Since the recognition that allopatric speciation can be induced by large-scale reconfigurations of the landscape that isolate formerly continuous populations, such as the separation of continents by plate tectonics, the uplift of mountains or the formation of large rivers, landscape change has been viewed as a primary driver of biological diversification. This process is referred to in biogeography as vicariance1. In the most species-rich region of the world, the Neotropics, the sundering of populations associated with the Andean uplift is ascribed this principal role in speciation2, 3, 4, 5. An alternative model posits that rather than being directly linked to landscape change, allopatric speciation is initiated to a greater extent by dispersal events, with the principal drivers of speciation being organism-specific abilities to persist and disperse in the landscape6, 7. Landscape change is not a necessity for speciation in this model8. Here we show that spatial and temporal patterns of genetic differentiation in Neotropical birds are highly discordant across lineages and are not reconcilable with a model linking speciation solely to landscape change. Instead, the strongest predictors of speciation are the amount of time a lineage has persisted in the landscape and the ability of birds to move through the landscape matrix. These results, augmented by the observation that most species-level diversity originated after episodes of major Andean uplift in the Neogene period, suggest that dispersal and differentiation on a matrix previously shaped by large-scale landscape events was a major driver of avian speciation in lowland Neotropical rainforests.
At a glance
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Extended data figures and tables
Extended Data Figures
- Extended Data Figure 1: Areas of endemism for lowland rainforest birds in Central and South America. (399 KB)
A full description of the geographical limits of each area is available in the Supplementary Information.
- Extended Data Figure 2: hABC output showing estimates of mean and dispersion indices of population divergence times and times of co-divergence pulses inferred from mitochondrial DNA. (277 KB)
The left panels illustrate the approximate joint posterior estimates of , the dispersion index of τ and , the mean of τ across n population pairs, where τi is the divergence time of the ith of n population-pairs and is scaled in coalescent time units of 4 generations where is the mean effective population size averaged across population-pairs. The right panels depict the posterior distributions of the relative times of the co-divergence pulses across barriers, scaled by coalescent units. The shading intensity of each distribution is conditional on the posterior probability of ψ, the associated number of different pulses of co-divergence across each barrier. Sample sizes for each barrier: Andes, n = 29; Isthmus of Panama, n = 14; Amazon River, n = 14; Negro River, n = 17; Madeira River, n = 14.
- Extended Data Figure 3: hABC output showing estimates of mean and dispersion indices of population divergence times across the Andes inferred from ultraconserved elements (UCEs). (100 KB)
Left panel illustrates the approximate joint posterior estimates of , the dispersion index of τ and , the mean of τ across n population-pairs, where τi is the divergence time of the ith of n population-pairs and is scaled in coalescent time units of 4 generations where is the mean effective population size averaged across population-pairs. The right panel depicts the posterior distribution of the relative times of the co-divergence pulses across the Andes (n = 5) scaled by coalescent units.
- Extended Data Figure 4: Bar plot showing the number of estimated species using a Bayesian general mixed Yule-coalescent (bGMYC) model from complete and randomly pruned data sets. (295 KB)
The coloured columns for each lineage correspond to the percentage (0–60%) of individuals randomly pruned from each data set.