Space–time clusters for early detection of grizzly bear predation

• Published in: Ecology and Evolution
• Main Authors: Kermish‐Wells, Joseph, Massolo, Alessandro, Stenhouse, Gordon B., Larsen, Terrence A., Musiani, Marco
• Other Authors: Weyerhaeuser Company, Shell, Alberta Conservation Association, Natural Sciences and Engineering Research Council of Canada
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: Ursus arctos
• Online Access: http://dx.doi.org/10.1002/ece3.3489; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fece3.3489; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.3489; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.3489

Abstract 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 (Sa TS can) 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 GLMM s 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 ...