Global nutrient cycling by commercially-targeted marine fish
Bibliographic record
Abstract
Abstract. Throughout the course of their lives fish ingest food containing essential elements, including nitrogen (N), phosphorus (P) and iron (Fe). Some of these elements are retained in the fish body to build new biomass, which acts as a stored reservoir of nutrients, while the rest is excreted or egested, providing a recycling flux to water. Fishing activity has modified the fish biomass distribution worldwide and consequently may have altered fish-mediated nutrient cycling, but this possibility remains largely unassessed, mainly due to the difficulty of estimating global fish biomass and metabolic rates. Here we quantify the role of commercially-targeted marine fish between 10 g and 100 kg () in the cycling of N, P and Fe in the global ocean, and its change due to fishing activity, by using a global size-spectrum model of marine fish populations calibrated to observations of fish catches. Our results show that the amount of nutrients stored in the global pristine , biomass was generally small compared to the ambient surface nutrient concentrations but significant in the nutrient-poor regions of the world: the North Atlantic for P, the oligotrophic gyres for N and the High Nutrient Low Chlorophyll (HNLC) regions for Fe. Similarly, the rate of nutrient removed from the ocean through fishing is globally small compared to the inputs, but can be important locally especially for Fe in the equatorial Pacific and along the western margin of South America and Africa. This model allowed us to compute the spatial distribution of the cycling of elements by the biomass at pristine and global peak catch state, which is relatively small compared to the estimated primary production demand for nutrients and estimated export production of nutrients. Pristine cycling (excretion + egestion) accounted for less than 2.7 % of the primary productivity demand for N, P and Fe globally. Relative to the export of nutrients, modeled global pristine egestion represents on average 2.3 %, 3.0 % and 1.1–22 % for N, P and Fe (low-high estimates), respectively, with a higher fraction in the low-export oligotrophic tropical gyres. Our study highlights the role of the fraction of the icthyosphere (i.e. does not include non-commercial species such as mesopelagic fish) on nutrient storage and cycling, and the potential role of fishing activities on this cycling, which could be of importance in regions of low nutrient concentration, high fish biomass and/or high productivity demand, and especially at the more local scale for Fe.
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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".