Cross-seasonal weather effects interact with breeding conditions to impact reproductive success in an alpine songbird
Bibliographic record
Abstract
Abstract In alpine habitats, fluctuating early-season weather conditions and short breeding seasons limit reproductive opportunities, such that arriving and breeding earlier or later than the optimum may be particularly costly for migratory species. Given early-season energy limitations, the influence of environmental conditions across the annual cycle on breeding phenology may have pronounced fitness consequences, yet our understanding of cross-seasonal dynamics in alpine breeding organisms is severely limited. For an alpine-breeding, migratory population of horned lark ( Eremophila alpestris ) in northern British Columbia, Canada (54.8°N latitude) we assessed how spatially explicit weather conditions from across the annual cycle influenced clutch initiation date and offspring development. We also addressed how cross-seasonal effects on breeding parameters interact to influence reproductive fitness. With 12 years of intensive breeding data and 3 years of migration data from archival light-level geolocators, we used a sliding window approach to identify critical points during the annual cycle where weather events most influenced breeding phenology and offspring development. Consequences for reproductive success were assessed using nest survival simulations. Average clutch initiation varied up to 11 days among years but did not advance from 2003 to 2019. Colder temperatures with greater precipitation at wintering habitats, as well as colder temperatures upon arrival at the breeding site delayed clutch initiation, independent of arrival time. Extreme cold (sub-zero temperatures) within a staging area just prior to arrival at the breeding site carried over to prolong offspring development rate, potentially by influencing parental investment. Nest survival decreased with both later clutch initiation and prolonged offspring development, such that females that nested earlier and fledged offspring at a younger age were up to 45% more likely to reproduce successfully. We demonstrate pronounced carry-over effects acting through mechanisms that influence breeding phenology and offspring development independently. We also highlight the potential importance of staging areas for alpine songbirds, particularly given that environmental conditions are becoming increasingly decoupled across seasons. Understanding the cross-seasonal mechanisms shaping breeding decisions in stochastic environments like the alpine enables more accurate predictions of future individual- and 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 | 0.000 | 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 | 0.001 | 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".