Modelling the Effects of Seston Food Quality on Zooplankton Growth: Implications for Broader food Web Dynamics
Bibliographic record
Abstract
An increasing number of contemporary studies in aquatic ecology emphasize the im- portance of highly unsaturated fatty acids (HUFAs) at the plant-animal interface. Studies have demonstrated a wide range of fatty acid profiles in primary producers, forcing her- bivorous zooplankton to differentially retain fatty acids to meet somatic requirements. Herbivores also vary in their somatic fatty acid profiles; cladocerans collect Eicosapen- taenoic Acid (EPA), copepods prefer Docosahexaenoic Acid (DHA). Fatty acid internal reserves can be broken down to meet structural needs (i.e. phospholipid synthesis), fuel reproduction and may play a role in cold weather adaptation. Several authors have noted increases in HUFA concentration with lowering ambient temperatures. Cladoceran membranes form a gel at lower temperatures, while copepod membranes remain fluid and allow active overwintering. Both fish and crustaceans accumulate high concentrations of HUFAs during periods of rapid growth, but colimitation with elemental resources may exist. Recent modeling results suggest food webs with high quality (nutritional and biochemical) primary producers can attain inverted biomass distributions with efficient energy transfer between trophic levels. The adoption rate for this material into man- agement studies remains low, and while other sectors of the scientific community thrive on the potential of HUFAs, planktonic food-web studies are choosing traditional view points over forward thinking. Bearing in mind the emerging hypotheses on the critical factors that drive the energy flow in the plant-animal interface, my dissertation will at- tempt to address the following general questions: What are the distinct signatures of food quality and food quantity on planktonic food web dynamics? How do nutritional and biochemical factors affect the flow of energy at the plant-animal interface? What is our current understanding of the role of highly unsaturated fatty acids (HUFAs) in aquatic food webs? To what extent can the current generation of plankton models reproduce the lower food web patterns when explicitly accounting for HUFAs? Is the integration of the HUFA role into water quality management models feasible? Explicitly accounting for HUFAs requires integrating factors of animal physiology with macro-ecology: what are the ramifications? Finally, what are the evolutionary aspects of animals coping with food quality?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".