Making the most of mechanistic models


Ries, Leslie


  • Poster




Mechanistic models of species distributions have been subjected to increased testing, but recent comparisons with correlative approaches have not shown their putative benefits, which include strengthened inference due to a priori predictions and the ability to identify evolutionary dynamics. Through a new implementation of a butterfly distribution model focused on the sachem skipper (Atalopedes campestris) subjected to past comparisons, we show these benefits by using abundance data derived from a large - scale citizen - science monitoring program. Abundance is a more amenable metric to a model that predicts relative performance under differing conditions. We show that abundances have a positive relationship to predicted growth rates, but only in the cooler parts of this butterfly’s range, suggesting an unidentified mortality source in warmer regions. We also identified two populations that may be experiencing local adaptation and suggest how this could be tested. Finally, we show how matching the year ranges of the environmental to distributional data provides a more robust test and suggests this species may not be in equilibrium with the recent climate.