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Space-time clusters for early detection of grizzly bear predation.
Kermish-Wells, Joseph; Massolo, Alessandro; Stenhouse, Gordon B; Larsen, Terrence A; Musiani, Marco.
Afiliação
  • Kermish-Wells J; Environmental Design University of Calgary Calgary AB Canada.
  • Massolo A; Ethology Unit Department of Biology University of Pisa Pisa Italy.
  • Stenhouse GB; Department of Ecosystem and Public Health Faculty of Veterinary Medicine University of Calgary Calgary AB Canada.
  • Larsen TA; UMR CNRS 6249 Chrono-Environnement Université Bourgogne Franche-Comté Besancon France.
  • Musiani M; fRI Research Grizzly Bear Program Hinton AB Canada.
Ecol Evol ; 8(1): 382-395, 2018 01.
Article em En | MEDLINE | ID: mdl-29321879
Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small-prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space-time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears (Ursus arctos) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013-2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006-2007, 187 of which were within space-time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013-2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based on space-time probability models allows for prompt visits to predation sites. This enables accurate identification of the carcass size and increases fieldwork efficiency in predation studies.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Idioma: En Revista: Ecol Evol Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Idioma: En Revista: Ecol Evol Ano de publicação: 2018 Tipo de documento: Article