Habitats and fish communities at mesophotic depths in the Mexican Pacific
Bibliographic record
Abstract
Abstract Aim Mesophotic ecosystems, found at the limit of light penetration in the ocean, are rich in biodiversity and harbour unique ecological communities. However, they remain among the least studied habitat zones on earth due to the high costs and technological limitations. Here, we characterize mesophotic communities in two marine reserves across a range of habitat types, depths and temperatures using submersible technologies, with the goal of understanding the processes that structure these communities across biogeographical regions. Location The Bay of La Paz and the Revillagigedo Archipelago, Mexico. Taxa Fish and algal species. Methods We used a small and inexpensive remotely operated vehicle (ROV) to conduct roving‐swim surveys of major habitat types in depths from 12 to 94 m. With the resulting binary data on the presence of fish species, we used generalized linear mixed models and canonical correspondence analysis to determine whether biogenic habitat, depth and/or temperature best explained species richness and community structure across reef and non‐reef substrate. Results We identified 72 species or genera, including new depth records for nine fish species and a new geographical record for one fish species. Our surveys included large undocumented rhodolith beds (free‐living coralline algae) and mesophotic algal communities, in addition to diverse communities of soft corals and sponges. Fish species richness was positively associated with rocky substrate and warmer water, and reef fish communities differed significantly by depth, temperature and biogenic habitat. Main conclusion Our results highlight the importance of biogenic habitat in structuring communities across gradients of depth and temperature. We also demonstrate the effectiveness of a small and economical ROV for conducting mesophotic surveys in remote regions. Our methods and results provide a framework that can be used to greatly increase the biogeographical and taxonomic scope of mesophotic research, especially for readily identifiable taxa such as fish.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".