Spring weather influences breeding propensity, the most important productivity component for Arctic-nesting lesser snow geese
Bibliographic record
Abstract
Animal reproduction is composed of several stages, which collectively determine overall productivity. Yet, it is not fully understood how different productivity components contribute to population change. To bridge this gap, we leveraged integrated population modelling and transient life-table response experiments, together with population-level data on lesser snow geese ( Anser caerulescens caerulescens ) breeding on Wrangel Island, Russia, from 1970 to 2022. We assessed contributions of breeding propensity, clutch size, nest success, egg survival, hatching success and pre-fledging survival to population change, and tested hypotheses about the effects of environmental drivers and density dependence on different components. Breeding propensity contributed the most to variation in population growth, followed by nest success. These two components were negatively affected by the timing of snow melt. We found no overall deleterious effects of climate change on productivity. Density dependence had a positive effect on multiple productivity components, likely through predator swamping. Our results show the importance of breeding propensity to the population dynamics of this long-lived animal, which is notable because this productivity component is often overlooked. Our results also demonstrate that the effects of environmental conditions and density dependence can differ among animal populations of different sizes, locations and life histories.
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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) | 0.001 | 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".