Year‐round effects of climate on demographic parameters of an arctic‐nesting goose species
Bibliographic record
Abstract
Understanding how climate change will affect animal population dynamics remains a major challenge, especially in long-distant migrants exposed to different climatic regimes throughout their annual cycle. We evaluated the effect of temperature throughout the annual cycle on demographic parameters (age-specific survival and recruitment, breeding propensity and fecundity) of the greater snow goose (Chen caerulescens atlantica L.), an arctic-nesting species. As this is a hunted species, we used the theory of exploited populations to estimate hunting mortality separately from natural mortality in order to evaluate climatic effects only on the latter form of mortality. Our analysis was based on a 22-year marking study (n = 27,150 females) and included live recaptures at the breeding colony and dead recoveries from hunters. We tested the effect of climatic covariates by applying a procedure that accounts for unexplained environmental variation in the demographic parameter to a multistate capture-mark-recapture recruitment model. Breeding propensity, clutch size and hatching probability all increased with high temperatures on the breeding grounds. First-year survival to natural causes of mortality increased when temperature was high at the end of the summer, whereas adult survival was not affected by temperature. On the contrary, accession to reproduction decreased with warmer climatic conditions during the non-breeding season. Survival was strongly negatively related to hunting mortality in adults, as expected, but not in first-year birds, which suggests the possibility of compensation between natural and hunting mortality in the latter group. We show that events occurring both at and away from the breeding ground can affect the demography of migratory birds, either directly or through carryover effects, and sometimes in opposite ways. This highlights the need to account for the whole life cycle of an animal when attempting to project the response of populations to future climatic changes.
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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 | 0.001 |
| 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) | 0.000 | 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".