D. J., O. Y. Martin, S. Wigby, T. Chapman, and D. J. Hodgson. 2009. “Sexual Conflict and Reproductive Isolation in Flies.” Journal Article. Biology Letters 5 (5): 697–99. doi:10.1098/rsbl.2009.0066.
House, Clarissa M., Zenobia Lewis, Dave J. Hodgson, Nina Wedell, Manmohan D. Sharma, John Hunt, and David J. Hosken. 2013. “Sexual and Natural Selection Both Influence Male Genital Evolution.” Journal Article. Plos One 8 (5). doi:10.1371/journal.pone.0063807.
Hunt, J., R. R. Snook, C. Mitchell, H. S. Crudgington, and A. J. Moore. 2012. “Sexual Selection and Experimental Evolution of Chemical Signals in Drosophila Pseudoobscura.” Journal Article. Journal of Evolutionary Biology 25 (11): 2232–41. doi:10.1111/j.1420-9101.2012.02603.x.
Immonen, Elina, Rhonda R. Snook, and Michael G. Ritchie. 2014. “Mating System Variation Drives Rapid Evolution of the Female Transcriptome in Drosophila Pseudoobscura.” Journal Article. Ecology and Evolution 4 (11): 2186–2201. doi:10.1002/ece3.1098.
Innocenti, Paolo, Ilona Flis, and Edward H. Morrow. 2014. “Female Responses to Experimental Removal of Sexual Selection Components in Drosophila Melanogaster.” Journal Article. Bmc Evolutionary Biology 14. doi:10.1186/s12862-014-0239-3.
Jacomb, Frances, Jason Marsh, and Luke Holman. 2016. “Sexual Selection Expedites the Evolution of Pesticide Resistance.” Journal Article. Evolution 70 (12): 2746–51. doi:10.1111/evo.13074.
Janicke, T., P. Sandner, S. A. Ramm, D. B. Vizoso, and L. Schaerer. 2016. “Experimentally Evolved and Phenotypically Plastic Responses to Enforced Monogamy in a Hermaphroditic Flatworm.” Journal Article. Journal of Evolutionary Biology 29 (9): 1713–27. doi:10.1111/jeb.12910.
Jarzebowska, Magdalena, and Jacek Radwan. 2010. “Sexual Selection Counteracts Extinction of Small Populations of the Bulb Mites.” Journal Article. Evolution 64 (5): 1283–9. doi:10.1111/j.1558-5646.2009.00905.x.
Kawecki, Tadeusz J., Richard E. Lenski, Dieter Ebert, Brian Hollis, Isabelle Olivieri, and Michael C. Whitlock. 2012. “Experimental Evolution.” Journal Article. Trends in Ecology & Evolution 27 (10): 547–60. doi:10.1016/j.tree.2012.06.001.
Klemme, I., and R. C. Firman. 2013. “Male House Mice That Have Evolved with Sperm Competition Have Increased Mating Duration and Paternity Success.” Journal Article. Animal Behaviour 85 (4): 751–58. doi:10.1016/j.anbehav.2013.01.016.
Lieshout, Emile van, Kathryn B. McNamara, and Leigh W. Simmons. 2014. “Rapid Loss of Behavioral Plasticity and Immunocompetence Under Intense Sexual Selection.” Journal Article. Evolution 68 (9): 2550–8. doi:10.1111/evo.12422.
Long, T. A. F., A. F. Agrawal, and L. Rowe. 2012. “The Effect of Sexual Selection on Offspring Fitness Depends on the Nature of Genetic Variation.” Journal Article. Current Biology 22 (3): 204–8. doi:10.1016/j.cub.2011.12.020.
Lumley, Alyson J., Lukasz Michalczyk, James J. N. Kitson, Lewis G. Spurgin, Catriona A. Morrison, Joanne L. Godwin, Matthew E. Dickinson, et al. 2015. “Sexual Selection Protects Against Extinction.” Journal Article. Nature 522 (7557): 470–+. doi:10.1038/nature14419.
MacLellan, K., L. Kwan, M. C. Whitlock, and H. D. Rundle. 2012. “Dietary Stress Does Not Strengthen Selection Against Single Deleterious Mutations in Drosophila Melanogaster.” Journal Article. Heredity 108 (3): 203–10. doi:10.1038/hdy.2011.60.
MacLellan, Kelsie, Michael C. Whitlock, and Howard D. Rundle. 2009. “Sexual Selection Against Deleterious Mutations via Variable Male Search Success.” Journal Article. Biology Letters 5 (6): 795–97. doi:10.1098/rsbl.2009.0475.
Maklakov, A. A., C. Fricke, and G. Arnqvist. 2007. “Sexual Selection Affects Lifespan and Aging in the Seed Beetle.” Journal Article. Aging Cell 6 (6): 739–44. doi:10.1111/j.1474-9726.2007.00333.x.
Maklakov, Alexei A., and Claudia Fricke. 2009. “Sexual Selection Did Not Contribute to the Evolution of Male Lifespan Under Curtailed Age at Reproduction in a Seed Beetle.” Journal Article. Ecological Entomology 34 (5): 638–43. doi:10.1111/j.1365-2311.2009.01113.x.
Maklakov, Alexei A., Russell Bonduriansky, and Robert C. Brooks. 2009. “Sex Differences, Sexual Selection, and Ageing: An Experimental Evolution Approach.” Journal Article. Evolution 63 (10): 2491–2503. doi:10.1111/j.1558-5646.2009.00750.x.
Mallet, M. A., and A. K. Chippindale. 2011. “Inbreeding Reveals Stronger Net Selection on Drosophila Melanogaster Males: Implications for Mutation Load and the Fitness of Sexual Females.” Journal Article. Heredity 106 (6): 994–1002. doi:10.1038/hdy.2010.148.
Mallet, Martin A., Jessica M. Bouchard, Christopher M. Kimber, and Adam K. Chippindale. 2011. “Experimental Mutation-Accumulation on the X Chromosome of Drosophila Melanogaster Reveals Stronger Selection on Males Than Females.” Journal Article. Bmc Evolutionary Biology 11. doi:10.1186/1471-2148-11-156.
Martin, O. Y., and D. J. Hosken. 2003. “Costs and Benefits of Evolving Under Experimentally Enforced Polyandry or Monogamy.” Journal Article. Evolution 57 (12): 2765–72. http://onlinelibrary.wiley.com/store/10.1111/j.0014-3820.2003.tb01518.x/asset/j.0014-3820.2003.tb01518.x.pdf?v=1&t=j3z3cgcu&s=03fd98bf0e04f07d109203a83ba57172ba707947.
———. 2004. “Reproductive Consequences of Population Divergence Through Sexual Conflict.” Journal Article. Current Biology 14 (10): 906–10. doi:10.1016/j.cub.2004.04.043.
Matsuyama, T., and H. Kuba. 2009. “Mating Time and Call Frequency of Males Between Mass-Reared and Wild Strains of Melon Fly, Bactrocera Cucurbitae (Coquillett) (Diptera: Tephritidae).” Journal Article. Applied Entomology and Zoology 44 (2): 309–14. doi:10.1303/aez.2009.309.
McGuigan, K., D. Petfield, and M. W. Blows. 2011. “Reducing Mutation Load Through Sexual Selection on Males.” Journal Article. Evolution 65 (10): 2816–29. doi:10.1111/j.1558-5646.2011.01346.x.
McKean, Kurt A., and Leonard Nunney. 2008. “Sexual Selection and Immune Function in Drosophila Melanogaster.” Journal Article. Evolution 62 (2): 386–400. doi:10.1111/j.1558-5646.2007.00286.x.
McLain, D. K. 1992. “Population Density and the Intensity of Sexual Selection on Body Length in Spatially or Temporally Restricted Natural Populations of a Seed Bug.” Journal Article. Behavioral Ecology and Sociobiology 30 (5): 347–56. doi:10.1007/BF00170602.
McNamara, Kathryn B., Emile van Lieshout, and Leigh W. Simmons. 2014. “A Test of the Sexy-Sperm and Good-Sperm Hypotheses for the Evolution of Polyandry.” Journal Article. Behavioral Ecology 25 (4): 989–95. doi:10.1093/beheco/aru067.
McNamara, Kathryn B., Stephen P. Robinson, Marta E. Rosa, Nadia S. Sloan, Emile van Lieshout, and Leigh W. Simmons. 2016. “Male-Biased Sex Ratio Does Not Promote Increased Sperm Competitiveness in the Seed Beetle, Callosobruchus Maculatus.” Journal Article. Scientific Reports 6. doi:10.1038/srep28153.
Meffert, Lisa M., Jennifer L. Regan, Sara K. Hicks, Nsuela Mukana, and Stacey B. Day. 2006. “Testing Alternative Methods for Purging Genetic Load Using the Housefly (Musca Domestica L.).” Journal Article. Genetica 128 (1-3): 419–27. doi:10.1007/s10709-006-7667-y.
Michalczyk, Lukasz, Anna L. Millard, Oliver Y. Martin, Alyson J. Lumley, Brent C. Emerson, and Matthew J. G. Gage. 2011. “Experimental Evolution Exposes Female and Male Responses to Sexual Selection and Conflict in Tribolium Castaneum.” Journal Article. Evolution 65 (3): 713–24. doi:10.1111/j.1558-5646.2010.01174.x.
Michalczyk, Lukasz, Anna L. Millard, Oliver Y. Martin, Alyson J. Lumley, Brent C. Emerson, Tracey Chapman, and Matthew J. G. Gage. 2011. “Inbreeding Promotes Female Promiscuity.” Journal Article. Science 333 (6050): 1739–42. doi:10.1126/science.1207314.
Morrow, E. H., A. D. Stewart, and W. R. Rice. 2008. “Assessing the Extent of Genome-Wide Intralocus Sexual Conflict via Experimentally Enforced Gender-Limited Selection.” Journal Article. Journal of Evolutionary Biology 21 (4): 1046–54. doi:10.1111/j.1420-9101.2008.01542.x.
Nakagawa, Shinichi, Robert Poulin, Kerrie Mengersen, Klaus Reinhold, Leif Engqvist, Malgorzata Lagisz, and Alistair M. Senior. 2015. “Meta-Analysis of Variation: Ecological and Evolutionary Applications and Beyond.” Journal Article 6 (2): 152.
Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen, and N. G. Prasad. 2014. “Experimental Evolution of Female Traits Under Different Levels of Intersexual Conflict in Drosophila Melanogaster.” Journal Article. Evolution 68 (2): 412–25. doi:10.1111/evo.12271.
Nandy, Bodhisatta, Pratip Chakraborty, Vanika Gupta, Syed Zeeshan Ali, and Nagaraj Guru Prasad. 2013. “Sperm Competitive Ability Evolves in Response to Experimental Alteration of Operational Sex Ratio.” Journal Article. Evolution 67 (7): 2133–41. doi:10.1111/evo.12076.
Nelson, Adam C., Kevin E. Colson, Steve Harmon, and Wayne K. Potts. 2013. “Rapid Adaptation to Mammalian Sociality via Sexually Selected Traits.” Journal Article. Bmc Evolutionary Biology 13. doi:10.1186/1471-2148-13-81.
Nie, Haiyan, and Kenneth Kaneshiro. 2016. “Sexual Selection and Incipient Speciation in Hawaiian Drosophila.” Journal Article. Science Bulletin 61 (2): 125–31. doi:10.1007/s11434-015-0976-8.
Palopoli, Michael F., Colin Peden, Caitlin Woo, Ken Akiha, Megan Ary, Lori Cruze, Jennifer L. Anderson, and Patrick C. Phillips. 2015. “Natural and Experimental Evolution of Sexual Conflict Within Caenorhabditis Nematodes.” Journal Article. Bmc Evolutionary Biology 15. doi:10.1186/s12862-015-0377-2.
Partridge, L. 1980a. “Mate Choice Increases a Component of Offspring Fitness in Fruit-Flies.” Journal Article. Nature 283 (5744): 290–91. doi:10.1038/283290a0.
———. 1980b. “Mate Choice Increases a Component of Offspring Fitness in Fruit-Flies.” Journal Article. Nature 283 (5744): 290–91. doi:10.1038/283290a0.
Perry, Jennifer C., Richa Joag, David J. Hosken, Nina Wedell, Jacek Radwan, and Stuart Wigby. 2016. “Experimental Evolution Under Hyper-Promiscuity in Drosophila Melanogaster.” Journal Article. Bmc Evolutionary Biology 16. doi:10.1186/s12862-016-0699-8.
Pélabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming, and G. Rosenqvist. 2014. “The Effects of Sexual Selection on Life-History Traits: An Experimental Study on Guppies.” Journal Article. Journal of Evolutionary Biology 27 (2): 404–16. doi:10.1111/jeb.12309.
Pischedda, A., and A. Chippindale. 2005. “Sex, Mutation and Fitness: Asymmetric Costs and Routes to Recovery Through Compensatory Evolution.” Journal Article. Journal of Evolutionary Biology 18 (4): 1115–22. doi:10.1111/j.1420-9101.2005.00915.x.
Pischedda, Alison, and Adam K. Chippindale. 2006. “Intralocus Sexual Conflict Diminishes the Benefits of Sexual Selection.” Journal Article. Plos Biology 4 (11): 2099–2103. doi:10.1371/journal.pbio.0040356.
Pitnick, S., W. D. Brown, and G. T. Miller. 2001. “Evolution of Female Remating Behaviour Following Experimental Removal of Sexual Selection.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 268 (1467): 557–63. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1088640/pdf/PB010557.pdf.
Pitnick, S., G. T. Miller, J. Reagan, and B. Holland. 2001. “Males’ Evolutionary Responses to Experimental Removal of Sexual Selection.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 268 (1471): 1071–80. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1088710/pdf/PB011071.pdf.
Plesnar, Agata, Magdalena Konior, and Jacek Radwan. 2011. “The Role of Sexual Selection in Purging the Genome of Induced Mutations in the Bulb Mite (Rizoglyphus Robini).” Journal Article. Evolutionary Ecology Research 13 (2): 209–16.
Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop, M. Kolasa, M. Dzialo, and J. Radwan. 2013. “No Evidence for Reproductive Isolation Through Sexual Conflict in the Bulb Mite Rhizoglyphus Robini.” Journal Article. PLoS ONE 8 (9). doi:10.1371/journal.pone.0074971.
Plesnar-Bielak, Agata, Anna M. Skrzynecka, Zofia M. Prokop, and Jacek Radwan. 2012. “Mating System Affects Population Performance and Extinction Risk Under Environmental Challenge.” Journal Article. Proceedings of the Royal Society B-Biological Sciences 279 (1747): 4661–7. doi:10.1098/rspb.2012.1867.
Power, D. J., and L. Holman. 2014. “Polyandrous Females Found Fitter Populations.” Journal Article. Journal of Evolutionary Biology 27 (9): 1948–55. doi:10.1111/jeb.12448.
———. 2015. “Assessing the Alignment of Sexual and Natural Selection Using Radiomutagenized Seed Beetles.” Journal Article. Journal of Evolutionary Biology 28 (5): 1039–48. doi:10.1111/jeb.12625.
Price, T. A. R., G. D. D. Hurst, and N. Wedell. 2010a. “Polyandry Prevents Extinction.” Journal Article. Current Biology 20 (5): 471–75. doi:10.1016/j.cub.2010.01.050.
———. 2010b. “Polyandry Prevents Extinction.” Journal Article. Current Biology 20 (5): 471–75. doi:10.1016/j.cub.2010.01.050.
Prokop, Z. M., M. A. Prus, T. S. Gaczorek, K. Sychta, J. K. Palka, A. Plesnar-Bielak, and M. Skarboń. 2017. “Do Males Pay for Sex? Sex-Specific Selection Coefficients Suggest Not.” Journal Article. Evolution 71 (3): 650–61. doi:10.1111/evo.13151.
Promislow, D. E. L., E. A. Smith, and L. Pearse. 1998. “Adult Fitness Consequences of Sexual Selection in Drosophila Melanogaster.” Journal Article. Proceedings of the National Academy of Sciences of the United States of America 95 (18): 10687–92. doi:10.1073/pnas.95.18.10687.
Radwan, J. 2004. “Effectiveness of Sexual Selection in Removing Mutations Induced with Ionizing Radiation.” Journal Article. Ecology Letters 7 (12): 1149–54. doi:10.1111/j.1461-0248.2004.00681.x.
Radwan, J., J. Unrug, K. Snigorska, and K. Gawronska. 2004. “Effectiveness of Sexual Selection in Preventing Fitness Deterioration in Bulb Mite Populations Under Relaxed Natural Selection.” Journal Article. Journal of Evolutionary Biology 17 (1): 94–99. doi:10.1046/j.1420-9101.2003.00646.x.
Re, A. C. Del. 2013. Compute.es: Compute Effect Sizes. R Package. http://cran.r-project.org/web/packages/compute.es.
Rundle, H. D., S. F. Chenoweth, and M. W. Blows. 2009. “The Diversification of Mate Preferences by Natural and Sexual Selection.” Journal Article. Journal of Evolutionary Biology 22 (8): 1608–15. doi:10.1111/j.1420-9101.2009.01773.x.
Rundle, H. D., A. Odeen, and A. 0 Mooers. 2007. “An Experimental Test for Indirect Benefits in Drosophila Melanogaster.” Journal Article. BMC Evolutionary Biology 7. doi:10.1186/1471-2148-7-36.
Rundle, Howard D., Stephen F. Chenoweth, and Mark W. Blows. 2006. “The Roles of Natural and Sexual Selection During Adaptation to a Novel Environment.” Journal Article. Evolution 60 (11): 2218–25. doi:10.1111/j.0014-3820.2006.tb01859.x.
Savic Veselinovic, M., S. Pavkovic-Lucic, Z. Kurbalija Novicic, M. Jelic, and M. Andelkovic. 2013a. “Sexual Selection Can Reduce Mutational Load in Drosophila Subobscura.” Journal Article. Genetika-Belgrade 45 (2): 537–52. doi:10.2298/GENSR1302537V.
———. 2013b. “Sexual Selection Can Reduce Mutational Load in Drosophila Subobscura.” Journal Article. Genetika-Belgrade 45 (2): 537–52. doi:10.2298/GENSR1302537V.
Senior, Alistair M., Alison K. Gosby, Jing Lu, Stephen J. Simpson, and David Raubenheimer. 2016. “Meta-Analysis of Variance: An Illustration Comparing the Effects of Two Dietary Interventions on Variability in Weight.” Journal Article. Evolution, Medicine, and Public Health 2016 (1): 244–55. doi:10.1093/emph/eow020.
Senior, Alistair M., Catherine E. Grueber, Tsukushi Kamiya, Malgorzata Lagisz, Katie O’Dwyer, Eduardo S. A. Santos, and Shinichi Nakagawa. 2016. “Heterogeneity in Ecological and Evolutionary Meta-Analyses: Its Magnitude and Implications.” Ecology 97 (12): 3293–9. doi:10.1002/ecy.1591.
Seslija, D., I. Marecko, and N. Tucic. 2008. “Sexual Selection and Senescence: Do Seed Beetle Males (Acanthoscelides Obtectus, Bruchidae, Coleoptera) Shape the Longevity of Their Mates?” Journal Article. Journal of Zoological Systematics and Evolutionary Research 46 (4): 323–30. doi:10.1111/j.1439-0469.2008.00469.x.
Sharma, Manmohan D., John Hunt, and David J. Hosken. 2012. “Antagonistic Responses to Natural and Sexual Selection and the Sex-Specific Evolution of Cuticular Hydrocarbons in Drosophila Simulans.” Journal Article. Evolution 66 (3): 665–77. doi:10.1111/j.1558-5646.2011.01468.x.
Sharp, N. P., and A. F. Agrawal. 2008. “Mating Density and the Strength of Sexual Selection Against Deleterious Alleles in Drosophila Melanogaster.” Journal Article. Evolution 62 (4): 857–67. doi:10.1111/j.1558-5646.2008.00333.x.
Sharp, Nathaniel P., and Aneil F. Agrawal. 2009. “Sexual Selection and the Random Union of Gametes: Testing for a Correlation in Fitness Between Mates in Drosophila Melanogaster.” Journal Article. American Naturalist 174 (5): 613–22. doi:10.1086/605960.
Simmons, Leigh W., and Renee C. Firman. 2014. “Experimental Evidence for the Evolution of the Mammalian Baculum by Sexual Selection.” Journal Article. Evolution 68 (1): 276–83. doi:10.1111/evo.12229.
Simmons, Leigh W., and Francisco Garcia-Gonzalez. 2008. “Evolutionary Reduction in Testes Size and Competitive Fertilization Success in Response to the Experimental Removal of Sexual Selection in Dung Beetles.” Journal Article. Evolution 62 (10): 2580–91. doi:10.1111/j.1558-5646.2008.00479.x.
———. 2011. “Experimental Coevolution of Male and Female Genital Morphology.” Journal Article. Nature Communications 2. doi:10.1038/ncomms1379.
Simmons, Leigh W., Clarissa M. House, John Hunt, and Francisco Garcia-Gonzalez. 2009. “Evolutionary Response to Sexual Selection in Male Genital Morphology.” Journal Article. Current Biology 19 (17): 1442–6. doi:10.1016/j.cub.2009.06.056.
Snook, Rhonda R., Nelly A. Gidaszewski, Tracey Chapman, and Leigh W. Simmons. 2013. “Sexual Selection and the Evolution of Secondary Sexual Traits: Sex Comb Evolution in Drosophila.” Journal Article. Journal of Evolutionary Biology 26 (4): 912–18. doi:10.1111/jeb.12105.
Tilszer, M., K. Antoszczyk, N. Sałek, E. Zajac, and J. Radwan. 2006. “Evolution Under Relaxed Sexual Conflict in the Bulb Mite Rhizoglyphus Robini.” Journal Article. Evolution 60 (9): 1868–73. doi:10.1554/06-060.1.
Whitlock, M. C., and D. Bourguet. 2000. “Factors Affecting the Genetic Load in Drosophila: Synergistic Epistasis and Correlations Among Fitness Components.” Journal Article. Evolution 54 (5): 1654–60. http://onlinelibrary.wiley.com/store/10.1111/j.0014-3820.2000.tb00709.x/asset/j.0014-3820.2000.tb00709.x.pdf?v=1&t=j3z3k4zk&s=e04e52c50923ef3f847e776940854982021862a1.
Wigby, S., and T. Chapman. 2004. “Female Resistance to Male Harm Evolves in Response to Manipulation of Sexual Conflict.” Journal Article. Evolution 58 (5): 1028–37. http://onlinelibrary.wiley.com/store/10.1111/j.0014-3820.2004.tb00436.x/asset/j.0014-3820.2004.tb00436.x.pdf?v=1&t=j3z3kjfs&s=e7d435680d787af13772d7111d209fb6d5125822.