Seddon, Botero et al.
2
Electronic Supplementary Material
Sexual selection accelerates signal evolution during
4
speciation in birds
6
8
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.
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Appendix S1: Supplementary Methods
Study species
Species pairs (sister species and clade sisters) with published data on spectral
28
reflectance [1] were identified from published phylogenetic trees of families or
genera generated using protein coding mtDNA in which > 70% of taxa had been
30
sampled and where node support was high (either posterior probability > 95%, or
maximum likelihood bootstrap > 70). More recent phylogenetic studies took
32
precedence unless earlier studies included more taxa with a different resolution of
sister relationships. When several molecular phylogenies were presented within a
34
paper, we only selected sister pairs resolved in all trees. In situations where nodal
support conflicted between different methods of phylogenetic reconstruction,
36
maximum likelihood bootstrap values took precedence. Consensus trees and trees
based on concatenated molecular datasets were presumed to depict the most
38
reliable phylogenetic relationships and thus, whenever possible, we assessed nodal
support based on the values given in these trees.
40
When selecting species from clades, we paired the focal species with
whichever member of its sister clade had plumage reflectance data. However, where
42
more than one clade member had reflectance data, we used range maps to select
the species with the closest possible breeding range to the focal species. By
44
choosing the geographically closest clade member, we selected lineages most likely
to have split recently, assuming historical species ranges can be inferred from
46
present day distributions and provide an indication of the mode of speciation. To
minimize the influence of species interactions on phenotypic divergence (e.g.
48
character displacement), we excluded all cases where one or more unsampled clade
members were sympatric with either the focal species or the sampled clade
50
member. In other words, a criterion of selection was that none of the breeding
ranges of the other clade members overlapped geographically with the focal species
52
or the clade member included in the main analysis. These criteria automatically
restricted the sample of sister clades to small, relatively young clades (≤ 5 species).
Seddon, Botero et al.
54
We then categorized species pairs as sympatric or allopatric based on
published datasets [2-4]. Remaining species were assigned to these categories
56
using high quality geographic range polygons, following the methods of Weir & Price
[3].
58
Sample size
60
Our final sample of species comparisons (Appendix S2) contained 84 species pairs,
including 39 true sister species pairs and 45 clade sisters (a focal species paired
62
with one member of their sister clade). However, because of differences in data
requirements and availability, sample size varied across our models (Table S1).
64
Data for both plumage and morphology were available for 69 pairs (hence sample
size of Analysis 1 [A1]). Meanwhile, data on plumage were available for all 84
66
species pairs, but the models of diversification rate (Analysis 3 [A3]) could only be
run using true sister pairs (n = 39). This was not a constraint for A1 and A2. In
68
addition, we removed 17 clade sisters from A2 as they were phylogenetically nested
(they shared one species with another species pair in our dataset) and were
70
therefore unsuitable for evolutionary rates models. We retained these 17 species
pairs in linear mixed effect models (LMMs) as all combinations of species were
72
unique, and thus we considered each divergence event to be independent. We
included species name as a random effect in the mixed models to control for the
74
inclusion of these repeated measures (see below).
76
Quantifying phenotype
Morphological traits. We measured beak, tarsus, and wing length from museum
78
specimens using digital callipers. Beaks were measured (to the nearest 0.01 mm) as
length from the anterior edge of the nostrils to the tip; tarsus length was measured
80
down the back of the leg from the middle of the ankle joint (i.e. the notch between
the tibia and tarsus) to the end of the last scale of the acrotarsium (usually the last
82
undivided scale); wing was measured as the distance from the carpal joint to the
longest primary of the unflattened wing. To ensure consistency, all measures for
Seddon, Botero et al.
84
members of a pair were taken by one researcher. Body mass data were compiled
from Dunning [5].
86
Plumage traits. All spectrophotometer measurements were collected using an
88
Ocean Optics (Dunedin, Florida) USB2000 spectrophotometer and a PX-2 pulsed
Xenon light source with the spectrophotometer probe at 90° to the plumage.
90
Measurements were standardized to a WS-1 white standard, considered >98%
reflective from 250−1500 nm wavelengths.
92
For each reflectance reading, we averaged the reflectance data into bins
covering 20 nm of the spectrum. We quantified colour using standard descriptors of
94
reflectance spectra: brightness and hue/chroma [7]. We calculated brightness or
intensity by summing its reflectance from 320 to 700 nm, the approximate visible
96
spectrum of most avian species [8]. Because a spectrum consists of reflectance at
each wavelength that is highly correlated, we then used a PCA to collapse these
98
reflectance variables into a few independent variables that summarize spectrum
shape [6, 7], a standard method to handle spectral data [9-13]. We first used
100
brightness to standardize all reflectance scans before PCA. The resulting principal
components (PC) values were thus indices of chroma and hue [6], independent of its
102
brightness. We then performed a principal component analysis (PCA) using the
standardized reflectance values from each specimen (19 values for each specimen
104
based on 20 nm bins). Although multiple methods have been previously used to
analyze spectral data, including those that take into account the spectral sensitivity
106
of each cone type, the reflectance of the sample, the background against which the
sample is viewed, and the irradiance spectrum of the ambient light [7, 14-17], when
108
different methods have been compared, they have yielded qualitatively similar
estimates of colour [17] and dichromatism [1]. We chose PCA analysis for its
110
simplicity and because it yields separate values that represent the shape of the
spectrum and chroma (e.g. purity of colour). In our analyses, the first two principal
112
components explained more than 75.69% of the variation in the data. We found that
principal component 1 (PC1) was positively correlated with reflectance in the 400–
114
480nm range and accounted for 50.54% of the variation in the data; PC2 was
Seddon, Botero et al.
positively correlated with reflectance in 320–380nm range, and accounted for
116
25.14% of the variation in the data. Therefore, we interpreted PC1 to represent
chroma in short wavelength and PC2 to represent chroma in UV. For PC1 and PC2,
118
we calculated the average for males and for females of each species for each body
region. For factor loadings, see Table S3. To calculate dichromatism scores, for
120
each body region, we calculated the Euclidean distance between PC scores for
males and females (y-axis) separately for PC1 and PC2. We then summed the
122
differences between males and females for each PC across all six body regions to
produce the overall dichromatism score.
124
Dichromatism as an index of sexual selection
126
Sexual dichromatism is not a perfect index of sexual selection, not least because a
variety of other mechanisms can result in sex-differences in plumage colouration
128
and conspicuousness, such as natural selection for female crypsis in species with
female-only incubation [reviewed in 18]. However, in the absence of detailed long-
130
term behavioural studies in which direct measures of sexual selection are obtained
(e.g. relative rate of reproduction), dichromatism is the best proxy currently available
132
for the purposes of comparative analyses. It can be easily estimated in all bird
species (unlike other indices such as relative testes size or rates of extra-pair
134
paternity which rely either on invasive sampling or intensive behavioural research).
Moreover, a number of studies have revealed strong positive associations between
136
dichromatism and other indices of sexual selection such as testes size, degree of
polygyny, and frequency of extra-pair paternity [19-21]. Consequently, dichromatism
138
has been used as a proxy for sexual selection in a large number of studies, including
those examining the effects of sexual selection on speciation in birds [22-28], lizards
140
[29], insects [30], and fish [31], as well as in comparative studies of the effects of
sexual selection on extinction [32-34], mortality [35], immune defense [36], signal
142
144
evolution [37], molecular evolution [38] and even response to climate change [39].
Seddon, Botero et al.
146
Classifying monomorphic taxa as mutually ornamented
For taxa where quantitative plumage data indicated a lack of plumage dichromatism,
148
we visually assessed whether this was due to mutual ornamentation (both males
and females are ornamented) or not (both males and females lack plumage
150
ornaments). Using photographs or illustrations from field guides or taxonomic
monographs, we identified a taxon as ‘mutually ornamented’ in cases where both
152
males and females had colourful (not brown) or iridescent plumage patches, stripes
or spots in any region of the body (e.g. bridled titmouse Baeolophus wollweberi). We
154
classified a taxon as ‘monomorphic-dull’ when both males and females had drab,
uniform or pale colouration lacking ornamentation in the form of patches of colour,
156
stripes or spots (e.g. oak titmouse Baeolophus inornatus).
158
Analytical approach
160
Analysis 1: Effect of sexual selection on extent of phenotypic divergence
We first used linear mixed effect models (LMMs) with maximum likelihood estimation
162
to investigate whether extent of phenotypic divergence varies according to levels of
sexual selection (prediction 1) and whether the effects of sexual selection on
164
phenotypic divergence differ between the sexes (prediction 2). We modelled the
maximum and total extent of phenotypic divergence (dependent variable) in relation
166
to several predictors including the index of sexual selection (mean value of sexual
dichromatism within a pair), sex, and the interaction between sex and dichromatism.
168
The variable 'sex' was included as a factor in the model to indicate the sex
associated with the trait diverging most in a particular pair of species (e.g. tarsus
170
length or back colour) came from males or females.
In our dataset, some species (n = 17) were represented in more than one
172
pairing (Appendix S2). The non-independence of these data points arising from
using the same species was taken into account by fitting both focal species (labeled
174
as Species 1 in analysis tables) and the species they were compared to (labeled
Species 2 in analysis tables) as random effects in our LMMs. In all analyses, only
176
unique combinations of species were included. To control for phylogenetic inertia in
Seddon, Botero et al.
the extent of phenotypic divergence between pairs of species, we included
178
taxonomy as a nested random effect [44]. Mixed-effect models including taxonomy
(Family [Genus]) had a significantly lower log-likelihood score than the model
180
excluding taxonomy (Table 1). The significance of fixed effects was examined using
Wald type F-tests [45]. Significance values derive from having all significant terms (P
182
< 0.05) fitted in the final model together; statistics associated with non-significant
terms were derived from having all significant terms in the model and each non-
184
significant term (P > 0.05) fitted individually. The significance of random effects was
tested using log-likelihood ratio tests with all fixed effects and their interactions
186
included in models [46].
Although our LMM approach controlled for phylogenetic inertia at higher
188
taxonomic levels (family and genus) it nonetheless assumed that species pairs are
equally related to one another and that the phylogenetic signal of phenotypic
190
divergence is weak. To test this assumption we estimated lambda (λ), which
measures the degree to which traits co-vary across a tree in line with Brownian
192
motion (Freckleton et al. 2002). A λ of 1 corresponds to the Brownian model, λ of 0
indicates a lack of phylogenetic structure, and λ values between 0 and 1 indicate the
194
degree of trait lability [47]. To determine whether λ values departed significantly from
a Brownian model, we compared the fit of the two models using a likelihood ratio
196
test. We found than both total phenotypic divergence (λ = 0.92) and maximum
phenotypic divergence (λ = 0.90) departed from a strict Brownian model. The
198
implication is that the extent to which phenotypes diverge among our species pairs
may have been influenced by shared ancestry. To correct for this, we used the
200
phylogenetic generalized least squares (PGLS) comparative method described in
Freckleton et al. [48], using the maximum likelihood tree (Fig. S1) as our
202
phylogenetic hypothesis. We present results from both the LMM and PGLS models
because the LMMs are robust to analysis of repeated measures and can therefore
204
be used on all unique species pairs in our dataset, but assume λ = 0, whereas the
PGLS approach estimates λ and then uses it to adjust the internal branch lengths
206
such that the data meet the assumption of Brownian motion [48].
Seddon, Botero et al.
For both the LMMs and PGLS models, response variables and predictors
208
were transformed to ensure model residuals were normally distributed and had
homogeneous variance: maximum and total phenotypic divergence were Box-Cox
210
transformed; mean dichromatism and mean body mass were log-transformed.
Parameter estimates are presented as mean ± SE.
212
Analysis 2: Effect of sexual selection on evolutionary rates of phenotypic divergence
214
The analyses presented in the main text indicate that sexual selection has different
effects on phenotypic evolution of males and females (Tables S5 and S6). To
216
evaluate the strength of these differences, we compared models in which rates of
phenotypic divergence were estimated separately for each sex to models in which
218
the data for both sexes were analyzed together. Joint AIC values for sex-specific
models were computed by adding the likelihood values of models in Tables S5 and
220
S6 and adjusting the number of parameters accordingly. For all traits, sex-specific
models were clearly better supported than models in which data from both sexes are
222
combined (Table S7).
224
Analysis 3: effect of sexual dichromatism on diversification
Birth-death trees with the correction for the lagtime to species recognition were
226
simulated in the R package PhyloGen [49]. We simulated with λ ranging from 0 to
0.15 in 0.01 intervals, and from 0.15 to 0.90 in 0.05 intervals. For λ ≤ 0.4, μ ranged
228
from 0λ, 0.05λ, 0.1λ, 0.2λ…0.9λ, 0.95λ, 0.99λ. For λ > 0.4, the same rates of μ were
used provided λ-μ < 0.45 (a necessary computational restriction given excessively
230
large trees sizes when λ-μ > 0.45). For each set of λ and μ 21 values of φ (φ = 0,
0.1, 0.2…1.9, 2.0) were used.
232
We adopt a simulation approach for this analysis because while there are
methods available for estimating speciation/extinction rates from phylogenies while
234
correcting for the lag-time to speciation, no such method exists for sister species
data. Moreover, the key advantage of the simulation method over the analytical
236
238
method is that it does not assume the survival of lineages.
Seddon, Botero et al.
Supplementary Figure
240
242
Figure S1 Maximum clade credibility tree illustrating the evolutionary relationships of
244
the passerine bird species included in this study
Seddon, Botero et al.
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248
250
Supplementary Tables
Table S1 Phenotypic traits measured and analyses in which they were included
Class of
phenotype
Morphology
Plumage
Specific trait
Beak length
Tarsus length
Wing-chord length
Crown (SW chroma)
Crown (UV chroma)
Throat (SW chroma)
Throat (UV chroma)
Back (SW chroma)
Back (UV chroma)
Belly (SW chroma)
Belly (UV chroma)
Tail (SW chroma)
Tail (UV chroma)
Wing-coverts (SW chroma)
Wing-coverts (UV chroma)
Number of species pairs included
252
Analysis Analysis Analysis
1
2
3
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
69
52
39
Seddon, Botero et al.
Table S2 Factor loadings from principal components
254
analysis on plumage reflectance data
Wavelength (nm)
320
340
360
380
400
420
440
460
480
500
520
540
560
580
600
620
640
660
680
256
258
260
262
264
266
268
270
272
274
276
PC1
-0.123
-0.128
-0.081
0.011
0.109
0.173
0.194
0.201
0.202
0.065
-0.014
-0.075
-0.088
-0.041
-0.033
-0.041
-0.023
-0.020
-0.043
PC2
0.253
0.288
0.251
0.133
0.010
-0.089
-0.123
-0.141
-0.164
-0.004
0.040
0.080
0.049
-0.067
-0.106
-0.104
-0.127
-0.115
-0.082
Seddon, Botero et al.
278
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
(dichromatism), sex, and other potentially confounding variables (n = 52 pairs).
280
(a) Total phenotypic divergence
Fixed effects
Dichromatism
Sex
Dichromatism * Sex
Evolutionary age
Sympatry
Body mass
Final model
AIC
AICc
Parameter
Estimate (b)
1.696
-3.437
5.396
1.099
0.081
2.660
Lambda
0.92
534.80
535.97
SE
t
P
1.461
0.863
1.340
1.120
0.749
1.348
F1,51
7.88
1.160
-3.981
4.026
0.981
0.108
1.973
R2
0.32
0.25
<0.0001
<0.0001
0.33
0.91
0.05
adj. R2
0.29
SE
t
P
0.369
0.229
0.349
0.022
0.196
0.335
F1,51
3.18
0.12
-2.88
2.84
0.40
1.51
1.50
R2
0.16
0.904
0.005
0.005
0.69
0.14
0.14
adj. R2
0.11
(b) Maximum phenotypic divergence
Fixed effects
Dichromatism
Sex
Dichromatism * Sex
Evolutionary age
Sympatry
Body mass
Final model
AIC
AICc
282
284
Parameter
Estimate (b)
0.04
-0.66
0.99
0.01
0.30
0.50
Lambda
0.90
260.3
261.5
Seddon, Botero et al.
Table S4 PGLS models of (a) total and (b) maximum phenotypic divergence
286
between species pairs in relation to the intensity of sexual selection within species
(dichromatism), sex, and other potentially confounding variables, excluding mutually
288
ornamented species (n = 45 species pairs).
(a) Total phenotypic divergence
Parameter
Fixed effects
Estimate (b)
Dichromatism
2.58
Sex
-3.08
Dichromatism * Sex
4.32
Evolutionary age
1.40
Sympatry
0.16
Body mass
2.46
Lambda
Final model
0.92
AIC
467.59
AICc
468.99
(b) Maximum phenotypic divergence
Parameter
Fixed effects
Estimate (b)
Dichromatism
0.16
Sex
-0.59
Dichromatism * Sex
0.75
Evolutionary age
0.15
Sympatry
0.34
Body mass
0.43
Lambda
Final model
0.88
AIC
233.47
AICc
234.87
290
SE
t
P
1.62
1.01
1.59
1.25
0.82
1.38
F1,44
4.78
1.59
-3.04
2.72
1.12
0.19
1.78
P
<0.0001
0.12
<0.0001
0.01
0.27
0.85
0.08
adj. R2
0.21
SE
t
P
0.37
-2.16
1.77
0.47
1.56
1.22
P
0.05
0.71
0.03
0.08
0.64
0.12
0.23
adj. R2
0.08
0.42
0.27
0.43
0.32
0.22
0.36
F1,51
2.24
Seddon, Botero et al.
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294
296
298
Table S5 Evolutionary divergence in male traits in relation to sexual selection. Comparison of support for models in which
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
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
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).
Trait
Beak
length
Tarsus
length
Wing-chord
length
Crown SW
chroma
Crown UV
chroma
Model type
Rate_0 ± SE †
CR-BM
VR-BM
CR-OU
VR-OU
0.032 ± 0.006
0.009 ± 0.005
0.148 ± 0.149
0.013 ± 0.021
CR-BM
VR-BM
CR-OU
VR-OU
0.025 ± 0.005
0.020 ± 0.008
0.031 ± 0.013
0.039 ± 0.01
CR-BM
VR-BM
CR-OU
VR-OU
0.020 ± 0.004
0.017 ± 0.007
0.020
0.017 ± 0.007
CR-BM
VR-BM
CR-OU
VR-OU
0.615 ± 0.121
0.745 ± 0.184
216.689
0.000
CR-BM
VR-BM
0.442 ± 0.087
0.000 ± 0.044
α_0 ± SE †
Slope for rate
± SE
Slope for α
± SE
AICc
Akaike
weight
0.044 ± 0.062
-50.24
-58.85
-58.69
-59.35
0.00
0.31
0.29
0.40
-0.032
-63.59
-61.86
-61.99
-58.25
0.52
0.22
0.23
0.04
0.000 ± 0.017
-75.22
-73.31
-73.06
-68.86
0.57
0.22
0.19
0.02
33.118
103.51
104.60
73.72
71.64
0.00
0.00
0.26
0.74
86.28
59.79
0.00
0.00
0.005 ± 0.002
0.967 ± 1.074
0.131 ± 0.257
0.012 ± 0.010
0.001 ± 0.002
0.075 ± 0.112
-0.002
0.221 ± 0.064
0.001 ± 0.001
0.001
0.000 ± 0.017
0.001 ± 0.002
-0.028 ± 0.019
121.623
249.237
184.007
0.061 ± 0.016
Seddon, Botero et al.
Throat SW
chroma
Throat UV
chroma
Back SW
chroma
Back UV
chroma
Belly SW
chroma
Belly UV
chroma
CR-OU
VR-OU
3.423 ± 3.134
0.000 ± 0.736
CR-BM
VR-BM
CR-OU
VR-OU
0.540 ± 0.106
0.000
2.544 ± 2.197
0.000 ± 0.599
CR-BM
VR-BM
CR-OU
VR-OU
0.518 ± 0.102
0.015 ± 0.120
4.660 ± 5.192
1.622 ± 3.081
CR-BM
VR-BM
CR-OU
VR-OU
1.198 ± 0.235
1.141 ± 0.349
29.431 ± 125.33
0.000 ± 0.216
CR-BM
VR-BM
CR-OU
VR-OU
0.494 ± 0.097
0 ± 0.118
5.101 ± 6.799
0.747 ± 4.398
CR-BM
VR-BM
CR-OU
VR-OU
0.765 ± 0.150
0.909 ± 0.251
19.561 ± 71.812
0.000
CR-BM
VR-BM
CR-OU
VR-OU
0.411 ± 0.081
0.000
1.792 ± 1.299
0.000
2.905 ± 2.684
1.347 ± 2.231
0.427 ± 0.365
0.040 ± 0.155
51.28
40.72
0.01
0.99
0.048 ± 0.124
96.78
91.69
85.89
82.15
0.00
0.01
0.13
0.86
0.022 ± 0.319
94.62
83.59
58.94
56.57
0.00
0.00
0.23
0.77
0.000 ± 0.779
138.20
140.31
94.69
81.00
0.00
0.00
0.00
1.00
0.000 ± 0.630
92.15
77.14
49.80
40.35
0.00
0.00
0.01
0.99
0.616
114.83
116.45
79.90
73.56
0.00
0.00
0.04
0.96
0.006
82.49
73.31
64.93
53.58
0.00
0.00
0.00
1.00
0.145
1.041 ± 0.978
1.330 ± 0.983
2.121 ± 1.01
0.118 ± 0.052
3.430 ± 3.86
0.779 ± 1.221
3.642 ± 5.643
0.013 ± 0.062
11.032 ± 46.995
15.842
9.166 ± 7.168
0.094 ± 0.044
4.502 ± 6.037
6.809 ± 16.531
1.519 ± 3.324
-0.03 ± 0.032
9.742 ± 35.800
12.217
7.430
0.083
1.103 ± 0.854
1.673
0.606
Seddon, Botero et al.
CR-BM
VR-BM
CR-OU
VR-OU
0.347 ± 0.068
0.313 ± 0.086
1.150 ± 0.607
0.587 ± 0.762
CR-BM
VR-BM
CR-OU
VR-OU
0.206 ± 0.04
0.171 ± 0.063
0.595 ± 0.448
0.590 ± 0.647
Wing
coverts SW
chroma
CR-BM
VR-BM
CR-OU
VR-OU
0.407 ± 0.08
0.000 ± 0.101
1.055 ± 0.555
0.000
Wing
coverts UV
chroma
CR-BM
VR-BM
CR-OU
VR-OU
0.307 ± 0.06
0.206 ± 0.095
0.684 ± 0.34
0.000 ± 0.625
Tail SW
chroma
Tail UV
chroma
300
0.322 ± 0.495
73.81
75.66
60.40
63.76
0.00
0.00
0.84
0.16
-0.016 ± 0.093
46.52
48.22
41.16
45.43
0.06
0.02
0.82
0.10
0.000
82.03
59.71
76.26
57.27
0.00
0.23
0.00
0.77
0.073 ± 0.118
67.31
67.69
63.81
66.16
0.11
0.09
0.61
0.19
0.007 ± 0.014
0.734 ± 0.425
0.044 ± 0.763
0.289 ± 0.467
0.008 ± 0.013
0.514 ± 0.482
-0.001 ± 0.102
0.582 ± 0.692
0.078 ± 0.031
0.450 ± 0.296
0.412
0.194
0.022 ± 0.024
0.342 ± 0.234
0.173 ± 0.337
0.245 ± 0.310
Seddon, Botero et al.
302
304
306
308
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
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 OrnsteinUhlenbeck (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
by the data among the four alternatives (i.e., Akaike weights > 70% and ΔAICc > 2 when compared to the next best
supported model).
Trait
Beak
length
Tarsus
length
Wing-chord
length
Crown SW
chroma
Crown UV
Model
type
Rate_0 ± SE†
CR-BM
VR-BM
CR-OU
VR-OU
0.087 ± 0.017
0.099 ± 0.024
0.239 ± 0.12
0.235 ± 0.197
CR-BM
VR-BM
CR-OU
VR-OU
0.065 ± 0.013
0.075 ± 0.017
0.157 ± 0.084
0.177 ± 0.102
CR-BM
VR-BM
CR-OU
VR-OU
0.068 ± 0.013
0.064
0.126 ± 0.062
0.167 ± 0.003
CR-BM
VR-BM
CR-OU
VR-OU
1.185 ± 0.232
0.92
78.014
0.000
CR-BM
0.405 ± 0.079
α_0 ± SE †
AICc
Akaike
weight
0.311 ± 0.349
1.65
3.03
-5.61
-5.91
0.01
0.01
0.45
0.53
0.018 ± 0.085
-13.04
-12.11
-17.38
-13.97
0.08
0.05
0.73
0.13
0.016 ± 0.020
-11.04
-14.06
-11.86
-14.57
0.08
0.35
0.12
0.45
683.890
137.61
132.84
96.03
99.34
0.00
0.00
0.84
0.16
81.76
0.00
Slope for rate ± SE Slope for α ± SE
-0.003 ± 0.003
0.506 ± 0.303
0.000 ± 0.588
0.041 ± 0.067
-0.002 ± 0.002
0.388 ± 0.275
-0.003 ± 0.011
0.344 ± 0.304
-0.003
0.230 ± 0.191
-0.008
0.201 ± 0.108
-0.043
28.511
976.495
2791.748
Seddon, Botero et al.
chroma
Throat SW
chroma
Throat UV
chroma
Back SW
chroma
Back UV
chroma
Belly SW
chroma
Belly UV
chroma
Tail SW
VR-BM
CR-OU
VR-OU
0.226 ± 0.117
2.250 ± 1.948
0.054 ± 2.303
CR-BM
VR-BM
CR-OU
VR-OU
0.592 ± 0.116
0.638 ± 0.164
1.961 ± 1.558
0.000
CR-BM
VR-BM
CR-OU
VR-OU
0.588 ± 0.115
0.548 ± 0.150
77.087
145.401
CR-BM
VR-BM
CR-OU
VR-OU
2.523 ± 0.495
2.940 ± 0.586
50.957
42.107
CR-BM
VR-BM
CR-OU
VR-OU
0.932 ± 0.183
0.947 ± 0.253
20.889± 117.423
15.075 ± 93.281
CR-BM
VR-BM
CR-OU
VR-OU
0.906 ± 0.178
1.063 ± 0.224
12007.112
0.000
CR-BM
VR-BM
CR-OU
VR-OU
0.409 ± 0.080
0.289 ± 0.106
3.884 ± 7.870
0.000 ± 19.819
CR-BM
0.484 ± 0.095
0.040 ± 0.033
1.403 ± 1.280
0.952 ± 1.698
1.152 ± 1.828
0.266 ± 0.684
80.84
65.36
66.10
0.00
0.59
0.41
0.119
101.57
103.59
95.33
94.30
0.02
0.01
0.37
0.61
0.000
101.18
103.20
82.50
85.10
0.00
0.00
0.79
0.21
0.821
176.91
172.18
99.41
102.98
0.00
0.00
0.86
0.14
0.000 ± 2.501
125.12
127.27
69.13
71.12
0.00
0.00
0.73
0.27
2.335
123.66
123.51
76.50
76.92
0.00
0.00
0.55
0.45
2.311 ± 11.185
82.31
82.59
55.75
57.73
0.00
0.00
0.73
0.27
91.03
0.00
-0.010 ± 0.022
0.622 ± 0.587
0.832
1.038
0.009 ± 0.024
36.539
101.918
15.228
-0.131 ± 0.029
17.449
11.164
-0.100
-0.003 ± 0.037
12.804± 71.974
14.152 ± 81.537
1.715 ± 3.685
-0.039 ± 0.016
6291.482
9.621
9.889
0.026 ± 0.025
3.029 ± 6.300
5.456 ± 25.582
6.125 ± 36.006
Seddon, Botero et al.
chroma
Tail UV
chroma
Wing
coverts SW
chroma
312
0.507 ± 0.117
1.500 ± 0.751
1.200 ± 1.096
CR-BM
VR-BM
CR-OU
VR-OU
0.307 ± 0.060
0.232 ± 0.099
3.103 ± 3.932
1.243 ± 2.547
CR-BM
VR-BM
CR-OU
VR-OU
0.304 ± 0.060
0.291 ± 0.128
1.303 ± 0.945
0.000
-0.005 ± 0.013
0.650 ± 0.363
0.000 ± 0.657
0.400 ± 0.473
0.401 ± 0.426
93.05
79.56
81.84
0.00
0.76
0.24
0.259 ± 0.7
67.44
68.89
50.98
54.23
0.00
0.00
0.84
0.16
0.243
66.85
69.00
54.15
57.39
0.00
0.00
0.83
0.16
0.721 ± 2.461
73.37
73.46
49.49
53.47
0.00
0.00
0.88
0.12
0.017 ± 0.023
2.640 ± 3.429
0.605 ± 1.235
1.967 ± 3.335
0.003 ± 0.026
0.974 ± 0.767
0.455
0.288
CR-BM
0.344 ± 0.068
VR-BM
0.209 ± 0.106
0.031 ± 0.028
CR-OU
3.660 ± 7.862
3.226 ± 7.097
VR-OU
0.000 ± 5.869
0.996 ± 2.869
0.408 ± 5.768
† Parameter estimate when the index of sexual selection is equal to zero
Wing
coverts UV
chroma
310
VR-BM
CR-OU
VR-OU
Seddon, Botero et al.
314
316
318
320
Table S7 Evolutionary divergence in traits when male and female data are combined. As in Tables S5 and S6 we present
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
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
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
sexes yields a better supported model than either of the sex-specific alternatives.
Trait
Beak
length
Tarsus
length
Wing-chord
length
Crown SW
chroma
Crown UV
chroma
Model type
Rate_0 ± SE †
CR-BM
VR-BM
CR-OU
VR-OU
0.059 ± 0.008
0.057 ± 0.012
0.173 ± 0.068
0.096 ± 0.112
CR-BM
VR-BM
CR-OU
VR-OU
0.045 ± 0.006
0.05 ± 0.009
0.089 ± 0.031
0.098 ± 0.035
CR-BM
VR-BM
CR-OU
VR-OU
0.044 ± 0.006
0.055
0.063 ± 0.019
0.079 ± 0.003
CR-BM
VR-BM
CR-OU
VR-OU
0.9 ± 0.125
1.08 ± 0.153
96.572
0.001
CR-BM
VR-BM
0.423 ± 0.059
0.019 ± 0.065
α_0 ± SE †
Slope for rate ± SE
Slope for α ± SE
AICc
0.272 ± 0.363
-38.28
-36.28
-55.49
-55.17
0.000 ± 0.030
-66.92
-65.81
-73.06
-69.65
0.002 ± 0.003
-69.73
-75.70
-70.12
-74.14
1705128
244.49
235.26
167.75
166.31
0.001 ± 0.002
0.547 ± 0.255
0.000 ± 0.578
0.044 ± 0.066
-0.001 ± 0.001
0.267 ± 0.144
0.265 ± 0.177
-0.002 ± 0.003
-0.002
0.122 ± 0.095
-0.004
0.117 ± 0.043
-0.049 ± 0.008
42.75
4748386
7280477
0.093 ± 0.028
166.01
141.02
Seddon, Botero et al.
Throat SW
chroma
Throat UV
chroma
Back SW
chroma
Back UV
chroma
CR-OU
VR-OU
3.071 ± 2.146
0.07 ± 0.753
CR-BM
VR-BM
CR-OU
VR-OU
0.566 ± 0.079
0
2.249 ± 1.349
0
CR-BM
VR-BM
VR-OU
0.553 ± 0.077
0.35 ± 0.095
8.926 ±
19.346
23.621
CR-BM
VR-BM
CR-OU
VR-OU
1.861 ± 0.258
2.235 ± 0.328
42.147 ± 293
0
CR-BM
VR-BM
0.713 ± 0.099
0.507 ± 0.138
14.025 ±
36.428
7.592 ±
28.750
CR-OU
CR-OU
VR-OU
Belly SW
chroma
Belly UV
chroma
CR-BM
VR-BM
VR-OU
0.835 ± 0.116
0.992 ± 0.155
21.805 ±
68.45
0
CR-BM
VR-BM
0.41 ± 0.057
0.167 ± 0.097
CR-OU
2.226 ± 1.596
0.595 ± 0.468
1.198 ± 1.109
0.118 ± 0.18
113.51
101.77
0.096
196.34
192.84
177.42
167.15
0.166
0.81 ± 0.549
0.871
0.895
193.89
190.67
0.044 ± 0.024
5.139 ± 11.266
3.698
139.86
24.277
-0.149 ± 0.837
137.19
1.993
320.03
316.34
189.84
182.20
-0.09 ± 0.02
15.086 ± 101.379
12.747
15.802
220.29
219.18
0.045 ± 0.032
10.164 ±26.416
12.532 ± 38.562
116.43
2.080 ± 3.033
0.000 ± 1.230
236.75
235.84
-0.036 ± 0.013
11.218 ± 35.227
11.529
108.84
152.08
8.876
0.057 ± 0.031
1.368
142.00
162.68
156.10
Seddon, Botero et al.
CR-OU
VR-OU
2.345 ± 1.58
0
CR-BM
VR-BM
CR-OU
VR-OU
0.416 ± 0.058
0.416 ± 0.073
1.323 ± 0.478
0.872 ± 0.636
CR-BM
VR-BM
CR-OU
VR-OU
0.256 ± 0.036
0.202 ± 0.058
1.702 ± 1.383
0.913 ± 1.205
Wing
coverts SW
chroma
CR-BM
VR-BM
CR-OU
VR-OU
0.355 ± 0.049
0.021 ± 0.071
1.109 ± 0.466
0 ± 0.456
Wing
coverts UV
chroma
CR-BM
VR-BM
CR-OU
VR-OU
0.326 ± 0.045
0.207 ± 0.071
1.273 ± 0.682
0 ± 0.754
Tail SW
chroma
Tail UV
chroma
322
†
1.614 ± 1.138
3.584
1.801
0.315
117.11
105.33
0.379 ± 0.322
164.14
166.22
137.36
138.08
0.118 ± 0.281
113.92
114.82
89.30
92.15
0.029 ± 0.067
147.87
130.15
128.95
112.23
0.183 ± 0.227
138.74
136.96
112.99
113.79
0 ± 0.01
0.683 ± 0.273
0 ± 0.489
0.358 ± 0.332
0.012 ± 0.013
1.525 ± 1.311
0.233 ± 0.384
1.153 ± 1.325
0.082 ± 0.03
0.608 ± 0.297
0.539 ± 0.397
0.300 ± 0.201
0.027 ± 0.019
0.859 ± 0.511
0.304 ± 0.527
0.422 ± 0.428
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
324
with and without sexual dichromatism
326
Constant rate*
Variable rate †
0
0.014
0
0.002
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
-73.55
-72.29
3
5
153.11
154.58
0
1.47
Model
Slope birth rate (b) across gradient
Slope death rate (d) across
gradient
Net slope
Maximum Likelihood Estimate
# Parameters in model
AIC
Δ AIC
*b and d do not vary across dichromatism gradient
328
330
†b
and d vary across dichromatism gradient
Seddon, Botero et al.
Supplementary References
332
1.
334
336
338
340
2.
3.
342
4.
344
5.
346
6.
348
7.
350
8.
352
9.
354
10.
356
358
360
362
364
366
368
370
372
374
11.
12.
13.
14.
15.
16.
17.
18.
Armenta J.K., Dunn P.O., Whittingham L.A. 2008 Quantifying avian sexual
dichromatism: a comparison of methods. J Exp Biol 211(15), 2423-2430.
(doi:10.1242/jeb.013094).
Phillimore A.B., Orme C.D., Thomas G.H., Blackburn T.M., Bennett P.M., Gaston K.J.,
Owens I.P.F. 2008 Sympatric speciation in birds is rare: insights from range data
and simulations. Am Nat 171, 646-657.
Weir J.T., Price T.D. 2011 Limits to speciation inferred from times to secondary
sympatry and ages of hybridizing species along a latitudinal gradient. Am Nat 177,
462-469.
Weir J.T., Wheatcroft D. 2011 A latitudinal gradient in rates of evolution of avian
syllable diversity and song length. Proc Roy Soc Lond B 278, 1713-1720.
Dunning J.B. 2010 CRC Handbook of Avian Body Masses. Boca Raton, Florida, CRC
Press.
Cuthill I.C., Bennett A.T.D., Partridge J.C., Maier E.J. 1999 Plumage reflectance and
the objective assessment of avian sexual dichromatism. Am Nat 153(2), 183-200.
Endler J.A. 1990 On the measurement and classification of color in studies of animal
color patterns. Biol J Linn Soc 41(4), 315-352.
Hart N.S. 2001 The visual ecology of avian photoreceptors. Prog Retin Eye Res
20(5), 675-703.
Bennett A.T.D., Cuthill I.C., Partridge J.C., Lunau K. 1997 Ultraviolet plumage colors
predict mate preferences in starlings. Phil Trans Roy Soc Lond B 94(16), 8618-8621.
Endler J.A., Théry M. 1996 Interacting effects of lek placement, display behavior,
ambient light, and color patterns in three neotropical forest-dwelling birds. Am Nat
148(3), 421-452.
Hunt S., Cuthill I.C., Bennett A.T.D., Griffiths R. 1999 Preferences for ultraviolet
partners in the blue tit. Anim Behav 58, 809-815.
Macedonia J.M. 2001 Habitat light, colour variation, and ultraviolet reflectance in
the Grand Cayman anole, Anolis conspersus. Biol J Linn Soc 73(3), 299-320.
Stein A.C., Uy J.A.C. 2006 Plumage brightness predicts male mating success in the
lekking golden-collared manakin. Behav Ecol 17, 41-47.
Vorobyev M., Osorio D. 1998 Receptor noise as a determinant of colour thresholds.
Proc Roy Soc Lond B 265(1394), 351-358.
Endler J.A., Mielke P.W. 2005 Comparing entire colour patterns as birds see them.
Biol J Linn Soc 86, 405-431.
Vorobyev M., Osorio D., Bennett A.T.D., Marshall N.J., Cuthill I.C. 1998
Tetrachromacy, oil droplets and bird plumage colours. J Comp Physio 183(5), 621633.
Stoddard M.C., Prum R.O. 2008 Evolution of avian plumage color in a tetrahedral
color space: a phylogenetic analysis of New World buntings. Am Nat 172(5), 740740. (doi:10.1086/593138).
Badyaev A.V., Hill G.E. 2003 Avian sexual dichromatism in relation to phylogeny and
ecology. Ann Rev Ecol Syst 34, 27-49.
24
Seddon, Botero et al.
376
378
380
382
384
386
388
390
392
394
396
398
400
402
404
406
408
410
412
414
416
418
420
19. Owens I.P.F., Hartley I.R. 1998 Sexual dimorphism in birds: why are there so many
forms of dimorphism? Proc Roy Soc Lond B 265, 397-407.
20. Møller A.P., Cuervo J.J. 1998 Speciation and feather ornamentation in birds.
Evolution 52, 859-869.
21. Dunn P.O., Whittingham L.A., Pitcher T.E. 2001 Mating systems, sperm competition
and the evolution of sexual dimorphism in birds. Evolution 55, 161-175.
22. Owens I.P.F., Bennett A.T.D., Harvey P.H. 1999 Species richness among birds: body
size, life history, sexual selection or ecology? Proc Roy Soc Lond B 266, 933-939.
23. Seddon N., Merrill R.M., Tobias J.A. 2008 Sexually selected traits predict patterns of
species richness in a diverse clade of suboscine birds. Am Nat 171, 620-631.
24. Barraclough T.G., Harvey P.H., Nee S. 1995 Sexual selection and taxonomic diversity
in passerine birds. Proc Roy Soc Lond B 259, 211-215.
25. Phillimore A.B., Freckleton R.P., Orme C.D.L., Owens I.P.F. 2006 Ecology predicts
large-scale patterns of phylogenetic diversification in birds. Am Nat 168, 220–229.
26. Morrow E.H., Pitcher T.E., Arnqvist G. 2003 No evidence that sexual selection is an
'engine of speciation' in birds. Ecol Lett 6, 228-234.
27. Kruger O. 2008 Engines of speciation: a comparative study in birds of prey. J Evol
Biol 21, 861–872.
28. Sol D., Stirling D.G., Lefebvre L. 2005 Behavioral drive or behavioral inhibition in
evolution: subspecific diversification in Holarctic passerines. Evolution 59, 26692677.
29. Stuart-Fox D., Owens I.P.F. 2003 Species richness in agamid lizards: chance, body
size, sexual selection or ecology? J Evol Biol 16, 659-669.
30. Misof B. 2002 Diversity of Anisoptera (Odonata): Infering speciation processes
from patterns of morphological diversity. Zoology 105(355–365).
31. Wagner C.E., Harmon L.J., Seehausen O. 2012 Ecological opportunity and sexual
selection together predict adaptive radiation. Nature doi:10.1038/nature11144.
32. McLain D.K., Moulton M.P., Redfearn T.P. 1995 Sexual selection and the risk of
extinction in oceanic islands. Oikos 74, 27-34.
33. Morrow E.H., Pitcher T.E. 2003 Sexual selection and the risk of extinction in birds.
Proc Roy Soc Lond B 270, 1793-1799.
34. Sorci G., Møller A.P., Clobert J. 1998 Plumage dichromatism of birds predicts
introduction success in New Zealand. J Anim Ecol 67(2), 263-269.
35. Promislow D.E.L., Montgomerie R., Martin T.E. 1992 Mortality costs of sexual
dimorphism in birds. Proc Roy Soc Lond B 250(1328), 143-150.
36. Møller A.P. 1997 Immune defence, extra-pair paternity, and sexual selection in
birds. 264, 561-566.
37. Garamszegi L.Z., Pavlova D.Z., Eens M., Møller A.P. 2007 The evolution of song in
female birds in Europe. Behav Ecol 18(1), 86-96.
38. Nadeau N., Burke T., Mundy N.I. 2007 Evolution of an avian pigmentation gene
correlates with a measure of sexual selection. Proc Roy Soc Lond B 274, 1807-1813.
39. Spottiswoode C.N., Tøttrup A.P., Coppack T. 2006 Sexual selection predicts
advancement of avian spring migration in response to climate change. Proc Roy Soc
Lond B 273, 3023–3029.
40. Drummond A.J., Rambaut A. 2007 BEAST: Bayesian evolutionary analysis by
sampling trees. BMC Evolutionary Biology 7, 214.
25
Seddon, Botero et al.
422
424
426
428
430
432
434
436
438
440
41. Swofford D.L. 2002 PAUP*. Phylogenetic Analysis Using Parsimony (*and Other
Methods). Sunderland, Massachusetts, Sinauer Associates.
42. Weir J.T., Schluter D. 2008 Calibrating the avian molecular clock. Mol Ecol 17, 2321–
2328.
43. Bromham L., Cardillo M. 2003 Testing the link between the latitudinal gradient in
species richness and rates of molecular evolution. J Evol Biol 16, 200-207.
44. Freckleton R.P. 2009 The seven deadly sins of comparative analysis. J Evol Biol 22,
1367 – 1375.
45. Kenward M.G., Roger J.H. 1997 Small sample inference for fixed effects from
restricted maximum likelihood. Biometrics 53, 983-997.
46. Self S.G., Liang K.Y. 1987 Large sample properties of the maximum likelihood
estimator and the likelihood ratio test on the boundary of the parameter space. . J
Am Stat Assoc 82, 605-611.
47. Pagel M.D. 1999 Inferring the historical patterns of biological evolution. Nature
401, 877-884.
48. Freckleton R.P., Harvey P.H., Pagel M.D. 2002 Phylogenetic analysis and
comparative data: A test and review of evidence. Am Nat 160, 712-726.
49. Weir J.T., Schluter D. 2007 The latitudinal gradient in recent speciation and
extinction rates of birds and mammals. Science 315, 1574–1576.
26