Statistical Methods for Identifying Wolf Kill Sites Using Global Positioning System Locations

Abstract

Accurate estimates of kill rates remain a key limitation to addressing many predator—prey questions. Past approaches for identifying kill sites of large predators, such as wolves (Canis lupus), have been limited primarily to areas with abundant winter snowfall and have required intensive ground‐tracking or aerial monitoring. More recently, attempts have been made to identify clusters of locations obtained using Global Positioning System (GPS) collars on predators to identify kill sites. However, because decision rules used in determining clusters have not been consistent across studies, results are not necessarily comparable. We illustrate a space—time clustering approach to statistically define clusters of wolf GPS locations that might be wolf kill sites, and we then use binary and multinomial logistic regression to model the probability of a cluster being a non—kill site, kill site of small‐bodied prey species, or kill site of a large‐bodied prey species. We evaluated our approach using field visits of kills and assessed the accuracy of the models using an independent dataset. The cluster‐scan approach identified 42–100% of wolf‐killed prey, and top logistic regression models correctly classified 100% of kills of large‐bodied prey species, but 40% of small‐bodied prey species were classified as nonkills. Although knowledge of prey distribution and vulnerability may help refine this approach, identifying small‐bodied prey species will likely remain problematic without intensive field efforts. We recommend that our approach be utilized with the understanding that variation in prey body size and handling time by wolves will likely have implications for the success of both the cluster scan and logistic regression components of the technique. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):798–807; 2008)

Effects of Time of Day and Activity Status on Bobcat (Lynx rufus) Cover-Type Selection in Southwestern Georgia

“…a cluster) was a kill site (Knopff et al 2009), fewer have attempted to develop models that researchers can use to increase efficacy of detecting kill sites (e.g. Webb et al 2008, Tambling et al 2010). Metrics used in previous studies include biological factors that may affect predator kill success (habitat type, time of day; Anderson & Lindzey 2003, Webb et al 2008, predator movement characteristics (rate of movement, site fidelity; Knopff et al 2009) and factors that may affect the probability of detecting prey at the kill site (number of locations at cluster, elapsed time from cluster formation to investigation; Sand et al 2005, Zimmerman et al 2007, Knopff et al 2009).…”

“…Webb et al 2008, Tambling et al 2010). Metrics used in previous studies include biological factors that may affect predator kill success (habitat type, time of day; Anderson & Lindzey 2003, Webb et al 2008, predator movement characteristics (rate of movement, site fidelity; Knopff et al 2009) and factors that may affect the probability of detecting prey at the kill site (number of locations at cluster, elapsed time from cluster formation to investigation; Sand et al 2005, Zimmerman et al 2007, Knopff et al 2009). Including multiple metrics in model development provides biologists with a comprehensive approach to efficiently identify potential kill sites based on the biological characteristics and movements of the predator while maximizing the probability of detecting a kill site in the field.…”

Hunting performance of captive-born South China tigers (Panthera tigris amoyensis) on free-ranging prey and implications for their reintroduction

“…Mean error was 4.49 m in one collar (minimum = 0.37 m, maximum = 23.24), and 4.31 m for the other collar (minimum = 0.28, maximum = 26.83 m). Collars were programmed to record location at 15 minute intervals (144 readings/tiger/day) to maximize opportunities of identifying small prey feeding locations (Webb et al, 2008). GPS data were downloaded and imported into ArcGIS v.10.2 (ESRI, Redlands, CA, USA) to identify GPS location clusters.…”

“…All clusters were visited the day after the tiger was removed from the enclosure at the end of its study period with a maximum of 33 days between the occurrence of a cluster and its visit. This search schedule avoided displacing tigers from kills and was adequate to prevent losing information to carcass decomposition or scavenging since caracals (Caracal caracal), black backed jackals (Canis mesomelas), and small scavengers such as members of the Herpestidae family were also present in the camps (Miller et al, 2013;Sand et al, 2008;Webb et al, 2008). Prey remains were photographed and, if unconfirmed in the field, representative material was collected for identification at the Centre of Wildlife Management, University of Pretoria (South Africa).…”

Gray Wolf (<em>Canis lupus</em>) Movements and Behavior Around a Kill Site and Implications for GPS Collar Studies

“…The use of Global Positioning System (GPS) radiocollars on Gray Wolves (Canis lupus) (Mech et al 1998;Merrill et al 1998) soon led to attempts to study wolf predation (Sand et al 2005;Demma et al 2007;Zimmerman et al 2007;Sand et al 2008;Webb et al 2008;Palacios and Mech 2010). Such attempts rely on the assumption that when wolves make a kill, they remain at or near the kill for a long enough period so that clusters of the GPS locations taken from the kill site can be distinguished from GPS locations recorded while the animals are traveling.…”