Contributions of food web modelling to the ecosystem approach to marine resource management in the Mediterranean Sea
Bibliographic record
Abstract
Abstract Ecological modelling tools are applied worldwide to support the ecosystem‐based approach of marine resources (EAM). In the last decades, numerous applications were attempted in the Mediterranean Sea, mainly using the Ecopath with Ecosim (EwE) tool. These models were used to analyse a variety of complex environmental problems. Many applications analysed the ecosystem impacts of fishing and assessed management options. Other studies dealt with the accumulation of pollution through the food web, the impact of aquaculture or the ecosystem effects of climate change. They contributed to the scientific aspects of an ecosystem‐based approach in the region because they integrated human activities within an ecosystem context and evaluated their impact on the marine food web, including environmental factors. These studies also gathered a significant amount of information at an ecosystem level. Thus, in the second part of this review, we used this information to quantify structural and functional traits of Mediterranean marine ecosystems at regional scales as the illustration of further potentialities of EwE for an EAM. Results highlighted differential traits between ecosystem types and a few between basins, which illustrate the environmental heterogeneity of the Mediterranean Sea. Moreover, our analysis evidenced the importance of top predators and small pelagic fish in Mediterranean ecosystems, in addition to the structural role of benthos and plankton organisms. The impact of fishing was high and of a similar intensity in the western, central and eastern regions and showed differences between ecosystem types. The keystone role of species was more prominent in protected environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".