Radio-tracking reveals insight into survival and dynamic habitat selection of fledgling Cerulean Warblers
Bibliographic record
Abstract
The Cerulean Warbler is a declining Nearctic-Neotropical migrant species of concern that breeds in hardwood forests of the eastern United States and Canada. While much knowledge has been gained about the nesting period of this canopy species, little is known about the post-fledging period. During the 2014 and 2015 breeding seasons, after locating and monitoring nests within a matrix of habitat conditions created by various forest management strategies in NW Pennsylvania, USA, we captured fledglings and used radio telemetry to estimate survival, track movements, and examine habitat selection during their dependent stage. Mortality occurred in 10 of 20 fledglings (50%) during this period, with 8 deaths occurring within three days post-fledging. Most birds left the parental territory within a week of fledging and the maximum distance moved from the nest after 5 d was 462 m; after 14 d it was 1.3 km; and after 24 d it was, somewhat unexpectedly, 4.4 km. Fledglings used a diversity of habitat and management types, including clear-cut patches, 25-yr-old regenerating clear-cuts, shelterwood cuts, and mature undisturbed forest. Over the entire tracking period (up to 35 d), fledglings selected for habitats with greater midstory cover, lower basal area, and lesser sapling cover (when compared with paired, random locations). However, lesser sapling cover may also be correlated with decreased survival for birds that come down from the canopy immediately upon fledging. Our results suggest that a diversity of management types, including some quite different from those that produce habitat typically selected by adults, and maintaining large blocks of contiguous forest would benefit fledgling Cerulean Warblers.
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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) | 0.001 | 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".