Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

摘要: In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators large areas difficult, data have a high level uncertainty. We devised practical multimethod multistate modeling approach based on Bayesian hierarchical-site-occupancy models combined multiple survey methods estimate different states for use at regional scale. used wolves (Canis lupus) as our model species generated reliable number sites with wolf reproduction (presence pups). 2 sets from Spain (Western Galicia 2013 Asturias 2004) test approach. Based howling surveys, naive estimation (i.e., only observations) was 9 25 Western Asturias, respectively. Our showed 33.4 (SD 9.6) 34.4 (3.9) reproduction, The occupied 0.67 0.19) 0.76 (0.11), This can be design more programs define sampling effort needed per site). should inspire well-coordinated surveys across administrative borders populations lead improved decision making carnivores landscape level. this framework provides simple way visualize degree uncertainty around population-parameter thus managers stakeholders an intuitive interpreting results. widely applied spatial scales wildlife where detection probabilities differ between several being parameters.