Functional responses in habitat selection: clarifying hypotheses and interpretations
Bibliographic record
Abstract
A fundamental challenge in habitat ecology and management is understanding the mechanisms generating animal distributions. Studies of habitat selection provide a lens into such mechanisms, but are often limited by unrealistic assumptions. For example, most studies assume that habitat selection is constant with respect to the availability of resources, such that habitat use remains proportional to availability. To the contrary, a growing body of work has shown the fallacy of this assumption, indicating that animals modify their behavior depending on the context at broader scales. This has been termed a functional response in habitat selection. Furthermore, a diversity of methods is employed to model functional responses in habitat selection, with little attention to how methodology might affect scientific and conservation conclusions. Here, we first review the conceptual and statistical foundations of methods currently used to model functional responses and clarify the ecological tests evaluated within each approach. We then use a combination of simulated and empirical data sets to evaluate the similarities and differences among approaches. Importantly, we identified multiple statistical issues with the most widely applied approaches to understand functional responses, including: (1) a complex and important role of random- or individual-level intercepts in adjusting individual-level regression coefficients as resource availability changes and (2) a sensitivity of results to poorly informed individual-level coefficients estimated for animals with low availability of a given resource. Consequently, we provide guidance on applying approaches that are insensitive to these issues with the goal of advancing our understanding of animal habitat ecology and management. Finally, we characterize the management implications of assuming similarity between the current approaches to model functional responses with two empirical data sets of federally threatened species: Canada lynx (Lynx canadensis) in the United States and woodland caribou (Rangifer tarandus caribou) in Canada. Collectively, our assessment helps clarify the similarities and differences among current approaches and, therefore, assists the integration of functional responses into the mainstream of habitat ecology and management.
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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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".