Individuality, diel time, and landscape context shape space-use of an elusive carnivore in a risky environment
Bibliographic record
Abstract
Individual animal’s perception of risk can alter how it navigates a landscape altered by anthropogenic and natural disturbances. As perception depends on experience, we should expect habitat selection to be context dependent and individualistic. We hypothesized that: (i) fine-scale habitat selection of fisher ( Pekania pennanti ) in a human dominated landscape is driven by multiple interacting spatio-temporal factors; and (ii) an individual’s response to these factors depend on their exposure to anthropogenic disturbance within their home range (i.e., functional response). We used fine-scale GPS location data of fisher in step-selection functions to make inference on the effects of human development, habitat loss, and road risk on fisher habitat selection. We found fisher habitat selection is individualistic, spatio-temporally dependent and a function of their exposure to anthropogenic disturbance in their home range. Fisher selected areas of lower road risk more frequently relative to availability, particularly during daylight hours. Higher road risk areas were only used more frequently when they were available at night. With a higher human land use in their home ranges fisher selected space near roads at night only, however when the extent of human use in their home range was lower, they selected areas further from roads at all times. Our study shows how individual variability allows fisher to adapt their diel activity to utilize resources in areas of high human land use. This further emphasizes the importance of accounting for individuality and multiple interacting spatio-temporal factors in habitat selection, particularly in highly human modified landscapes.
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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) | 0.003 | 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".