The influence of multiple stressors on the spatial distribution of corals
Bibliographic record
Abstract
Abstract Coral reef ecosystems are widely threatened by global change, yet the cumulative impacts of multiple interacting stressors remain difficult to quantify over space and time. We evaluate how long‐term artisanal fishing effort, blast fishing, human population density and marine protected areas (MPAs) interact with biophysical and seascape variables to influence the spatial distribution of corals in a biodiverse, but heavily impacted ecosystem. To address these challenges, we combined satellite habitat mapping, Indigenous and local knowledge and generalized linear mixed‐effects models to assess how stressors and seascape characteristics shape the occurrence of living coral. This integrative approach allowed us to capture both ecological and social processes at an ecosystem scale. Coral was the dominant habitat in only 30% of the study area. The strongest predictor of coral distribution was seascape configuration: corals were more likely to be in compact reef patches. Coral presence was also positively associated with MPAs (18% higher probability inside MPAs) and depth (8% increase in probability of corals per 2.25 m). Increasing distance to seagrass was associated with higher coral probability (6% per 100 m), but this effect diminished at greater distances, reflecting a nonlinear quadratic response. Disturbance effects were complex. Coral probability declined 6% for each 35 days of blast fishing (fishing with explosives), with an antagonistic interaction with human population density: impacts were strongest in low‐population areas (−7.6%) and weaker in high‐population areas (−2.4%). Total fishing effort (excluding blast fishing) reduced coral probability by 3.2% for every ~530 fishing days at a site, with a notable 10‐year lag, highlighting delayed ecosystem responses. Our findings emphasize the importance of long time series and the need to account for lagged effects of fishing, which may otherwise be underestimated. They also underscore that conservation outcomes depend on both managing harmful stressors and enhancing beneficial seascape features. Key priorities include reducing spatial overlap of destructive activities, protecting reef configurations that support coral persistence and addressing stressors with delayed impacts. The approach developed here—integrating spatial, ecological and social data—offers a framework for adaptively managing coral reef ecosystems that are changing under interacting pressures. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 |
| 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 | 0.000 |
| 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".