Multi-year succession of cyanobacteria blooms in a highland reservoir with changing nutrient status, Guizhou Province, China
Bibliographic record
Abstract
Over the last 22 years significant phytoplankton changes in Hongfeng lake reservoir have been observed with multiple years of harmful cyanobacteria blooms (cHABs). Fish farming and other anthropogenic activities from 1994-2001 triggered the harmful blooms. Nine years after the cessation of aquaculture, a conversion from problematic species (Microcystis spp, Aphanizomenon flos-aquae) to the less problematic species P. limnetica and other associated non-cyanobacteria taxa was recorded. Through this period of change, trophic factors (bottom-up) were re-examined, and correlations between cHABs and selected environmental variables were observed. Higher temperatures, nutrients (TN, TP) and available light significantly favored the development of Microcystis spp blooms. With declining nutrient loads, and a decline in TP relative to TN there was a competitive shift from Microcystis summer blooms to the growth of Pseudanabaena limnetica and other non-cyanobacteria. Pseudanabaena limnetica was favored over Microcystis spp when temperatures were <20°C and TP was <0.03 mg L-1. The apparent species succession to P. limnetica was enhanced by a competitive advantage under varied light conditions. Multiple environmental and biotic conditions (not always nutrients) were driving cHABs. Although only a selected number of environmental variables were examined, the CCA analysis supports observations that temperature and nutrients were associated with the species shift. The replacement of cHABs with the growth of less toxic cyanobacteria like P. limnetica, and other algae creates an interesting scenario (new community condition) for the removal of problematic taxa in reservoir systems. Diverting or controlling blooms will have direct implications on water quality and economic remediation initiatives in reservoir and lake 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".