Predicting mammalian communities in Mesoamerican 'sky islands' using species traits and spatiotemporal patterns of environmental suitability

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Understanding and predicting shifts in species geographic distributions (or ranges) is important to inform decision-making on a range of pressing issues in health, agriculture, and natural-resource management. Many human activities increase the patchiness of habitats in an area, thus affecting species distributions. However, predicting the effects of fragmentation on particular species remains difficult. To forecast which species will remain in each patch and which will move into the areas between them, scientists need to consider not only their preferred habitats but also the environmental history of the region and other traits of the species themselves (like body size, dispersal ability, and reproductive rates). Here, the researchers will test a new model to forecast species distributions in fragmented landscapes, applying it to mammals associated with mountain forests. The research will evaluate the roles of environmental history and species traits in predicting a species' presence or absence in particular forest patches. Graduate and undergraduate students contributing to the project will gain training in data collection, analyses using Geographic Information Systems (GIS), and computer coding. In addition to publishing scientific papers on the findings, the team will conduct workshops and produce computer code, tutorials, and webinars. In a system of naturally fragmented, montane habitat islands or sky islands, the researchers will test hypotheses regarding differential colonization and extinction among species using traits, allometric scaling (based on body size), and spatial patterns of present and past climate. To predict the particular species occurring in given patches, they will implement the new Constraint-based model of Dynamic Island Biogeography (C-DIB). The team will do so studying small non-volant mammals associated with currently isolated mesic montane forests of the Sierra Madre Oriental in Mexico (the mainland of this system). Specifically, the researchers will: 1. obtain occurrence records and trait data for species of the mainland; 2. make predictions for each species and patch within one sky island complex using body size, trophic level, and measures of connectivity and area from ecological niche models applied to present and past conditions; 3. determine the species composition of the patches by conducting field inventories; and 4. perform statistical tests of differential colonization and extinction among species. This project will advance understanding of the factors that affect species ranges across space and over time, empowering researchers to improve biodiversity prediction and spark further development and use of the C-DIB in basic and applied science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.