Re-Evaluation of the Impacts of Dietary Preferences on Macroinvertebrate Trophic Sources: An Analysis of Seaweed Bed Habitats Using the Integration of Stable Isotope and Observational Data
Bibliographic record
Abstract
Stable isotope analyses of food webs have been used in previous decades to determine trophic sources and food web structures. The use of stable isotope models to estimate consumption contributions is based on a type of multivariate beta distribution called the Dirichlet distribution. The Dirichlet distribution does not conclude the pi = 0 and pi = 1 situation. Thus, scientists have previously assumed that every potential trophic source contributes to consumption in stable isotope models. However, animals have dietary preferences and some trophic resources may not contribute to consumption. Less is known about the effects of species-specific dietary processes on stable isotope analyses, especially in regard to trophic contribution estimation. In this study, we develop methods to determine consumers’ “real potential trophic sources” and “discrimination factors” using lab-based observations and lab-based discrimination experiments. We describe a dietary process-based stable isotope mixing model (D-SIMM) that integrates lab-based dietary preference observations and the stable isotope mixing model (SIMM) to estimate trophic contributions. Then, we present the application of D-SIMM on three representative macroinvertebrate species in our study area (sea urchin: Anthocidaris crassispina (A. crassispina); gastropod: Turbo cornutus (T. cornutus); and mussel: Septifer virgatus (S. virgatus)) to re-evaluate source-consumer contributions. Thus, we compare the differences between the source contribution estimation results of SIMM and D-SIMM by calculating the standardized convex hull area (TA) of species-specific trophic sources and the consumer standard ellipses area (SEA) of the potential trophic source group. Three examples illustrate significant differences in species-specific dietary preferences between consumers, resulting in systematic difference for TA, SEA and trophic source contribution estimation results between SIMM and D-SIMM. As such, D-SIMM explains pi = 0 of certain trophic sources, which often causes uncertainty and is ignored in previous SIMM research. In addition, species-specific discrimination factors should be noticed during trophic source estimation. For estimation of the trophic contribution of source-consumers, our findings imply that the dietary preferences of consumers should be fully considered before SIMM analysis, and that D-SIMM is a more ecological process and robust measure. Additionally, we found high macroalgae (MAC) coverage in seaweed beds and a high detritus contribution of MAC to sedimentary organic matter (SOM). These findings, combined with the high contributions of MAC and SOM to consumers, suggest that MAC and its debris are the basal trophic sources for gastropods, sea urchins and mussels in seaweed bed habitats. The conservation of seaweed beds should be fully considered to ensure sustainable utilization of shellfish.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".