Coralline barrens and benthic mega-invertebrates: An intimate connection
Bibliographic record
Abstract
Despite considerable progress in understanding the transition from algal forests to coralline barrens, knowledge of coralline barren ecosystems in terms of community composition and functioning is still sparse and important gaps remain to be filled. Using a barren/forest patch system, we tested the hypothesis that the presence of coralline barren enhances the abundance and diversity of benthic mega-invertebrates. We also analysed trophic functional diversity through isotopic analyses of δ13C and δ15N. The distribution of benthic mega-invertebrates biomass differed markedly between coralline barren and algal forest, being more abundant and diverse in the barren state. Isotopic diversity metrics of the benthic mega-invertebrates assemblage indicated comparable trophic structure between the two states, although higher isotopic uniqueness in coralline barren was determined by sea urchins, especially A. lixula, and carnivorous starfish. We showed that in a patchy coralline barren/ algal forest system, a more diversified benthic mega-invertebrates assemblage in the barren caused limited trophodynamic changes. This was possibly determined by the behaviour of some trophic groups such as filter feeders, deposit feeders and omnivores. Finally, our results evidence the close association between coralline barrens and benthic mega-invertebrates, contradicting the common view of coralline barrens as depauperate habitats with low diversity and productivity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".