Abstract
BackgroundDensity estimation is a key issue in wildlife management but is particularly challenging and labour-intensive for elusive species. Recently developed approaches based on remotely collected data and capture-recapture models, though representing a valid alternative to more traditional methods, have found little application to species with limited morphological variation. We implemented a camera trap capture-recapture study to survey wolf packs in a 560-km2 area of Central Italy. Individual recognition of focal animals (alpha) in the packs was possible by relying on morphological and behavioural traits and was validated by non-invasive genotyping and inter-observer agreement tests. Two types (Bayesian and likelihood-based) of spatially explicit capture-recapture (SCR) models were fitted on wolf pack capture histories, thus obtaining an estimation of pack density in the area.ResultsIn two sessions of camera trapping surveys (2014 and 2015), we detected a maximum of 12 wolf packs. A Bayesian model implementing a half-normal detection function without a trap-specific response provided the most robust result, corresponding to a density of 1.21 ± 0.27 packs/100 km2 in 2015. Average pack size varied from 3.40 (summer 2014, excluding pups and lone-transient wolves) to 4.17 (late winter-spring 2015, excluding lone-transient wolves).ConclusionsWe applied for the first time a camera-based SCR approach in wolves, providing the first robust estimate of wolf pack density for an area of Italy. We showed that this method is applicable to wolves under the following conditions: i) the existence of sufficient phenotypic/behavioural variation and the recognition of focal individuals (i.e. alpha, verified by non-invasive genotyping); ii) the investigated area is sufficiently large to include a minimum number of packs (ideally 10); iii) a pilot study is carried out to pursue an adequate sampling design and to train operators on individual wolf recognition. We believe that replicating this approach in other areas can allow for an assessment of density variation across the wolf range and would provide a reliable reference parameter for ecological studies.
Highlights
Density estimation is a key issue in wildlife management but is challenging and labourintensive for elusive species
We studied an Italian wolf population, where individual recognition was facilitated by the introgression of canine genes [33] and validated by the support of non-invasive genetic sampling (NGS) data coming from a long-term research project
Wolf recognition During 303 sampling occasions (5197 trap days), from the 1st of April 2014 to the 11th of June 2015, we achieved a total of 909 wolf videos corresponding to 657 independent capture events (CE) (1.38 videos/CE) and 1240 individuals captured (1.89 individuals/CE)
Summary
Density estimation is a key issue in wildlife management but is challenging and labourintensive for elusive species. Non-invasive genetic sampling (NGS) and camera trapping (CT), combined with capture-recapture (CR) models, revolutionized field studies on many large terrestrial predators in forested habitats [4], as they allow individual recognition and the obtainment of encounter history data without animal manipulation. In the last few years, CT has been increasingly used for species lacking evident natural markings, like puma (Puma concolor [16]), lowland tapir (Tapirus terrestris [17]), forest elephant (Loxodonta cyclotis [18]) chimpanzee (Pan troglodytes [19]), and some canids (coyote Canis latrans [20]; maned wolf Chrysocyon brachyurus [21] and red fox Vulpes vulpes [22]) In these studies, individual recognition was obtained from different morphological traits like tail shape and carriage or fur colour markings in specific areas (e.g. head, legs or tail)
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