Identification of kill sites from GPS clusters for jaguars (Panthera onca) in the southern Pantanal, Brazil
Eric M. Gese A C , Patricia A. Terletzky B and Sandra M. C. Cavalcanti B
+ Author Affiliations
- Author Affiliations
A United States Department of Agriculture, Wildlife Services, National Wildlife Research Center, Department of Wildland Resources, Utah State University, Logan, UT 84322-5230, USA.
B Department of Wildland Resources, Utah State University, Logan, UT 84322-5230, USA.
C Corresponding author. Email: eric.gese@usu.edu
Wildlife Research 43(2) 130-139 https://doi.org/10.1071/WR15196
Submitted: 16 October 2015 Accepted: 27 February 2016 Published: 2 May 2016
Abstract
Context: Understanding predator–prey relationships is important for making informed management decisions. Knowledge of jaguar (Panthera onca) predation on livestock and native prey is imperative for future conservation of jaguars in Central and South America.
Aim: As part of an investigation to determine predation patterns of jaguars in the southern Pantanal, Brazil, we examined spatial, temporal and habitat variables, which are useful in categorising location clusters as kill sites and non-kill sites.
Methods: Using GPS-collars on 10 jaguars we obtained a total of 11 784 locations, from which 877 clusters were identified, visited and examined for prey remains. Of the 877 clusters, 421 were associated with a kill and 456 clusters were not associated with a kill. We used univariate and multivariate models to examine the influence of spatial (distance to nearest: water, dense cover, road; dispersion of points), temporal (season, time, number of nights, duration) and habitat (percentage of seven habitat classes, dominant habitat class) variables on categorising clusters as kill or non-kill sites.
Key results: We found the time a jaguar spent at a cluster (duration), the dispersion of points around the centre of the cluster (dispersion) and the number of nights spent at the cluster were all reliable predictors of whether a cluster was a kill or non-kill site. The best model predicting the likelihood a cluster was a jaguar kill site was a combination of duration and dispersion. Habitat variables were not important in discriminating kills from non-kill sites.
Conclusion: We identified factors useful for discriminating between kills and non-kill sites for jaguars. We found that as a jaguar spent more time at a cluster and as the dispersion of points around the centre of the cluster increased, the higher likelihood the cluster was a jaguar kill. Similarly, as the number of nights spent at the cluster increased, the greater the probability the cluster was a kill.
Implications: Our results will increase the efficiency of field investigations of location clusters in determining predation patterns of jaguars in Central and South America. Being able to prioritise which location clusters should be investigated will assist researchers with limited time and resources.
Additional keywords: cluster analysis, global positioning systems (GPS) radio-collars, kill sites, non-kill sites, predation.
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