Climate change disturbances contextualize the outcomes of coral-reef fisheries management across Micronesia
Bibliographic record
Abstract
Climate change is increasing disturbance events on coral reefs with poorly understood consequences for fish population dynamics and fisheries management. Given growing concerns over food security for the tropical Pacific, we assessed fisheries management policies across a suite of Micronesian islands since 2014 as climate disturbance events have intensified. Disturbances associated with the 2015–2017 ENSO led to significant mortality of corals and calcifying substrates and replacement with algae and detritus, followed by a doubling of biomass across all fish guilds that was proportional to their starting points for all islands. Increased fish biomass was equally attributed to growth of individuals evidenced by increased size structures, and recruitment/survival evidenced by larger population densities. However, the pulsed increase of fish biomass lasted 1–2 years for islands with limited and isolated MPA but remained high for islands with effective MPA networks for 4 years until the study ended. Meanwhile, policies to protect grouper spawning seasons resulted in increased occurrences that were magnified by disturbances and MPA. Grouper increases were largest where both spawning season bans and MPA networks existed, helping to tease apart the management-from-disturbance responses. Smaller rates of increases over longer time were observed for species with commercial fishing bans (bumphead parrotfish, Napoleon wrasse, and sharks). Yet, occurrences remain low in comparison to remote-island baselines, and MPA only provided benefits for juveniles in inner lagoons. Recent trends for these species were less influenced by climate disturbances compared to groupers. The results cautioned how short-term responses of fish assemblages following climate disturbances can provide false signs of success for some management policies without contextual reference baselines that may not exist. Positively, improvements were noted for both MPA and species policies in our region that are expected to benefit reef resilience.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".