Estimation of pack density in grey wolf ( Canis lupus ) by applying spatially explicit capture-recapture models to camera trap data supported by genetic monitoring

摘要: Density estimation is a key issue in wildlife management but particularly challenging and labour-intensive for elusive species. Recently developed approaches based on remotely collected data capture-recapture models, though representing valid alternative to more traditional methods, have found little application species with limited morphological variation. We implemented camera trap study survey wolf packs 560-km2 area of Central Italy. Individual recognition focal animals (alpha) the was possible by relying behavioural traits validated non-invasive genotyping inter-observer agreement tests. Two types (Bayesian likelihood-based) spatially explicit (SCR) models were fitted pack capture histories, thus obtaining an density area. In two sessions trapping surveys (2014 2015), we detected maximum 12 packs. A Bayesian model implementing half-normal detection function without trap-specific response provided most robust result, corresponding 1.21 ± 0.27 packs/100 km2 2015. Average size varied from 3.40 (summer 2014, excluding pups lone-transient wolves) 4.17 (late winter-spring 2015, wolves). applied first time camera-based SCR approach wolves, providing estimate showed that this method applicable wolves under following conditions: i) existence sufficient phenotypic/behavioural variation individuals (i.e. alpha, verified genotyping); ii) investigated sufficiently large include minimum number (ideally 10); iii) pilot carried out pursue adequate sampling design train operators individual recognition. believe replicating other areas can allow assessment across range would provide reliable reference parameter ecological studies.