Data from: Natal habitat preference induction in large mammals – Like mother, like child?
Bibliographic record
Abstract
Habitat selection has received considerable attention from ecologists during the last decades, yet the underlying forces shaping individual differences in habitat selection are poorly documented. Some of these differences could be explained by the early experience of individuals in their natal habitat. By selecting habitat attributes like those encountered early in life, individuals could improve resource acquisition, survival, and ultimately fitness. This behaviour, known as natal habitat preference induction (NHPI), could be particularly common in large mammals, because offspring generally stay with their mother for an extended period. We used three complementary approaches to assess NHPI in a marked population of woodland caribou (Rangifer tarandus caribou): 1) population-based resource selection functions (RSFs), 2) individual-based RSFs, and 3) behavioural repeatability analyses. All approaches compared the behaviour of calves in their natal range to their behaviour as independent subadults during the snow-covered (Dec – Apr) and snow-free (May – Nov) seasons. Using RSFs, we found that the magnitude of habitat selection between calf and subadult stages differed for most covariates, yet the signs of statistically significant effects (selection vs. avoidance) were generally the same. We also found that some habitat selection tactics were highly repeatable across life stages. Notably, caribou responses to habitat disturbances were highly repeatable year-round, meaning that different individuals reacted differently, but consistently, to disturbances. This study highlights the potential role of natal habitat preference induction in shaping individual differences in habitat selection in large mammals and provides valuable knowledge for the management and conservation of a threatened species.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".