Highly diverse mesophotic reef fish communities in Raja Ampat, West Papua
Bibliographic record
Abstract
Abstract Mesophotic coral ecosystems (MCEs; reefs 30–150 m depth) are poorly studied, with existing research heavily geographically biased away from the most species-rich reef regions. Yet, MCEs are of high interest because of their unique species and potential to act as refuges from the impacts of fishing. Using baited remote underwater video systems, we surveyed reef fish communities from 2 to 85 m depths throughout the Raja Ampat archipelago in West Papua, Indonesia—an area considered the heart of the Coral Triangle where coral reef biodiversity is greatest. We sought to provide the first assessment of fish communities across this depth gradient in the region and identify whether human population density and market access differently affected fish abundance based on depth. Here we show that—similar to shallow reefs—Raja Ampat MCEs are exceptionally diverse, with 152 fish species recorded at depths greater than 40 m. We found that fish community structures were highly depth driven, with declines in fish abundance at increased depth. In contrast to previous studies elsewhere in the world, we found that the proportion of planktivores declined across the shallow reef to MCE depth gradient. While greater human population density correlated with lower Epinephelidae and Lutjanidae abundance (two key fisheries families), we did not find evidence that MCEs provide a depth refuge from fishing. Surprisingly, we also found that fish abundance declined at greater distances from the major regional market—likely caused by historical fisheries pressure in more remote areas. These results both expand upon and contrast some previously established MCE-depth patterns and human impact patterns on fish communities, suggesting that regional context and historical pressures matters. Our findings highlight the need for future MCE studies within the Coral Triangle region.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".