Coupled Dynamics of Wetlandscape Properties and Phytoplankton Bloom Magnitude and Extent in Lake Winnipeg
Bibliographic record
Abstract
Abstract Wetlandscapes—networks of hydrologically connected wetlands—can influence the transport and transformation of nutrients across watersheds. As climate change and human activity reshape wetland extent and connectivity, these landscape‐scale processes are being altered in ways that may intensify eutrophication in downstream lakes. We used Landsat‐derived inundation data (1984–2020) to evaluate how long‐term changes in wetlandscape properties have affected nutrient loading and phytoplankton bloom dynamics in Lake Winnipeg, Canada. Over this period, wetlands generally increased in number and size and exhibited greater connectivity to rivers and the lake but with declines observed after ∼2015. These changes coincided with periods of substantial increases in the magnitude and spatial extents of phytoplankton blooms followed by declines in 2015 in the North Basin. Sub‐watersheds with shorter runoff travel distances to the lake showed stronger relationships between wetland connectivity and bloom metrics ( p ≤ 0.10), suggesting reduced opportunity for nutrient retention and transformation. Incorporating runoff travel distance into wetlandscape assessments improved correlations with nutrient inputs and bloom extent. Rising surface temperatures further contributed to bloom intensification. These findings highlight how climate‐driven changes in wetland connectivity influence lake nutrient dynamics and demonstrate the potential for globally available satellite data to support spatially targeted water quality management.
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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 | 0.000 |
| 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".