Improvements on GPS location cluster analysis for the prediction of large carnivore feeding activities: ground-truth detection probability and inclusion of activity sensor measures

Dublin Core

Title

Improvements on GPS location cluster analysis for the prediction of large carnivore feeding activities: ground-truth detection probability and inclusion of activity sensor measures

Description

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

Bibliographic Citation

Blecha, K. A., and M. W. Alldredge. 2015. Improvements on GPS location cluster analysis for the prediction of large carnivore feeding activities: ground-truth detection probability and inclusion of activity sensor measures. PLoS One 10(9): e0138915. https://doi.org/10.1371/journal.pone.0138915

Creator

Blecha, Kevin A.
Alldredge, Mathew W.

Subject

Geographic Information Systems (GIS)
Quality Improvement
Animal Distribution
Cluster Analysis
Puma
Research
Animal behavior
Global positioning systems (GPS)
Mathematical models
Telemetry

Extent

19 pages

Date Created

2015-09-23

Type

Text

Format

application/pdf

Language

English

Is Part Of

PLoS One

Collection

Citation

Blecha, Kevin A. and Alldredge, Mathew W., “Improvements on GPS location cluster analysis for the prediction of large carnivore feeding activities: ground-truth detection probability and inclusion of activity sensor measures,” CPW Digital Collections, accessed October 4, 2022, https://cpw.cvlcollections.org/items/show/98.