An introduction to statistical models used to characterize species-habitat associations with animal movement data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".