Identification of Vulnerable Marine Ecosystems on Seamounts in the North Pacific Fisheries Commission Convention Area using Visual Surveys and Distribution Models
Bibliographic record
Abstract
The United Nations General Assembly called upon States to manage fisheries sustainably and protect vulnerable marine ecosystems (VMEs) from destructive fishing practices when they adopted Resolution 61/105 in 2006. The Convention on the Conservation and Management of High Seas Fisheries Resources in the North Pacific Ocean requires North Pacific Fisheries Commission (NPFC) Members, including Canada, to develop a process to identify VMEs and areas where they are likely to occur (i.e., likely VMEs) using the best scientific information available. As of January 2024, the NPFC included four groups of corals and two classes of sponges as VME indicator taxa and recently adopted a quantitative methodology that can be used to identify VMEs based on visual surveys (NPFC-SSC BFME 2022, NPFC-SC 2022a; NPFC-COM 2023). This quantitative approach was originally developed by Rowden et al. (2020) for the South Pacific Regional Fisheries Management Organization and applied by Warawa et al. (2022, 2023a, 2023b) to the eastern NPFC Convention Area (CA). The NPFC also adopted a framework that identifies predictive models as one means to identify likely VMEs (NPFC 2023a; NPFC 2023b). As a Contracting Party to the NPFC, and the only Member operating a bottom fishery in the northeast (NE) part of the NPFC’s Convention Area, Canada has the responsibility to identify VMEs and likely VMEs in this region. As of January 2024, Canada has identified five VME areas on Cobb Seamount, which were endorsed by the NPFC’s Scientific Committee in December 2023. These were adopted by the Commission in April 2024. We describe how these five VMEs were identified using Rowden et al.’s (2020) approach (see Warawa et al. 2022, 2023a). We used visual data collected in 2012 (Curtis et al. 2015) to estimate a regional VME indicator density threshold to identify the five VMEs on Cobb Seamount by applying our threshold to these visual data. The five VMEs ranged from approximately 50 – 200 m2 in size and 500 – 1,200 m in depth. We also identify likely VMEs in the Cobb-Eickelberg seamount chain by applying the regional VME indicator density threshold to model predictions of the density of VME indicator taxa (Warawa et al. 2023b). Likely VMEs are probably present on all seamounts in the Cobb-Eickelberg seamount chain. Cobb Seamount had the largest total area identified as likely VME (27.55 km2 , 15.39% of modeled area on Cobb Seamount), followed by Brown Bear North (20.4 km2 , 19.88% of modeled area on Brown Bear North), Brown Bear South (13.74 km2 , 4.40% modeled area on Brown Bear South) and Eickelberg Ridge (10.95 km2 , 22.71% of modeled area on Eickelberg Ridge).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".