Ecosystem-based management of seaweed harvesting
Bibliographic record
Abstract
Abstract Harvesting wild seaweeds has a long history and is still relevant today, even though aquaculture now supplies >96% of global seaweed production. Current wild harvests mostly target canopy-forming kelp, rockweed and red macroalgae that provide important ecosystem roles, including primary production, carbon storage, nutrient cycling, habitat provision, biodiversity and fisheries support. Harvest methods range from selective hand-cutting to bottom trawling. Resulting ecosystem impacts depend on extraction method and scale, ranging from changes in primary production to habitat disruption, fragmentation, food-web alterations and bycatch of non-target species. Current management often aims for sustainable harvesting in a single-species context, although some agencies acknowledge the wider ecosystem structure, functions and services seaweeds provide. We outline potential ecosystem-based management approaches that would help sustain productive and diverse seaweed-based ecosystems. These include maintaining high canopy biomass, recovery potential, habitat structure and connectivity, limiting bycatch and discards, while incorporating seasonal closures and harvest-exclusion zones into spatial management plans. Other sustainability considerations concern monitoring, enforcement and certification standards, a shift to aquaculture, and addressing cumulative human impacts, invasive species and climate change. Our review provides a concise overview on how to define and operationalize ecosystem-based management of seaweed harvesting that can inform ongoing management and conservation efforts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".