Testing Global Positioning System Telemetry to Study Wolf Predation on Deer Fawns

ABSTRACT We conducted a pilot study to test the usefulness of Global Positioning System (GPS) collars for investigating wolf (Canis lupus) predation on white-tailed deer (Odocoileus virginianus) fawns. Using GPS collars with short location-attempt intervals on 5 wolves and 5 deer during summers 2002–2004 in northeastern Minnesota, USA, demonstrated how this approach could provide new insights into wolf hunting behavior of fawns. For example, a wolf traveled ≥1.5–3.0 km and spent 20–22 hours in the immediate vicinity of known fawn kill sites and ≥0.7 km and 8.3 hours at scavenging sites. Wolf travel paths indicated that wolves intentionally traveled into deer summer ranges, traveled ≥0.7–4.2 km in such ranges, and spent <1–22 hours per visit. Each pair of 3 GPS-collared wolf pack members were located together for ≤6% of potential locations. From GPS collar data, we estimated that each deer summer range in a pack territory containing 5 wolves ≥1 year old and hunting individually would be visited by a wolf on average every 3–5 days. This approach holds great potential for investigating summer hunting behavior of wolves in areas where direct observation is impractical or impossible.     

Methods for Locating African Lion Kills Using Global Positioning System Movement Data

ABSTRACT Knowledge of the range, behavior, and feeding habits of large carnivores is fundamental to their successful conservation. Traditionally, the best method to obtain feeding data is through continuous observation, which is not always feasible. Reliable automated methods are needed to obtain sample sizes sufficient for statistical inference. Identification of large carnivore kill sites using Global Positioning System (GPS) data is gaining popularity. We assessed performance of generalized linear regression models (GLM) versus classification trees (CT) in a multipredator, multiprey African savanna ecosystem. We applied GLMs and CTs to various combinations of distance-traveled data, cluster durations, and environmental factors to predict occurrence of 234 female African lion (Panthera leo) kill sites from 1,477 investigated GPS clusters. Ratio of distance moved 24 hours before versus 24 hours after a cluster was the most important predictor variable in both GLM and CT analysis. In all cases, GLMs outperformed our cost-complexity-pruned CTs in their discriminative ability to separate kill from nonkill sites. Generalized linear models provided a good framework for kill-site identification that incorporates a hierarchal ordering of cluster investigation and measures to assess trade-offs between classification accuracy and time constraints. Implementation of GLMs within an adaptive sampling framework can considerably increase efficiency of locating kill sites, providing a cost-effective method for increasing sample sizes of kill data.     

Evaluating Global Positioning System Telemetry Techniques for Estimating Cougar Predation Parameters

ABSTRACT Using clusters of locations obtained from Global Positioning System (GPS) telemetry collars to identify predation events may allow more efficient estimation of behavioral predation parameters for the study and management of large carnivore predator-prey systems. Applications of field- and model-based GPS telemetry cluster techniques, however, have met with mixed success. To further evaluate and refine these techniques for cougars (Puma concolor), we used data from visits to 1,735 GPS telemetry clusters, 637 of which were locations where cougars killed prey >8 kg in a multi-prey system in west-central Alberta. We tested 1) whether clusters were reliably created at kill locations, 2) the ability of logistic regression models to identify kill occurrence (prey >8 kg) and multinomial regression models to identify the prey species at a kill cluster, and 3) the duration of monitoring required to accurately estimate kill rate and prey composition. We found that GPS collars programmed to attempt location fixes every 3 hours consistently identified locations where prey >8 kg were handled, and cluster creation was robust to GPS location acquisition failures (poor collar fix success). The logistic regression model was capable of estimating cougar kill rate with a mean 5-fold cross validation error of <10%, provided the appropriate probability cutoff distinguishing kill clusters from non-kill clusters was selected. Logistic models also can be used to direct visits to clusters, reducing field efforts by as much as 25%, while still locating >95% of all kills. The multinomial model overpredicted occurrence of primary prey (deer) in the diet and underpredicted consumption of alternate prey (e.g., elk and moose) by as much as 100%. We conclude that a purely model-based approach should be used cautiously and that field visitation is required to obtain reliable information on species, sex, age, or condition of prey. Ultimately, we recommend a combined approach that involves using models to direct field visitation when estimating behavioral predation parameters. Regardless of the monitoring approach, long continuous monitoring periods (i.e., >100 days of a 180-day period) were necessary to reduce bias and imprecision in kill rate and prey composition estimates.