Impacts of hydrologic management on the eutrophication of shallow lakes in an intensive agricultural landscape (Saskatchewan, Canada)
Bibliographic record
Abstract
Abstract Hydrologic management of shallow lakes is often undertaken to prevent fluctuations in lake level, and to ensure sufficient water volume for economic, domestic, and recreational uses, but there is inconsistent evidence of whether lake‐level stabilisation through hydrological management promotes or hinders eutrophication. Here we used multi‐proxy paleolimnological assessments of water quality (sedimentary carbon, nitrogen, total phosphorus, fossil pigments), and zooplankton community ecology (fossil Cladocera assemblages), combined with Landsat‐derived estimates of lake surface area in two shallow eutrophic lakes, in the Prairies of southern Saskatchewan, Canada, to quantify how 8 decades of contrasting hydrological management strategies (continuous or intermittent) affect primary production and phytoplankton composition. Analysis revealed that irregular hydrological management of Pelican Lake led to sharp increases in primary production concomitant with lake‐level decline. In contrast, continuously managed Buffalo Pound Lake, a drinking water reservoir for regional cities, exhibited slow, persistent eutrophication over decades despite active regulation of water levels. In both lakes, strong correlations of δ 15N values with pigments from diazotrophic cyanobacteria (canthaxanthin) showed that N2‐fixation increased during eutrophication irrespective of the timing of change. Finally, variation in fossil cladoceran density and composition reflected changes in pelagic and littoral habitats (e.g., reduced macrophyte cover) due to changes in both lake level and water quality. Basin comparison shows that while hydrologic management can moderate water quality degradation due to lake‐level change, it does not prevent eutrophication when nutrient influx remains high. Given that regional water availability is forecast to decline in coming decades, we anticipate that continued hydrological management will be unavoidable and will be unable to improve water quality unless nutrient influx is also controlled.
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".