Abstract
AbstractThe study of biogeographic barriers have been instrumental in understanding the evolution and distribution of taxa. Now with the increased availability of empirical datasets, it is possible to infer emergent patterns from communities by synthesizing how barriers filter and structure populations across species. We assemble phylogeographic data for a barrier and perform spatially-explicit simulations to quantify temporal and spatial patterns of divergence, the influence of species traits on these patterns, and understand the statistical power of differentiating alternative diversification modes. We incorporate published datasets to examine taxa around the Cochise Filter Barrier, separating the Sonoran and Chihuahuan deserts of North America, to synthesize phylogeographic structuring across the community with respect to organismal functional traits. We then use a simulation and machine learning pipeline to assess the power of phylogeographic model selection. Taxa distributed across the Cochise Filter Barrier show heterogeneous responses to the barrier in levels of gene flow, phylogeographic structure, divergence timing, barrier width, and divergence mechanism. These responses vary concordantly with locomotor and thermoregulatory traits. Many taxa show a Pleistocene population genetic break, often with introgression after divergence. Allopatric isolation and isolation-by-environment are the primary mechanisms purported to structure taxa. Simulations reveal that in spatially-explicit isolation-with-migration models across the barrier, age of divergence, presence of gene flow, and presence of isolation-by-distance can confound the interpretation of evolutionary history and model selection by producing easily-confusable results. By synthesizing phylogeographic data for the Cochise Filter Barrier we show a pattern where barriers interact with species traits to differentiate taxa in communities over millions of years. Identifying the modes of differentiation across the barriers for these taxa remains challenging because commonly invoked demographic models may not be identifiable across a range of likely parameter space.
Publisher
Cold Spring Harbor Laboratory