Forest management influences the effects of streamside wet areas on stream ecosystems
Bibliographic record
Abstract
Riparian zones contain areas of strong hydrological connectivity between land and stream, referred to as variable source areas (VSAs), and are considered biogeochemical control points. However, little is known about whether VSAs influence stream communities and whether this connectivity is affected by forest management. To address this, we used multiple biotic and abiotic indicators to (1) examine the influence of VSAs on riparian vegetation and stream ecosystems by comparing VSA and non-VSA reaches and (2) explore how forest management may affect the influence of VSAs on stream ecosystems. We detected some significant differences between VSA and non-VSA reaches in the riparian vegetation (greater understory and lower tree density) and stream ecosystem indicators (greater dissolved organic matter aromaticity, microbial biomass, peroxidase activity and collector-gatherer density, and lower dissolved organic carbon concentrations, algal biomass, and predatory macroinvertebrate density), which suggests that VSAs may create a more heterotrophic ecosystem locally. However, we show some evidence that forest management activities (specifically, road density) can alter the influence of VSAs and eliminate the differences observed at lower forest management intensities, and that the most hydrologically connected areas seem more sensitive to disturbance. Therefore, we suggest that the heterogeneity in hydrological connectivity along riparian zones should be considered when planning forest harvesting operations and road building (e.g., wider riparian buffers around VSAs).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".