Ecology and Trophic Relationships Among Fishes and Invertebrates in the Hawaiian Archipelago: Insights from Fatty Acid Signatures Analysis
Bibliographic record
Abstract
Healthy coral reefs have become increasingly rare, and their continuous degradation has serious implications for loss of marine biodiversity. There is an urgent need to assess the strength of top-down versus bottom-up effects on reef communities, to better understand how food web alterations can change the structure and function of these vulnerable marine systems. In this study, I used fatty acid (FA) analysis to investigate the trophic and ecological relationships among potential key forage species of the critically endangered monk seal in the Hawaiian archipelago.\nA series of multivariate tests performed on groups of closely related and ecologically equivalent species of fishes and invertebrates using a restricted number of FAs revealed that FA differences among groups primarily reflected diet, but could also be related to habitat and ecology. The same groups were subsequently analysed using an alternate method in quantitative FA signature analysis (QFASA) simulations, which allowed for the effects of using various subsets of FAs to be evaluated. Overall, species groups were relatively well characterized using both methods. When present, overlap in FA composition principally occurred among groups with similar diet/ecology, and were more prominent at higher trophic levels. A last set of analyses which combined the multivariate and QFASA simulation methods revealed that despite taxonomical relatedness and similarities in trophic ecology, individual species of carnivorous fish could be reliably distinguished using FAs. Therefore, while increasing the number of FAs used in the analyses might be useful to refine the resolution of distinctions, using a restricted number of FAs can also result in reliable differentiation among species. My results suggested that despite tremendous diversity, finer scale variations in FA composition could be detected among groups, and among species which shared the same diet and trophic ecology. These findings have important implications for the study of food web interactions in the Hawaiian archipelago, as they provide the foundation for using the same species groups in diets estimations of monk seal, as well as other top predators in this ecosystem. Moreover, they provide a framework for using multiple approaches to link FA patterns to the foraging ecology of individual species.
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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 | 0.000 |
| 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".