Methods for locating african lion kills using global positioning system movement data

摘要

Knowledge of the range, behavior, and feeding habits of large carnivores isfundamental to their successful conservation. Traditionally, the best method to obtain feedingdata is through continuous observation, which is not always feasible. Reliable automatedmethods are needed to obtain sample sizes sufficient for statistical inference. Identification oflarge carnivore kill sites using Global Positioning System (GPS) data is gaining popularity. Weassessed performance of generalized linear regression models (GLM) versus classification trees(CT) in a multi-predator, multi-prey African savanna ecosystem. We applied GLMs and CTs tovarious combinations of distance travelled data, cluster durations, and environmental factors topredict 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 themost important predictor variable in both GLM and CT analysis. In all cases, GLMsoutperformed our cost-complexity-pruned CTs in their discriminative ability to separate kill fromnon-kill sites. Generalized linear models provided a good framework for kill site identificationthat incorporates a hierarchal ordering of cluster investigation and measures to assess trade-offsbetween classification accuracy and time constraints. Implementation of GLMs within anadaptive sampling framework can considerably increase efficiency of locating kill sites,providing a cost-effective method for increasing sample sizes of kill data.