An introduction to statistical models used to characterize species-habitat associations with animal movement data

Simple item page
dc.contributor.authorFlorko, Katie R. N.
dc.contributor.authorTogunov, Ron R.
dc.contributor.authorGryba, Rowenna
dc.contributor.authorSidrow, Evan
dc.contributor.authorFerguson, Steven H
dc.contributor.authorYurkowski, David
dc.contributor.authorAuger-Méthé, Marie
dc.date.accessioned2025-05-02T18:53:44Z
dc.date.available2025-05-02T18:53:44Z
dc.date.issued2025-04-17
dc.date.updated2025-05-01T03:37:37Z
dc.description.abstractAbstract Understanding species-habitat associations is fundamental to ecological sciences and for species conservation. Consequently, various statistical approaches have been designed to infer species-habitat associations. Due to their conceptual and mathematical differences, these methods can yield contrasting results. In this paper, we describe and compare commonly used statistical models that relate animal movement data to environmental data. Specifically, we examined selection functions which include resource selection function (RSF) and step-selection function (SSF), as well as hidden Markov models (HMMs) and related methods such as state-space models. We demonstrate differences in assumptions while highlighting advantages and limitations of each method. Additionally, we provide guidance on selecting the most appropriate statistical method based on the scale of the data and intended inference. To illustrate the varying ecological insights derived from each statistical model, we apply them to the movement track of a single ringed seal (Pusa hispida) in a case study. Through our case study, we demonstrate that each model yields varying ecological insights. For example, while the selection coefficient values from RSFs appear to show a stronger positive relationship with prey diversity than those of the SSFs, when we accounted for the autocorrelation in the data none of these relationships with prey diversity were statistically significant. Furthermore, the HMM reveals variable associations with prey diversity across different behaviors, for example, a positive relationship between prey diversity and a slow-movement behaviour. Notably, the three models identified different “important” areas. This case study highlights the critical significance of selecting the appropriate model as an essential step in the process of identifying species-habitat relationships and specific areas of importance. Our comprehensive review provides the foundational information required for making informed decisions when choosing the most suitable statistical methods to address specific questions, such as identifying expansive corridors or protected zones, understanding movement patterns, or studying behaviours. In addition, this study informs researchers with the necessary tools to apply these methods effectively.
dc.identifier.citationMovement Ecology. 2025 Apr 17;13(1):27
dc.identifier.doi10.1186/s40462-025-00549-2
dc.identifier.urihttp://hdl.handle.net/1993/39064
dc.language.isoeng
dc.language.rfc3066en
dc.publisherBMC
dc.rights.holderThe Author(s)
dc.subjectAnimal movement
dc.subjectBiologging
dc.subjectHabitat selection
dc.subjectHidden Markov models
dc.subjectIntegrated step‑selection functions
dc.subjectMovement ecology
dc.subjectPoisson point process models
dc.subjectResource selection functions
dc.subjectRinged seal (Pusa hispida)
dc.subjectStep‑selection functions
dc.subjectTelemetry
dc.titleAn introduction to statistical models used to characterize species-habitat associations with animal movement data
dc.typereview article
local.author.affiliationFaculty of Science::Department of Biological Sciences
oaire.citation.startPage27
oaire.citation.titleMovement Ecology
oaire.citation.volume13
project.funder.identifierhttps://doi.org/10.13039/501100000038
project.funder.nameNatural Sciences and Engineering Research Council of Canada
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
40462_2025_Article_549.pdf
Size:
3.1 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
770 B
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
University of Manitoba Scholarship