Supporting Information
Environmental harshness is positively correlated
with intraspecific divergence in mammals and birds
Carlos A. Botero, Roi Dor, Christy M. McCain, and Rebecca J. Safran
Contents
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Data S1
Spatial sensitivity analysis
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Data S2
Sample code for Bayesian Phylogenetic Mixed Models (BPMM) in R
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Figure S1 Elevation gradients of the world and three representative examples
of species whose breeding ranges are dissected by mountains
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Table S1
Principal components analyses of continuous bio-ecological variables
in the spatial sensitivity analysis
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Table S2
Summary of results for the Bayesian Phylogenetic Mixed Models of
subspecies richness in the spatial sensitivity analysis
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Table S3
Summary of results for the Bayesian Phylogenetic Mixed Models of
subspecies richness in north temperate mammals and birds
Data S1. Spatial sensitivity analysis
To explore the extent to which our results are influenced by spatial variation in
environmental conditions, we replicated our analyses 100 times using datasets in
which environmental variables were extracted from randomly chosen localities
within each species’ range (rather than averaged across the entire breeding range).
Table S1 summarizes the results of a PCA combining these 100 datasets for birds
and 100 datasets for mammals. The resulting principal components are nearly
identical to those derived from the analysis of environmental means (see Table 1 in
the main text), including a similarly decreasing amount of variance explained by
‘Environmental Harshness’ (PC1), Geographic Coverage (PC2), Precipitation
unpredictability (PC3), and Residual Body Size (PC4).
The combined results of BPMMs on these 100 datasets per group (Table S2)
indicate that the effects described in Table 3 of the main text are quite robust to
spatial variation in environmental variables. With the exception of rainfall
unpredictability and body size in mammals, the estimated effects of the remaining
putative predictors are consistent with our earlier findings in both magnitude and
significance. In particular, the mean coefficients estimated for environmental
harshness and geographic coverage are nearly identical to those presented in the
main text.
Data S2. Example code for Bayesian Phylogenetic Mixed Models (BPMM) in R
The following is an example of the general stipulation of our MCMCglmm models:
# fully parameterized model
require (MCMCglmm)
Ainv<-inverseA(mytree)$Ainv
prior = list( B = list(mu = rep(0,14), V = diag(14)), R = list(V = diag(1), nu = 0.002), G =
list( G1 = list(V = diag(1), n = 0.002 ) ) )
fullmod <- MCMCglmm( (subspecies - 1) ~ 1 + DissectedByMountains + EnvShielding
+ Glaciation + IslandDwelling + LogAge + LogEnvHarshness + Area +
RainfallUnpred + I(RainfallUnpred^2) + BS +
LogEnvHarshness*DissectedByMountains + LogEnvHarshness* EnvShielding
+ RainfallUnpred* EnvShielding, data = mydata, random=~Species,
ginverse=list(Species=Ainv), family = "poisson", prior= prior, nitt = 300000,
burnin = 10000, thin = 50)
# evaluate convergence
geweke.diag(fullmod$Sol) # Geweke (1992) diagnostic test
heidel.diag(fullmod$Sol) # Heidelberg and Welch (1983) diagnostic test
plot(fullmod$Sol) # visual inspection of mixing properties of the MCMC chain
# now explore results...
summary(fullmod)
Figure S1. Elevation gradients of the world (dark green) as defined by slopes equal
or higher than 5 degrees (criterion based on UN Mountain Watch category 5, UNEP
World Conservation Monitoring Centre 2002). The bottom panels depict
representative examples of species whose breeding ranges (red areas) are divided
into two or more geographically isolated sections by the intersection of mountain
chains: Apodemus sylvaticus (A); Bradypus variegatus (B); Aonyx cinerea (C).
Table S1. Principal components analyses of continuous bio-ecological variables in the spatial
sensitivity analysis. Data from 100 randomly selected locations within the breeding range of each
mammal and bird species in the main model are included. Standardized loadings of the main
contributors to each component are highlighted in boldface.
Environmental
harshness
(PC1)
Geographic
coverage
(PC2)
Precipitation
unpredictability
(PC3)
Residual
body size
(PC4)
Uniqueness
0.90
0.01
0.13
0.03
0.18
Predictability of
temperature cycles
-0.87
0.01
-0.15
0.02
0.22
sqrt (mean
precipitation)
-0.86
0.10
0.15
-0.05
0.22
(Mean
temperature)2
-0.75
0.20
0.05
0.01
0.40
Net primary
productivity
-0.69
0.20
0.07
-0.21
0.43
-0.71
0.16
0.44
0.01
0.28
0.61
0.36
0.24
-0.33
0.34
0.29
0.81
-0.03
-0.32
0.17
0.09
0.57
-0.21
0.77
0.03
-0.18
0.06
-0.87
-0.29
0.13
4.33
1.22
1.12
0.93
0.43
0.55
0.67
0.76
Bio-ecological
variable
ln (Annual
variance in
temperature)
sqrt (Annual
variance in
precipitation)
Habitat
heterogeneity
ln (Area)
ln (Body size)
Predictability of
precipitation cycles
SS loadings
% Cumulative
variance explained
Table S2. Summary of results for Bayesian Phylogenetic Mixed Models of subspecies
richness in the spatial sensitivity analysis. Estimates of posterior means reflect in this
case the average parameters estimated from 100 datasets using random localities for
every species.
Mammals †
Birds †
Parameter
Posterior mean (95% CI)
f‡
Posterior mean (95% CI)
f‡
Intercept
-1.92 (-3.33, -0.52)**
1.00
-2.32 (-2.96, -1.68)***
1.00
0.87 (0.70, 1.05)***
1.00
0.42 (0.34, 0.50)***
1.00
Environmental shielding §
N.S.
0.00
N.S.
0.00
Glaciation
N.S.
0.00
N.S.
0.00
Island dwelling
0.57 (0.31, 0.84)***
1.00
0.62 (0.51, 0.73)***
1.00
ln( species age )
N.S.
0.00
N.S.
0.00
ln( environmental harshness,
PC1 )
1.17 (0.87, 1.47)***
1.00
0.79 (0.59, 0.98)***
1.00
Geographic coverage, PC2
0.85 (0.72, 0.99)***
1.00
0.69 (0.61, 0.77)***
1.00
Precipitation unpredictability,
PC3
N.S.
0.03
0.05 (0.01, 0.09)*
0.82
PC32
N.S.
0.30
N.S.
0.09
Residual body size, PC4
N.S.
0.00
-0.35 (-0.43, -0.27)***
1.00
ln( environmental harshness) *
Environmental shielding
N.S.
0.00
N.S.
0.20
Precipitation unpredictability *
Environmental shielding
N.S.
0.00
N.S.
0.00
Dissected by mountains
† N.S. = not-significant;  p MCMC = 0.05; * p MCMC < 0.05;
**
p MCMC < 0.01; *** p
MCMC < 0.001
‡ f = frequency of spatial datasets in which MCMC p-values < 0.05
§ Hibernation in mammals, migration in birds
Table S3. Summary of results for the Bayesian Phylogenetic Mixed Models of subspecies
richness in north temperate mammals and birds †
Mammals ‡
Birds ‡
Parameter
Posterior mean (95% CI)
Posterior mean (95% CI)
f§
Intercept
-0.90 (-2.03, 0.22)
-0.41 (-1.07, 0.24)
0.00
Glaciation
N.S.
N.S.
0.00
Island dwelling
N.S.
N.S.
0.00
ln( species age )
N.S.
N.S.
0.00
0.46 (0.09, 0.87)*
0.53 (0.16, 0.90)*
1.00
0.96 (0.71, 1.22)***
0.60 (0.26, 0.94)***
1.00
N.S.
N.S.
0.00
-0.28 (-0.52, -0.03)*
-0.35 (-0.59, -0.12)*
1.00
Environmental harshness, PC1
Geographic coverage, PC2
Precipitation unpredictability, PC3
Residual body size, PC4
† Only non-migratory species whose ranges are entirely above 23.4°N latitude were considered
for these analyses
‡ N.S. = not-significant;  p MCMC = 0.05; * p MCMC < 0.05;
< 0.001;
§ f = frequency of trees for which MCMC p-values < 0.05
**
p MCMC < 0.01; *** p MCMC
Supporting References
UNEP World Conservation Monitoring Centre (2002). UNEP World Conservation
Monitoring Centre: Mountain Watch. Cambridge, UK.