Factors driving minimal focal species response to the implementation of habitat guidelines on private lands
Bibliographic record
Abstract
Effective conservation of breeding habitat for migratory birds benefits from the development and use of science-based management guidelines. The Cerulean Warbler (<em>Setophaga cerulea</em>) is a rapidly declining migratory songbird whose decline is understood to be driven, in large part, by breeding grounds habitat loss. In 2013, science-informed habitat management guidelines were developed that described a series of silvicultural techniques to enhance Cerulean Warbler nesting habitat in the Appalachian Mountains. From 2016–2020, the Natural Resources Conservation Service (NRCS) and several partners implemented these guidelines across more than 3800 ha of privately owned forest. From 2017–2020, we surveyed for Cerulean Warblers and sampled vegetation at 139 locations on private forests enrolled in NRCS programs in Pennsylvania and Maryland. Cerulean Warbler occupancy probability was low (<em>ᴪ</em> = 0.16) and appeared to decline with increasing distance to the nearest Cerulean Warbler subpopulation (especially beyond 2 km). Even after guideline implementation, only 25% of posttreatment locations we monitored met guideline targets for average tree diameter. The lack of large-diameter trees is characteristic of prior unsustainable harvest practices (e.g., high grading) that commonly occurred on private lands in eastern deciduous forests. Although most opportunities to manage Cerulean Warbler habitat in Appalachia exist on private lands, Cerulean Warbler habitat guidelines were developed from studies conducted on public lands, where a history of sustainable management is more common; this disparity appears to drive drastic differences in how the species responds to conservation on private vs public lands. Future efforts to implement Cerulean Warbler habitat guidelines should prioritize sites that are proximate to existing Cerulean Warbler breeding populations and those where exploitative timber harvests have not recently occurred. Our work also provides a cautionary example of recognizing that habitat recommendations developed on public lands may not yield similar results on comparable private lands, especially if guidelines were not designed with private lands in mind.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".