Interacting effect of wolves and climate on recruitment in a northern mountain caribou population
Bibliographic record
Abstract
There is limited research on the influence of Pacific‐based climate in large herbivore populations. Additionally, much of our understanding on the effect of large‐scale climate on ungulate population dynamics has occurred on forage‐limited rather than predator‐limited populations. We compared the influence of the Pacific Decadal Oscillation (PDO), North Pacific Index, and local weather on recruitment in a predator‐limited mountain‐dwelling caribou Rangifer tarandus caribou population in the Yukon Territory, Canada, across a range of wolf Canis lupus densities. A large‐scale wolf removal program allowed us to examine the role of Pacific climate and weather when wolves were reduced to ∼15% of their pre‐removal levels. Recruitment was best explained by the interaction of wolf density and April‐PDO, with wolf density explaining the most deviance. Predicted recruitment during good springs was 0.45 (SE = 0.04) during wolf removal and 0.29 (SE = 0.03) with no wolf removal. During poor springs (low PDO, increased snow depth) predicted recruitment was 0.55 (SE = 0.10) during wolf removal and 0.12 (SE = 0.03) with no wolf removal. With non‐altered wolf densities, there was a positive relationship between April‐PDO and recruitment due to reduced snow depth at calving, allowing parturient females to disperse up in elevation away from predators. When wolf densities were substantially reduced there was a slight negative relationship between April‐PDO and recruitment, possibly due to a more rapid vegetation green‐up reducing the temporal availability of highly nutritious forage necessary for lactation and subsequent calf growth. Attempts to find general relationships between climate and ungulate population dynamics have proven difficult due to different ecological mechanisms by which climate affects individuals across populations. Temporally varying factors, such as predator density, may also play an important role in uncovering the mechanistic relationship between climate and population dynamics.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".