An improved procedure to estimate wolf abundance using non-invasive genetic sampling and capture–recapture mixture models

Abstract

Non-invasive genetic sampling (NGS) is increasingly used to estimate the abundance of rare or elusive species such as the wolf (Canis lupus), which cannot be directly counted in forested mountain habitats. Wolf individual and familial home ranges are wide, potentially connected by long-range dispersers, and their populations are intrinsically open. Appropriate demo- graphic estimators are needed, because the assumptions of homogeneous detection probability and demographic closeness are violated. We compiled the capture-recapture record of 418 individual wolf genotypes identified from ca. 4,900 non-invasive samples, collected in the northern Italian Apennines from January 2002 to June 2009. We analysed this dataset using novel capture-recapture multi- event models for open populations that explicitly account for individual detection heterogeneity (IDH). Overall, the detection probability of the weakly detectable individuals, probably pups, juveniles and migrants (P = 0.08), was ca. six times lower than that of the highly detectable wolves (P = 0.44), probably adults and dominants. The apparent annual survival rate of weakly detectable individuals was lower (U = 0.66) than those of highly detectable wolves (U = 0.75). The population mean annual finite rate of increase was k = 1.05 ± 0.11, and the mean annual size ranged from N = 117 wolves in 2003 to N = 233 wolves in 2007. This procedure, combining large-scale NGS and multievent IDH demographic models, provides the first estimates of abundance, multi-annual trend and survival rates for an open large wolf population in the Apennines. These results contribute to deepen our understanding of wolf population ecology and dynamics, and provide new information to implement sound long-term conservation plans.