Essential fatty acids in the planktonic food web and their ecological role for higher trophic levels
Bibliographic record
Abstract
We measured concentrations of essential fatty acids (EFAs) in four size categories of planktonic organisms— seston (10–64 µm), microzooplankton (100–200 µm), mesozooplankton (200–500 µm), and macrozooplankton (≫500 230m)—and in rainbow trout ( Oncorhynchus mykiss ) in coastal lakes. Size‐dependent patterns in concentrations of specific fatty acids (FAs) are important for ecosystem function, because planktivorous fish and some invertebrates are size‐selective predators. We demonstrate that the retention of individual FAs differs among the four size categories of planktonic organisms in our study systems. Changes in individual EFA concentrations within the planktonic food web were similar in all coastal lakes sampled, which indicates the generality of our findings. Although concentrations of arachidonic acid, eicosapentaenoic acid (EPA), and linoleic acid increased steadily with plankton size, the concentration of a ‐linolenic acid decreased slightly in larger size fractions of zooplankton. Concentrations of another EFA, docosahexaenoic acid (DHA), declined sharply from mesozooplankton to the cladoceran‐dominated macrozooplankton size class. Our results indicate that the retention of EFAs, as a function of plankton size, is related, in part, to the taxonomic composition of planktonic food webs. We suggest that, in general, zooplankton exhibit an EPA‐retentive metabolism with increasing body size, whereas different taxonomic groups within the planktonic food web retain DHA differently. Finally, we conclude that EPA is highly retained in zooplankton, whereas in rainbow trout DHA is highly retained.
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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 | 0.001 | 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".