Linking habitat, predators and alternative prey to explain recruitment variations of an endangered caribou population
Bibliographic record
Abstract
Habitat loss, fragmentation and alteration are frequently identified as important threats to biodiversity, inducing major changes in the structure and composition of species communities and the resulting interspecific interactions. North American woodland caribou (Rangifer tarandus caribou) populations suffer from habitat modifications and most are currently in decline. It has been suggested that the conversion of old-growth coniferous forests into early-seral stages has increased cervid abundances, which have, in turn, stimulated a numerical response of predator populations, ultimately threatening caribou populations via a habitat-mediated apparent competition mechanism. Using a long-term dataset (1984–2012) of the Atlantic-Gaspésie caribou population, we quantified changes in interspecific interactions triggered by apparent competition between moose (Alces americanus) and caribou via the responses of two incidental predators, coyote (Canis latrans) and black bear (Ursus americanus). We also documented calf recruitment rates and analysed temporal trends (last three decades) in this vital rate. Inter-annual variations in autumn calf recruitment were mostly affected by the proxy of regional abundance of coyotes, which was highly correlated with moose and black bear proxies of abundance. The increase in coyote abundance proxy in the Gaspésie Peninsula following anthropogenic habitat modifications seems to be the main mechanism responsible for the current decline in the Atlantic-Gaspésie caribou population. Our analyses revealed some impacts of habitat alteration and the complexity of the resulting trophic cascades.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".