Improvements on GPS Location Cluster Analysis for the Prediction of Large Carnivore Feeding Activities: Ground-Truth Detection Probability and Inclusion of Activity Sensor Measures

摘要: Animal space use studies using GPS collar technology are increasingly incorporating behavior based analysis of spatio-temporal data in order to expand inferences resource use. location cluster is one such technique applied large carnivores identify the timing and feeding events. For logistical financial reasons, researchers often implement predictive models for identifying these We present two separate improvements that future practitioners can implement. Thus far, prediction have incorporated a small range covariates, usually limited characteristics data. Using collared cougar (Puma concolor) we include activity sensor as an additional covariate increase performance presence/absence. Integral modeling events ground-truthing component, which clusters visited by human observers confirm presence or absence remains. Failing account sources false-absences bias number predicted be low. some error directly model with covariates when applying predictions. Accounting errors resulted 10% double-observer design, show false-absence rate relatively low (4%) search delay 2–60 days. Overall, provide techniques expanded upon implemented interested behaviors carnivores.