Trophic upgrading via the microbial food web may link terrestrial dissolved organic matter to Daphnia
Bibliographic record
Abstract
Direct consumption of allochthonous resources generally yields poor growth and reproduction in zooplankton, but it is still unclear how trophic upgrading of terrestrial dissolved organic matter (tDOM) via the microbial food web may support zooplankton. We compared survival, somatic growth and reproduction of Daphnia magna fed with the heterotrophic flagellate Paraphysomonas vestita and three algal diets. Paraphysomonas was fed lake bacteria that used tDOM as a substrate to simulate an allochthonous diet that zooplankton encounter in lakes. The highest survival, growth and reproduction was achieved with a diet of Cryptomonas, while Daphnia performance was the worst when fed Microcystis. Paraphysomonas and Scenedesmus diets lead to intermediate growth and reproduction. Cryptomonas contained high amounts of essential polyunsaturated fatty acids (PUFA) and phytosterols that supported high somatic growth and reproduction, whereas poor performance of Daphnia on the cyanobacterial diet was most likely due to lack of sterols. Paraphysomonas contained some phytosterols, but not in sufficient amounts, and also essential PUFA (eicosapentaenoic and arachidonic acid) that enhance zooplankton growth and reproduction. Our results indicate that the tDOM-based microbial food web supports Daphnia performance even as a sole food source, and may be important in providing zooplankton with essential biochemical components when phytoplankton quantity or quality is low.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".