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

• Main Authors: Mattioli, Luca, Canu, Antonio, Passilongo, Daniela, Scandura, Massimo, Apollonio, Marco
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Figshare 2018
• Subjects: 59999 Environmental Sciences not elsewhere classified; FOS Earth and related environmental sciences; Ecology; FOS Biological sciences; 69999 Biological Sciences not elsewhere classified; 19999 Mathematical Sciences not elsewhere classified; FOS Mathematics; Cancer; Lone; Canis lupus
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.4253942; https://figshare.com/collections/Estimation_of_pack_density_in_grey_wolf_Canis_lupus_by_applying_spatially_explicit_capture-recapture_models_to_camera_trap_data_supported_by_genetic_monitoring/4253942

Abstract Background Density 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. Results In 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). Conclusions We 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.