Habitat associations at multiple scales identify areas of management priority for American woodcock in Nova Scotia
Bibliographic record
Abstract
Abstract Identifying optimal habitat and predicting species distributions are essential components in developing management priorities for species of concern. In Nova Scotia, Canada, the breeding population of American woodcock (Scolopax minor) has been in decline over the past 50 years, likely in part because of reduced availability of habitat. We aimed to identify regions in Nova Scotia expected to support high numbers of woodcock, and whether monitoring efforts sufficiently capture the distribution of habitat. We generated a species distribution model to identify areas of highest relative predicted abundance and optimal habitat based on 15 years of standardized singing ground survey monitoring conducted along 50 routes and a candidate set of 56 habitat variables representative of composition and configuration at each of 2 ecologically relevant spatial scales (i.e., describing habitat immediately available to displaying males and the broader landscape used by the local breeding population). The species distribution model indicated extensive areas of optimal habitat in the central mainland characterized by moist organic soils, large, irregularly shaped patches of early successional forest, and large patches of open space created by developed, urban areas. We identified additional smaller pockets of habitat throughout the province likely to support relatively high local abundances. Much of the area identified as optimal habitat is not currently surveyed by singing ground survey routes. Expanding survey route coverage into regions identified as potentially optimal habitat could help clarify drivers of population declines. Overall, partnerships with provincial forest managers will be key to ensuring the maintenance of woodcock habitat while balancing the needs of a suite of avian species toward the goal of biodiversity conservation in Nova Scotia.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".