Minimal effects of latitude on present-day speciation rates in New World birds Daniel L. Rabosky*, Pascal O. Title, Huateng Huang Supplementary Figures Supporting figures Ericson tree tip speciation (log) 1 0 -1 -2 -3 -3 -2 -1 0 1 Hackett tree tip speciation (log) Figure S1. Estimates of present-day speciation rates for 6670 species of birds are highly correlated between the Hackett and Ericson phylogenetic datasets (Pearson r = 0.90). Posterior probability 0.08 0.06 0.04 0.02 0.00 0 10 20 30 40 50 60 70 80 90 Number of shifts Figure S2. Prior (blue) and posterior (red) distributions of the number of rate shifts for the Hackett dataset. These distributions have no observed overlap, indicating that the data contain substantial evidence for diversification rate heterogeneity. Virtually identical results were obtained for the Ericson backbone topology. a 2 Passerines Muscisaxicola flycatchers b Tropical Other Tyrannidae flycatchers Speciation rate (log-scale) Sporophila tanagers (Thraupidae) Temperate Ovenbirds (Furnariidae) 1 Thrushes(Turdidae) Tits (Paridae) All other species 0.5 0.25 0.125 0.0625 0.031 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 0 Latitude Speciation rate (log-scale) c 200 400 600 800 Species in rate bin d Non-passerines 0.5 0.25 0.125 0.0625 0.031 -60 -50 -40 -30 -20 -10 0 Latitude 10 20 30 40 50 60 70 80 0 100 200 300 400 Species in rate bin Figure S3. Relationship between present-day speciation rate and centroid latitude for (a) passerines and (c) non-passerines. Legend/colors in (a) are included to illustrate a representative set of taxa that span a gradient from fast (orange/red) to slow (blue/green) speciation rates. Marginal rate distributions for (a) and (c) are shown in (b) and (d), respectively. As in Figure 4c, one can see that - for any given value of speciation rate there are far more tropical than temperate species. If faster speciation in the tropics drives the LDG, there should be an excess of fast-speciating tropical lineages. The correlation between speciation and centroid midpoint of breeding range is slightly higher for nonpasserine (r = 0.07, p = 0.038) than for non-passerine lineages (r = 0.037; p = 0.13). These analyses do not correct for phylogenetic non-independence: indeed, many closely related species have highly autocorrelated rates. However, in this context, it is appropriate to investigate this relationship both with and without correcting for nonindependence of rate estimates (the cohort correlations, Figure 4, explicitly address statistical non-independence). 1.0 0.8 0.8 Number of species Number of species 1.0 0.6 0.4 0.2 Hackett topology 0.0 0.03 0.12 0.5 1 Speciation rate (logscale) 0.6 0.4 0.2 Ericson topology 0.0 2 0.03 0.12 0.5 1 2 Speciation rate (logscale) Figure S4. Speciation rate distributions (rank-ordered percentile plots) for Hackett (left) and Ericson (right) topologies. In both cases, the distribution of speciation rates for tropical taxa (orange) is similar to the distribution for temperate taxa (blue), although the Ericson topology suggests that temperate zone speciation rates are slightly higher than tropical speciation rates. 1.6 Passerines Density 1.4 1.2 Tropical 1.0 Temperate 0.8 0.6 0.4 0.2 0.0 0.031 0.063 0.125 0.25 0.5 1 2 Speciation rate (log scale) 1.6 Non-passerines Density 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0.031 0.063 0.125 0.25 0.5 1 2 Speciation rate (log scale) Figure S5. Distributions of speciation rates for tropical (orange) and temperate (blue) taxa, inferred using kernel density estimation, for passerine (N = 1726) and non-passerine (N = 845) lineages considered separately. B Speciation rate A Cohort membership > 0.30 0.25 0.20 0.15 0.10 0.05 Figure S6. Macroevolutionary cohorts for a representative sample from the posterior distribution of rate shift configurations simulated with BAMM. Phylogeny on left is Hackett backbone phylogeny for 2571 NW land birds, with branches colored to reflect instantaneous rates of species formation. Each vertical bar (gray/white) reflects a different macroevolutionary cohort; all species inferred to belong to the same cohort "light up" in the same column. There is no requirement that cohorts be monophyletic, because a given clade may include nested clades that have undergone secondary shifts in diversification rates. a 200 Frequency 150 100 50 0 -1.0 -0.5 0.0 0.5 1.0 Correlation (tropical fraction & speciation) b 200 Frequency 150 100 50 0 -1.0 -0.5 0.0 0.5 1.0 Correlation (mean latitude & speciation) Figure S7. Correlations between two indices of "tropicalness" and speciation rates for macroevolutionary cohorts. Results show distribution of Spearman correlations between cohort speciation rate and (a) the percentage of tropical species and (b) the mean latitude of all species in each cohort, taken across the full posterior of macroevolutionary rate shift configurations simulated with BAMM. Each cohort (see figure S6 for example) is statistically independent from other cohorts under the BAMM model. a speciation-trait correlations 0.8 frequency 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 b trait-trait correlations 0.8 frequency 0.6 0.4 0.2 0.0 0.0 0.2 0.4 0.6 0.8 1.0 p-value Figure S8. Results of simulations to test whether cohort analyses (Fig. 4) would be characterized by elevated Type I error rates. For each sample from the posterior simulated with BAMM, we created a "cohort phylogeny" consisting of a single species from each macroevolutionary rate regime. Because each species is from a different cohort, they are assumed to be statistically independent, for the purposes of correlating tipspecific speciation rates with other traits. We then simulated a trait dataset on each cohort phylogeny under Brownian motion and tested whether tip-specific speciation rates were significantly correlated with the trait values. Because the traits are simulated on each phylogeny independently of the speciation rates, a significant association (+ or -) between speciation rate and traits represents a Type I error. We found that simulations conducted in this fashion (a) have very low Type I error rates (2.8% of simulations characterized by p < 0.05). To ensure that this low Type I error rate was not merely a result of the relatively small size of the cohort phylogeny, we performed a parallel analysis where we simulated two sets of traits on the same phylogeny under Brownian motion, with no correlation between the traits. We then tested the correlation between the two sets of traits while explicitly ignoring phylogeny. The distribution of p-values is shown in (b) and suggests a relatively high Type I error rate (21.4% of simulated datasets show p < 0.05). Hence, the cohort analysis (a) has statistical properties that are much more consistent with fully independent data relative to trait data simulated under Brownian motion (b). (a) 1.6 Time-constant 1.4 Density 1.2 1.0 0.8 Tropical 0.6 Temperate 0.4 0.2 0.0 0.031 0.063 0.125 0.25 0.5 1 2 Speciation rate (log scale) (b) 1.2 Time-variable Density 1.0 0.8 0.6 0.4 0.2 0.0 0.031 0.063 0.125 0.25 0.5 1 2 Speciation rate (log scale) Figure S9. Comparison of speciation rate distributions for tropical and temperate lineages under time-constant (a) and time-variable (b) models in BAMM. Speciation rates are faster in the temperate zone when speciation rates are constrained to be constant in time. Frequency distributions are kernel density estimates for tropical and temperate taxa, inferred with the constraint that speciation rates do not vary through time (a) and after relaxing this assumption (b). There is a clear increase in the proportion of "fastspeciation" temperate taxa relative to the tropics. When the assumption of constant-rates through time is relaxed (b), this effect disappears, and the distribution of speciation rates is invariant with respect to latitude. Results presented in Figures 2,3 and 4 are based on the more general model that allows speciation rates to vary through time.