Drivers of trophodynamics of the open-ocean and deep-sea environments of the Azores, NE Atlantic
Bibliographic record
Abstract
• The drivers of trophodynamics of the Azores’s open-ocean and deep-sea environments were investigated using a food-web model. • Findings highlight the role of deep-sea fisheries exploitation and environmental variability in shaping historical trends. • Integrating environmental factors is crucial for achieving biodiversity conservation and sustainable management objectives. Marine ecosystems associated with mid-oceanic elevations harbour unique pelagic and benthic biodiversity and sustain food webs critical for Nature’s contributions to people (NCP). The United Nations Sustainable Development Goals and the Convention on the Law of the Sea recognize the need to implement ecosystem-based management approaches to conserve the structure and functioning of oceanic and deep-sea ecosystems within sustainable reference points. However, uncertainties regarding the interactions between multiple drivers of change, and their impacts on the state of these ecosystems and the NCP, present significant challenges to effective management. Trophic models offer a holistic approach to identify the main drivers affecting the dynamics of marine ecosystems. Here, we used a food web model of the open-ocean and deep-sea environments of the Azores for identifying the drivers that best explain historical biomass trends of demersal fish of high commercial value. Our hindcast simulations suggested that historical trends can be explained by the combined effects of deep-sea fisheries exploitation and variability in environmental conditions, likely dominated by primary productivity anomalies. In particular, deficits in primary production and high levels of fishing exploitation might have contributed to the pronounced decline in biomass observed between 2008 and 2012. These findings reinforce that failure to consider environmental factors in ecosystem-based management may result in shortfalls at achieving biodiversity conservation and sustainability objectives, particularly in the context of climate change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".