Riparian habitat disturbed by reservoir management does not function as an ecological trap for the Yellow Warbler (<i>Setophaga petechia</i>)
Bibliographic record
Abstract
Ecological traps arise when anthropogenic change creates habitat that appears suitable but when selected reduces the fitness of an individual. We evaluated whether riparian habitat within the drawdown zone of the Arrow Lakes Reservoir, British Columbia, creates an ecological trap for Yellow Warblers ( Setophaga petechia (L., 1766)) by investigating habitat preferences and the fitness consequences of habitat selection decisions. Preferences were inferred by examining how habitat variables influenced settlement order, and comparing habitat at nest sites and random locations. Males preferred to settle in territories with more riparian shrub and tree cover, higher shrub diversity, and less high canopy cover. Females built nests in taller shrubs surrounded by a greater density of shrub stems. Habitat preferences were positively associated with fitness: nest sites in taller shrubs surrounded by higher shrub-stem densities were more likely to avoid predation and fledge young, whereas territories with more riparian cover, higher shrub diversity, and less high canopy cover had higher annual productivity. We therefore found no evidence that riparian habitat affected by reservoir operations functions as an ecological trap. Current habitat selection decisions may be associated with fitness because Yellow Warblers are adapted to breeding in a heterogeneous environment subject to periodic flooding.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".