Effects of phytoplankton blooms on fluxes and emissions of greenhouse gases in a eutrophic lake
Bibliographic record
Abstract
Lakes are important sources of greenhouse gases (GHGs) to the atmosphere. Factors controlling CO2, CH4 and N2O fluxes include eutrophication and warming, but the integrated influence of climate-warming-driven stratification, oxygen loss and resultant changes in bloom characteristics on GHGs are not well understood. Here we assessed the influence of contrasting meteorological conditions on stratification and phytoplankton bloom composition in a eutrophic lake, and tested for associated changes in GHGs inventories in both the shallow and deep waters, over three seasons (2010-2012). Atmospheric heatwaves had one of the most dramatic effects on GHGs. Indeed, cyanobacterial blooms that developed in response to heatwave events in 2012 enhanced both sedimentary CH4 concentrations (reaching up to 1mM) and emissions to the atmosphere (up to 8 mmol m−2 d−1). That summer, CH4 contributed 52% of the integrated warming potential of GHGs produced in the lake (in CO2 equivalents) as compared to between 34 and 39% in years without cyanobacterial blooms. High CH4 accumulation and subsequent emission in 2012 were preceded by CO2 and N2O consumption and under-saturation at the lake surface (uptakes at -30 mmol m−2 d−1 and -1.6 µmol m−2 d−1, respectively). Fall overturn presented a large efflux of N2O and CH4, particularly from the littoral zone after the cyanobacterial bloom. We provide evidence that, despite cooling observed at depth during hot summers, CH4 emissions increased via stronger stratification and surface warming, resulting in enhanced cyanobacterial biomass deposition and intensified bottom water anoxia. Our results, supported by recent literature reports, suggests a novel interplay between climate change effects on lake hydrodynamics that impacts both bloom characteristics and GHGs production in shallow eutrophic lakes. Given global trends of warming and enrichment, these interactive effects should be considered to more accurately predict the future global role of lakes in GHG emissions.
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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) | 0.001 | 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 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".