The relative influence of cross‐seasonal and local weather effects on the breeding success of a migratory songbird
Bibliographic record
Abstract
In seasonal environments, fluctuating early-season weather conditions and short breeding windows limit reproductive opportunities such that breeding earlier or later than the optimum may be particularly costly. Given the risk of early-season energy limitations, time- and energy-based carry-over effects stemming from environmental conditions across the annual cycle may have pronounced consequences for breeding phenology and fitness. Generally, when and where environmental conditions are most influential are poorly understood, limiting our ability to predict the future of climate-sensitive populations. For an alpine-breeding, migratory population of horned lark Eremophila alpestris in northern British Columbia, Canada (54.8°N), we assessed how weather conditions across the annual cycle influenced clutch initiation date and offspring development. We also addressed how cross-seasonal effects on breeding parameters combine to influence reproductive fitness. With 12 years of breeding data and 3 years of migration data, we used a sliding window approach to identify points during the annual cycle when weather events most influenced breeding phenology and offspring development. Consequences for breeding success were assessed using nest survival simulations. Average clutch initiation date varied up to 11 days among years but did not advance from 2003 to 2019. Warmer temperatures at stopover and breeding sites advanced clutch initiation, but winter conditions had no effect. Sub-zero stopover temperatures carried over to prolong offspring development independent of clutch initiation date, potentially indicating energy-based carry-over effects acting on parental investment. Nest survival decreased with both later clutch initiation and prolonged offspring development such that females nesting earlier and fledging offspring at a younger age were up to 45% more likely to reproduce successfully. We demonstrate that stronger carry-over effects originated from environmental conditions closer to the breeding site in time and space, as well as the potential for energy-based mechanisms to link pre-breeding conditions to reproductive fitness. We also highlight the importance of extended stopovers for songbirds breeding in seasonal environments, particularly given that climatic conditions are becoming increasingly decoupled across stages of the annual cycle. Understanding the cross-seasonal mechanisms shaping breeding decisions in stochastic environments allows for more accurate predictions of population-level responses to climate change.
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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 | 0.001 | 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".