Breeding season space use and habitat selection by Blue-winged Warblers in managed shrublands
Bibliographic record
Abstract
The Blue-winged Warbler (Vermivora cyanoptera) is a relatively understudied shrubland-associated species that has experienced sustained population declines in portions of its breeding range. Detailed evaluations of Blue-winged Warbler breeding season habitat requirements are needed to inform ongoing and future conservation efforts and, ultimately, stem population declines. Here, we use radio telemetry, field-measured vegetation data, and Light Detection and Ranging (LiDAR) to assess Blue-winged Warbler breeding season space use and habitat selection in southwest Pennsylvania. During the 2019 and 2020 breeding seasons, we tracked 27 male Blue-winged Warblers and mapped their core home ranges (50% kernel density estimate) and total home ranges (95% kernel density estimate). The scale of Blue-winged Warbler space use was similar to that of other Vermivora with a mean total home range size of 12.9 ha and a mean core home range (i.e., high use area of the home range) size of 2.9 ha. Blue-winged Warbler core home ranges had more shrub cover and herbaceous cover but less overhead cover and leaf litter than peripheral (area of total home range outside of core area) home ranges. Core areas were also dominated by shrubland and forest-shrubland ecotone, while peripheral home ranges contained greater forest cover. Finally, LiDAR data suggested that core home ranges contained more structural heterogeneity (rugosity metrics) and more short-stature vegetation (% returns between 1 and 5 m) than peripheral home ranges. These results suggest that although Blue-winged Warblers require shrubland communities in the core of their breeding season home ranges, the availability of adjacent forest cover that forms structurally complex ecotones is also essential. Therefore, conservation practices that aim to create or maintain habitat for this declining bird should promote structurally complex shrubland adjacent to forest-shrubland ecotones.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".