Spatial Management Units as an Ecosystem-Based Approach for Managing Bottom-Towed Fisheries in the Central Mediterranean Sea
Bibliographic record
Abstract
Marine ecosystems are being continually impacted by human activities and, among these, fisheries have been one of the most damaging. Fisheries modify the structure and functioning of food-webs through biomass removal and physical damage to the seabed, leading to loss of biodiversity and ecosystem services provided by the oceans. The ecosystem-based approach to fisheries is considered the most efficient way to achieve the goal of sustainable use of marine resources while allowing for biodiversity protection. The Strait of Sicily is a biologically important area of the central Mediterranean Sea characterised by high habitat complexity and rich biodiversity; however, due to the multispecific nature of local fisheries and weak implementation of the adopted management plans, this region is particularly vulnerable. We used fishery independent time series (1994-2016) to identify the main demersal assemblages and map their spatial distribution. The pressure of fishing effort on each of these defined assemblages was then quantified in order to evaluate the impact of bottom trawling on demersal communities. Our results showed four spatially distinct and temporally stable assemblages of the Strait of Sicily. These have a clear spatial distribution, different species composition and biodiversity values and are driven primarily by environmental gradients (i.e. mainly depth and, to a lesser extent, surface salinity). The demersal assemblages were subsequently grouped in homogeneous areas characterised by specific communities of commercial and non-commercial species and response to trawling impacts. These areas are proposed as Spatial Managements Units to evaluate and manage demersal mixed fisheries, while also considering biodiversity conservation in the central Mediterranean Sea.
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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 | 0.001 | 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".