Functional response of wolves to human development across boreal North America
Bibliographic record
Abstract
AIM: The influence of humans on large carnivores, including wolves, is a worldwide conservation concern. In addition, human-caused changes in carnivore density and distribution might have impacts on prey and, indirectly, on vegetation. We therefore tested wolf responses to infrastructure related to natural resource development (i.e., human footprint). LOCATION: Our study provides one of the most extensive assessments of how predators like wolves select habitat in response to various degrees of footprint across boreal ecosystems encompassing over a million square kilometers of Canada. METHODS: We deployed GPS-collars on 172 wolves, monitored movements and used a generalized functional response (GFR) model of resource selection. A functional response in habitat selection occurs when selection varies as a function of the availability of that habitat. GFRs can clarify how human-induced habitat changes are influencing wildlife across large, diverse landscapes. RESULTS: Wolves displayed a functional response to footprint. Wolves were more likely to select forest harvest cutblocks in regions with higher cutblock density (i.e., a positive functional response to high-quality habitats for ungulate prey) and to select for higher road density in regions where road density was high (i.e., a positive functional response to human-created travel routes). Wolves were more likely to use cutblocks in habitats with low road densities, and more likely to use roads in habitats with low cutblock densities, except in winter when wolves were more likely to use roads regardless of cutblock density. MAIN CONCLUSIONS: These interactions suggest that wolves trade-off among human-impacted habitats, and adaptively switch from using roads to facilitate movement (while also risking encounters with humans), to using cutblocks that may have higher ungulate densities. We recommend that conservation managers consider the contextual and interacting effects of footprints when assessing impacts on carnivores. These effects likely have indirect impacts on ecosystems too, including on prey species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".