Herbicide application improves plethodontid salamander habitat conditions in regenerating clear-cut forests
Bibliographic record
Abstract
Forestry activities, including harvesting and herbicide application, alter both overstory and understory vegetation communities, reshaping ecosystem structure and condition. These changes likely impact wildlife sensitive to environmental change, such as the Eastern Red-backed Salamander ( Plethodon cinereus). We compared canopy cover, soil temperature, soil moisture, soil pH, and salamander abundance across unharvested reference stands and clear-cut harvest blocks treated and untreated with a glyphosate-based herbicide. Overall, reference stands exhibited the highest canopy cover and soil moisture, and lowest soil temperature. Herbicide-treated blocks showed decreasing soil temperature and increasing moisture with time since harvest, whereas untreated blocks exhibited the opposite trend. Salamander abundance in reference stands was 4 and 18 times higher than in herbicide-treated and untreated blocks, respectively, and 3 times higher in herbicide-treated than untreated blocks. Greater canopy cover and soil moisture in herbicide-treated blocks likely improve habitat suitability, promoting higher salamander abundance compared to untreated blocks during forest regeneration. Our study suggests that herbicide application in clear-cut forests may accelerate the recovery of microhabitat conditions to preharvest levels, partially mitigating the impacts of harvesting on forest specialists like salamanders. We emphasize the need for holistic approaches in forestry management to sustain biodiversity and ecosystem integrity in increasingly changing landscapes.
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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 | 0.000 | 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".