Seasonal effects of Pacific‐based climate on recruitment in a predator‐limited large herbivore
Bibliographic record
Abstract
1. Climate is an important factor influencing the population dynamics of large herbivores operating directly on individuals or through its effect on forage characteristics. However, the seasonal effect of climate may differ between forage- and predator-limited populations because of a climatic influence on predation rates. The influence of climate on predator-limited large herbivores is less well known than on forage-limited populations. Further, the effect of Pacific-based climate on large herbivore populations has been rarely assessed. 2. We investigated the effect of the Pacific Decadal Oscillation (PDO), across different seasons, on recruitment in 10 populations (herds) of mountain-dwelling caribou Rangifer tarandus caribou L. in the Yukon Territory, Canada. These low-density populations occur in highly seasonal environments and are considered predator-limited with high neonatal calf mortality. Hence, in most years females do not spend resources through lactational support during the summer and resource intake is devoted to self-maintenance. We predicted that climate affecting environmental conditions at calving would have a strong effect on recruitment via its influence on predation rates. We also predicted that climatic conditions prior to conception could have an effect on recruitment through its influence on female fecundity. We modelled recruitment (n = 165) by seasonal PDO values using generalized linear mixed-effects models with herd-varying coefficients. 3. We found that recruitment variability was best explained by variation in winter climate (beta = 0.110, SE = 0.007) prior to birth (in utero) and May climate (beta = 0.013, SE = 0.006) at calving. There was little support for a pre-conception climate effect influencing female body condition and hence fecundity. These results confirm that recruitment in these populations is limited by predation and that forage-limitation is not a significant factor in their population dynamics. There was considerable variability in herd-specific relationships between the PDO and recruitment. Incorporating herd-specific characteristics, such as variable predator densities or terrain characteristics within a herd range, may shed greater light on the complex relationship between climate and ungulate 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".