Assessing shellfish farming-mediated benthic impacts based on organic carbon flux simulation and composition of macrofaunal community
Bibliographic record
Abstract
Marine benthic environments serve as the ultimate sink for sediment organic matter (SOM), but shellfish farming can potentially disturb the natural sink of seston, altering ecosystem functioning. Understanding the potential disturbance of a shellfish farm and its ecological effects is therefore important for a responsible management of shellfish-mediated marine ecosystem. In this study, the variations in benthic organic carbon flux of a bottom-based Manila clam (Ruditapes philippinarum) farm in Laizhou Bay, China were estimated by using a carbon flux model coupled with hydrodynamic and individual growth models. SOM and macrofaunal community were monitored for 3 years to investigate their changes to the carbon fluxes. Model simulations illustrated that the carbon flux in an area of 247 km2 was altered due to seston depletion and biodeposition, which caused decrease and increase in SOM in different areas, respectively. Cluster analysis based on taxonomic composition of macrofaunal community divided the sites into four groups, which corresponded with predicted changes of carbon flux. Increased carbon flux caused higher disturbance level (indicated by AMBI) to the macrofaunal community but increased species richness, abundance, and Shannon-Wiener index, suggesting the community was both disturbed and benefited from clam farming. This study confirmed that the benthic organic carbon flux is a key factor causing differences in SOM and macrofaunal community outside the farm, and thus can be used as an efficient method for estimating the benthic impacts of shellfish farming both in and outside the farming area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".