Are forested buffers an effective conservation strategy for riparian fauna? An assessment using meta‐analysis
Bibliographic record
Abstract
Historically, forested riparian buffers have been created to provide protection for aquatic organisms and aquatic ecosystem functions. Increasingly, new and existing riparian buffers are being used also to meet terrestrial conservation requirements. To test the effectiveness of riparian buffers for conserving terrestrial fauna, we conducted a meta-analysis using published data from 397 comparisons of species abundance in riparian buffers and unharvested (reference) riparian sites. The response of terrestrial species to riparian buffers was not consistent between taxonomic groups; bird and arthropod abundances were significantly greater in buffers relative to unharvested areas, whereas amphibian abundance decreased. Edge-preferring species were more abundant in buffer sites than reference sites, whereas species associated with interior habitat were not significantly different in abundance. The degree of buffer effect on animal abundance was unrelated to buffer width; wider buffers did not result in greater similarity between reference and buffer sites. However, responses to buffer treatment were more variable in buffers <50 m wide, a commonly prescribed width in many management plans. Our results indicate that current buffer prescriptions do not maintain most terrestrial organisms in buffer strips at levels comparable to undisturbed sites.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".