Context-specific “silver-spoon” effects of wetlands, climate, and temperature on lifetime fitness in a short-lived temperate-breeding migratory songbird
Bibliographic record
Abstract
Drivers of global change are creating strongly contrasting early life conditions for developing offspring, which may have carry-over effects on lifetime fitness. We tested for “silver-spoon” effects of natal conditions (environmental conditions and maternal quality) and individual quality (pre-fledging) on the lifetime reproductive success (LRS) of aerial insectivorous adult tree swallows (Tachycineta bicolor) hatched in distinct populations with contrasting environments in Saskatchewan and British Columbia, Canada. In both populations, LRS of adults was influenced by environmental conditions they experienced as developing nestlings, but silver-spoon effects were context-dependent, indicating population-specific responses to the local environment. Higher natal temperature in Saskatchewan had positive silver-spoon effects on the LRS of adult swallows, but the opposite was observed in British Columbia, likely because the highest temperatures local recruits experienced as nestlings occurred during heat extremes. In Saskatchewan, where wetter conditions reflect higher wetland abundance and food supply, we detected a negative effect of good natal wetland conditions on adult LRS, contrary to our hypothesis. However, since current breeding wetland conditions are a strong driver of adult fitness, and adults experiencing high natal wetland abundance generally bred when wetland abundance was lower, we suspect any potential benefits of natal wetland abundance on LRS were overridden by wetland conditions during breeding. As hypothesized, wetter natal conditions in British Columbia, which reflect an unfavorable environment for developing nestlings, had negative silver-spoon effects on the LRS of adults. No maternal or pre-fledging quality effects were detected at either site. Therefore, LRS of individuals within distinct populations is influenced, at least in part, by carry-over effects of the natal environment that vary locally. Consequently, natal environmental conditions that affect fitness, with putative population-level consequences, may underly spatially-varying population trends of regionally distinct populations within a species’ range.
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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 | 0.000 |
| 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 | 0.001 |
| 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".