Beyond Eutrophication: Vancouver Lake, WA, USA as a Model System for Assessing Multiple, Interacting Biotic and Abiotic Drivers of Harmful Cyanobacterial Blooms
Bibliographic record
Abstract
Eutrophication of lakes and reservoirs has contributed to an increase in the magnitude and frequency of harmful cyanobacterial blooms; however, the interactive effects of nutrient availability (eutrophication) and other abiotic and biotic drivers have rarely been comprehensively studied in the field. We undertook an eight-year (2005–2013) research program that assessed the interaction of multiple factors driving cyanobacterial blooms in Vancouver Lake, a large, shallow eutrophic lake in Washington, USA. Our program consisted of nearly continuous monthly or weekly monitoring of water quality and plankton community composition over eight years, as well as multiple zooplankton grazing experiments over three years. We found a relatively consistent seasonal succession of phytoplankton and zooplankton assemblages, and a pattern of interacting factors influencing cyanobacterial bloom dynamics. Typically, a combined effect of decreased dissolved inorganic nitrogen (N), a sudden increase of dissolved inorganic phosphorus (P), and a cascading effect of zooplankton grazing created a ‘perfect storm’ of conditions that promoted the rapid proliferation of cyanobacteria over the two to three weeks before a bloom. At the blooms’ peaks, cyanobacterial carbon biomass reached as high as 20 µg L−1, with total [chl a] often exceeding 750 µg L−1. In the weeks following the blooms’ peaks, [PO4-P] and [NH4-N] dropped and copepod feeding rates fell to near zero, whereas microzooplankton grazing rates reached their maxima. Microzooplankton grazing impact, combined with low nutrient availability, then drove down cyanobacteria abundance. Vancouver Lake serves as a model for understanding multiple, interacting drivers of cyanobacterial bloom dynamics in shallow, temperate lakes, and is therefore an important system in which to investigate new questions related to the science and management of harmful algal blooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".