Prof William Fagan1, Dr. Kumar Mainali1, Prof. Leslie Ries2, Prof. Trevor Hefley3
1University Of Maryland, College Park,, United States, 2Georgetown University, Washington,, United States, 3Kansas State University, Manhattan,, United States
Facing climate change, species unable to adapt locally must disperse if they are to persist. But how much dispersal is possible? How can we distinguish highly mobile species from those with lesser dispersal abilities? And how does this mobility depend on species’ characteristics and contexts? To explore these issues, we analyzed the mobility of North American butterflies using compiled occurrence records. As a group, butterflies comprise some of the best-studied invertebrates with regard to geographic distribution. Using >650,000 occurrence records across 344 species (obtained from the North American Butterfly Association, the Butterflies and Moths of North America, and the Global Biodiversity Information Facility), we built statistically robust maps of species’ distributions by fitting general additive models to latitude and longitude coordinate data. These models yielded probability surfaces from which we could identify ‘vagrant’ records in which a butterfly was observed well outside the geographic range of its species. The frequency of such vagrant records, and their displacements relative to the corresponding geographic ranges, are indicators of a species’ long-range mobility. We then built predictive models to distinguish species with extensive vagrancy from less mobile species in terms of their ecological characteristics and environmental contexts. These distinctions are integral to conservation measures as indicators of relative risk from climate change. Moreover, with dedicated observer networks, these predictions are eminently testable as new occurrence records pour in and species’ ranges shift.
Bill Fagan is Professor and Chair of Biology at the University of Maryland. He is a quantitative ecologist with special interests in mathematical theory and spatial data analysis. His lab has a long history of working with challenging datasets (tracking records, occurrence records, population timeseries) to understand aspects of species behavior, spatial distribution, and population dynamics. Often these projects have strong connections to conservation biology.