Assessing the environmental status of selected North Atlantic deep-sea ecosystems
Bibliographic record
Abstract
The deep sea is the largest biome on Earth but the least explored. Our knowledge of it comes from scattered sources spanning different spatial and temporal scales. Implementation of marine policies like the European Union’s Marine Strategy Framework Directive (MSFD) and support for Blue Growth in the deep sea are therefore hindered by lack of data. Integrated assessments of environmental status require tools to work with different and disaggregated datasets (e.g. density of deep-sea habitat-forming species, body-size distribution of commercial fishes, intensity of bottom trawling) across spatial and temporal scales. A feasibility study was conducted as part of the four-year ATLAS project to assess the effectiveness of the open-access Nested Environmental status Assessment Tool (NEAT) to assess deep-sea environmental status. We worked at nine selected study areas in the North Atlantic focusing on five MSFD descriptors (D1-Biodiversity, D3-Commercial fish and shellfish, D4-Food webs, D6-Seafloor integrity, D10-Marine litter). The objectives of the present study were to i) explore and propose indicators that could be used in the assessment of deep-sea environmental status, ii) evaluate the performance of NEAT in the deep sea, and iii) identify challenges and opportunities for the assessment of deep-sea status. Based on data availability, data quality and expert judgement, in total 24 indicators (one for D1, one for D3, seven for D4, 13 for D6, two for D10) were used in the assessment of the nine study areas, their habitats and ecosystem components. NEAT analyses revealed differences among the study areas for their environmental status ranging from “poor” to “high”. Overall, the NEAT results were in moderate to complete agreement with expert judgement, previous assessments, scientific literature on human-pressure gradients and expected management outcomes. We suggest that the assessment of deep-sea environmental status should take place at habitat and ecosystem level (rather than at species level) and at relatively large spatial scales, in comparison to shallow-water areas. Limited knowledge across space (e.g. distribution of habitat-forming species) and the scarcity of long-term data sets limit our knowledge about natural variability and human impacts in the deep sea preventing a more systematic assessment of habitat and ecosystem components in the deep sea. However, stronger cross-sectoral collaborations, the use of novel technologies and open data-sharing platforms will be critical for establishing environmental baseline indicator values in the deep sea that will contribute to the science base supporting the implementation of marine policies and stimulating Blue Growth.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".