Data from: Temporally dynamic habitat suitability predicts genetic relatedness among caribou
Bibliographic record
Abstract
Landscape heterogeneity plays a central role in shaping ecological and evolutionary processes. While species utilization of the landscape is usually viewed as constant within a year, the spatial distribution of individuals is likely to vary in time in relation to particular seasonal needs. Understanding temporal variation in landscape use and genetic connectivity has direct conservation implications. Here, we modelled the daily use of the landscape by caribou in Quebec and Labrador, Canada and tested its ability to explain the genetic relatedness among individuals. We assessed habitat selection using locations of collared individuals in migratory herds and static occurrences from sedentary groups. Connectivity models based on habitat use outperformed a baseline isolation-by-distance model in explaining genetic relatedness, suggesting that variations in landscape features such as snow, vegetation productivity and land use modulate connectivity among populations. Connectivity surfaces derived from habitat use were the best predictors of genetic relatedness. The relationship between connectivity surface and genetic relatedness varied in time and peaked during the rutting period. Landscape permeability in the period of mate searching is especially important to allow gene flow among populations. Our study highlights the importance of considering temporal variations in habitat selection for optimizing connectivity across heterogeneous landscape and counter habitat fragmentation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".