Reef fish functional groups show variable declines due to deforestation-driven sedimentation, while flexible harvesting mitigates this damage
Bibliographic record
Abstract
Sedimentation is a major coral reef stressor, with effects including suppressing algae consumption by herbivorous fish. This puts pressure on reef fish populations and the fisheries that harvest them. Deforestation causes much sedimentation on reefs, and is an ongoing concern in Pacific island states. Although ecosystem processes like deforestation and fish population dynamics are usually far from equilibrium, explicitly time-dependent analyses of reef fish vulnerability to deforestation are rare. Additionally, optimization methods for fisheries on heavily sedimented reefs are generally unexplored. Here, we construct a model coupling four reef fish functional groups with seabed dynamics and deforestation, fit using data for the Solomon Islands. We show that with predicted human population increases, highland deforestation could cause herbivorous and omnivorous fish abundances to halve within 15-30 years, but that piscivorous fish and top predators are resilient to lowland deforestation. We demonstrate that flexible fishing approaches could lead to high and temporally stable populations of herbivorous fish and top predators, offsetting sedimentation-related stress. We show that the combination of deforestation and increased fishing demand due to human population growth may cause medium-term local reef fish extirpation. Our results provide sustainability guidelines for reef fisheries, and demonstrate nonlinear interactions between overfishing and deforestation.
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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 | 0.001 |
| 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) | 0.004 | 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".