Space–time clusters for early detection of grizzly bear predation

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 (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.

Discriminating grey wolf kill sites using GPS clusters

“…Anderson and Lindzey (2003) first developed a method to evaluate if spatio‐temporal clustered GPS locations were associated with kill sites of cougars ( Puma concolor ). Since then, the use of spatio‐temporal GPS clusters to identify kill sites has been refined for cougars (Knopff et al 2009, Ruth et al 2010, Elbroch and Wittmer 2013, Wilckens et al 2015), and applied to other predators including lions ( Panthera leo ; Tambling et al 2010, 2012), leopards ( Panthera pardus ; Martins et al 2010, Pitman et al 2012), jaguars ( Panthera onca ; Cavalcanti and Gese 2010, Gese et al 2016), Eurasian lynx ( Lynx lynx ; Mattisson et al 2011, Krofel et al 2013), bobcats ( Lynx rufus ; Svoboda et al 2013), grizzly bears ( Ursus arctos ; Rauset et al 2012, Cristescu et al 2015, Ebinger et al 2016, Kermish‐Wells et al 2017), black bears ( Ursus americanus ; Kindschuh et al 2016), wolverines ( Gulo gulo ; Scrafford and Boyce 2018), and wolves ( Canis lupus ; Zimmerman et al 2007, Webb et al 2008, DeCesare 2012, Lake et al 2013).…”

“…Characteristics of recursion represent biological components of predation events such as handling time, diel variation, and space‐use around a prey carcass. Cluster duration variables indicating handling time have been characteristic in distinguishing predation events among predator species (Ruth et al 2010, Svoboda et al 2013, Kermish‐Wells et al 2017, Scrafford and Boyce 2018).…”

Conducting social network analysis with animal telemetry data: Applications and methods using spatsoc

“…Meanwhile, the clustering method will identify spatially and temporally clustered relocations for individuals, or groups of individuals, and could have applications for identifying preferred habitats for groups as well as locations of scavenging or predation (e.g. for similar applications see Knopff, Knopff, Warren, & Boyce, 2009;Kermish-Wells, Massolo, Stenhouse, Larsen, & Musiani, 2018;Cristescu, Stenhouse, & Boyce, 2014).…”