Cumulative Effects of Beaver Ponds and Forest Harvest on Streamwater Chemistry in Boreal Watersheds
Bibliographic record
Abstract
Canada’s boreal forests provide many important ecosystem services, including but not limited to biomass production, habitat provisioning, and soil and water protection. As forests are an important part of Canada’s economy and landscape, it is crucial that they are managed sustainably. In the province of Ontario, sustainable forest management is largely based on emulating natural disturbances. With respect to stream biogeochemistry, beaver ponds are one of the most common and significant disturbances against which to compare. Thus, there is a need to further our understanding of how beaver ponds and forest management interact and modify the effects of one another. This study examined a suite of surface water chemistry variables, with a focus on the bioaccumulative neurotoxin methylmercury, in 28 headwater catchments across 3 years in northwestern Ontario. Some were undisturbed; some were impacted by active or abandoned beaver ponds; some were undergoing active harvest, while others were harvested previously; and some were impacted by active or abandoned beaver ponds and new or previous harvest. Forest harvest impacts on organic carbon, suspended sediments, and mercury fell largely within the range of natural variation seen at undisturbed sites, except where significant soil and water disturbance from stream crossings occurred upstream. Pond impacts were highly variable, but more strongly related to catchment characteristics (such as mean slope and channel length) than to pond characteristics (such as shape and in-pond vegetation cover). Though downstream impacts were found to be greatest at a new pond in a catchment undergoing active harvest, pond and harvest impacts were not consistently additive in catchments where both occurred. Understanding the impacts of beaver ponds in conjunction with forest harvest is important for predicting the ultimate effectiveness of management decisions aimed at protecting terrestrial-aquatic ecosystems commonly affected by both disturbances.
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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 | 0.001 | 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) | 0.001 | 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".