Forest management impacts on stream integrity at varying intensities and spatial scales: Do biological effects accumulate spatially?
Bibliographic record
Abstract
The effects of forest harvesting on headwaters are quite well understood, yet our understanding of whether impacts accumulate or dissipate downstream is limited. To address this, we investigated whether several biotic indicators changed from smaller to larger downstream sites (n = 6) within three basins that had intensive, extensive or minimal forest management in New Brunswick (Canada). Biofilm biomass and grazer abundance significantly increased from upstream to downstream, whereas organic matter decomposition and the autotrophic index of biofilms decreased. However, some spatial trends differed among basins and indicated either cumulative (macroinvertebrate abundance, predator density, sculpin GSI) or dissipative (autotrophic index, cotton decomposition) effects downstream, potentially explained by sediment and nutrient dynamics related to harvesting. No such among-basin differences were observed for leaf decomposition, biofilm biomass, macroinvertebrate richness or sculpin condition. Additionally, results suggest that some of the same biological impacts of forestry observed in small headwaters also occurred in larger systems. Although the intensive and extensive basins had lower macroinvertebrate diversity, there were no other signs of biological impairment, suggesting that, overall, current best management practices protect biological integrity downstream despite abiotic effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".