Environmental drivers of ophiuroid species richness on seamounts
Bibliographic record
Abstract
Abstract Benthic communities on seamounts are frequently characterised as being species rich, yet there is considerable variation in observed species richness. Although large‐scale patterns of species richness have been described from many marine and terrestrial habitats, their environmental drivers often remain poorly understood. We compared species richness of ophiuroids (brittle‐stars) on 60 seamounts throughout the South West Pacific Ocean, and used an information‐theoretic approach and generalized linear models to determine the relative importance of predictor variables. Due to high correlation among many environmental variables, we used a reduced set of predictors in an a priori model framework. Temperature was the only environmental predictor of any importance in these models over the bathymetric range of the study. Post‐hoc analyses of other potential environmental predictor variables showed that depth, calcite saturation state, temperature range, modelled current velocity and latitude all had some predictive value, but were also highly correlated with temperature or other environmental variables included in the a priori model. Longitude, large‐area species richness, habitat suitability for stony corals, and modelled POC flux did not have high predictive value. We hypothesise that temperature affects richness by constraining species distributions; in particular fewer species can tolerate the conditions on relatively warm shallow seamount summits.
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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 | 0.001 |
| 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".