Effects of experimental nitrogen fertilization on planktonic metabolism and CO2 flux in a hypereutrophic hardwater lake
Bibliographic record
Abstract
Hardwater lakes are common in human-dominated regions of the world and often experience pollution due to agricultural and urban effluent inputs of inorganic and organic nitrogen (N). Although these lakes are landscape hotspots for CO2 exchange and food web carbon (C) cycling, the effect of N enrichment on hardwater lake food web functioning and C cycling patterns remains unclear. Specifically, it is unknown if different eutrophication scenarios (e.g., modest non point vs. extreme point sources) yield consistent effects on auto- and heterotrophic C cycling, or how biotic responses interact with the inorganic C system to shape responses of air-water CO2 exchange. To address this uncertainty, we induced large metabolic gradients in the plankton community of a hypereutrophic hardwater Canadian prairie lake by adding N as urea (the most widely applied agricultural fertilizer) at loading rates of 0, 1, 3, 8 or 18 mg N L-1 week-1 to 3240-L, in-situ mesocosms. Over three separate 21-day experiments, all treatments of N dramatically increased phytoplankton biomass and gross primary production (GPP) two- to six-fold, but the effects of N on autotrophs plateaued at ~3 mg N L-1. Conversely, heterotrophic metabolism increased linearly with N fertilization over the full treatment range. In nearly all cases, N enhanced net planktonic uptake of dissolved inorganic carbon (DIC), and increased the rate of CO2 influx, while planktonic heterotrophy and CO2 production only occurred in the highest N treatments late in each experiment, and even in these cases, enclosures continued to in-gas CO2. Chemical effects on CO2 through calcite precipitation were also observed, but similarly did not change the direction of net CO2 flux. Taken together, these results demonstrate that atmospheric exchange of CO2 in eutrophic hardwater lakes remains sensitive to increasing N loading and eutrophication, and that even modest levels of N pollution are capable of enhancing autotrophy and CO2 in-gassing in P-rich lake 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".