Incorporating movement patterns to discern habitat selection: black bears as a case study
Dana L. Karelus
A E , J. Walter McCown B , Brian K. Scheick B , Madelon van de Kerk C , Benjamin M. Bolker D and Madan K. Oli A
+ Author Affiliations
- Author Affiliations
A Department of Wildlife Ecology and Conservation, and School of Natural Resources and Environment, 110 Newins-Ziegler Hall, University of Florida, Gainesville, FL 32611, USA.
B Florida Fish and Wildlife Conservation Commission, 1105 SW Williston Road, Gainesville, FL 32601, USA.
C School of Environmental and Forest Sciences, 114 Winkenwerder, University of Washington, Seattle, WA 98195, USA.
D Departments of Mathematics & Statistics and Biology, McMaster University, 314 Hamilton Hall, Hamilton, Ontario L8S 4K1, Canada.
E Corresponding author. Email: dana.karelus@sulross.edu
Wildlife Research 46(1) 76-88 https://doi.org/10.1071/WR17151
Submitted: 30 October 2017 Accepted: 4 November 2018 Published: 15 February 2019
Abstract
Context: Animals’ use of space and habitat selection emerges from their movement patterns, which are, in turn, determined by their behavioural or physiological states and extrinsic factors.
Aim: The aims of the present study were to investigate animal movement and incorporate the movement patterns into habitat selection analyses using Global Positioning System (GPS) location data from 16 black bears (Ursus americanus) in a fragmented area of Florida, USA.
Methods: Hidden Markov models (HMMs) were used to discern the movement patterns of the bears. These results were then used in step-selection functions (SSFs) to evaluate habitat selection patterns and the factors influencing these patterns.
Key results: HMMs revealed that black bear movement patterns are best described by three behavioural states: (1) resting (very short step-lengths and large turning angles); (2) encamped (moderate step-lengths and large turning angles); and (3) exploratory (long step-lengths and small turning angles). Bears selected for forested wetlands and marsh wetlands more than any other land cover type, and generally avoided urban areas in all seasons and when in encamped and exploratory behavioural states. Bears also chose to move to locations farther away from major roads.
Conclusions: Because habitat selection is influenced by how animals move within landscapes, it is essential to consider animals’ movement patterns when making inferences about habitat selection. The present study achieves this goal by using HMMs to first discern black bear movement patterns and associated parameters, and by using these results in SSFs to investigate habitat selection patterns. Thus, the methodological framework developed in this study effectively incorporates state-specific movement patterns while making inferences regarding habitat selection. The unified methodological approach employed here will contribute to an improved understanding of animal ecology as well as informed management decisions.
Implications: Conservation plans focused on preserving forested wetlands would benefit bears by not only providing habitat for resting and foraging, but also by providing connectivity through fragmented landscapes. Additionally, the framework could be applied to species that follow annual cycles and may provide a tool for investigating how animals are using dispersal corridors.
Additional keywords: black bear movement, hidden Markov models, step-selection functions, step-length, Ursus americanus.
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