Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models

Abstract

Background: Animal movement expressed through home ranges or space-use can offer insights into spatial and habitat requirements. However, different classes of estimation methods are currently instinctively applied to answer home range, space-use or movement-based research questions regardless of their widely varying outputs, directly impacting conclusions. Recent technological advances in animal tracking (GPS and satellite tags), have enabled new methods to quantify animal space-use and movement pathways, but so far have primarily targeted mammal and avian species.

Methods: Most reptile spatial ecology studies only make use of two older home range estimation methods: Minimum Convex Polygons (MCP) and Kernel Density Estimators (KDE), particularly with the Least Squares Cross Validation (LSCV) and reference (h _ref ) bandwidth selection algorithms. These methods are frequently applied to answer space-use and movement-based questions. Reptile movement patterns are unique (e.g., low movement frequency, long stop-over periods), prompting investigation into whether newer movement-based methods -such as dynamic Brownian Bridge Movement Models (dBBMMs)- apply to Very High Frequency (VHF) radio-telemetry tracking data. We simulated 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 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.

Results: Although originally designed for GPS tracking studies, dBBMMs outperformed MCPs and KDE h _ref across all tracking regimes in accurately revealing movement pathways, with only KDE LSCV performing comparably at some higher frequency sampling regimes. However, the LSCV algorithm failed to converge with these high-frequency regimes due to high site fidelity, and was unstable across sampling regimes, making its use problematic for species exhibiting long-term sheltering behaviours. We found that dBBMMs minimized the effect of individual variation, maintained low error rates balanced between omission (false negative) and commission (false positive), and performed comparatively well even under low frequency sampling regimes (e.g., once a month).

Conclusions: We recommend dBBMMs as a valuable alternative to MCP and KDE methods for reptile VHF telemetry data, for research questions associated with space-use and movement behaviours within the study period: they work under contemporary tracking protocols and provide more stable estimates. We demonstrate for the first time that dBBMMs can be applied confidently to low-resolution tracking data, while improving comparisons across regimes, individuals, and species.

Supplementary information: Supplementary information accompanies this paper at 10.1186/s40462-020-00229-3.

Keywords: Dynamic Brownian Bridge Movement Models; Kernel density; Lizard; Minimum convex polygon; Reptile; Simulation; Snake; Spatial ecology; Squamate.

Fig. 1

Example two-month period showing how data is thinned to represent different tracking regimes

Fig. 2

Example two-month period illustrating how step distance (m) and its frequency differs between our three species archetypes

Fig. 3

Minimum number of sampling days until the autocorrelation becomes insignificant and data points can be considered independent

Fig. 4

Percentage error from the true movement pathway using 95% contours. a Commission error (Type I, false positive), and b omission error (Type II, false negative). Error bars represent standard error (SE) of means across species (3) and individuals (96). Note, panels a and b have different scales for error because omission error cannot exceed 100% of the true movement pathway

Fig. 5

An example individual from species 1 showing how method and regime can interact to produce different levels of commission (Type I, false negative; blue areas), and omission (Type II, false positive; red areas) error compared to the true movement pathway (grey areas). All contours shown are produced from the 95% contours. Example individuals from species 2 and 3 are in Additional file 1.3

Fig. 6

Comparison between the commission (Type I, false positive; blue areas) and omission (Type II, false negative; red areas) error rates produced by the KDE LSCV and dBBMM 95% contours when using data from sampling regime 2 (four locations per day) and regime 8 (four locations per day, relocations only)

Fig. 7

Model results that aimed to predict F-measures using method, regime, and individual ID by species. Tracking regime 1–7 are shown left to right with lowering levels of opacity. Fitted draws were taken only from the first 5000 samples