Exotic plants and other habitat characteristics do not impact Veery reproductive success at multiple spatial scales in northeastern Pennsylvania
Bibliographic record
Abstract
It is expected that birds have evolved nest site preferences that, on average, lead to positive fitness outcomes. It is possible, however, that mismatches occur between these preferences and reproductive success when a population experiences sudden changes in its habitat such as through invasion by exotic plants. From 2012 to 2016, we studied breeding habitat use and nesting success of the Veery (Catharus fuscescens) in mature forests of northeastern Pennsylvania that had been invaded by exotic shrubs. Our objectives were to document the prevalence of use of exotic shrubs as nesting substrates in this population, determine if use of exotic shrubs reduced nesting success or other measures of productivity, and to evaluate whether habitat or other characteristics of individual nests were useful predictors of nest survival. Although many native substrates were used, almost 60% of 289 nests used exotic substrates, particularly Japanese barberry (Berberis thunbergii) and multiflora rose (Rosa multiflora). Estimates of nest survival from logistic exposure modeling, mean clutch size, and mean number of young fledged per successful nest did not differ between nests placed in native versus exotic substrates. Annual nest survival rates varied substantially (range = 0.17–0.44). Nest success was also positively related to the age of a nest and decreased when a nest camera was present. However, survival of individual nests showed little relationship with vegetative/habitat variables at three spatial scales. Overall, exotic shrubs did not appear to negatively impact Veery nesting success and instead appeared to be of similar quality as native plants for nesting substrates.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".