Abundance and species composition of amphibians, small mammals, and songbirds in riparian forest buffer strips of varying widths in the boreal mixedwood of Alberta
Bibliographic record
Abstract
Forested buffer strips are left along water bodies after forest harvesting to protect water quality and fish stocks, but little is known about their utility as reserves for forest species in managed landscapes. We report on changes in terrestrial vertebrate communities from pre- to post-harvest in experimentally created buffer strips (20, 100, 200, and 800 m wide) in a boreal mixedwood forest in Alberta, Canada. We trapped anuran amphibians and small mammals and spot-mapped bird territories around 12 lakes (4 treatment levels, 3 replicates) before and after harvesting. Changes in small mammal or amphibian abundance were not detected for any treatment relative to controls; however, these species are habitat generalists that used and even bred in clearcuts. Total bird abundance did not change after harvesting, with the exception of crowding in 20-m buffers 1 year post-harvest. Species composition did not change for amphibians and small mammals after harvest, but forest-dependent bird species declined as buffer width narrowed from 200 to 100 m and narrower. We concluded that 20100 m buffers would not serve as reserves for forest songbirds in managed landscapes, but that 200 m wide strips conserved the pre-harvest passerine bird community, at least up to 3 years post-harvest.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".