Ecosystem-based management of seaweed harvesting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it