Impacts of nest predators and weather on reproductive success and population limitation in a long‐distance migratory songbird
Bibliographic record
Abstract
Although avian nesting success is much studied, little is known about the relative importance of the factors that contribute to annual reproductive success and population limitation, especially for long‐distance migratory songbird species. We combined a field experiment limiting access to nests by mammalian predators with modeling of long‐term field data of American redstarts (Parulidae: Setophaga ruticilla ) to assess the effects of multiple environmental variables on breeding success and population limitation. Experimental treatment (baffles placed around tree boles beneath active nests; n = 71) increased nesting success of this single‐brooded species significantly (77 vs 50% in controls; n = 343), demonstrating that scansorial mammals, primarily red squirrels Tamiasciurus hudsonicus and eastern chipmunks Tamias striatus , reduced reproductive success. Based on unbaffled nests (n = 466), daily nest survival varied annually, and was positively influenced by May temperature and negatively by sciurid nest predator abundance. Daily nest survival was also influenced positively by June rainfall, and declined with nest age but not with calendar date. Since nest failure was overwhelmingly caused by nest predation, these significant climate and nest‐age effects in our models are indirect, likely influencing nest predator and/or nesting bird behaviors that in turn influenced nest predation. Redstart population density had no effect on nesting success, after accounting for other factors. Annual reproductive success accounted for 34% of the variability in annual population change in redstarts in our study area. Our findings document 1) breeding season population limitation in this species, 2) a link between tree masting and bird population dynamics via mammal population fluctuations, 3) the independent contributions of summer versus winter population processes in a migratory species, and 4) the potential complexity of climate‐biotic interactions.
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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".