An assessment of red wolf (Canis rufus) population persistence using classical and individual-based modeling approaches

Abstract

Population viability analysis (PVA) has become the quintessential analytical tool for conservation biologists. However, as interest in PVA has grown, many notable concerns have been raised about the predictive accuracy of PVA models. Model uncertainty, the analytical limitations of sparse data, and error propagation are a few of the many limitations discussed in the literature. Unfortunately, PVA models are rarely re-evaluated or tested relative to future trends within populations, limiting our understanding of how robust PVA can be to many of the uncertainties raised by the conservation community. Here, we explore how model uncertainties and model complexity influence predictions of population persistence with twenty one years of red wolf (Canis rufus) demographic data in an effort to better understand PVA, as well as guide more informed red wolf management in the future, using both classical and individual-based approaches.