Insights for Estimating Animal Movement Step Selection Functions
- 1University of Maryland
Abstract
Ecologists remotely track movement steps of animals (e.g., via global positioning systems) and use step selection functions to study the effect of environmental factors upon their movement decisions. Constructing such functions requires pairing each observed step with some number of unobserved but feasible comparison steps. Larger numbers of comparison steps generally yield better estimates but also incur potentially challenging computational demands. Thus, it is important to determine an appropriate number of comparison steps. No established guidance exists for this decision. Here, we use simulated tracks to assess how many comparison steps are needed, fitting each set of steps to a conditional logistic regression model. We monitor errors in estimated effects for several classes of tracks, identifying the number of comparison steps for which mean relative absolute error in estimated effects is consistently low. By this criterion, 32 comparison steps per observed step are needed for our primary class of simulated tracks. Tracks in more homogeneous landscapes, tracks with shorter mean step lengths, or shorter tracks generally require more comparison steps (ranging from 64 to 128 per observed step) to achieve the same level of accuracy. Longer tracks generally require fewer comparison steps (16 per observed step). These results clearly demonstrate that the number of comparison steps influences how well step selection functions estimate covariate effects and provides initial direction in a research area that currently lacks quantitative guidance. Movement ecologists should take care when selecting the number of comparison steps paired with each observed step because those decisions matter.
Related
- Social anchors: how previous social locations shape current animal movement and habitat selection
- Behavioural state inference from movement and environmental data using Markovian step selection functions
- Movement mechanisms explain current animal density patterns and predict their reorganization under global change
- Perceptual constraints of motion shape animal movement strategies
- Representing short-term movement to reveal animal identity and learning progress
BibTeX
@misc{koshute2026insights,
title = {{Insights for Estimating Animal Movement Step Selection Functions}},
author = {P. Koshute and W. F. Fagan},
howpublished = {bioRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.29.748012},
url = {https://www.biorxiv.org/content/10.64898/2026.08.29.748012v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Insights for Estimating Animal Movement Step Selection Functions
AU - P. Koshute
AU - W. F. Fagan
PY - 2026
DA - 2026/08/29
PB - bioRxiv
DO - 10.64898/2026.08.29.748012
UR - https://www.biorxiv.org/content/10.64898/2026.08.29.748012v1
AB - Ecologists remotely track movement steps of animals (e.g., via global positioning systems) and use step selection functions to study the effect of environmental factors upon their movement decisions. Constructing such functions requires pairing each observed step with some number of unobserved but feasible comparison steps. Larger numbers of comparison steps generally yield better estimates but also incur potentially challenging computational demands. Thus, it is important to determine an appropriate number of comparison steps. No established guidance exists for this decision. Here, we use simulated tracks to assess how many comparison steps are needed, fitting each set of steps to a conditional logistic regression model. We monitor errors in estimated effects for several classes of tracks, identifying the number of comparison steps for which mean relative absolute error in estimated effects is consistently low. By this criterion, 32 comparison steps per observed step are needed for our primary class of simulated tracks. Tracks in more homogeneous landscapes, tracks with shorter mean step lengths, or shorter tracks generally require more comparison steps (ranging from 64 to 128 per observed step) to achieve the same level of accuracy. Longer tracks generally require fewer comparison steps (16 per observed step). These results clearly demonstrate that the number of comparison steps influences how well step selection functions estimate covariate effects and provides initial direction in a research area that currently lacks quantitative guidance. Movement ecologists should take care when selecting the number of comparison steps paired with each observed step because those decisions matter.
ER -