Avian responses to experimental harvest in southern boreal mixedwood shoreline forests: implications for riparian buffer management
Bibliographic record
Abstract
Conventional management of shoreline forest in harvested boreal landscapes involves retention of treed buffer strips to provide habitat for wildlife species and protect aquatic habitats from deleterious effects of harvesting. With shoreline forests being considered for harvest in several jurisdictions, it is important to determine the potential impacts of this disturbance on birds. In this study, responses of riparian- and upland-nesting birds to three levels of harvest (0%–50%, 50%–75%, and 75%–100% within 100 m of the water) in shoreline forests around boreal wetlands were assessed 1 year before and each year for 4 years after harvest relative to unharvested reference sites. Upland-nesting species showed variable responses to harvest, with greatest declines in abundance of interior forest nesting species (e.g., Ovenbird, Seiurus aurocapillus L.) with the highest levels of harvest. Shrub-nesting and generalist species increased in abundance in harvest treatments relative to reference sites. Riparian birds showed little response to harvest, suggesting that shoreline forest harvest has little effect on their abundance up to 4 years after harvest. Retention of small buffers may not be an effective management strategy for conservation of birds occupying shoreline forests, particularly interior forest nesting species. We suggest that alternatives to conventional buffer management be explored.
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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 | 0.000 | 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 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".