Predicting suitable habitat for deep-water gorgonian corals on the Atlantic and Pacific Continental Margins of North America
Why this work is in the frame
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Bibliographic record
Abstract
Mapping marine habitats and species distributions is essential in conservation and resource management. The generation of such maps, however, is particularly challenging for the poorly sampled deep-sea species. In this study, we explored the spatial suitability of deep-water coral (Families Paragorgiidae and Primnoidae) habitat on both the Pacific and Atlantic Continental Margins of North America (PCM and ACM) using Biomapper, a modeling program which can determine habitat suitability using presence-only data. The PCM study area was divided into 2 regions to limit the geographic size of the modelled area: PCM:AK, which encompasses Alaska and PCM:BC-CA, which encompasses British Columbia, Washington, Oregon, and California. Suitable habitat was determined based on quantitative relationships between physical seascape factors and biological data. For the PCM study area, the most accurate model for Paragorgiidae in PCM:AK combined temperature, slope, current and chlorophyll (chl) a concentration (Spearman's = 0.79), whereas in the PCM:BC-CA it combined depth and chl a concentration ( = 0.66). For Primnoidae, in the PCM:AK the most accurate combination included depth, slope, current and chl a concentration ( = 0.90), and in the PCM:BC-CA, it included depth, temperature, slope and current ( = 0.85). In the ACM study area, the most accurate model for Paragorgiidae combined temperature, slope and chl a concentration ( = 0.71), whereas the one for Primnoidae combined temperature, slope, current and chl a concentration ( = 0.74). In both study areas, corals were predicted to occur in areas of complex topography, mainly along the continental shelf break and on seamounts. Sensitivity analyses indicated that predicted mean values of seascape factors, in coral habitat as well as niche breadth, varied with number of coral locations, but to a much lesser extent with spatial resolution. To our knowledge, this is the first study to use Biomapper for the prediction of suitable habitat in marine species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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