Despite a recent surge of interest in geographic range limits, a large conceptual divide exists between approaches that assume range margins represent stable, equilibrial niche limits, held in place by limits to adaptation, and approaches that assume range margins represent dynamic, nonequilibrial limits, providing current snapshots of expanding or contracting ranges. The historical demography of populations from across a species’ geographic range can provide insight into the existence of equilibrial or nonequilibrial limits, lend support to particular range limit hypotheses, and provide a window of inference into the past that is unattainable using purely ecological approaches. Here we outline an approach to using inference of historical demographic parameters (e.g., population sizes, population growth, and migration) from population genetic data to help elucidate if a current range margin is equilibrial or nonequilibrial, and if a given range limit hypothesis (e.g., swamping gene flow) is tenable given patterns of polymorphism across the range. We simulated various equilibrial and nonequilibrial range histories, representative of a suite of range limit hypotheses, and used demographic analyses of these simulations (Bayesian skyline plots, coalescent sampler methods) to contrast the demographic histories and genetic attributes of central and marginal populations.
Results/Conclusions
Our results demonstrate that central and marginal populations can have detectably different historical demographic signals under varying equilibrial and nonequilibrial historical scenarios. The signals under equilibrial scenarios like negative population growth, lower effective populations sizes, and asymmetrical gene flow into marginal populations were correctly recovered in analyses of simulated scenarios. However, some nonequilibrial scenarios (e.g., recent range expansion) resulted in demographic signatures similar to equilibrial processes (e.g., asymmetrical migration). Historical demography can provide valuable insights for studies of geographic range limits. However, the commonality of some processes across different scenarios makes fully distinguishing hypotheses using genetic data alone improbable in some cases. Hence, we emphasize the utility of combining multiple forms of empirical data and analyses (historical demography, demographic projections, transplant experiments, and distribution modeling) to gain a comprehensive understanding of geographic range limits. We also contrast the results of our simulations with range-wide polymorphism data from the scarlet monkeyflower, Mimulus cardinalis. Uncovering which processes set species’ range limits, and how these limits may track or be altered by climatic change, is critically important for conservation, while providing insight into a fundamental problem in ecology, the regulation of species’ distributions.