Nitrogen addition enhances terrestrial phosphorous retention in grassland mesocosms
Bibliographic record
Abstract
Nitrogen (N) and phosphorus (P) are fundamental for plant biomass production in grasslands, are often co-limiting, and have become major freshwater pollutants. By factorially applying gradients of N and P to field-based grassland mesocosms, we tested for saturating thresholds of plant uptake as nutrients increase and whether simultaneous and potentially additive growing-season demand reduces flows of dissolved nutrients to subsurface leachate. We quantified the seasonality of nutrient losses, differences in uptake by functional group (grasses, forbs), the impacts of increasing nutrients on root:shoot ratios, and contrasted vegetated and unvegetated treatments to isolate edaphic influences. Overall, most added nutrients were retained by plants and soil–80% for N and 99% for P. Co-limitation dynamics were powerful but asymmetrical with N additions reducing P in leachate, but P having little influence on N. N retention was primarily influenced by season—most N was lost prior to peak biomass when plant demand was presumably lower. Nutrients reduced root:shoot ratios by increasing foliage but with no detectable effect on retention, possible because root biomass remained unchanged. Similarly, there was no impact of functional group on nutrient loss. Despite substantial plant uptake, leachate concentrations of N and P still exceeded regional levels for safe drinking water and prevention of algal blooms. This work reveals how nutrient co-limitation can accelerate the capture of P by N in grasslands, indicating that plant uptake can significantly reduce dissolved subsurface nutrients. However, the offseason flows of N and the failure to meet regional water-quality standards despite capture levels as high as 99% reveal that vegetative-based solutions to nutrient capture by grasslands are important but likely insufficient without complimentary measures that reduce inputs.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".