DOI: 10.5061/dryad.0p2ngf277
title: Data from: Evolutionary lability of sexual selection and its implications for speciation and macroevolution

description: Sexual selection is widely hypothesized to facilitate speciation and
 phenotypic evolution, but evidence from comparative studies has been
 mixed. Many previous studies have relied on proxy variables to quantify
 the intensity of sexual selection, raising the possibility that
 inconclusive results may reflect, in part, the imperfect measurement of
 this evolutionary process. Here, we test the relationship between
 phylogenetic speciation rates and indices of the opportunity for sexual
 selection drawn from populations of 82 vertebrate taxa. These indices
 provide a much more direct assessment of sexual selection intensity than
 proxy traits and allow straightforward comparisons among distantly related
 clades. We find no correlation between the opportunity for sexual
 selection and speciation rate, and this result is consistent across many
 complementary analyses. In addition, widely used proxy variables – sexual
 dimorphism and dichromatism – are not correlated with indices employed
 here. Moreover, we find that the opportunity for sexual selection has low
 phylogenetic signal and that intraspecific variability in selection
 indices for many species approaches the range of variation observed across
 all vertebrates as a whole. Our results potentially reconcile a major
 paradox in speciation biology at the interface between microevolution and
 macroevolution: sexual selection can be important for speciation, yet the
 evolutionary lability of the process over deeper timescales restricts its
 impact on broad-scale patterns of biodiversity.
description: <strong>Meta-analytic procedure used for data
 collection</strong> Estimates of the opportunity
 for sexual selection and Bateman gradients were obtained after an
 extensive search in the literature that follows meta-analytic procedures
 (see more details in Macedo-Rego et al
 2024.  While screening the literature,
 studies were selected whether they provided at least one estimate of
 φ<sub>Is</sub> or φ<sub>If</sub> and one estimate
 of φ<sub>I</sub> for a given sex (see main text). The search
 was performed in two databases: ISI Web of Science (all databases) and
 Scopus, and its step-by-step PRISMA design (see Nakagawa et al, 2017) is
 illustrated in Supplementary figure 2. The following keywords were
 used:"reproductive success" AND "mating success" OR
 "fitness" AND "mating success" OR
 "paternity" AND "mating success" OR
 "offspring" AND "mating success" OR "litter"
 AND "mating success" OR "fertilization success" AND
 "mating success" OR "breeding success" AND
 "mating success" OR "fecundity" AND "mating
 success" OR "reproductive rate" AND "mating
 success" OR "post-mating sexual selection" OR
 "post-mating selection" OR "Bateman*" OR
 "opportunit* for selection" OR "opportunit* for sexual
 selection" OR "selection gradient*" OR "Morisita
 index" OR "monopolization index for reproductive success"
 OR "Jones index" OR "copulation success" OR
 "opportunit* for natural selection" OR "intensit* of sexual
 selection" OR "mating success" AND "survival
 rate" OR "reproductive success" AND "number of
 mat*" OR "mixed paternity" OR "mating and reproductive
 success" OR "opportunit* for natural selection and sexual"
 OR "natural and sexual selection" OR "sexual and natural
 selection". Combined, Scopus and Web of Science
 provided 7,624 studies. After reading titles and abstracts of each study,
 1,659 were selected for further evaluation. Rejections were due to various
 reasons: studies not on sexual selection (54.89%), studies that focus only
 on pre-mating events of sexual selection (15.22%), studies on sexual
 selection but with no measurement of mating and reproductive success
 (13.23%), study on non-animal species (7.82%), studies that focus only on
 post-mating events of sexual selection (4.79%), studies on humans (3.08%),
 and studies whose complete versions were not found (0.97%).
 Before/while reading texts in full, 185 additional studies were
 obtained from other sources (e.g., while checking reference lists).
 Consequently, 1,844 studies were read in full. At this step, studies were
 rejected due to various reasons: study on sexual selection but that does
 not provide at least one estimate of φ<sub>Is</sub> or
 φ<sub>If</sub> and one estimate of φ<sub>I</sub>
 (females: 41.88%; males: 38.88%), studies that focus only on pre-mating
 events of sexual selection (females: 18.41%; males: 20.31%), studies that
 focus only on post-mating events of sexual selection (females: 17.80%;
 males: 18.92%), studies not on sexual selection (females: 11.41%; males:
 11.49%), studies that focus solely on males (males: 3.14%), or on females
 (males: 3.02%), studies whose complete versions were not found (both
 sexes: 3.02%), and other (both sexes: 4.35%). After
 this second round of filtering, estimates of sexual selection intensity
 were extracted from 153 studies. Original studies either (I) directly
 provided calculations of φ<sub>Is</sub>,
 φ<sub>If</sub>, φ<sub>I</sub> and the Bateman
 gradient or (II) provided the raw data needed (i.e., mean mating success,
 mean fertilization success and mean reproductive success per individual
 and/or mean estimates per population, with the respective standard
 deviations). From this initial dataset, estimates on non-Osteichthyes (1
 study) and turtles (1 study) were excluded, as well as estimates based on
 studies run under mesocosm (14 studies) or laboratory (15 studies)
 conditions. Finally, while running this project, three studies that met
 inclusion criteria were found sporadically in the literature, and were
 thus added to the final version of the dataset. In
 total, 101 studies provided 634 estimates of the intensity of sexual
 selection (conting all indices for all sexes).<br><br>The
 methods used to obtain data other than indices for the opportunity for
 sexual selection are described in the main text of the article that this
 dataset refers to
description: # Data from: Evolutionary lability of sexual selection and its
 implications for speciation and macroevolution
 [https://doi.org/10.5061/dryad.0p2ngf277](https://doi.org/10.5061/dryad.0p2ngf277) This folder contains the dataset & analytical scripts of the manuscript entitled "Evolutionary lability of sexual selection and its implications for speciation and macroevolution". ## Authors & institutions: Matheus Januario[a] Renato C. Macedo-Rego[b,c], Daniel L. Rabosky[a] Author affiliations: a. Museum of Zoology & Department of Ecology and Evolutionary Biology, University of Michigan, 2032 Biological Sciences Building, Ann Arbor, MI, 48109, (USA) b. Programa de Pós-Graduação em Ecologia, Universidade de São Paulo, Rua do Matão, 321, Travessa 14, São Paulo, SP, 05508-090 (Brazil) c. Departamento de Biologia Geral, Universidade Federal de Viçosa, Av. Peter Henry Rolfs s/n, Viçosa, MG, 36570-000 (Brazil) ## Description of the data and file structure Responsible for collecting data: Renato C. Macedo-Rego Responsible for WRITING CODE: Matheus Januario List of data files from "Time lability decouples sexual selection intensity from the origins of vertebrate biodiversity" data.csv: measurements at population level (selection indices) species_data_full.csv: measurements at species level, full dataset (averaged selection indices, speciation rates, dichromatism data, size dimorphism indices) species_data_zero_only: measurements at species level, zero-only dataset (averaged selection indices, speciation rates, dichromatism data, size dimorphism indices) finalTree.tre: phylogenetic tree used in all analyses SSD_vertebrates.tsv: sex-specific size data (used as raw data for sexual size dimorphism analyses) Armenta_etal_2008_data.txt: Dataset containing dichromatism measurements for many species of birds (Armenta et al 2008 Journal of Experimental Biology 211:2423–2430). Blomberg_etal_2003_appendix.csv: Estimates of phylogenetic signal “Blomberg’s K/kappa” (only vertebrate estimates estimates from the Appendix 6 of Blomberg et al 2003 Evolution 57 no. 4, pages 717-745) all_vert_tips_lambda.csv: Cooney & Thomas (2020) tip-specific speciation rate dataset. speciation_rates_taxonomy: dataset used in the power comparison across vertebrate families with less than 200 spp (see details in Supplementary text part IV) Variables (columns) within data files: data.csv: \[1] "obs_ID" = unique ID for each observation \[2] "study" = unique ID for each study incorporated in sampling \[3] "paper" = Reference in short format for each study incorporated in sampling \[4] "year" = Publication year of study incorporated in sampling \[5] "species_unmatched_with_tips" = species name matching original reference \[6] "species" = species name that match with phylogeny (finalTree.tre) tips. If different from what the original references says, it reflects either a taxonomical change (usually just name spelling, not an actual decision on species concept). If name does not match phylo tips, use equivalent name assigne din column "phylo_species" \[7] "sex" = Biological sex \[8] "group" = short code for vertebrate order \[9] "intrasex_competition" = short code indicating if we were sure the studied individuals faced intrassexual competition \[10] "includes_ms_0" = short code for certainty (= 1) or uncertainty/absence of information (= 0) regarding the reporting of zero-valued individuals \[11] "ms_determination" = If the authors somehow influenced (=1) or did not influenced (=0) the mating success (e.g. offering more males for a set of females). \[12] "Is" = opportunity for sexual selection value \[13] "ms_n" = Sample size for Is \[14] "ms_mean" = Mean mating success \[15] "ms_var" = Variance in mating success \[16] "ms_lnCVR" = Difference between sexes \[17] "ms_meaning" = Whether mating success was measured by counting the number of mating events or the number of sexual partners \[18] "ms_repeat" = If the information on mating success is repeated in another line. \[19] "ms_exclusion" = logical indicating if Is data should be excluded by any reason found after the data was collected (e.g. because it represents a repeated measurement) \[20] "reason1" = reason for exclusion of mating success \[21] "If" = opportunity for selection operationalized with fertilization \[22] "fs_n" = Sample size for If \[23] "fs_mean" = Mean fertilization success \[24] "fs_var" = Variance in fertilization success \[25] "fs_lnCVR" = Difference between sexes \[26] "fs_repeat" = If the information on fertilization success is repeated in another line. \[27] "fs_exclusion" = logical indicating if If data should be excluded by any reason found after the data was collected (e.g. because it represents a repeated measurement) \[28] "reason2" = reason for exclusion of fertilization success \[29] "I" = opportunity for selection operationalized with reproduction \[30] "rs_n" = Sample size for I \[31] "rs_mean" = Mean reproduction success \[32] "rs_var" = Variance in reproduction success \[33] "rs_lnCVR" = Difference between sexes \[34] "offspring_age" = short code for offspring development stage where I was measured \[35] "rs_exclusion" = logical indicating if I data should be excluded by any reason found after the data was collected (e.g. because it represents a repeated measurement) \[36] "reason3" = reason for exclusion of reproduction success \[37] "bg_ms_n" = sample size for calculation of mating-based Bateman gradient \[38] "bg_ms_slope" = best estimate of slope of mating-based Bateman gradient \[39] "bg_ms_slope_2.5" = Slope 2.5% CI of mating-based Bateman gradient \[40] "bg_ms_slope_97.5" = Slope 97.5% CI of mating-based Bateman gradient \[41] "bg_ms_intercept" = best estimate of intercept of mating-based Bateman gradient \[42] "bg_ms_intercept_2.5" = intercept 2.5% CI of mating-based Bateman gradient \[43] "bg_ms_intercept_97.5" = intercept 97.5% CI of mating-based Bateman gradient \[44] "bg_ms_residual_se" = residual standard error of mating-based Bateman gradient \[45] "bg_ms_r2" = Pearson's correlation coefficient for calculation of mating-based Bateman gradient \[46] "bg_ms_exclusion" = logical indicating if bg_ms_slope data should be excluded by any reason found after the data was collected (e.g. because it represents a repeated measurement) \[47] "reason4" = reason for exclusion of mating-based Bateman gradient \[48] "bg_fs_n" = sample size for calculation of fertilization-based Bateman gradient \[49] "bg_fs_slope" = best estimate of slope of fertilization-based Bateman gradient \[50] "bg_fs_slope_2.5" = intercept 2.5% CI of fertilization-based Bateman gradient \[51] "bg_fs_slope_97.5" = Slope 97.5% CI of fertilization-based Bateman gradient \[52] "bg_fs_intercept" = best estimate of intercept of fertilization-based Bateman gradient \[53] "bg_fs_intercept_2.5" = intercept 2.5% CI of fertilization-based Bateman gradient \[54] "bg_fs_intercept_97.5" = intercept 97.5% CI of fertilization-based Bateman gradient \[55] "bg_fs_residual_se" = residual standard error of fertilization-based Bateman gradient \[56] "bg_fs_r2" = Pearson's correlation coefficient for calculation of fertilization-based Bateman gradient \[57] "bg_fs_exclusion" = logical indicating if bg_ms_slope data should be excluded by any reason found after the data was collected (e.g. because it represents a repeated measurement) \[58] "reason5" = = reason for exclusion of mating-based Bateman gradient \[59] "obs" = relevant observations \[60] "BAMM" = species-specific phylogenetic speciation (tip) rate, calculated from BAMM (i.e., lambda_{BAMM}) \[61] "DR" = species-specific phylogenetic speciation (tip) rate, measured by the DR statistic (i.e., lambda_{DR}) \[62] "clade" = shortcode for vertebrate order \[63] "phylo_species" = species name that match with phylogeny (finalTree.tre) tips. If different from what column "species" says, it reflects the replacement of a species missing form the DNA-based tree by another closely related species \[64] "study_type" = shortocode for general way in which data was obtained species_data_full.csv \[1] "sp" \[2] "clade" = shortcode for vertebrate order \[3] "sex" = Biological sex \[4] "Is_avg" = opportunity for sexual selection (species average, calculated from population_data.csv) \[5] "Is_var" = opportunity for sexual selection (species variance, calculated from population_data.csv) \[6] "Is_min" = opportunity for sexual selection (species minimum value, calculated from population_data.csv) \[7] "Is_max" = opportunity for sexual selection (species maximum value, calculated from population_data.csv) \[8] "Is_n" = opportunity for sexual selection (species sample size of index values, calculated from population_data.csv) \[9] "I_avg" = opportunity for selection operationalized with reproduction (species average, calculated from population_data.csv) 10] "I_var" = opportunity for selection operationalized with reproduction (species variance, calculated from population_data.csv) \[11] "I_min" = opportunity for selection operationalized with reproduction (species minimum value, calculated from population_data.csv) \[12] "I_max" = opportunity for selection operationalized with reproduction (species maximum value, calculated from population_data.csv) \[13] "I_n" = opportunity for selection operationalized with reproduction (species sample size of index values, calculated from population_data.csv) \[14] "If_avg" = opportunity for selection operationalized with reproduction (species average, calculated from population_data.csv) \[15] "If_var" = opportunity for selection operationalized with fertilization (species variance, calculated from population_data.csv) \[16] "If_min" = opportunity for selection operationalized with fertilization (species minimum value, calculated from population_data.csv) \[17] "If_max" = opportunity for selection operationalized with fertilization (species maximum value, calculated from population_data.csv) \[18] "If_n" = opportunity for selection operationalized with fertilization (species sample size of index values, calculated from population_data.csv) \[19] "Bgm_avg" = mating-based Bateman gradient (species average, calculated from population_data.csv) \[20] "Bgm_var" = mating-based Bateman gradient (species variance, calculated from population_data.csv) \[21] "Bgm_min" = mating-based Bateman gradient (species minimum, calculated from population_data.csv) \[22] "Bgm_max" = mating-based Bateman gradient (species maximum, calculated from population_data.csv) \[23] "Bgm_n" = mating-based Bateman gradient (species sample size of index values, calculated from population_data.csv) \[24] "Bgf_avg" = fertilization-based Bateman gradient (species average, calculated from population_data.csv) \[25] "Bgf_var" = fertilization-based Bateman gradient (species variance, calculated from population_data.csv) \[26] "Bgf_min" = fertilization-based Bateman gradient (species minimum, calculated from population_data.csv) \[27] "Bgf_max" = fertilization-based Bateman gradient (species maximum, calculated from population_data.csv) \[28] "Bgf_n" mating-based Bateman gradient (species sample size of index values, calculated from population_data.csv) \[29] "phylo_sp" = species name that match with phylogeny (finalTree.tre) tips. If different from what column "species" says, it reflects the replacement of a species missing form the DNA-based tree by another closely related species \[30] "DR" = species-specific phylogenetic speciation (tip) rate, measured by the DR statistic (i.e., lambda_{DR}) \[31] "BAMM" = species-specific phylogenetic speciation (tip) rate, calculated from BAMM (i.e., lambda_{BAMM}) \[32] "male_weight_g" = Male weight in grams \[33] "female_weight_g" = Female weight in grams \[34] "male_SVL_mm" = Male snout vent length in millimeters \[35] "female_SVL_mm" = Female snout vent length in millimeters \[36] "male_length_at_maturity_mm" = Male length at maturity in millimeters \[37] "female_length_at_maturity_mm" = Female length at maturity in millimeters \[38] "reference_dimo" = Long code for reference in sex-specific size \[39] "SSD_w" = Lovich & Gibbons sexual size dimorphism index calculated with weight \[40] "SSD_svl" = Lovich & Gibbons sexual size dimorphism index calculated with svl \[41] "SSD_len" = Lovich & Gibbons sexual size dimorphism index calculated with length at maturity \[42] "dichro_PCA" = Armenta et al 2008 sexual dichromatism index calculated with a PCA using data on plumage reflectance color \[43] "dichro_segment_classif" = Armenta et al 2008 sexual dichromatism index calculated with segment classification, using data on plumage reflectance color \[44] "dichro_col_discriminab" = Armenta et al 2008 sexual dichromatism index calculated with a discriminability model species_data_zero_only.csv Same columns/variables as species_data_full.csv, but species-level selection indices estimates are different SSD_vertebrates.tsv \[1] "sp" = species name that match with phylogeny (finalTree.tre) tips. If different from what column "species" says, it reflects the replacement of a species missing form the DNA-based tree by another closely related species \[2] "clade" = shortcode for vertebrate order \[3] "male_weight_g" = Male weight in grams \[4] "female_weight_g" = Female weight in grams \[5] "male_SVL_mm" = Male weight in grams \[6] "female_SVL_mm" = Female weight in grams \[7] "male_length_at_maturity_mm" = Male length at maturity in millimeters \[8] "female_length_at_maturity_mm" = Female length at maturity in millimeters \[9] "reference_dimo" = = Long code for reference in sex-specific size all_vert_tips_lambda.csv \[1] "species" = species name in Cooney & Thomas (2020) dataset. \[2] "bamm.mcc.tree" = species-specific phylogenetic speciation (tip) rate, calculated from BAMM (i.e., lambda_{BAMM}) \[3] "dr.mcc.tree" = species-specific phylogenetic speciation (tip) rate, measured by the DR statistic (i.e., lambda_{DR}) \[4] "clade" = shortcode for vertebrate order Armenta_etal_2008_data.txt \[1] "species" = species name in Cooney & Thomas (2020) dataset. \[2] "PCA" = Armenta et al 2008 sexual dichromatism index calculated with a PCA using data on plumage reflectance color \[3] "Segment_classification" = Armenta et al 2008 sexual dichromatism index calculated with segment classification, using data on plumage reflectance color \[4] "Colour_discriminability" = Armenta et al 2008 sexual dichromatism index calculated with a discriminability model Blomberg_etal_2003_appendix.csv \[1] "Vertebrate" = Indicator if clade contains only vertebrates (1) or not (0). \[2] "Group" = shortcode for vertebrate order. \[3] "trait" = short description of trait. Originally from Blombe0rg et al 2003. \[4] "Trait_type" = Class of trait (i.e. "Body size", "Morphology", "Physiology". "Life history", "Ecology", or "Behavior"). Originally from Blomberg et al 2003. \[5] "samp_size" = number of species in original data source. \[6] "p.value" = P-value for signifance, based on permutation test (see Blomberg et al 2003 for details). \[7] "estimate" = Value of "Blomberg's K" (= "Kappa"). \[8] "Source" = Short name for reference of original data source (see Blomberg et al 2003 for full references). speciation_rates_taxonomy.csv \[1] "bamm.mcc.tree" = species-specific phylogenetic speciation (tip) rate, calculated from BAMM (i.e., lambda_{BAMM}) \[2] "mean.dr.full.tree" = species-specific phylogenetic speciation (tip) rate, measured by the DR statistic (i.e., lambda_{DR}) \[3] "clade" = Factor telling if species is a bird, a fish, an amphibian, a mammal, or a squamate \[4] "Family" = Taxonomic family of the species, as assigned by the "taxize" R package List of tables (output) files from "Time lability decouples sexual selection intensity from the origins of vertebrate biodiversity" PGLS_lambda_full_dataset.csv: results from PGLS analysis among speciation and selection indices (full dataset). See main text or script "PGLS_lambda_full_dataset.R" for more details on calculation (this is also Supplementary table 1) PGLS_lambda_zero_only.csv: results from PGLS analysis among speciation and selection indices (zero-only dataset). See main text or script "PGLS_lambda_full_dataset.R" for more details on calculation (this is also Supplementary table 2) PGLS_traits_full_dataset.csv: results from PGLS analysis among proxy traits and selection indices (full dataset). See main text or script PGLS_traits_full_dataset.R" for more details on calculation (this is also Supplementary table 3) PGLS_traits_zero_only.csv: results from PGLS analysis among proxy traits and selection indices (zero-only dataset). See main text or script PGLS_traits_full_dataset.R" for more details on calculation (this is also Supplementary table 4) phylosig_charac_evo.csv: trait evolution modeling results & phylogenetic signal results (see script "phylosig_charac_evo_mods.R" for details). (this is also Supplementary table 5) summary_stat_signif: Summary of significant analyses. (this is also Supplementary table 6) tip_lipas.csv: result of LIPA analysis. See main text or script "phylosig_charac_evo_mods.R" for more details on calculations PGLS_lambda_stepwiseMS.csv: Results of the automatic stepwise model selection between speciation rate and indices, modulated by clade. See main text or script "PGLS_lambda_stepwiseMS.R" for more details on calculations. PGLS_trait_stepwiseMS.csv: Results of the automatic stepwise model selection between proxy traits and indices, modulated by clade. See main text or script "PGLS_trait_stepwiseMS.R" for more details on calculations. power_vert_families: Results of the power comparison across vertebrate families with less than 200 spp (see details in Supplementary text part IV) Variables (columns) within data files (see main text for key on the labels of indices & speciation rate shortcodes): PGLS_lambda_full_dataset.csv OR PGLS_lambda_zero_only.csv: \[1] "clade" = shortcode for clade used in analysis. "All" means all vertebrate orders \[2] "lambda" = shortcode for type of phylogenneitc speciation rate tip values \[3] "Index" = Selection index name \[4] "sex" = biological sex \[5] "sample_size" = Number of species used in analysis \[6] "intercept" = intercept of PGLS \[7] "slope" = slope of PGLS \[8] "delta_AICc_alt_mod" = Personal codification for easily handling of Delta AICc among models. Note that for code/programing convenience this was calculated as AICc_{Non-NULL} - AICc_{NULL}, so negative values appear when the null model was selected \[9] "p_intercept" = p-value of (non-null) intercept (test if different from zero) \[10] "p_slope" = p-value of (non-null) PGLS slope (test if different from zero) \[11] "rsq" = R-squared of (non-null) PGLS \[12] "pwr1_rate" = "r" parameter of the model employed in power analysis I \[13] "pwr1_delay" = "t" ("threshold") parameter of the model employed in power analysis I \[14] "pwrQuant_0.025" = Empirical 2.5% quantiles of simulated correlation values (power analysis II) \[15] "pwrQuant_0.5" = Empirical 50% quantiles (median) of simulated correlation values (power analysis II) \[16] "pwrQuant_0.975" = Empirical 2.5% quantiles of simulated correlation values (power analysis II) \[17] "pwr_n_signif" = Number of simulated datasets which we found to be signfiicant in power analysis II \[18] pwr_n_reject" = number of simulations excluded from final calculations. This is checking column, and all values should be equal to zero. PGLS_traits_full_dataset.csv OR PGLS_traits_zero_only.csv: \[1] "clade" = shortcode for clade used in analysis. "All" means all vertebrate orders \[2] "Trait" = shortcode for proxy trait used \[3] "Index" = Selection index name \[4] "sex" = biological sex \[5] "sample_size" = Number of species used in analysis \[6] "intercept" = intercept of PGLS \[7] "slope" = slope of PGLS \[8] "delta_AICc_alt_mod" = Personal codification for easily handling of Delta AICc among models. Note that for code/programing convenience this was calculated as AICc_{Non-NULL} - AICc_{NULL}, so negative values appear when the null model was selected \[9] "p_intercept" = p-value of (non-null) intercept (test if different from zero) \[10] "p_slope" = p-value of (non-null) PGLS slope (test if different from zero) \[11] "rsq" = R-squared of (non-null) PGLS \[12] "pwr1_rate" = "r" parameter of the model employed in power analysis I \[13] "pwr1_delay" = "t" ("threshold") parameter of the model employed in power analysis I \[14] "pwrQuant_0.025" = Empirical 2.5% quantiles of simulated correlation values (power analysis II) \[15] "pwrQuant_0.5" = Empirical 50% quantiles (median) of simulated correlation values (power analysis II) \[16] "pwrQuant_0.975" = Empirical 2.5% quantiles of simulated correlation values (power analysis II) \[17] "pwr_n_signif" = Number of simulated datasets which we found to be signfiicant in power analysis II \[18] pwr_n_reject" = number of simulations excluded from final calculations. This is checking column, and all values should be equal to zero. phylosig_charac_evo.csv: \[1] "Index" = Selection index name \[2] "sex" = biological sex \[3] "phylosig" = shortcode for type of phylogenetic singla measured \[4] "estimate" = Estimate for phylogenetic signal. Note every type of measurement works in a different scale \[5] "p-value" = p-value of test for significant phylogenentic singal \[6] "samp_size" = Number of species used in analysis summary_stat_signif.csv: \[1] "clade" = shortcode for clade used in analysis. "All" means all vertebrate orders \[2] "sex" = biological sex \[3] "Independent variable" = the independent variable used in the PGLS \[4] "Predicted variable" = the predictedt variable used in the PGLS \[5] "Dataset" = The dataset used as the source of data \[6] "sample_size" = Number of species used in analysis \[7] "Effect" = Polarity of the effect in the PGLS tip_lipas.csv: \[1] "Lcl_lipa" = LIPA vale \[2] "sp" = species name \[3] "sex" = biological sex \[4] "var" = Selection index name \[5] "significant" = logical indicating if LIPA value significantly deviates from others PGLS_lambda_stepwiseMS.csv: \[1] "lambda" = shortcode for type of phylogenneitc speciation rate tip values \[2] "Index" = Selection index name \[3] "sex" = biological sex \[4] "sample_size" = Number of species used in analysis \[5] "intercept" = intercept of PGLS \[6] "sel_mod" = Name of selected model. Options are Full (lambda ~ index * clade), Simple (lambda ~ index+clade), or Null (lambda ~ 1) \[7] "loglik" = Log-likelihood of the best (selected) model. \[8] "AIC" = Akaike Information Criteria of the best (selected) model. \[9] "p_intercept" = p-value of the test for significant deviations from zero in the intercept \[10] "rsq" = R-square of the model, as implemented in the R package "phylolm" \[11] "N_nonzeropars" = Number of parameters that significantly deviate from zero. \[12] "dataset" = Indicator for "full" or "Zero-only" datasets. PGLS_trait_stepwiseMS.csv: \[1] "lambda" = shortcode for type of phylogenneitc speciation rate tip values \[2] "Index" = Selection index name \[3] "sex" = biological sex \[4] "sample_size" = Number of species used in analysis \[5] "intercept" = intercept of PGLS \[6] "sel_mod" = Name of selected model. Options are Full (lambda ~ index * clade), Simple (lambda ~ index+clade), or Null (lambda ~ 1) \[7] "loglik" = Log-likelihood of the best (selected) model. \[8] "AIC" = Akaike Information Criteria of the best (selected) model. \[9] "p_intercept" = p-value of the test for significant deviations from zero in the intercept \[10] "rsq" = R-square of the model, as implemented in the R package "phylolm" \[11] "N_nonzeropars" = Number of parameters that significantly deviate from zero. \[12] "dataset" = Indicator for "full" or "Zero-only" datasets. power_vert_families.csv: \[1] "clade" = shortcode for clade used in analysis. "All" means all vertebrate orders \[2] "family" = taxonomic family \[3] "pwr_1_delay" = "t" ("threshold") parameter of the model employed in power analysis I \[4] "ntips" = Number of species in the phylogeny of the evaluated family Missing data code: NA List of scripts from "Time lability decouples sexual selection intensity from the origins of vertebrate biodiversity" Symbols used: Script names, what they do (#), and which files they need (&gt;) or create (*): prepare_dataset.R ``` \# removes negative bateman gradients \# aggregates selection intensity values by species and sex \# removes zero-valued species \# log-transforms sex sel indexes and spec rates: \# Reads data on sex size and calculate SSD using Lovich & Gibbons (1992) index \# Reads data bird dichromatism \# incorporates trait (dichromatism and dimorphism) into dataset \&gt; data.csv [ measurements of pop-level intensity of selection ] \&gt; finalTree.tre [phylogeny with all species included in this study] \&gt; SSD_vertebrates.tsv [sexual size dimorphism primary data] \&gt; Armenta_etal_2008_data.txt [sexual dichromatism data] \* creates "sp_melt" and "sp_val" objects. Updates "data" object ``` prepare_dataset_zero_included.R ``` \# same as make_dataset.R, BUT it only includes data from population which we are certain that zero-success individuals are reported \* creates "sp_melt_zero_included" and "sp_val_zero_included", and "data_zero_included" objects ``` PGLS_lambda_full_dataset.R ``` \# Runs PGLS analysis among selection indices and phylogenetic speciation rates, and run power analyses (Full dataset) \&gt; objects created by script "prepare_dataset.R" \* tables/PGLS_lambda_full_dataset.csv \* figs/supp_figs/supfig_12_power_analysis_plots_lambda_full.pdf ``` PGLS_lambda_zero_included.R ``` \# Runs PGLS analysis among selection indices and phylogenetic speciation rates, and run power analyses (Zero-only dataset) \&gt; objects created by script "prepare_dataset_zero_included.R" \* tables/PGLS_lambda_zero_only.csv \* figs/supp_figs/supfig_13_power_analysis_plots_lambda_zero_only.pdf ``` PGLS_traits_full_dataset.R ``` \# Runs PGLS analysis among selection indices and proxy traits, and run power analyses (Full dataset) \&gt; objects created by script "prepare_dataset.R" \* tables/PGLS_traits_full_dataset.csv \* figs/supp_figs/supfig_14_power_analysis_plots_traits_full.pdf ``` PGLS_lambda_zero_included.R ``` \# Runs PGLS analysis among selection indices and proxy traits, and run power analyses (Zero-only dataset) \&gt; objects created by script "prepare_dataset_zero_included.R" \* tables/PGLS_traits_zero_only.csv \* figs/supp_figs/supfig_15_power_analysis_plots_traits_zero_only.pdf ``` phylosig_charac_evo_mods.R ``` \# Calculates phylogenetic signal, LIPAs, and does model testing (between Brownian, Early-Burst, Ornstein-Uhlenbeck, and White Noise models) for indices, proxy traits, and for body size (i.e. the "positive control") \&gt; objects created by script "prepare_dataset.R" \* tables/phylosig_charac_evo.csv \* tables/tip_lipas.csv ``` PGLS_lambda_stepwiseMS.R ``` \# Runs the automatic stepwise model selection between speciation rate and indices, modulated by clade. See main text for more info, and see table "PGLS_lambda_stepwiseMS.csv" for results". ``` PGLS_trait_stepwiseMS.R ``` \# Runs the automatic stepwise model selection between proxy traits and indices, modulated by clade. See main text for more info, and see table "PGLS_trait_stepwiseMS.csv" for results". ``` Scripts related to particular figures read the appropriate dataset and generate the file, usually as .pdf and .png. Important: some details (e.g. color legends) are added later manually. **For further details:** Please send questions to januarioml.eco [at] gmail [dot] com.