North Atlantic Basin-Scale Multi-Criteria Assessment Database to Inform Effective Management and Protection of Vulnerable Marine Ecosystems
Bibliographic record
Abstract
The identification of areas that fit the Food and Agriculture Organization of the United Nations (FAO) criteria to define what constitutes a Vulnerable Marine Ecosystem (VME) has been the main policy driver for the protection of deep-sea environments in Areas Beyond National Jurisdictions (United Nations General Assembly, 2006; FAO, 2009) in relation to bottom fisheries. At the same time, the Convention on Biological Diversity advocates for the implementation of representative networks of Marine Protected Areas (MPAs) in the open ocean and the deep sea, and calls for the identification of Ecologically or Biologically Significant marine Areas (EBSAs; Convention on Biological Diversity, 2008, Decision IX/20). Although VMEs and EBSAs are conceptually different, Ardron et al. (2014) argue that the designation of VMEs, EBSAs, and large open-ocean MPAs should be aligned to ensure that VMEs are incorporated within area-based management tools. \n \nThe International Council for the Exploration of the Sea (ICES) adopted a Multi-Criteria Assessment (MCA) methodology for informing the identification of VMEs in the North-East Atlantic (ICES, 2016a,b; Morato et al., 2018). The MCA is a taxa-dependent spatial method that incorporates the fact that not all VME indicators are equally vulnerable to human impacts, and thus should not be weighted equally. By including a measure of the confidence associated with each VME record, this methodology also considers some of the uncertainties associated with the sampling methodologies, the reported taxonomy, and data quality issues. Equally important, it highlights areas in the North Atlantic that have been poorly sampled and that require further attention. Finally, this methodology also allows for the evaluation and comparison of VME index with spatial fisheries data that may directly generate significant adverse impacts on VMEs. Although the VME Index has been used since 2018 in ICES advice, several caveats and limitations have been identified (ICES, 2018, 2019, 2020). The main criticism refers to the fact that the VME index signals the presence of VME indicator taxa that are considered to be the most important rather than showing the likelihood of an area containing a spatially explicit VME. Also, concerns over the abundance scores adopted have been raised and it has been suggested that abundance thresholds should be defined for each VME indicator. It is, therefore, recognized that improvements of the VME index and the way actual VMEs are identified are still necessary. \n \nThe identification of representative areas that can form a network of MPAs in the deep sea requires ocean basin-scale approaches grounded on ocean basin-scale datasets. In this regard, the H2020 ATLAS project (GA 678760) performed a unique trans-Atlantic assessment of deep-water ecosystems to inform Atlantic Ocean basin-scale governance. The ATLAS project compiled the best available data on VME indicator taxa for the North Atlantic (Ramiro-Sánchez et al., 2020) in order to assist with the identification of locations that may constitute VMEs and EBSAs, as a precursor to the development of a North Atlantic wide network of MPAs. Here, we applied the ICES MCA method to the ATLAS VME indicator taxa database to produce and make publicly available a new “North Atlantic Ocean basin-scale VME index dataset,” facilitating further consultation and use by scientists, managers, or other relevant stakeholders.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".