The attraction of the known: the importance of spatial familiarity in habitat selection in wapiti <i>Cervus elaphus</i>
Bibliographic record
Abstract
Most studies of habitat selection by large herbivores focus on the resource availability and interactions with other species, but neglect the importance of an animal being familiar with an area due to past use. Yet, studies of the establishment and retention of territories, home ranges, birth sites, and feeding site choices in experimental settings have shown the importance of spatial familiarity at these scales. We used GPS locations of translocated wapiti Cervus elaphus , resource selection functions (RSF), and time‐to‐return to examine whether previous site use was important for selection of sites by wapiti in west‐central Alberta. To construct RSFs, we used logistic regression that included spatial familiarity (presence of a previous GPS location within a 50‐m radius) as well as estimates of herbaceous and shrub biomass, elevation, aspect, slope, and predation risk to wapiti from wolf predation, as dependent variables. We found that previous use had a strong positive relationship with subsequent site use, indicating that wapiti were not avoiding previously visited locations, as would be expected if memory of forage depletion (which we did not measure) determined response to familiar locations. Revisited sites were of higher quality, i.e. had more moderate terrain, higher forage, and lower predation risk, than sites that were not revisited, indicating that the selection of familiar locations was likely not the result of avoidance of unfamiliar locations. Finally, animals demonstrated preference for familiar locations that it had visited most recently, indicating that memory (which would decline with time) of higher site quality, rather than high quality alone, influenced selection for familiar locations. We conclude that spatial familiarity is important not only for large scale processes such as selection of home range and territory, but for smaller scale habitat selection and foraging as well.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".