Beaver reservoirs have variable effects on downstream mercury in boreal stream food webs across harvested watersheds
Bibliographic record
Abstract
Abstract Beaver reservoirs and forestry are landscape disturbances that can increase the transfer of mercury (Hg) to stream food webs through increased mobilization from forest soils and methylation within the landscape, and through changes to food web structure. Both disturbances are widespread and co-occur throughout Canada’s boreal region, yet their combined effects on Hg bioaccumulation have not been studied. We sampled upstream and downstream of beaver reservoirs in harvested ( n = 3) and non-harvested ( n = 3) watersheds in Northwestern Ontario, Canada, for water, food sources (biofilm, leaves, and detritus), macroinvertebrate consumers (herbivorous and predatory), and top predators (fishes). When only harvesting effects were considered (upstream of reservoirs), Hg concentrations ([Hg]) of water, aquatic food sources and consumers, and trophic magnification slopes were highest in harvested compared to non-harvested watersheds; such effects were mostly absent downstream, indicating that effects of beaver reservoirs and harvest were not cumulative. In non-harvested landscapes, significantly higher biotic [Hg] was observed downstream of one reservoir only, potentially due to shifts in consumer diets from terrestrial to aquatic food sources and higher downstream availability of Hg. The most extensively harvested site displayed significant downstream decreases in biotic [Hg] and dissolved organic carbon, while the other two harvested sites had either increases in biotic [Hg], or no changes, indicating that reservoirs vary in their contribution to downstream Hg transfer, but may buffer the overall effects of extensive harvest on downstream Hg dynamics. Results provide new knowledge that helps forest managers incorporate natural disturbances (beaver impoundments) into evaluations and mitigations of forest management impacts on Hg in aquatic food webs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".