Linking stream ecosystem integrity to catchment and reach conditions in an intensively managed forest landscape
Bibliographic record
Abstract
Abstract Forests are vital to maintaining headwater stream integrity in forested biomes, which ensures the delivery of aquatic ecosystem services downstream. Forest harvesting, however, can alter land–water linkages and compromise stream integrity. Historically, the main effects of forestry on streams have been documented by studies that used relatively few (mainly abiotic) indicators and which focused on single harvesting events. However, forest management is expected to intensify in the future to meet increasing global wood demand and it is likely that our present understanding does not adequately capture the cumulative effects that streams will be subjected to under intensive forest management. To address this, we assessed the effects of varying forest management intensities on the integrity of 15 forest headwater streams in northwestern New Brunswick, Canada. We used a comprehensive approach to link multiple biotic and abiotic indicators of stream ecosystem integrity to reach‐ and catchment‐level characteristics including forest management (e.g., cumulative harvesting over time, road density, forest condition). Most indicators detected the gradient in forest management intensity with abiotic indicators responding most strongly. Streams in catchments with highest management intensity (especially road density) tended to have higher fine inorganic sediment deposition and entrainment, water cations and carbon, dissolved organic matter humification, and water temperature. These abiotic differences were associated with higher biofilm biomass and shredder densities, but lower leaf decomposition. Evidence from our multi‐indicator approach elucidated a potential effects pathway of higher inorganic sediment content in biofilms of organic matter potentially limiting or altering its use by microbial and benthic macroinvertebrate (BMI) communities and resulting in reduced leaf decomposition rates. Overall, this study shows that current best management practices in an intensively managed watershed (and legacy effects from past management such as older road systems) do not fully protect against an increased delivery of terrestrial materials to streams with resulting habitat and biotic changes, but that they are mostly effective at preventing the impairment of BMI communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".