Habitat use and characteristics of Connecticut Warbler during the nesting and post-fledging period
Bibliographic record
Abstract
Widespread declines in breeding bird populations have been documented across North America since the 1970s, and concerns about loss of avian biodiversity are growing. Species with narrow habitat preferences are of particular conservation concern. Connecticut Warblers (Oporornis agilis) breed in forests in central Canada and around the western Great Lakes; throughout much of its breeding range, this species is associated with lowland black spruce (Picea mariana) and tamarack (Larix laricina) forests. Population trends of the Connecticut Warbler indicate it is one of the most rapidly declining bird species in North America, but the species is understudied because it occurs in relatively low densities across its breeding range. To better understand the breeding ecology of the Connecticut Warbler, we studied its nesting and post-fledging habitat use and survival in northern Minnesota, USA at two study areas in 2019 and 2020. We mapped territories of 49 singing males, located and monitored 11 nests, and tracked the post-fledging movements of individuals from five broods. Nest sites were located in lowland conifer stands with a semi-open canopy and dense understory. The average fledging age was 7.5 days post-hatch, and the individuals (n = 14) tracked during 0–7 days post-fledging had a mean daily distance from nests of 35.5 m and a maximum distance from nests of 104 m. Connecticut Warblers were not observed making movements of more than 100 m from the nest until 7 days post-fledging. Microsite areas with high stem density were important features for post-fledgling birds, and that the same habitats were used for breeding and the post-fledging period. The results of this study can be used by managers to develop conservation strategies that will provide critical habitat to support this species.
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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 | 0.001 |
| 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".