Integrating ensemble species distribution modelling and statistical phylogeography to inform projections of climate change impacts on species distributions
Species distribution models (SDMs) are commonly used to forecast climate change impacts. These models, however, are subject to important assumptions and limitations. By integrating two independent but complementary methods, ensemble SDMs and statistical phylogeography, we addressed key assumptions and created robust assessments of climate change impacts on species distributions while improving the conservation value of these projections.
North American cordillera.
This approach was demonstrated using the arctic-alpine plant Rhodiola integrifolia (Crassulaceae). SDMs were fitted to current and past climates using eight models, two thresholds and one to three climate data sets. These projections were combined to create a map of stable climate (refugia) since the Last Interglacial (124,000 kya). Five biogeographic hypotheses were developed based on the configuration of refugia and tested using statistical phylogeography. Projection of SDMs into the future was contingent on agreement across approaches; future projections (to 2085) used five climate data sets and two greenhouse gas scenarios.
A multiple-refugia hypothesis was supported by both methods, confirming the assumption of niche conservatism in R. integrifolia and justifying the projection of SDMs onto future climates. Future projections showed substantial loss of climatically suitable habitat. Southern populations had the greatest losses, although the biogeographic scale of modelling may overpredict extinction risks in areas of topographic complexity. Past and future SDMs were assessed for novel values of climate variables; areas of novel climate were flagged as having higher uncertainty.
Integrating molecular approaches with spatial analyses of species distributions under global change has great potential to improve conservation decision-making. Molecular tools can support and improve current methods for understanding the vulnerability of species to climate change and provide additional data upon which to base conservation decisions, such as prioritizing the conservation of areas of high genetic diversity to build evolutionary resiliency within populations.