Climate warming is predicted to reduce omega‐3, long‐chain, polyunsaturated fatty acid production in phytoplankton
Bibliographic record
Abstract
Phytoplankton are the main source of energy and omega-3 (n-3) long-chain essential fatty acids (EFA) in aquatic ecosystems. Their growth and biochemical composition are affected by surrounding environmental conditions, including temperature, which continues to increase as a result of climate warming. Increasing water temperatures may negatively impact the production of EFA by phytoplankton through the process of homeoviscous adaptation. To investigate this, we conducted an exploratory data synthesis with 952 fatty acid (FA) profiles from six major groups of marine and freshwater phytoplankton. Temperature was strongly correlated with a decrease in the proportion of n-3 long-chain polyunsaturated FA (LC-PUFA) and an increase in omega-6 FA and saturated FA. Based on linear regression models, we predict that global n-3 LC-PUFA production will be reduced by 8.2% for eicosapentaenoic acid (EPA) and 27.8% for docosahexaenoic acid (DHA) with an increase in water temperature of 2.5 °C. Using a previously published estimate of the global production of EPA by diatoms, which contribute to most of the world's supply of EPA, we predict a loss of 14.2 Mt of EPA annually as a result of ocean warming. The n-3 LC-PUFA are vitally important for an array of key physiological functions in aquatic and terrestrial organisms, and these FA are mainly produced by phytoplankton. Therefore, reduced production of these EFA, as a consequence of climate warming, is predicted to negatively affect species that depend on these compounds for optimum physiological function. Such profound changes in the biochemical composition of phytoplankton cell membranes can lead to cascading effects throughout the world's 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".