Effects of disturbance on macrofaunal<scp>biodiversity‐ecosystem</scp>functioning relationships in seagrass habitats
Bibliographic record
Abstract
Abstract Seagrass beds support diverse macrofaunal communities, and collectively they influence carbon and nutrient cycles; however, we know little on how seagrass disturbance alters this relationship. In Newfoundland, Canada, the invasive European green crabCarcinus maenasthreatens the seagrassZostera marinaby snipping and uprooting seagrasses while foraging and burrowing. In order to understand the effects of seagrass disturbance on macrofaunal diversity and ecosystem functioning within sediments, we experimentally uprooted small patches of seagrass and compared rates of oxygen and nutrient fluxes from sediment cores from uprooted (disturbed) patches, seagrasses, and unvegetated sediments nearby. In parallel, we assessed macrofaunal biodiversity (taxonomic and functional) and sedimentary (granulometric properties and organic matter content/freshness) variables in all three of these treatments over a three‐month period. As expected, macrofaunal abundance, species richness and functional richness declined significantly initially in disturbed cores, although this decrease had little effect on benthic flux rates. Over 3 months, macrofaunal colonization of the disturbed sediments resulted in abundances similar to the natural seagrass and unvegetated treatments. We also observed a change in nutrient flux rates that we attribute to seasonal shifts in regeneration pathways rather than macrofaunal community recovery, suggesting a lesser role for macrofaunal diversity in carbon and nutrient cycling in dynamic nearshore habitats than in deeper water. Our results demonstrate the impacts of green crab‐mediated seagrass disturbance on macrofaunal abundance and community structure while highlighting their potential capacity for rapid stabilization, and emphasize the strength of large‐scale seasonal environmental changes on ecosystem processes.
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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 | 0.001 |
| 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".