No evidence of a demographic response to experimental herbicide treatments by the White-crowned Sparrow, an early successional forest songbird
Bibliographic record
Abstract
Abstract Early-successional forest birds, which depend on disturbance events within forested landscapes, have received increased conservation concern because of long-term population declines. Herbicides are often used to control vegetation within early-successional forests, with unknown effects on avian vital rates. We used a large-scale experiment to test how nest and post-fledging survival were influenced by herbicide intensity within managed conifer plantations across 2 breeding seasons. We created a gradient of 4 stand-scale herbicide treatments (light, moderate, and intensive, and no-spray control) and evaluated the reproductive response of the White-crowned Sparrow (Zonotrichia leucophrys), a declining songbird in managed forest landscapes of the Pacific Northwest. Against initial predictions, we found no evidence that either daily nest survival (n > 760 nests across all treatments) or post-fledging survival (n = 70 individuals reared in control and moderate treatments) were influenced by herbicide application intensity. Increased herbicide intensity resulted in an extensive reduction in vegetation cover at both stand and nest-patch scales; in contrast, vegetative cover at nest sites did not differ across herbicide treatments, nor was nest survival related to vegetation concealment measures. As the largest experimental investigation to assess forest herbicide effects on songbird demography, our study indicates that components of sparrow reproductive success were not influenced by experimental vegetation control measures, although additional work on other early-successional species will be useful to evaluate the generalities of our findings.
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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 | 0.001 |
| 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".