Sexual Selection Accelerates Signal Evolution During Speciation in Birds

Seddon, Botero et al.

Electronic Supplementary Material 2 Sexual selection accelerates signal evolution during

4 speciation in birds

10 Contents

Supplementary Methods (Appendix S1)

12 Supplementary Figure

Evolutionary relationships of study species (Fig S1)

14 Supplementary Tables

16 Phenotypic traits (Table S1)

Factor loadings for plumage reflectance data (Table S2)

18 Statistical tables (Tables S3 to S8)

Supplementary References

20 External Database as an Excel file (Appendix S2) Seddon, Botero et al.

22 Appendix S1: Supplementary Methods

24 Study species 26 Species pairs (sister species and clade sisters) with published data on spectral reflectance [1] were identified from published phylogenetic trees of families or 28 genera generated using protein coding mtDNA in which > 70% of taxa had been sampled and where node support was high (either posterior probability > 95%, or 30 maximum likelihood bootstrap > 70). More recent phylogenetic studies took precedence unless earlier studies included more taxa with a different resolution of 32 sister relationships. When several molecular phylogenies were presented within a paper, we only selected sister pairs resolved in all trees. In situations where nodal 34 support conflicted between different methods of phylogenetic reconstruction, maximum likelihood bootstrap values took precedence. Consensus trees and trees 36 based on concatenated molecular datasets were presumed to depict the most reliable phylogenetic relationships and thus, whenever possible, we assessed nodal 38 support based on the values given in these trees. When selecting species from clades, we paired the focal species with 40 whichever member of its sister clade had plumage reflectance data. However, where more than one clade member had reflectance data, we used range maps to select 42 the species with the closest possible breeding range to the focal species. By choosing the geographically closest clade member, we selected lineages most likely 44 to have split recently, assuming historical species ranges can be inferred from present day distributions and provide an indication of the mode of speciation. To 46 minimize the influence of species interactions on phenotypic divergence (e.g. character displacement), we excluded all cases where one or more unsampled clade 48 members were sympatric with either the focal species or the sampled clade member. In other words, a criterion of selection was that none of the breeding 50 ranges of the other clade members overlapped geographically with the focal species or the clade member included in the main analysis. These criteria automatically 52 restricted the sample of sister clades to small, relatively young clades (≤ 5 species). Seddon, Botero et al.

We then categorized species pairs as sympatric or allopatric based on 54 published datasets [2-4]. Remaining species were assigned to these categories using high quality geographic range polygons, following the methods of Weir & Price 56 [3].

58 Sample size Our final sample of species comparisons (Appendix S2) contained 84 species pairs, 60 including 39 true sister species pairs and 45 clade sisters (a focal species paired with one member of their sister clade). However, because of differences in data 62 requirements and availability, sample size varied across our models (Table S1). Data for both plumage and morphology were available for 69 pairs (hence sample 64 size of Analysis 1 [A1]). Meanwhile, data on plumage were available for all 84 species pairs, but the models of diversification rate (Analysis 3 [A3]) could only be 66 run using true sister pairs (n = 39). This was not a constraint for A1 and A2. In addition, we removed 17 clade sisters from A2 as they were phylogenetically nested 68 (they shared one species with another species pair in our dataset) and were therefore unsuitable for evolutionary rates models. We retained these 17 species 70 pairs in linear mixed effect models (LMMs) as all combinations of species were unique, and thus we considered each divergence event to be independent. We 72 included species name as a random effect in the mixed models to control for the inclusion of these repeated measures (see below). 74 Quantifying phenotype 76 Morphological traits. We measured beak, tarsus, and wing length from museum specimens using digital callipers. Beaks were measured (to the nearest 0.01 mm) as 78 length from the anterior edge of the nostrils to the tip; tarsus length was measured down the back of the leg from the middle of the ankle joint (i.e. the notch between 80 the tibia and tarsus) to the end of the last scale of the acrotarsium (usually the last undivided scale); wing was measured as the distance from the carpal joint to the 82 longest primary of the unflattened wing. To ensure consistency, all measures for Seddon, Botero et al.

members of a pair were taken by one researcher. Body mass data were compiled 84 from Dunning [5].

86 Plumage traits. All spectrophotometer measurements were collected using an Ocean Optics (Dunedin, Florida) USB2000 spectrophotometer and a PX-2 pulsed 88 Xenon light source with the spectrophotometer probe at 90° to the plumage. Measurements were standardized to a WS-1 white standard, considered >98% 90 reflective from 250−1500 nm wavelengths. For each reflectance reading, we averaged the reflectance data into bins 92 covering 20 nm of the spectrum. We quantified colour using standard descriptors of reflectance spectra: brightness and hue/chroma [7]. We calculated brightness or 94 intensity by summing its reflectance from 320 to 700 nm, the approximate visible spectrum of most avian species [8]. Because a spectrum consists of reflectance at 96 each wavelength that is highly correlated, we then used a PCA to collapse these reflectance variables into a few independent variables that summarize spectrum 98 shape [6, 7], a standard method to handle spectral data [9-13]. We first used brightness to standardize all reflectance scans before PCA. The resulting principal 100 components (PC) values were thus indices of chroma and hue [6], independent of its brightness. We then performed a principal component analysis (PCA) using the 102 standardized reflectance values from each specimen (19 values for each specimen based on 20 nm bins). Although multiple methods have been previously used to 104 analyze spectral data, including those that take into account the spectral sensitivity of each cone type, the reflectance of the sample, the background against which the 106 sample is viewed, and the irradiance spectrum of the ambient light [7, 14-17], when different methods have been compared, they have yielded qualitatively similar 108 estimates of colour [17] and dichromatism [1]. We chose PCA analysis for its simplicity and because it yields separate values that represent the shape of the 110 spectrum and chroma (e.g. purity of colour). In our analyses, the first two principal components explained more than 75.69% of the variation in the data. We found that 112 principal component 1 (PC1) was positively correlated with reflectance in the 400– 480nm range and accounted for 50.54% of the variation in the data; PC2 was Seddon, Botero et al.

114 positively correlated with reflectance in 320–380nm range, and accounted for 25.14% of the variation in the data. Therefore, we interpreted PC1 to represent 116 chroma in short wavelength and PC2 to represent chroma in UV. For PC1 and PC2, we calculated the average for males and for females of each species for each body 118 region. For factor loadings, see Table S3. To calculate dichromatism scores, for each body region, we calculated the Euclidean distance between PC scores for 120 males and females (y-axis) separately for PC1 and PC2. We then summed the differences between males and females for each PC across all six body regions to 122 produce the overall dichromatism score.

124 Dichromatism as an index of sexual selection Sexual dichromatism is not a perfect index of sexual selection, not least because a 126 variety of other mechanisms can result in sex-differences in plumage colouration and conspicuousness, such as natural selection for female crypsis in species with 128 female-only incubation [reviewed in 18]. However, in the absence of detailed long- term behavioural studies in which direct measures of sexual selection are obtained 130 (e.g. relative rate of reproduction), dichromatism is the best proxy currently available for the purposes of comparative analyses. It can be easily estimated in all bird 132 species (unlike other indices such as relative testes size or rates of extra-pair paternity which rely either on invasive sampling or intensive behavioural research). 134 Moreover, a number of studies have revealed strong positive associations between dichromatism and other indices of sexual selection such as testes size, degree of 136 polygyny, and frequency of extra-pair paternity [19-21]. Consequently, dichromatism has been used as a proxy for sexual selection in a large number of studies, including 138 those examining the effects of sexual selection on speciation in birds [22-28], lizards [29], insects [30], and fish [31], as well as in comparative studies of the effects of 140 sexual selection on extinction [32-34], mortality [35], immune defense [36], signal evolution [37], molecular evolution [38] and even response to climate change [39]. 142

144 Seddon, Botero et al.

Classifying monomorphic taxa as mutually ornamented 146 For taxa where quantitative plumage data indicated a lack of plumage dichromatism, we visually assessed whether this was due to mutual ornamentation (both males 148 and females are ornamented) or not (both males and females lack plumage ornaments). Using photographs or illustrations from field guides or taxonomic 150 monographs, we identified a taxon as ‘mutually ornamented’ in cases where both males and females had colourful (not brown) or iridescent plumage patches, stripes 152 or spots in any region of the body (e.g. bridled titmouse Baeolophus wollweberi). We classified a taxon as ‘monomorphic-dull’ when both males and females had drab, 154 uniform or pale colouration lacking ornamentation in the form of patches of colour, stripes or spots (e.g. oak titmouse Baeolophus inornatus). 156 Analytical approach 158 Analysis 1: Effect of sexual selection on extent of phenotypic divergence 160 We first used linear mixed effect models (LMMs) with maximum likelihood estimation to investigate whether extent of phenotypic divergence varies according to levels of 162 sexual selection (prediction 1) and whether the effects of sexual selection on phenotypic divergence differ between the sexes (prediction 2). We modelled the 164 maximum and total extent of phenotypic divergence (dependent variable) in relation to several predictors including the index of sexual selection (mean value of sexual 166 dichromatism within a pair), sex, and the interaction between sex and dichromatism. The variable 'sex' was included as a factor in the model to indicate the sex 168 associated with the trait diverging most in a particular pair of species (e.g. tarsus length or back colour) came from males or females. 170 In our dataset, some species (n = 17) were represented in more than one pairing (Appendix S2). The non-independence of these data points arising from 172 using the same species was taken into account by fitting both focal species (labeled as Species 1 in analysis tables) and the species they were compared to (labeled 174 Species 2 in analysis tables) as random effects in our LMMs. In all analyses, only unique combinations of species were included. To control for phylogenetic inertia in Seddon, Botero et al.

176 the extent of phenotypic divergence between pairs of species, we included taxonomy as a nested random effect [44]. Mixed-effect models including taxonomy 178 (Family [Genus]) had a significantly lower log-likelihood score than the model excluding taxonomy (Table 1). The significance of fixed effects was examined using 180 Wald type F-tests [45]. Significance values derive from having all significant terms (P < 0.05) fitted in the final model together; statistics associated with non-significant 182 terms were derived from having all significant terms in the model and each non- significant term (P > 0.05) fitted individually. The significance of random effects was 184 tested using log-likelihood ratio tests with all fixed effects and their interactions included in models [46]. 186 Although our LMM approach controlled for phylogenetic inertia at higher taxonomic levels (family and genus) it nonetheless assumed that species pairs are 188 equally related to one another and that the phylogenetic signal of phenotypic divergence is weak. To test this assumption we estimated lambda (λ), which 190 measures the degree to which traits co-vary across a tree in line with Brownian motion (Freckleton et al. 2002). A λ of 1 corresponds to the Brownian model, λ of 0 192 indicates a lack of phylogenetic structure, and λ values between 0 and 1 indicate the degree of trait lability [47]. To determine whether λ values departed significantly from 194 a Brownian model, we compared the fit of the two models using a likelihood ratio test. We found than both total phenotypic divergence (λ = 0.92) and maximum 196 phenotypic divergence (λ = 0.90) departed from a strict Brownian model. The implication is that the extent to which phenotypes diverge among our species pairs 198 may have been influenced by shared ancestry. To correct for this, we used the phylogenetic generalized least squares (PGLS) comparative method described in 200 Freckleton et al. [48], using the maximum likelihood tree (Fig. S1) as our phylogenetic hypothesis. We present results from both the LMM and PGLS models 202 because the LMMs are robust to analysis of repeated measures and can therefore be used on all unique species pairs in our dataset, but assume λ = 0, whereas the 204 PGLS approach estimates λ and then uses it to adjust the internal branch lengths such that the data meet the assumption of Brownian motion [48]. Seddon, Botero et al.

206 For both the LMMs and PGLS models, response variables and predictors were transformed to ensure model residuals were normally distributed and had 208 homogeneous variance: maximum and total phenotypic divergence were Box-Cox transformed; mean dichromatism and mean body mass were log-transformed. 210 Parameter estimates are presented as mean ± SE.

212 Analysis 2: Effect of sexual selection on evolutionary rates of phenotypic divergence The analyses presented in the main text indicate that sexual selection has different 214 effects on phenotypic evolution of males and females (Tables S5 and S6). To evaluate the strength of these differences, we compared models in which rates of 216 phenotypic divergence were estimated separately for each sex to models in which the data for both sexes were analyzed together. Joint AIC values for sex-specific 218 models were computed by adding the likelihood values of models in Tables S5 and S6 and adjusting the number of parameters accordingly. For all traits, sex-specific 220 models were clearly better supported than models in which data from both sexes are combined (Table S7). 222 Analysis 3: effect of sexual dichromatism on diversification 224 Birth-death trees with the correction for the lagtime to species recognition were simulated in the R package PhyloGen [49]. We simulated with λ ranging from 0 to 226 0.15 in 0.01 intervals, and from 0.15 to 0.90 in 0.05 intervals. For λ ≤ 0.4, μ ranged from 0λ, 0.05λ, 0.1λ, 0.2λ…0.9λ, 0.95λ, 0.99λ. For λ > 0.4, the same rates of μ were 228 used provided λ-μ < 0.45 (a necessary computational restriction given excessively large trees sizes when λ-μ > 0.45). For each set of λ and μ 21 values of φ (φ = 0, 230 0.1, 0.2…1.9, 2.0) were used. We adopt a simulation approach for this analysis because while there are 232 methods available for estimating speciation/extinction rates from phylogenies while correcting for the lag-time to speciation, no such method exists for sister species 234 data. Moreover, the key advantage of the simulation method over the analytical method is that it does not assume the survival of lineages. 236 Seddon, Botero et al.

Supplementary Figure 238

240 Figure S1 Maximum clade credibility tree illustrating the evolutionary relationships of 242 the passerine bird species included in this study Seddon, Botero et al.

244 Supplementary Tables

246 Table S1 Phenotypic traits measured and analyses in which they were included 248

Class of Analysis Analysis Analysis Specific trait phenotype 1 2 3

Morphology Beak length X X Tarsus length X X Wing-chord length X X Plumage Crown (SW chroma) X X X Crown (UV chroma) X X X Throat (SW chroma) X X X Throat (UV chroma) X X X Back (SW chroma) X X X Back (UV chroma) X X X Belly (SW chroma) X X X Belly (UV chroma) X X X Tail (SW chroma) X X X Tail (UV chroma) X X X Wing-coverts (SW chroma) X X X Wing-coverts (UV chroma) X X X

Number of species pairs included 69 52 39 Seddon, Botero et al.

Table S2 Factor loadings from principal components 250 analysis on plumage reflectance data

Wavelength (nm) PC1 PC2 320 -0.123 0.253 340 -0.128 0.288 360 -0.081 0.251 380 0.011 0.133 400 0.109 0.010 420 0.173 -0.089 440 0.194 -0.123 460 0.201 -0.141 480 0.202 -0.164 500 0.065 -0.004 520 -0.014 0.040 540 -0.075 0.080 560 -0.088 0.049 580 -0.041 -0.067 600 -0.033 -0.106 620 -0.041 -0.104 640 -0.023 -0.127 660 -0.020 -0.115 680 -0.043 -0.082 252

272 Table S3 PGLS models of (a) total and (b) maximum phenotypic divergence between species pairs in relation to the intensity of sexual selection within species 274 (dichromatism), sex, and other potentially confounding variables (n = 52 pairs).

(a) Total phenotypic divergence

Parameter Fixed effects SE t P Estimate (b) Dichromatism 1.696 1.461 1.160 0.25 Sex -3.437 0.863 -3.981 <0.0001 Dichromatism * Sex 5.396 1.340 4.026 <0.0001 Evolutionary age 1.099 1.120 0.981 0.33 Sympatry 0.081 0.749 0.108 0.91 Body mass 2.660 1.348 1.973 0.05 2 2 Lambda F1,51 R adj. R Final model 0.92 7.88 0.32 0.29 AIC 534.80 AICc 535.97

(b) Maximum phenotypic divergence

Parameter Fixed effects SE t P Estimate (b) Dichromatism 0.04 0.369 0.12 0.904 Sex -0.66 0.229 -2.88 0.005 Dichromatism * Sex 0.99 0.349 2.84 0.005 Evolutionary age 0.01 0.022 0.40 0.69 Sympatry 0.30 0.196 1.51 0.14 Body mass 0.50 0.335 1.50 0.14 2 2 Lambda F1,51 R adj. R Final model 0.90 3.18 0.16 0.11 AIC 260.3 AICc 261.5 276

Table S4 PGLS models of (a) total and (b) maximum phenotypic divergence 280 between species pairs in relation to the intensity of sexual selection within species (dichromatism), sex, and other potentially confounding variables, excluding mutually 282 ornamented species (n = 45 species pairs). (a) Total phenotypic divergence Parameter Fixed effects SE t P Estimate (b) Dichromatism 2.58 1.62 1.59 0.12 Sex -3.08 1.01 -3.04 <0.0001 Dichromatism * Sex 4.32 1.59 2.72 0.01 Evolutionary age 1.40 1.25 1.12 0.27 Sympatry 0.16 0.82 0.19 0.85 Body mass 2.46 1.38 1.78 0.08 2 Lambda F1,44 P adj. R Final model 0.92 4.78 <0.0001 0.21 AIC 467.59 AICc 468.99

(b) Maximum phenotypic divergence Parameter Fixed effects SE t P Estimate (b) Dichromatism 0.16 0.42 0.37 0.71 Sex -0.59 0.27 -2.16 0.03 Dichromatism * Sex 0.75 0.43 1.77 0.08 Evolutionary age 0.15 0.32 0.47 0.64 Sympatry 0.34 0.22 1.56 0.12 Body mass 0.43 0.36 1.22 0.23 2 Lambda F1,51 P adj. R Final model 0.88 2.24 0.05 0.08 AIC 233.47 AICc 234.87

Table S5 Evolutionary divergence in male traits in relation to sexual selection. Comparison of support for models in which 286 the rate of evolutionary divergence in traits is assumed to be independent of the strength of sexual selection (constant rate model, CR) or linearly associated with the strength of sexual selection (variable rates model, VR). For each model 288 type we explore results under a Brownian motion (BM) model of evolution and an Ornstein-Uhlenbeck (OU) process. In the variable rates OU model, we allow the possibility that sexual selection is also linearly associated with the constraint 290 parameter, α. Bold denotes models that are unambiguously supported by the data in a given candidate set (i.e., Akaike weights > 70% and ΔAICc > 2 when compared to the next best supported model). 292 Slope for Akaike Trait Model type Rate_0 ± SE † α_0 ± SE † Slope for α ± SE AICc rate ± SE weight CR-BM 0.032 ± 0.006 -50.24 0.00 0.005 ± VR-BM 0.009 ± 0.005 -58.85 0.31 0.002 Beak 0.967 ± length CR-OU 0.148 ± 0.149 -58.69 0.29 1.074 0.131 ± 0.012 ± VR-OU 0.013 ± 0.021 0.044 ± 0.062 -59.35 0.40 0.257 0.010 CR-BM 0.025 ± 0.005 -63.59 0.52 0.001 ± VR-BM 0.020 ± 0.008 -61.86 0.22 0.002 Tarsus 0.075 ± length CR-OU 0.031 ± 0.013 -61.99 0.23 0.112 0.221 ± VR-OU 0.039 ± 0.01 -0.002 -0.032 -58.25 0.04 0.064

CR-BM 0.020 ± 0.004 -75.22 0.57 0.001 ± VR-BM 0.017 ± 0.007 -73.31 0.22 Wing-chord 0.001 length CR-OU 0.020 0.001 -73.06 0.19 0.000 ± 0.001 ± VR-OU 0.017 ± 0.007 0.000 ± 0.017 -68.86 0.02 0.017 0.002 Seddon, Botero et al.

CR-BM 0.615 ± 0.121 103.51 0.00 -0.028 ± Crown SW VR-BM 0.745 ± 0.184 104.60 0.00 0.019 chroma CR-OU 216.689 121.623 73.72 0.26 VR-OU 0.000 249.237 184.007 33.118 71.64 0.74

CR-BM 0.442 ± 0.087 86.28 0.00 0.061 ± VR-BM 0.000 ± 0.044 59.79 0.00 0.016 Crown UV 2.905 ± chroma CR-OU 3.423 ± 3.134 51.28 0.01 2.684 1.347 ± 0.427 ± VR-OU 0.000 ± 0.736 0.040 ± 0.155 40.72 0.99 2.231 0.365

CR-BM 0.540 ± 0.106 96.78 0.00 VR-BM 0.000 0.145 91.69 0.01 Throat SW 1.041 ± CR-OU 2.544 ± 2.197 85.89 0.13 chroma 0.978

1.330 ± VR-OU 0.000 ± 0.599 2.121 ± 1.01 0.048 ± 0.124 82.15 0.86 0.983

CR-BM 0.518 ± 0.102 94.62 0.00 0.118 ± VR-BM 0.015 ± 0.120 83.59 0.00 Throat UV 0.052 chroma CR-OU 4.660 ± 5.192 3.430 ± 3.86 58.94 0.23

0.779 ± 3.642 ± VR-OU 1.622 ± 3.081 0.022 ± 0.319 56.57 0.77 1.221 5.643

Back SW CR-BM 1.198 ± 0.235 138.20 0.00 chroma VR-BM 1.141 ± 0.349 0.013 ± 140.31 0.00 0.062 Seddon, Botero et al.

11.032 ± CR-OU 29.431 ± 125.33 94.69 0.00 46.995 9.166 ± VR-OU 0.000 ± 0.216 15.842 0.000 ± 0.779 81.00 1.00 7.168

CR-BM 0.494 ± 0.097 92.15 0.00 0.094 ± VR-BM 0 ± 0.118 77.14 0.00 Back UV 0.044 4.502 ± chroma CR-OU 5.101 ± 6.799 49.80 0.01 6.037 6.809 ± 1.519 ± VR-OU 0.747 ± 4.398 0.000 ± 0.630 40.35 0.99 16.531 3.324

CR-BM 0.765 ± 0.150 114.83 0.00 Belly SW VR-BM 0.909 ± 0.251 -0.03 ± 0.032 116.45 0.00 9.742 ± chroma CR-OU 19.561 ± 71.812 79.90 0.04 35.800 VR-OU 0.000 12.217 7.430 0.616 73.56 0.96

CR-BM 0.411 ± 0.081 82.49 0.00 Belly UV VR-BM 0.000 0.083 73.31 0.00 1.103 ± chroma CR-OU 1.792 ± 1.299 64.93 0.00 0.854 VR-OU 0.000 1.673 0.606 0.006 53.58 1.00

CR-BM 0.347 ± 0.068 73.81 0.00 0.007 ± VR-BM 0.313 ± 0.086 75.66 0.00 Tail SW 0.014 chroma 0.734 ± CR-OU 1.150 ± 0.607 60.40 0.84 0.425

0.044 ± 0.289 ± VR-OU 0.587 ± 0.762 0.322 ± 0.495 63.76 0.16 0.763 0.467 Seddon, Botero et al.

CR-BM 0.206 ± 0.04 46.52 0.06 0.008 ± VR-BM 0.171 ± 0.063 48.22 0.02 Tail UV 0.013 0.514 ± chroma CR-OU 0.595 ± 0.448 41.16 0.82 0.482 -0.001 ± 0.582 ± VR-OU 0.590 ± 0.647 -0.016 ± 0.093 45.43 0.10 0.102 0.692

CR-BM 0.407 ± 0.08 82.03 0.00 0.078 ± Wing VR-BM 0.000 ± 0.101 59.71 0.23 coverts SW 0.031 0.450 ± chroma CR-OU 1.055 ± 0.555 76.26 0.00 0.296 VR-OU 0.000 0.412 0.194 0.000 57.27 0.77

CR-BM 0.307 ± 0.06 67.31 0.11 0.022 ± VR-BM 0.206 ± 0.095 67.69 0.09 Wing 0.024 coverts UV 0.342 ± CR-OU 0.684 ± 0.34 63.81 0.61 chroma 0.234 0.173 ± 0.245 ± VR-OU 0.000 ± 0.625 0.073 ± 0.118 66.16 0.19 0.337 0.310 Seddon, Botero et al.

294 Table S6 Evolutionary divergence in female traits in relation to sexual selection. AICc values are used to compare the support for models in which the rate of evolutionary divergence in traits is assumed to be independent of the strength of 296 sexual selection (constant rate model, CR) versus linearly associated with the strength of sexual selection (variable rates model, VR). For each model type we explore results under a Brownian motion (BM) model of evolution and an Ornstein- 298 Uhlenbeck (OU) process. In the variable rates OU model, we allow the possibility that sexual selection is also linearly associated with the constraint parameter, α. For each trait, bold denotes the model that is unambiguously best supported 300 by the data among the four alternatives (i.e., Akaike weights > 70% and ΔAICc > 2 when compared to the next best supported model).

302 Model Slope for Akaike Trait Rate_0 ± SE† α_0 ± SE † Slope for α ± SE AICc type rate ± SE weight CR-BM 0.087 ± 0.017 1.65 0.01 -0.003 ± VR-BM 0.099 ± 0.024 3.03 0.01 0.003 Beak 0.506 ± length CR-OU 0.239 ± 0.12 -5.61 0.45 0.303 0.000 ± 0.041 ± VR-OU 0.235 ± 0.197 0.311 ± 0.349 -5.91 0.53 0.588 0.067 CR-BM 0.065 ± 0.013 -13.04 0.08 -0.002 ± VR-BM 0.075 ± 0.017 -12.11 0.05 0.002 Tarsus 0.388 ± length CR-OU 0.157 ± 0.084 -17.38 0.73 0.275 -0.003 ± 0.344 ± VR-OU 0.177 ± 0.102 0.018 ± 0.085 -13.97 0.13 0.011 0.304 Wing-chord CR-BM 0.068 ± 0.013 -11.04 0.08 length VR-BM 0.064 -0.003 -14.06 0.35 CR-OU 0.126 ± 0.062 0.230 ± -11.86 0.12 0.191 Seddon, Botero et al.

0.201 ± VR-OU 0.167 ± 0.003 -0.008 0.016 ± 0.020 -14.57 0.45 0.108 CR-BM 1.185 ± 0.232 137.61 0.00 Crown SW VR-BM 0.92 -0.043 132.84 0.00 chroma CR-OU 78.014 28.511 96.03 0.84 VR-OU 0.000 976.495 2791.748 683.890 99.34 0.16 CR-BM 0.405 ± 0.079 81.76 0.00 0.040 ± VR-BM 0.226 ± 0.117 80.84 0.00 0.033 Crown UV 1.403 ± chroma CR-OU 2.250 ± 1.948 65.36 0.59 1.280 0.952 ± 1.152 ± VR-OU 0.054 ± 2.303 0.266 ± 0.684 66.10 0.41 1.698 1.828 CR-BM 0.592 ± 0.116 101.57 0.02 -0.010 ± VR-BM 0.638 ± 0.164 103.59 0.01 Throat SW 0.022 chroma 0.622 ± CR-OU 1.961 ± 1.558 95.33 0.37 0.587 VR-OU 0.000 0.832 1.038 0.119 94.30 0.61

CR-BM 0.588 ± 0.115 101.18 0.00 0.009 ± Throat UV VR-BM 0.548 ± 0.150 103.20 0.00 0.024 chroma CR-OU 77.087 36.539 82.50 0.79 VR-OU 145.401 101.918 15.228 0.000 85.10 0.21

CR-BM 2.523 ± 0.495 176.91 0.00 -0.131 ± Back SW VR-BM 2.940 ± 0.586 172.18 0.00 0.029 chroma CR-OU 50.957 17.449 99.41 0.86 VR-OU 42.107 11.164 -0.100 0.821 102.98 0.14 Seddon, Botero et al.

CR-BM 0.932 ± 0.183 125.12 0.00 -0.003 ± VR-BM 0.947 ± 0.253 127.27 0.00 0.037 Back UV 12.804± chroma CR-OU 20.889± 117.423 69.13 0.73 71.974 14.152 ± 1.715 ± VR-OU 15.075 ± 93.281 0.000 ± 2.501 71.12 0.27 81.537 3.685

CR-BM 0.906 ± 0.178 123.66 0.00 -0.039 ± Belly SW VR-BM 1.063 ± 0.224 123.51 0.00 0.016 chroma CR-OU 12007.112 6291.482 76.50 0.55 VR-OU 0.000 9.621 9.889 2.335 76.92 0.45

CR-BM 0.409 ± 0.080 82.31 0.00 0.026 ± VR-BM 0.289 ± 0.106 82.59 0.00 0.025 Belly UV 3.029 ± chroma CR-OU 3.884 ± 7.870 55.75 0.73 6.300 5.456 ± 6.125 ± VR-OU 0.000 ± 19.819 2.311 ± 11.185 57.73 0.27 25.582 36.006

CR-BM 0.484 ± 0.095 91.03 0.00 -0.005 ± VR-BM 0.507 ± 0.117 93.05 0.00 0.013 Tail SW 0.650 ± chroma CR-OU 1.500 ± 0.751 79.56 0.76 0.363 0.000 ± 0.400 ± VR-OU 1.200 ± 1.096 0.401 ± 0.426 81.84 0.24 0.657 0.473

Tail UV CR-BM 0.307 ± 0.060 67.44 0.00 chroma VR-BM 0.232 ± 0.099 0.017 ± 68.89 0.00 0.023 Seddon, Botero et al.

2.640 ± CR-OU 3.103 ± 3.932 50.98 0.84 3.429 0.605 ± 1.967 ± VR-OU 1.243 ± 2.547 0.259 ± 0.7 54.23 0.16 1.235 3.335

CR-BM 0.304 ± 0.060 66.85 0.00 0.003 ± Wing VR-BM 0.291 ± 0.128 69.00 0.00 0.026 coverts SW 0.974 ± chroma CR-OU 1.303 ± 0.945 54.15 0.83 0.767 VR-OU 0.000 0.455 0.288 0.243 57.39 0.16

CR-BM 0.344 ± 0.068 73.37 0.00 0.031 ± VR-BM 0.209 ± 0.106 73.46 0.00 Wing 0.028 coverts UV 3.226 ± CR-OU 3.660 ± 7.862 49.49 0.88 chroma 7.097 0.996 ± 0.408 ± VR-OU 0.000 ± 5.869 0.721 ± 2.461 53.47 0.12 2.869 5.768 † Parameter estimate when the index of sexual selection is equal to zero 304 Seddon, Botero et al.

Table S7 Evolutionary divergence in traits when male and female data are combined. As in Tables S5 and S6 we present 306 here the results for models in which the rate of evolutionary divergence in traits is assumed to be independent of (constant rate model, CR) or linearly associated with the strength of sexual selection (variable rates model, VR), each under a 308 Brownian model of evolution, BM, and an Ornstein-Uhlenbeck process, OU. In the variable rates OU model, we allow the possibility that sexual selection is also linearly associated with the constraint parameter, α. AICc values are used here 310 to compare the support for models derived from sexes-combined versus sex-specific data. Model variants for each trait are compared to their equivalent models in Tables S5 and S6 and bold highlights traits for which combining data for both 312 sexes yields a better supported model than either of the sex-specific alternatives.

Model Trait Rate_0 ± SE † α_0 ± SE † Slope for rate ± SE Slope for α ± SE AICc type CR-BM 0.059 ± 0.008 -38.28 VR-BM 0.057 ± 0.012 0.001 ± 0.002 -36.28 Beak length CR-OU 0.173 ± 0.068 0.547 ± 0.255 -55.49 VR-OU 0.096 ± 0.112 0.000 ± 0.578 0.044 ± 0.066 0.272 ± 0.363 -55.17 CR-BM 0.045 ± 0.006 -66.92 VR-BM 0.05 ± 0.009 -0.001 ± 0.001 -65.81 Tarsus length CR-OU 0.089 ± 0.031 0.267 ± 0.144 -73.06 VR-OU 0.098 ± 0.035 0.265 ± 0.177 -0.002 ± 0.003 0.000 ± 0.030 -69.65 CR-BM 0.044 ± 0.006 -69.73 Wing-chord VR-BM 0.055 -0.002 -75.70 length CR-OU 0.063 ± 0.019 0.122 ± 0.095 -70.12 VR-OU 0.079 ± 0.003 -0.004 0.117 ± 0.043 0.002 ± 0.003 -74.14 CR-BM 0.9 ± 0.125 244.49 Crown SW VR-BM 1.08 ± 0.153 -0.049 ± 0.008 235.26 chroma CR-OU 96.572 42.75 167.75 VR-OU 0.001 4748386 7280477 1705128 166.31 Crown UV CR-BM 0.423 ± 0.059 166.01 chroma VR-BM 0.019 ± 0.065 0.093 ± 0.028 141.02 Seddon, Botero et al.

CR-OU 3.071 ± 2.146 2.226 ± 1.596 113.51 VR-OU 0.07 ± 0.753 0.595 ± 0.468 1.198 ± 1.109 0.118 ± 0.18 101.77 CR-BM 0.566 ± 0.079 196.34 Throat SW VR-BM 0 0.166 192.84 chroma CR-OU 2.249 ± 1.349 0.81 ± 0.549 177.42 VR-OU 0 0.871 0.895 0.096 167.15

CR-BM 0.553 ± 0.077 193.89 VR-BM 0.35 ± 0.095 0.044 ± 0.024 190.67 Throat UV 8.926 ± chroma CR-OU 5.139 ± 11.266 139.86 19.346 VR-OU 23.621 3.698 24.277 -0.149 ± 0.837 137.19

CR-BM 1.861 ± 0.258 320.03 Back SW VR-BM 2.235 ± 0.328 -0.09 ± 0.02 316.34 chroma CR-OU 42.147 ± 293 15.086 ± 101.379 189.84 VR-OU 0 12.747 15.802 1.993 182.20

CR-BM 0.713 ± 0.099 220.29 VR-BM 0.507 ± 0.138 0.045 ± 0.032 219.18 Back UV 14.025 ± CR-OU 10.164 ±26.416 116.43 chroma 36.428 7.592 ± VR-OU 12.532 ± 38.562 2.080 ± 3.033 0.000 ± 1.230 108.84 28.750

CR-BM 0.835 ± 0.116 236.75 VR-BM 0.992 ± 0.155 -0.036 ± 0.013 235.84 Belly SW 21.805 ± chroma CR-OU 11.218 ± 35.227 152.08 68.45 VR-OU 0 11.529 8.876 1.368 142.00

Belly UV CR-BM 0.41 ± 0.057 162.68 chroma VR-BM 0.167 ± 0.097 0.057 ± 0.031 156.10 Seddon, Botero et al.

CR-OU 2.345 ± 1.58 1.614 ± 1.138 117.11 VR-OU 0 3.584 1.801 0.315 105.33

CR-BM 0.416 ± 0.058 164.14 Tail SW VR-BM 0.416 ± 0.073 0 ± 0.01 166.22 chroma CR-OU 1.323 ± 0.478 0.683 ± 0.273 137.36 VR-OU 0.872 ± 0.636 0 ± 0.489 0.358 ± 0.332 0.379 ± 0.322 138.08

CR-BM 0.256 ± 0.036 113.92 Tail UV VR-BM 0.202 ± 0.058 0.012 ± 0.013 114.82 chroma CR-OU 1.702 ± 1.383 1.525 ± 1.311 89.30 VR-OU 0.913 ± 1.205 0.233 ± 0.384 1.153 ± 1.325 0.118 ± 0.281 92.15

CR-BM 0.355 ± 0.049 147.87 Wing coverts VR-BM 0.021 ± 0.071 0.082 ± 0.03 130.15 SW chroma CR-OU 1.109 ± 0.466 0.608 ± 0.297 128.95 VR-OU 0 ± 0.456 0.539 ± 0.397 0.300 ± 0.201 0.029 ± 0.067 112.23

CR-BM 0.326 ± 0.045 138.74 Wing coverts VR-BM 0.207 ± 0.071 0.027 ± 0.019 136.96 UV chroma CR-OU 1.273 ± 0.682 0.859 ± 0.511 112.99 VR-OU 0 ± 0.754 0.304 ± 0.527 0.422 ± 0.428 0.183 ± 0.227 113.79 314 † Parameter estimate when the index of sexual selection is equal to zero Seddon, Botero et al.

Table S8 Comparison of support for diversification rates models fitted to the data 316 with and without sexual dichromatism

Model Constant rate* Variable rate † Slope birth rate (b) across gradient 0 0.014 Slope death rate (d) across 0 0.002 gradient Net slope 0 0.011 b at dichromatism = 0 0.25 0.20 b at dichromatism = 22 0.25 0.50 d at dichromatism = 0 0 0 d at dichromatism = 22 0 0.05 Net at dichromatism = 0 0.25 0.20 Net at dichromatism = 22 0.25 0.45 Maximum Likelihood Estimate -73.55 -72.29 # Parameters in model 3 5 AIC 153.11 154.58 Δ AIC 0 1.47 *b and d do not vary across dichromatism gradient

320 †b and d vary across dichromatism gradient

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