Understanding the effects of forest management on streams and rivers: A synthesis of research conducted in New Brunswick (Canada) 2014–2018
Bibliographic record
Abstract
Forests play a major role in maintaining healthy streams and in providing ecosystem services such as clean drinking water, flood/drought protection and biodiversity, but studies have shown that some forestry operations can compromise these benefits. To assess whether current forest management practices impact stream ecosystems, a five-year study was conducted in J.D. Irving, Limited’s Black Brook Forestry District (New Brunswick, Canada) and in other watersheds with varying forest management intensity. This study was divided into two phases, with each addressing one main research question: 1) how different intensities of forest management affect the ecological health of headwater streams and, 2) whether the changes observed in headwater streams accumulate or dissipate in larger downstream rivers. A comprehensive approach to examining these research questions was taken by measuring multiple abiotic and biotic indicators to assess the integrity of stream ecosystems (sediments, water chemistry, insect communities, leaf decomposition, fish condition, mercury concentrations). The purpose of this paper is: 1) to synthesize the results of numerous scientific articles, and 2) to present the science and management implications in terms that regulatory and industrial forest managers can use to incorporate the lessons learned into their decision making. Results in Phase I show that streams in the most intensively managed catchments had greater inputs of terrestrial materials such as sediments, and these were incorporated into food webs, resulting in more terrestrial diets of aquatic consumers. The important stream function of leaf litter breakdown was negatively influenced by increased management intensity. Management practices related to roads warrant special attention, as roads tended to be more related to changes in stream indicators than tree removal. Additionally, results suggest that wet riparian areas were more sensitive to disturbance than drier riparian areas, which has implications for riparian buffer zone configurations. Regarding Phase II, some of the effects of forest management on small streams accumulated in larger downstream rivers (e.g., sediments, use of terrestrial resources by aquatic organisms), while others dissipated (e.g., water temperature, mercury contents). Interestingly, the impacts of forest management on streams were greater in the basin with tree removal but less silviculture than in the basin with more of both, suggesting that greater overall intensity of forest practices does not necessarily translate into greater environmental impacts, for example when considering partial versus clearcut harvesting. Overall, the study suggests that while current best management practices do not eliminate all effects, they do still offer good protection of biological integrity downstream.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".