Nitrogen loss processes in response to upwelling in a Peruvian coastal setting dominated by denitrification – a mesocosm approach
Bibliographic record
Abstract
Abstract. Upwelling of nutrient-rich deep waters make eastern boundary upwelling systems (EBUSs), such as the Humboldt Current system, hot spots of marine productivity. Associated settling of organic matter to depth and consecutive aerobic decomposition results in large subsurface water volumes being oxygen depleted. Under these circumstances, organic matter remineralisation can continue via denitrification, which represents a major loss pathway for bioavailable nitrogen. Additionally, anaerobic ammonium oxidation can remove significant amounts of nitrogen in these areas. Here we assess the interplay of suboxic water upwelling and nitrogen cycling in a manipulative offshore mesocosm experiment. Measured denitrification rates in incubations with water from the oxygen-depleted bottom layer of the mesocosms (via 15N label incubations) mostly ranged between 5.5 and 20 nmol N2 L−1 h−1 (interquartile range), reaching up to 80 nmol N2 L−1 h−1. However, actual in situ rates in the mesocosms, estimated via Michaelis–Menten kinetic scaling, did most likely not exceed 0.2–4.2 nmol N2 L−1 h−1 (interquartile range) due to substrate limitation. In the surrounding Pacific, measured denitrification rates were similar, although indications of substrate limitation were detected only once. In contrast, anammox (anaerobic ammonium oxidation) made only a minor contribution to the overall nitrogen loss when encountered in both the mesocosms and the Pacific Ocean. This was potentially related to organic matter C / N stoichiometry and/or process-specific oxygen and hydrogen sulfide sensitivities. Over the first 38 d of the experiment, total nitrogen loss calculated from in situ rates of denitrification and anammox was comparable to estimates from a full nitrogen budget in the mesocosms and ranged between ∼ 1 and 5.5 µmol N L−1. This represents up to ∼ 20 % of the initially bioavailable inorganic and organic nitrogen standing stocks. Interestingly, this loss is comparable to the total amount of particulate organic nitrogen that was exported into the sediment traps at the bottom of the mesocosms at about 20 m depth. Altogether, this suggests that a significant portion, if not the majority of nitrogen that could be exported to depth, is already lost, i.e. converted to N2 in a relatively shallow layer of the surface ocean, provided that there are oxygen-deficient conditions like those during coastal upwelling in our study. Published data for primary productivity and nitrogen loss in all EBUSs reinforce such conclusion.
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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 | 0.002 |
| 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) | 0.000 | 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".