Role of seagrass physical structure in macrofaunal biodiversity-ecosystem functioning relationships
Bibliographic record
Abstract
Seagrass above-ground shoot canopies and below-ground rhizome networks provide structurally complex habitat that supports diverse macrofaunal communities. Seagrasses also support biodiversity through their biological activity by influencing food availability. While numerous studies have demonstrated that seagrass physical and biological habitat elements influence macrofaunal diversity and community structure, we lack an understanding of how these elements potentially interact with sedimentary macrofaunal communities to influence ecosystem processes. To understand how physical seagrass structure affects macrofaunal biodiversity and the processes of carbon and nutrient cycling, we deployed artificial seagrass patches that mimicked canopies and surface rhizomes, in tandem with parallel observations of natural seagrass Zostera marina , unvegetated habitat, seagrass patch edge, and canopy control treatments. After 3 mo, we recorded rates of oxygen and nutrient flux from sediment cores and assessed macrofaunal biodiversity and environmental variables to relate them to benthic flux patterns. We found significantly higher macrofaunal abundance and diversity in natural seagrass treatments compared to unvegetated, patch edge, canopy control, and artificial seagrass treatments. However, we found no difference in benthic fluxes across all treatments, suggesting a lesser role for macrofaunal bioturbation in nutrient regeneration in these nearshore sediments. Our results also suggest lesser importance for the structural benefits of seagrasses than their biological contributions for supporting macrofaunal biodiversity. Negative edge effects on macrofaunal abundance and diversity suggest potential consequences for macrofaunal communities of fragmented seagrass habitats associated with anthropogenic disturbance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".