Adjustments in habitat selection to changing availability induce fitness costs for a threatened ungulate
Bibliographic record
Abstract
Summary Functional responses in habitat selection occur when individuals adjust their selection of habitat features as a function of the availability of those features. Functional responses in habitat selection are generally assumed to be fitness‐rewarding tactics and are used to guide conservation actions. Fitness consequences of functional responses, however, have rarely been evaluated. Eighty‐three caribou were followed with GPS collars to establish the link between functional responses in habitat selection and adult female survival, a strong fitness correlate for caribou. We measured how caribou avoidance of mixed/deciduous stands and 6‐ to 20‐year‐old clear‐cuts varied with the proportion of 6‐ to 20‐year‐old clear‐cuts within their 100% minimal convex polygon ( MCP ), and if these functional responses were linked to survival. Mixed/deciduous stands and 6‐ to 20‐year‐old clear‐cuts are risky for caribou because they are selected by moose, thereby attracting wolves and increasing predation risk for caribou. Caribou avoided mixed/deciduous stands, especially when 6‐ to 20‐year‐old clear‐cuts comprised a large proportion of their MCP , but this functional response did not differ between caribou that died and those that survived. When the proportion of 6‐to 20‐year‐old clear‐cuts in the MCP was low, caribou generally had low odds of occurring near 6‐ to 20‐year‐old clear‐cuts. However, when the proportion of clear‐cuts in the MCP was relatively high, caribou that strongly increased their odds of being near 6‐ to 20‐year‐old clear‐cuts were generally those that died. Synthesis and applications . Assessing the fitness consequences of how animals respond to habitat disturbances is central to wildlife conservation. We demonstrate that distinct functional responses in habitat selection involve different mortality risks and that population dynamics should depend on the frequency of the different tactics observed within populations. Individuals that persistently select riskier areas should be important drivers of population decline. Caribou mortality could be reduced by decreasing the appeal of 6‐ to 20‐year‐old clear‐cuts for moose by removing deciduous vegetation through cleaning, which should reduce the selection of wolves for these stands. Removing deciduous vegetation should be especially effective in areas where those clear‐cuts comprise a large proportion of the landscape, because this is where a subset of the caribou population experiences high mortality rates by selecting 6‐ to 20‐year‐old clear‐cuts.
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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 | 0.001 | 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) | 0.000 | 0.000 |
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".