Differential Response of Fish Assemblages to Coral Reef-Based Seaweed Farming
Bibliographic record
Abstract
As the global demand for seaweed-derived products drives the expansion of seaweed farming onto shallow coral ecosystems, the effects of farms on fish assemblages remain largely unexplored. Shallow coral reefs provide food and shelter for highly diverse fish assemblages but are increasingly modified by anthropogenic activities. We hypothesized that the introduction of seaweed farms into degraded shallow coral reefs had potential to generate ecological benefits for fish by adding structural complexity and a possible food source. We conducted 210 transects at 14 locations, with sampling stratified across seaweed farms and sites adjacent to and distant from farms. At a seascape scale, locations were classified by their level of exposure to human disturbance. We compared sites where (1) marine protected areas (MPAs) were established, (2) neither MPAs nor blast fishing was present (hence "unprotected"), and (3) blast fishing occurred. We observed 80,186 fish representing 148 species from 38 families. The negative effects of seaweed farms on fish assemblages appeared stronger in the absence of blast fishing and were strongest when MPAs were present, likely reflecting the positive influence of the MPAs on fish within them. Species differentiating fish assemblages with respect to seaweed farming and disturbance were typically small but also included two key target species. The propensity for seaweed farms to increase fish diversity, abundance, and biomass is limited and may reduce MPA benefits. We suggest that careful consideration be given to the placement of seaweed farms relative to MPAs.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".