Changes in landscape composition influence the decline of a threatened woodland caribou population
Bibliographic record
Abstract
1. Large-scale habitat loss is frequently identified with loss of biodiversity, but examples of the direct effect of habitat alterations on changes in vital rates remain rare. Quantifying and understanding the relationship between habitat composition and changes in vital rates, however, is essential for the development of effective conservation strategies. 2. It has been suggested that the decline of woodland caribou Rangifer tarandus caribou populations in North America is precipitated by timber harvesting that creates landscapes of early seral forests. Such habitat changes have altered the predator-prey system resulting in asymmetric predation, where predators are maintained by alternative prey (i.e. apparent competition). However, a direct link between habitat condition and caribou population declines has not been documented. 3. We estimated survival probabilities for the threatened arboreal lichen-feeding ecotype of woodland caribou in British Columbia, Canada, at two different spatial scales. At the broader scale, observed variation in adult female survival rates among 10 distinct populations (range = 0.67-0.93) was best explained by variation in the amount of early seral stands within population ranges and population density. At the finer scale, home ranges of caribou killed by predators had lower proportions of old forest and more mid-aged forest as compared with multi-annual home ranges where caribou were alive. 4. These results are consistent with predictions from the apparent competition hypothesis and quantify direct fitness consequences for caribou following habitat alterations. We conclude that apparent competition can cause rapid population declines and even extinction where changes in species composition occur following large scale habitat change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".