Integrated species distribution models reveal spatiotemporal patterns of human-wildlife conflict.

Abstract

To mitigate human-wildlife conflict it is imperative to know where and when conflict occurs. However, standard methods used to predict the occurrence of human-wildlife conflict often fail to recognize how a species distribution likely limits where and when conflict may happen. As such, methods that predict human-wildlife conflict could be improved if they could identify where conflict will occur relative to species' underlying distribution. To this end, we used an integrated species distribution model that combined presence-only wildlife complaints with data from a systematic camera trapping survey throughout Chicago, Illinois. This model draws upon both data sources to estimate a latent distribution of species; in addition, the model can estimate where conflict is most likely to occur within that distribution. We modeled the occupancy and conflict potential of coyote (Canis latrans), Virginia opossum (Didelphis virginiana), and raccoon (Procyon lotor) as a function of urban intensity, per capita income, and home vacancy rates throughout Chicago. Overall, the distribution of each species constrained the spatiotemporal patterns of conflict throughout the city of Chicago. Within each species distribution, we found that human-wildlife conflict was most likely to occur where humans and wildlife habitat overlap (e.g., featuring higher-than-average canopy cover and housing density). Furthermore, human-wildlife conflict was most likely to occur in high-income neighborhoods for Virginia opossum and raccoon, despite the fact that those two species have higher occupancy in low-income neighborhoods. As such, knowing where species are distributed can inform guidelines on where wildlife management should be focused, especially if it overlaps with human habitats. Finally, because this integrated model can incorporate data that have already been collected by wildlife managers or city officials, this approach could be used to develop stronger collaborations with wildlife management agencies and conduct applied research that will inform landscape-scale wildlife management.