Influence of diet on chitobiase-based production rates for the harpacticoid copepod Tigriopus californicus
Bibliographic record
Abstract
The influence of diet on copepod production is important when examining energy transfer within marine food webs, yet a direct link between diet and copepod production is lacking. Recently, measurements of the crustacean moulting enzyme chitobiase have allowed for rapid estimates of production rates for zooplankton communities. We tested the sensitivity of this method as a tool for measuring the productivity response of copepods to different diets by rearing the harpacticoid splash-pool copepod, Tigriopus californicus, on single and mixed species phytoplankton diets. Chitobiase-based productivity measurements demonstrate that a single species algal diet of the diatom Thalassiosira weissflogii was the best food item for T. californicus. In contrast, the dinoflagellate Amphidinium carterae was likely toxic given that copepods reared on this diet spent more time in early developmental stages and ultimately failed to survive. Copepod production and daily growth rates of T. californicus were higher on the T. weissflogii and natural food assemblage diets compared with the A. carterae or mixed (A. carterae/T. weissflogii) diets. Our use of the chitobiase method in a lab setting demonstrates the potential utility and sensitivity of this approach for field studies examining the impact and significance of short-term shifts in food quality on entire crustacean zooplankton communities.
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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 | 0.000 | 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".