Short‐term forest harvesting effects on emergence rates of aquatic insects from small streams treated with different riparian management
Bibliographic record
Abstract
Abstract Riparian zone management significantly influences adjacent aquatic habitats, particularly in the context of commercial forest harvesting. Tree removal can significantly alter light exposure, organic matter supplies and nutrient inputs, among other impacts. Riparian buffers are often used to mitigate harvesting's impact on freshwater ecosystems, but their effectiveness is limited. We experimentally tested the effects of riparian forest harvesting, using treatments of different buffer widths, on adult stream insect emergence from temperate, coniferous rainforest streams of coastal British Columbia. We measured the emergence of Plecoptera and Trichoptera taxa before and after harvesting activities. Using a replicated, Before‐After‐Control‐Impact (BACI) design, we examined emergence from 13 streams each treated with one of three riparian buffer widths (30, 10 and 0 m) versus unharvested reference streams. Insects were captured at regular intervals over the three‐and‐a‐half‐year study period using 0.5 m 2 emergence traps. At the level of Order, Plecoptera emergence was negatively impacted by harvesting activity, whereas Trichoptera emergence was not. Mean Plecoptera abundance at reference streams doubled (an inter‐annual effect unrelated to harvesting) after the harvesting period, whereas the observed increases in 10 and 0 m streams were, respectively, 92% and 114% less than the expected increase based on reference streams. At the genus level, several Plecoptera taxa were significantly lower in harvested sites (two Nemouridae and one Leuctridae), while one leuctrid species, Despaxia augusta , increased significantly following harvest in streams with 30‐m buffers relative to reference streams. Synthesis and applications . These results highlight the detrimental impact of harvesting along stream ecosystems where riparian buffer width is insufficient and demonstrate that a 30‐m buffer may offer substantial protection, strengthening the scientific foundation for sustainable riparian forest management.
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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) | 0.000 | 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 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".