Nest usurpation by non‐native birds and the role of people in nest box management
Bibliographic record
Abstract
Abstract Invasive species are a threat to global biodiversity, yet the impacts of invasive birds on the native birds with which they compete are understudied. Humans have a long history of providing and managing nest boxes to support native birds; however, their management of non‐native birds has received limited research attention. We surveyed people who maintain nest boxes in North America to examine the extent of interference competition for nest sites between native and non‐native birds and the human behaviors intended to reduce nest site competition. Our specific objectives were to examine observations of nest usurpation of native birds by non‐native birds across the United States and Canada, to ascertain whether and how people who maintain nest boxes control non‐native bird species in favor of native species, and to quantify various factors correlated with the likelihood of engaging in management activities. We found that nearly one‐third of the 871 respondents had observed a non‐native species usurp a nest box occupied by a native species. Among respondents who reported nest usurpations, species‐specific nest usurpation rates varied (range = 3–35%). We found that witnessing a nest usurpation is the most important predictor of whether or not someone will engage in management activities. Management activity was also associated with the extent to which respondents believed non‐native birds to be a problem at the continental scale. Our study shows that people's observations of threats from introduced species are correlated with the environmental management actions people take, and that these actions can mitigate the threats, and potentially support the survival of native birds.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".