Impacts of a century of land‐use change on the eutrophication of large, shallow, prairie Lake Manitoba in relation to adjacent Lake Winnipeg (Manitoba, Canada)
Bibliographic record
Abstract
Abstract Evaluation of large lake response to centennial changes in land use and climate can be complicated by high spatial and hydrological complexity within their catchments, particularly in regions of low relief. Furthermore, large lakes can exhibit abrupt changes in structure and function that obscure causes of eutrophication. We provide the first quantification of historical trends in lake production, cyanobacterial abundance, sediment geochemistry and diatom composition since c. 1800 in Lake Manitoba, the 29th largest lake in the world, and compared them to Lake Winnipeg, a morphologically similar, adjacent basin with a 10‐fold larger catchment and an abrupt increase in production around 1990. Before 1900, Lake Manitoba was mesotrophic, with low sedimentary concentrations of carbon, phosphorus, nitrogen, cyanobacteria and algal pigments, as well as assemblages of low‐light‐adapted benthic diatoms. Analysis of pigment time‐series with hierarchical generalised additive models revealed that Lake Manitoba eutrophied during 1900–1930 as a consequence of the development of intensive agriculture within its local catchment, but thereafter exhibited stable cyanobacterial densities with limited expansion of N2‐fixing cyanobacteria despite persistent eutrophication. Lake Manitoba did not undergo an abrupt change as seen in Lake Winnipeg. These findings suggest that catchment size had little influence on water quality degradation and that nutrient influx from proximal agricultural sources was sufficient to initially degrade these large prairie lakes. The abrupt change in Lake Winnipeg around 1990 required additional intensification of local land use that did not occur in the Lake Manitoba catchment.
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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 | 0.001 | 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".