Woodland caribou calving fidelity: Spatial location, habitat, or both?
Bibliographic record
Abstract
Individuals that isolate themselves to give birth can use more than one strategy in choosing birth sites to maximize reproductive success. Previous research has focused on the consistency in the use of the same birth-site across years (i.e., spatial fidelity), but individuals alternatively may use similar habitat conditions across years (i.e., habitat fidelity). Using GPS telemetry, we determined whether woodland caribou expressed spatial or habitat fidelity during calving, and evaluated intrinsic and extrinsic factors associated with expressing either type of fidelity. We identified 56 individuals with ≥2 putative birth events, via a movement-based model, across northern Ontario between 2010 and 2014. Individuals were classified as expressing (1) spatial fidelity by comparing sequential calving locations to a random spatial distribution of available calving locations, (2) habitat fidelity using a logistic use model compared to a null (intercept only) model, (3) no fidelity (neither criterion met), or (4) both spatial and habitat fidelity (both criteria met). Across all individuals, 37% expressed no fidelity (36 of 98), 15% expressed only spatial fidelity (15 of 99), 35% expressed only habitat fidelity (34 of 98), and 14% expressed both spatial and habitat fidelity (14 of 98). Older individuals were more likely to express spatial fidelity, whereas lower availability of upland and lowland conifer forests without linear features increased the probability an individual expressed habitat fidelity. Our results indicate that managing for caribou calving needs to consider protecting both specific, known birthing sites, but also broad-scale areas of preferred habitat for calving. Understanding the mechanisms that influence caribou expressing calving fidelity, and associated fitness costs, is crucial for the conservation of the 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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".