Drivers of polychaetes functional α- and β-diversity at regional scale: Disentangling the role of biogenic habitats and environmental variability
Bibliographic record
Abstract
Alterations of the seafloor, particularly loss of biogenic habitats, are homogenizing benthic environments and their associated biota. Apprehending the functional consequences of these changes is critical but requires a thorough understanding of the functional β-diversity of benthic communities. Here, using data from 3 years (2007, 2010, 2013) of the REBENT monitoring programme and 51 sampling locations along Brittany’s coastline (France), we assess taxonomic and functional α- and β-diversity of polychaetes assemblages and disentangle their drivers at the regional scale and over four habitats: subtidal and intertidal bare sediments, subtidal maerl (coralline red algae) beds and intertidal Zostera marina meadows. The 1061 sediment samples yielded 137,319 polychaetes belonging to 242 species. Eleven traits and 43 modalities were used to describe the functional effect and response of these species. Among the highly contrasted environments of Brittany, strong within-habitat taxonomic variability was observed, which blurred among-habitat differences. Here, we relate taxonomic patterns and functional variations, in order to propose a model linking environmental and habitat conditions to taxonomic and functional α- and β-diversity. Linking these various facets of diversity facilitates the identification of sites with particular conservation interests.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".