Nutrient function over form: Organic and inorganic nitrogen additions have similar effects on lake phytoplankton nutrient limitation
Bibliographic record
Abstract
Abstract The concentration of dissolved organic nitrogen (DON) is increasing in many northern hemisphere lakes, yet its use by phytoplankton and fate in the environment seldom have been quantified. We conducted 1 week, in situ, microcosm incubations across 25 lakes in northeastern North America to understand how DON, dissolved inorganic nitrogen (DIN), and dissolved inorganic phosphorus (P) affected phytoplankton biomass. In addition, we tested whether lakes were limited by single macronutrients (N or P) or colimited by both. Phytoplankton biomass in 80% of lakes responded similarly to DON and DIN additions. Of the lakes where N form produced differential responses, the majority of phytoplankton communities exhibited greater biomass accumulation with DON than DIN. Colimitation was the most common type of nutrient limitation among the study lakes, followed by P limitation. Limitation type shifted with N form in 40% of the study lakes, but without consistent patterns explaining how shifts occurred. Regardless of N form, lakes with watersheds more dominated by agriculture and higher total dissolved nitrogen (TDN) tended to show P‐limited phytoplankton responses, while lakes with less agricultural watersheds and lower TDN tended to show colimited phytoplankton responses. Finally, ambient TDN and total phosphorus (TP) nutrient concentrations were stronger predictors of limitation type than ambient TDN : TP ratios. The different contributions of DON and DIN to phytoplankton biomass in some of our study lakes suggest that DON loading from surrounding watersheds may be an overlooked component in predicting phytoplankton productivity and nutrient limitation dynamics in freshwater 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 | 0.000 | 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 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".