Estimating concentrations of essential omega-3 fatty acids in the ocean: supply and demand
Bibliographic record
Abstract
Abstract Vertebrates have a universal requirement for essential fatty acids (FAs), but in the ocean these FAs are synthesized only by phytoplankton. All other marine organisms must source their essential FA directly from phytoplankton or indirectly through the food web. Thus, the growth and abundance of all organisms in the marine ecosystem is constrained not just by the rate of carbon fixation in photosynthesis but also by the rate of synthesis of essential FAs. Despite the significance of this controlling step, we have had until now only very limited knowledge of the amount, distribution and rate of synthesis of essential FAs in the sea. Here, we report results on the quantity of a specific essential omega-3 FA, eicosapentaenoic acid (EPA) in the ocean, obtained with a novel application of ocean-colour data collected by remote sensing. Using in situ samples collected in the Northwest Atlantic, we developed a simple model to describe the relationship between total FAs and total chlorophyll-a. We refined these by examining the relationships of FAs produced predominantly by diatoms with the fraction of total chlorophyll-a derived from diatoms. These models were then applied to satellite data to map the distribution of EPA relative to diatom carbon in the Northwest Atlantic. With extrapolation to the global oceans, we were able to provide a first estimate of annual production of EPA, which demonstrated that the supply was barely sufficient to meet the nutritional demand of the world population in the present day; as the world population increases, this resource may become inadequate to meet those demands. This approach will allow us to begin to address issues such as the budget of essential FAs in the ocean and the maximum sustainable rate at which these FAs could be harvested from the ocean without compromising the integrity of the marine ecosystem.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".