IUCN / SSC Cat Specialist Group - Digital Cat Library |
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| Mahoney, P.J.; Young, J.K.; Shivik, J.A.; Stoner, D.C. | |
| Program cluster: identifying predation events through clustered GPS data | |
| 2014 Conference Proceeding | |
Non-parametric kernel density estimators are commonly used to classify home ranges or utilization distributions from animal location data. Here, we extend the r-LoCoH estimator of Getz et al. (2007) using time-constrained convex hulls. This extension was originally developed with the intent of identifying predation events using global positioning system data derived from predator space-use monitoring efforts, but can be applied to any system where frequent and accurate relocation data are available and relocation clustering in space and time are indicative of behaviors of interest (i.e. denning, nesting). The present method is wrapped in a Python program called Program Cluster to allow easy and rapid implementation by users with limited programming knowledge. The output from Program Cluster resembles a fragmented utilization distribution, with each fragment corresponding to areas identified as intensely used in the location data based on user-specified spatial and temporal tuning parameters. Further, each fragment can be interpreted as an individual utilization distribution with more heavily shaded regions (i.e. greatest hull overlap) corresponding to areas with the highest intensity of use. We discuss the extended r-LoCoH method, program implementation, and output in the context an on-going cougar (_Felis conolor_) study on Monroe Mountain, Fishlake National Forest, Utah. |
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