Using habitat distribution models to evaluate large-scale landscape priorities for spatially dynamic species.
Abstract
Large-scale conservation planning requires the identification of priority areas in which species have a high likelihood of long-term persistence. This typically requires high spatial resolution data on species and their habitat. Such data are rarely available at a large geographical scale, so distribution modelling is often required to identify the locations of priority areas. However, distribution modelling may be difficult when a species is either not recorded, or not present, at many of the locations that are actually suitable for it. This is an inherent problem for species that exhibit metapopulation dynamics. Rather than basing species distribution models on species locations, we investigated the consequences of predicting the distribution of suitable habitat, and thus inferring species presence/absence. We used habitat surveys to define a vegetation category which is suitable for a threatened species that has spatially dynamic populations (the butterfly Euphydryas aurinia), and used this as the response variable in distribution models. Thus, we developed a practical strategy to obtain high resolution (1 ha) large scale conservation solutions for E. aurinia in Wales, UK. Habitat-based distribution models had high discriminatory power. They could generalize over a large spatial extent and on average predicted 86% of the current distribution of E. aurinia in Wales. Models based on species locations had lower discriminatory power and were poorer at generalizing throughout Wales. Surfaces depicting the connectivity of each grid cell were calculated for the predicted distribution of E. aurinia habitat. Connectivity surfaces provided a distance-weighted measure of the concentration of habitat in the surrounding landscape, and helped identify areas where the persistence of E. aurinia populations is expected to be highest. These identified successfully known areas of high conservation priority for E. aurinia. These connectivity surfaces allow conservation planning to take into account long-term spatial population dynamics, which would be impossible without being able to predict the species' distribution over a large spatial extent. Synthesis and applications. Where species location data are unsuitable for building high resolution predictive habitat distribution models, habitat data of sufficient quality can be easier to collect. We show that they can perform as well as or better than species data as a response variable. When coupled with a technique to translate distribution model predictions into landscape priority (such as connectivity calculations), we believe this approach will be a powerful tool for large-scale conservation planning.