Early life conditions influence fledging success and subsequent local recruitment rates in a declining migratory songbird, the Whinchat <i>Saxicola rubetra</i>
Bibliographic record
Abstract
Abstract Life history traits and environmental conditions influence reproductive success in animals, and consequences of these can influence subsequent survival and recruitment into breeding populations. Understanding influences on demographic rates is required to determine the causes of decline. Migratory species experience spatially and temporally variable conditions across their annual cycle, making identifying where the factors influencing demographic rates operate challenging. Here, we use the Whinchat Saxicola rubetra as a model declining long‐distance migrant bird. We analyse 10 years of data from 247 nesting attempts and 2519 post‐fledging observations of 1193 uniquely marked nestlings to examine the influence of life history traits, habitat characteristics and weather on survival of young from the nestling stage to local recruitment into the natal population. We detected potential silver spoon effects where conditions during the breeding stage influence subsequent apparent local recruitment rates, with higher recruitment for fledglings from larger broods, and recruitment rate negatively related to rainfall that chicks experienced in‐nest. Additionally, extreme temperatures experienced pre‐ and post‐fledging increased fledging success and recruitment rate. However, we could not determine whether this was driven by temperature influencing mortality during the post‐fledging period or later in the annual cycle. Brood size declined with hatching date. In‐nest survival increased with brood size and was highest at local temperature extremes. Furthermore, nest survival was highest at nests surrounded with 40%–60% vegetation cover of Bracken Pteridium aquilinum within 50 m of the nest. Our results show that breeding phenology and environmental factors may influence fledging success and recruitment in songbird populations, with conditions experienced during the nestling stage influencing local recruitment rates in Whinchats (i.e. silver spoon effect). Recruitment rates are key drivers of songbird population dynamics. Our results help identify some of the likely breeding season mechanisms that could be important population drivers.
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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) | 0.002 | 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".