Why ponds concentrate nutrients: the roles of internal features, land use, and climate
Bibliographic record
Abstract
Abstract Ponds are key freshwater habitats supporting biodiversity and ecosystem services, yet they remain understudied in the context of land use and climate change. We examined 240 ponds across eight countries (seven in Europe and Uruguay) to assess how internal pond characteristics, surrounding land cover and livestock intensity, seasonal climatic variation, and climate influence nutrient concentrations across spatial and temporal scales. Nutrient concentrations were strongly associated with internal features: shallow ponds and short hydroperiods had higher total nitrogen (TN) and total phosphorus (TP) concentrations, while thermal stratification, typically found in deeper ponds, was associated with higher TN, indicating enhanced internal nutrient recycling. Land use also played a significant role with agricultural intensity increasing nutrient concentration (both TN and TP), whereas forest cover reduced TP. Seasonal variation modulated these patterns, with higher TP concentrations observed in summer, and with dilution effects during wetter and cooler periods, particularly for TN in semi-permanent ponds. These findings underscore the combined influence of physical characteristics, landscape context, and climate variability on nutrient concentrations in ponds and highlight the need for integrated, multi-scale approaches to anticipate the impacts of global climate change effects on these ecologically valuable ecosystems.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".