Abstract

Simulating South American biodiversity The emergence, distribution, and extinction of species are driven by interacting factors—spatial, temporal, physical, and biotic. Rangel et al. simulated the past 800,000 years of evolution in South America, incorporating these factors into a spatially explicit dynamic model to explore the geographical generation of diversity. Their simulations, based on a paleoclimate model on a 5° latitude-longitude scale, result in shifting maps of speciation, persistence, and extinction (or cradles, museums, and graves). The simulations culminate in a striking resemblance to contemporary distribution patterns across the continent for birds, mammals, and plants—despite having no target patterns and no empirical data parameterizing them. Science, this issue p. eaar5452 Mechanistic simulations of climate dynamics, speciation, and adaptive evolution yield realistic geographical patterns of biodiversity. INTRODUCTION Individual processes that shape geographical patterns of biodiversity are increasingly understood, but their complex interactions on broad spatial and temporal scales remain beyond the reach of analytical models and traditional experiments. To meet this challenge, we built a spatially explicit, mechanistic model that simulates the history of life on the South American continent, driven by modeled climates of the past 800,000 years. Operating at the level of geographical ranges of populations, our simulations implemented adaptation, geographical range shifts, range fragmentation, speciation, long-distance dispersal, competition between species, and extinction. Only four parameters were required to control these processes (dispersal distance, evolutionary rate, time for speciation, and intensity of competition). To assess the effects of topographic heterogeneity, we experimentally smoothed the climate maps in some treatments. RATIONALE The simulations had no target patterns. Instead, the study took a fundamental approach, relying on the realism of the modeled ecological and evolutionary processes, theoretical derivations of parameter values, and the climatic and topographic drivers to produce meaningful biogeographical patterns. The model encompassed only the Late Quaternary (last 800,000 years), with its repeated glacial-interglacial cycles, beginning at a time when South America was already populated with a rich biota, comprising many distinct lineages. Nonetheless, current consensus holds that the contemporary flora and vertebrate fauna of South America include numerous lineages that have undergone rapid diversification during the Quaternary, particularly in the Andes. In our model, over the course of each simulation, a complete phylogeny emerged from a single founding species. On the basis of the full historical records for each species range, at each 500-year interval, we recorded spatial and temporal patterns of speciation (“cradles”), persistence (“museums”), extinction (“graves”), and species richness. RESULTS Simulated historical patterns of species richness, as recorded by maps of the richness of persistent (museum) species, proved quite successful in capturing the broad features of maps of contemporary species richness for birds, mammals, and plants. Factorial experiments varying parameter settings and initial conditions revealed the relative impact of the evolutionary and ecological processes that we modeled, as expressed in spatial and temporal patterns of cradles, museums, graves, and species richness. These patterns were most sensitive to the geographical location of the founding species and to the rate of evolutionary adaptation. Experimental topographic smoothing confirmed a crucial role for climate heterogeneity in the diversification of clades, especially in the Andes. Analyses of temporal patterns of speciation (cradles) and extinction (graves) emerging from the simulations implicated Quaternary glacial-interglacial cycles as drivers of both diversification and extinction on a continental scale. CONCLUSION Our biogeographical simulations were constructed from the bottom up, integrating mechanistic models of key ecological and evolutionary processes, following well-supported, widely accepted explanations for how these processes work in nature. Despite being entirely undirected by any target pattern of real-world species richness and covering only a tiny slice of the past, surprisingly realistic continental and regional patterns of species richness emerged from the model. Our simulations confirm a powerful role for adaptive niche evolution, in the context of diversification and extinction driven by topography and climate. Observed species richness versus modeled (simulated) richness. Upper map: Contemporary South American bird richness (2967 species). Lower map: Simulated spatial pattern for the cumulative richness of persistent (museum) species, arising from the model. The map show results averaged over all parameter values for an Atlantic Rainforest founder, excluding the climate-smoothing experimental treatments. Simulated species richness is highly correlated with observed species richness for birds (r2 = 0.6337). Individual processes shaping geographical patterns of biodiversity are increasingly understood, but their complex interactions on broad spatial and temporal scales remain beyond the reach of analytical models and traditional experiments. To meet this challenge, we built a spatially explicit, mechanistic simulation model implementing adaptation, range shifts, fragmentation, speciation, dispersal, competition, and extinction, driven by modeled climates of the past 800,000 years in South America. Experimental topographic smoothing confirmed the impact of climate heterogeneity on diversification. The simulations identified regions and episodes of speciation (cradles), persistence (museums), and extinction (graves). Although the simulations had no target pattern and were not parameterized with empirical data, emerging richness maps closely resembled contemporary maps for major taxa, confirming powerful roles for evolution and diversification driven by topography and climate.