Trait‐based approach to monitoring marine benthic data along 500 km of coastline
Bibliographic record
Abstract
Abstract Aim β diversity and its linkages with ecosystem functioning remain poorly documented. This impedes our capacity to predict biodiversity changes and how they affect ecosystem functioning at scales relevant for conservation. Here, we address the functional implications of ongoing seafloor changes by characterizing at regional scale the taxonomic and functional α and β diversities of benthic habitats currently threatened by biotic homogenization. Location Western Europe. Methods Combining a trait‐based approach to benthic community monitoring data covering a 7‐year period and 500 km of coast, we explored the mechanisms governing community assembly in habitats associated with two types of foundation species, intertidal seagrass and subtidal maerl beds, compared to bare sediment at similar tidal level. We assessed their spatial and temporal variability and linked these mechanisms to their repercussions at regional scale through analyses of taxonomic and functional β diversity. Results Foundation species locally promote taxonomic and functional diversity. Maerl fine‐scale heterogeneity promotes niche diversity and leads to high functional redundancy for the whole subtidal compartment, providing insurance for seafloor functioning. Seagrass high diversity seems more reliant on transient species and is associated with redundancy of only a few functions. Maintaining the seascapes in which seagrass are embedded seems essential to ensure their long‐term functioning. At regional scale, the locally poorer bare sediment harbour similar functional richness as biogenic habitats because of higher within‐habitat β diversity. Main conclusions Our study reinforces the conservation value of biogenic habitats but highlights that different mechanisms underlie their local diversity, which has implications for the vulnerabilities of their associated communities. Accounting for β diversity at regional scale also stressed a potential underrated conservation value of bare sediment for benthic ecosystem functioning. Coupling trait‐based approaches to monitoring data can help link broad‐scale β diversity to its underlying drivers, bringing local mechanistic understanding closer to the scales at which biodiversity loss and management actions occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".