Finding the hotspots within a biodiversity hotspot: fine‐scale biological predictions within a submarine canyon using high‐resolution acoustic mapping techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Submarine canyons are complex geomorphological features that have been suggested as potential hotspots for biodiversity. However, few canyons have been mapped and studied at high resolution (tens of m). In this study, the four main branches of Whittard Canyon, Northeast Atlantic, were mapped using multibeam and sidescan sonars to examine which environmental variables were most useful in predicting regions of higher biodiversity. The acoustic maps obtained were ground truthed by 13 remotely operated vehicle ( ROV ) video transects at depths ranging from 650 to 4000 m. Over 100 h of video were collected, and used to identify and georeference megabenthic invertebrate species present within specific areas of the canyon. Both general additive models ( GAM s) and random forest ( RF ) were used to build predictive maps for megafaunal abundance, species richness and biodiversity. Vertical walls had the highest diversity of organisms, particularly when colonized by cold‐water corals such as Lophelia pertusa and Solenosmilia variabilis . GAM s and RF gave different predictive maps and external assessment of predictions indicated that the most adequate technique varied based on the response variable considered. By using ensemble mapping approaches, results from more than one model were combined to identify vertical walls most likely to harbour a high biodiversity of organisms or cold‐water corals. Such vertical structures were estimated to represent less than 0.1% of the canyon's surface. The approach developed provides a cost‐effective strategy to facilitate the location of rare biological communities of conservation importance and guide further sampling efforts to help ensure that appropriate monitoring can be implemented.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it