Greenhouse gas dynamics in fishponds: management versus environmental controls in a complex aqua-agriculture system, France
Bibliographic record
Abstract
Fishponds are hotspots for carbon sequestration, processing, and emissions. However, the environmental drivers of greenhouse gas (GHG) fluxes in these ecosystems remain poorly understood. This study investigates how management practices influence GHG dynamics during the wet phase. We hypothesize that management alters water and sediment characteristics, thereby affecting GHG dynamics. In summer 2023, we measured CO 2 and CH 4 concentrations, along with water and sediment characteristics, in 38 ponds in La Dombes (France) in response to fertilization, feeding, liming and drying. GHG concentrations varied widely (CO 2 : 0.99–275.6 μmol L −1 , CH 4 : 0.20–32.45 μmol L −1 ) and fluxes ranged from −10.1-148.4 mmol m 2 ·d −1 for CO 2 and from 0.1 to 20.1 mmol m 2 ·d −1 for CH 4 . Fish-farming practices primarily influenced sediment-rather than water-properties. CO 2 concentrations were negatively related to dissolved oxygen and pH, positively influenced by feeding, which together explained 75 % of variability. CH 4 dynamics appeared more complex, likely due to shallow depths, ebullition, and local environmental heterogeneity. This study highlights the strong variability in GHG emissions among ponds, partly mediated by fish-farming practices, and provides insights into their environmental drivers. Further research, including seasonal monitoring and experimental approaches, is needed to identify key management-related factors to help mitigate the climate impact of fish-farming.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".