Many organisms exhibit distinct breeding seasons tracking food availability. If conspecific populations inhabit areas that experience different temporal cycles in food availability spurred by variation in precipitation regimes, then they should display asynchronous breeding seasons. Thus, such populations might exhibit a temporal barrier to gene flow, which may potentially promote genetic differentiation. We test a central prediction of this hypothesis, namely, that individuals living in areas with more asynchronous precipitation regimes should be more genetically differentiated than individuals living in areas with more similar precipitation regimes. Using mitochondrial DNA sequences, climatic data, and geographical/ecological distances between individuals of 57 New World bird species mostly from the tropics, we examined the effect of asynchronous precipitation (a proxy for asynchronous resource availability) on genetic differentiation. We found evidence for a positive and significant cross-species effect of precipitation asynchrony on genetic distance after accounting for geographical/ecological distances, suggesting that current climatic conditions may play a role in population differentiation. Spatial asynchrony in climate may thus drive evolutionary divergence in the absence of overt geographic barriers to gene flow; this mechanism contrasts with those invoked by most models of biotic diversification emphasizing physical or ecological changes to the landscape as drivers of divergence.
FIG S1 (Cloud-Cover forest plot)
Figure S1. Overall effect of precipitation asynchrony (measured from Cloud-Cover data) on genetic differentiation, as shown by the meta-analysis forest plot. From left to right: Species name; number of individuals per species; an horizontal axis where the small vertical line indicates the Mantel correlation coefficient, the horizontal line the 95% confidence interval (CI) and the size of the square the specific weight of the species' result to the overall meta analysis (based on the sample size). Exact values for the coefficient, the 95% CI and the weight percentage are given in columns to the right. The continuous vertical line displays 0 in the x axis and the vertical dotted line denotes the summary cross-species correlation coefficient; the width of the bottom diamond represents the 95% CI. For more specific details see Table S3.
FIG S2 (Worldclim forest plot)
Figure S2. Overall effect of precipitation asynchrony (measured from WorldClim data) on genetic differentiation, as shown by the meta-analysis forest plot. From left to right: Species name; number of individuals per species; an horizontal axis where the small vertical line indicates the Mantel correlation coefficient, the horizontal line the 95% confidence interval (CI) and the size of the square the specific weight of the species' result to the overall meta analysis (based on the sample size). Exact values for the coefficient, the 95% CI and the weight percentage are given in columns to the right. The continuous vertical line displays 0 in the x axis and the vertical dotted line denotes the summary cross-species correlation coefficient; the width of the bottom diamond represents the 95% CI. For more specific details see Table S3.
Fig S3 (different sample cut-offs CLoudCover)
Figure S3. Overall effect of precipitation asynchrony (measured from Cloud-Cover data) on genetic differentiation, as shown by the meta-analysis forest plot when excluding species with less than 5, 10 and 15 individuals. From left to right: Species name; number of individuals per species; an horizontal axis where the small vertical line indicates the Mantel correlation coefficient, the horizontal line the 95% confidence interval (CI) and the size of the square the specific weight of the species' result to the overall meta analysis (based on the sample size). Exact values for the coefficient, the 95% CI and the weight percentage are given in columns to the right. The continuous vertical line displays 0 in the x axis and the vertical dotted line denotes the summary cross-species correlation coefficient; the width of the bottom diamond represents the 95% CI.
Fig S4 (different sample cut-offs WorldClim)
Figure S4. Overall effect of precipitation asynchrony (measured from WorldClim data) on genetic differentiation, as shown by the meta-analysis forest plot when excluding species with less than 5, 10 and 15 individuals. From left to right: Species name; number of individuals per species; an horizontal axis where the small vertical line indicates the Mantel correlation coefficient, the horizontal line the 95% confidence interval (CI) and the size of the square the specific weight of the species' result to the overall meta analysis (based on the sample size). Exact values for the coefficient, the 95% CI and the weight percentage are given in columns to the right. The continuous vertical line displays 0 in the x axis and the vertical dotted line denotes the summary cross-species correlation coefficient; the width of the bottom diamond represents the 95% CI.
Table S1 (Genetic Information)
Table S1. Data sources, number of individuals sampled and references for each of the species used in this study.
Table S2 (Monthly Cloud-Cover)
Table S2. Geographical coordinates for all the individuals included in analyses and their respective monthly Cloud-Cover data. Cloud-Cover data for each month are given as percentage of days in which each locality was covered with clouds, averaged over a dozen years (2000-2012).
Table S3 (Mantel results)
Table S3. Results of partial Mantel tests examining the relationship between precipitation asynchrony and genetic distance, accounting for dispersal distance, for each species. Results based on Cloud-Cover and WorldClim data are given.
Table S4 (Mantel correlogram)
Table S4. Results of partial Mantel spatial correlograms assessing spatial autocorrelation in precipitation asynchrony (based on Cloud-Cover data) and genetic distance.
Table S5 (Phylogenetic Signal)
Table S5. Results of tests for phylogenetic signal in Mantel coefficients describing the relationship between precipitation asynchrony and genetic distances.
Table S6 (Species variables)
Table S6. Species variables used for the ad hoc regressions trying to explain variation among species in Mantel coefficients describing the relationship between precipitation asynchrony and genetic distance. References from which the variables were obtained are included.
Table S7 (Separate ad hoc)
Table S7. Results of weighted least-squares regression assessing relationships between Mantel coefficients and species variables; results are shown for WorldClim and Could-Cover data.
Table S8 (Stepwise)
Table S8. Results of the stepwise model selection using weighted least squares examining relationships between Mantel coefficients and species variables using the AIC and the BIC for both WorldClim and Cloud-Cover data.