Global changes may be promoting a rise in select cyanobacteria in nutrient‐poor northern lakes
Bibliographic record
Abstract
The interacting effects of global changes-including increased temperature, altered precipitation, reduced acidification and increased dissolved organic matter loads to lakes-are anticipated to create favourable environmental conditions for cyanobacteria in northern lakes. However, responses of cyanobacteria to these global changes are complex, if not contradictory. We hypothesized that absolute and relative biovolumes of cyanobacteria (both total and specific genera) are increasing in Swedish nutrient-poor lakes and that these increases are associated with global changes. We tested these hypotheses using data from 28 nutrient-poor Swedish lakes over 16 years (1998-2013). Increases in cyanobacteria relative biovolume were identified in 21% of the study sites, primarily in the southeastern region of Sweden, and were composed mostly of increases from three specific genera: Merismopedia, Chroococcus and Dolichospermum. Taxon-specific changes were related to different environmental stressors; that is, increased surface water temperature favoured higher Merismopedia relative biovolume in low pH lakes with high nitrogen to phosphorus ratios, whereas acidification recovery was statistically related to increased relative biovolumes of Chroococcus and Dolichospermum. In addition, enhanced dissolved organic matter loads were identified as potential determinants of Chroococcus suppression and Dolichospermum promotion. Our findings highlight that specific genera of cyanobacteria benefit from different environmental changes. Our ability to predict the risk of cyanobacteria prevalence requires consideration of the environmental condition of a lake and the sensitivities of the cyanobacteria genera within the lake. Regional patterns may emerge due to spatial autocorrelations within and among lake history, rates and direction of environmental change and the niche space occupied by specific cyanobacteria.
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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 | 0.001 |
| 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".