RT Journal Article
SR Electronic
T1 Revisiting reptile home ranges: moving beyond traditional estimators with dynamic Brownian Bridge Movement Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.10.941278
DO 10.1101/2020.02.10.941278
A1 Inês Silva
A1 Matt Crane
A1 Benjamin Michael Marshall
A1 Colin Thomas Strine
YR 2020
UL http://biorxiv.org/content/early/2020/02/10/2020.02.10.941278.abstract
AB Animal movement, expressed through home ranges, can offer insights into spatial and habitat requirements. However, home range estimation methods vary, directly impacting conclusions. Recent technological advances in animal tracking (GPS and satellite tags), have enabled new methods for home range estimation, but so far have primarily targeted mammal and avian movement patterns. Most reptile home range studies only make use of two older estimation methods: Minimum Convex Polygons (MCP) and Kernel Density Estimators (KDE), particularly with the Least Squares Cross Validation (LSCV) and reference (href) bandwidth selection algorithms. The unique characteristics of reptile movement patterns (e.g. low movement frequency, long stop-over periods), prompt an investigation into whether newer movement-based methods –such as dynamic Brownian Bridge Movement Models (dBBMMs)– are applicable to Very High Frequency (VHF) radio-telemetry tracking data. To assess home range estimation methods for reptile telemetry data, we simulated animal movement data for three archetypical reptile species: a highly mobile active hunter, an ambush predator with long-distance moves and long-term sheltering periods, and an ambush predator with short-distance moves and short-term sheltering periods. We compared traditionally used home range estimators, MCP and KDE, with dBBMMs, across eight feasible VHF field sampling regimes for reptiles, varying from one data point every four daylight hours, to once per month. Although originally designed for GPS tracking studies, we found that dBBMMs outperformed MCPs and KDE href across all tracking regimes, with only KDE LSCV performing comparably at some higher-frequency sampling regimes. The performance of the LSCV algorithm significantly declined with lower-tracking-frequency regimes, whereas dBBMMs error rates remained more stable. We recommend dBBMMs as a viable alternative to MCP and KDE methods for reptile VHF telemetry data: it works under contemporary tracking protocols and provides more stable estimates, improving comparisons across regimes, individuals and species.