Diet predicts fatty acids of zooplankton more than environmental or geographical variables: evidence from subarctic to subtropical aquatic ecosystems
Bibliographic record
Abstract
Dietary transfer of fatty acids (FA) across the plant–animal interface of aquatic ecosystems is important for zooplankton fitness and, subsequently, consumers at higher trophic levels. To date, our knowledge of trophic FA transfer is limited to aquatic ecosystems of similar climatic areas. In this field study, aquatic ecosystems ranging from subarctic to temperate and subtropical climates were selected to examine FA of seston and zooplankton. Results show that physicochemical and geographic/morphometric factors predicted only 35% of the FA variation in seston. While the overall FA composition among each zooplankton taxon differed significantly across habitats, taxon-specific FA patterns grouped cladocerans (via eicosapentaenoic acid) and pooled zooplankton (mostly copepods; via docosahexaenoic acid). Seston FA predicted up to 3 times more of the FA variation in zooplankton than physicochemical and geographic/morphometric variables, and the sum of all predictors explained up to 75% of the FA variation in cladocerans and 55% in pooled zooplankton. The FA dispersion patterns between consumers and seston indicated a strong reorganization/ bioconversion of polyunsaturated FA in zooplankton, suggesting zooplankton resilience to environmental change. Therefore, this study provides field evidence that dietary FA transfer and the concurrent ability of zooplankton to organize their FA seem to be key for their adaptation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".