Incorporating ecological drivers and uncertainty into a demographic population viability analysis for the island fox

Biometricians have made great strides in the generation of reliable estimates of demographic rates and their uncertainties from imperfect field data, but these estimates are rarely used to produce detailed predictions of the dynamics or future viability of at-risk populations. Conversely, population viability analysis (PVA) modelers have increased the sophistication and complexity of their approaches, but most do not adequately address parameter and model uncertainties in viability assessments or include important ecological drivers. Merging the advances in these two fields could enable more defensible predictions of extinction risk and better evaluations of management options, but only if clear and interpretable PVA results can be distilled from these complex analyses and outputs. Here, we provide guidance on how to successfully conduct such a combined analysis, using the example of the endangered island fox (Urocyon littoralis), endemic to the Channel Islands of California, USA. This more rigorous demographic PVA was built by forming a close marriage between the statistical models used to estimate parameters from raw data and the details of the subsequent PVA simulation models. In particular, the use of mark-recapture analyses and other likelihood and information-theoretic methods allowed us to carefully incorporate parameter and model uncertainty, the effects of ecological drivers, density dependence, and other complexities into our PVA. Island fox populations show effects of density dependence, predation, and El Nino events, as well as substantial unexplained temporal variation in survival rates. Accounting not only for these sources of variability, but also for uncertainty in the models and parameters used to estimate their strengths, proved important in assessing fox viability with different starting population sizes and predation levels. While incorporating ecological drivers into PVA assessments can help to predict realistic dynamics, we also show that unexplained process variance has important effects even in our extremely well-studied system, and therefore must not be ignored in PVAs. Overall, the treatment of causal factors and uncertainties in parameter values and model structures need not result in unwieldy models or highly complex predictions, and we emphasize that future PVAs can and should include these effects when suitable data are available to support their analysis.