Linking songbird nest predation to seedling density: Sugar maple masting as a resource pulse in a forest food web
Bibliographic record
Abstract
Abstract The ecological literature presents considerable evidence for top‐down forcing on the maintenance of species diversity. Yet, in temperate forests, bottom‐up forces often exert a strong influence on ecosystem functioning. Here, we report on the indirect influence of a pulsed resource, sugar maple (Acer saccharum) seed production, on nest survival in a migratory songbird. We hypothesized that seed production in year t would determine daily nest survival rate in year t + 1 through its effects on seed‐eating rodents. We used the density of sugar maple seedlings (with cotyledons) in year t + 1 as a proxy for seed production in year t and predicted that it would be inversely related to songbird nest survival the same year. We estimated the density of sugar maple seedlings, eastern chipmunk (Tamias striatus) activity, and daily nest survival rate in the ovenbird (Seiurus aurocapilla) over four successive years in a northern hardwood forest of New Brunswick, Canada. Seedling density varied by two orders of magnitude between years, whereas an index of chipmunk activity changed by an order of magnitude. Both variables were positively correlated and negatively correlated to daily nest survival rate. A logistic‐exposure model including only seedling density received the greatest level of support (lowest AICc). Previous studies have reported the effect of sugar maple masting on seed‐eating rodent populations, but the strong link we report between seedling density and songbird nest survival is novel. A nocturnal seed‐eating nest predator, deer mouse (Peromyscus maniculatus), was not considered in our models, which may explain why chipmunk was not the best predictor of daily nest survival rate. The trophic linkages we observed are remarkably strong for a temperate forest ecosystem and might become more prevalent in northeastern North America, at least on calcium‐rich soils, with the loss of large‐diameter beech trees as a result of beech bark disease.
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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 | 0.001 | 0.001 |
| 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".