Forest harvesting impacts on small, temperate zone lakes: a review
Bibliographic record
Abstract
Small lakes (<1 km2) are physically, chemically, and biologically linked to their watersheds through hydrologic, terrestrial, and aerial delivery of materials. Inputs affecting lake function and structure, such as nutrient, energy, sediment, and large wood subsidies, arrive at the lake on three spatial scales, that is, the lake watershed, lake-inflow streams, and lake riparian areas. Lakes are sinks in the waterscape and integrate cumulative inputs from the landscape. Lakes can, therefore, react to landscape disturbances such as forest harvesting. Forest harvesting and associated activities can induce changes in watershed water yields, inputs of organic and inorganic materials to lakes, and in lake temperatures and wind. These changes result in stressors that can alter the lake’s physical habitat, water quality, and food webs. Here we review the reported impacts of forest harvesting on small, temperate zone lakes. The magnitude of the lake response to harvesting can depend on factors such as the proportion of the watershed harvested, the intensity of this harvesting, silvicultural practices and other activities, and road construction and density. Other additional factors include the proximity of the lake to harvesting and its impact on hydrological pathways connecting perturbed areas to stream and lake systems. The majority of surveyed studies reported short-term increases in nutrient and energy inputs, increased sedimentation due to harvesting, and increases in wind speeds, where about half reported increases in primary production or decreases in zooplankton biomass. Results on benthic macroinvertebrate, fish, and amphibians were less frequently reported and were geographically variable. Variation in the direction and magnitude of a lake’s food web response is likely due to studies of lakes with differences in watershed characteristics and forestry activities that can affect the magnitude of the lake response (i.e., proportion of watershed harvesting, forestry operations methods, hydrological connections to the lake, and site-specific attributes). We also discuss watershed forestry management and the implementation of lakeshore riparian buffers in the context of reducing forestry impacts on lakes. Most studies have been short-term, and the long-term, cumulative effects of forest harvesting on lake ecosystems remain unclear.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".