Fishing impacts on marine ecosystems off Brazil : with emphasis on the northeastern region
Bibliographic record
Abstract
This study is the first contribution towards the development of ecosystem-based fisheries management in northeastern Brazil, through the exploration of fishing policies based on a trophic model. The following objectives were addressed: 1) analysis of the richness of common names of Brazilian fishes; 2) reconstruction of time series of marine catches; 3) modelling of trophic interactions off northeastern Brazil; and 4) assessment of fishing policies. The analysis of common names indicated a high richness of names per species (average = 6) and the use of the same common name for different species, with a negative impact on the accuracy of catch statistics. The reconstruction of catch time series was based on landings from national yearbooks, and from ICC AT and FAO's databases (1978-2000), allowing for the detection of 'fishing down the food web' in northeastern Brazil. The trophic model estimated a total biomass for this ecosystem of 222 tonnes km⁻² (excluding detritus), and indicated a low degree of omnivory and the high importance of detritus. Simulations for 2001-2028 indicated that current fishing effort is unsustainable for lobsters and swordfish; however, the model inadequately described the dynamics of swordfish, tunas, and other large pelagics, which have large distribution areas. The simulation of optimum fishing policies led to a diverse fleet when ecosystem health was emphasized. If the main objective was economic or social (or a combination of both and ecosystem health), manual collection of coastal resources, and demersal industrial fisheries could be boosted, while the lobster and longline fisheries should be phased out. A 50% reduction in effort for lobster fisheries would not produce significant changes in lobster biomass; a reduction in effort to the 1978 level (f[sub MSY]) would lead to biomass recovery. The instability of institutions responsible for fisheries management in Brazil has had a deleterious impact on the resources. This negative impact is expected to increase due to the current split of responsibility between two institutions with diverse agendas. An improvement in the collection system of catch statistics is recommended, which would consider a standardized set of common names, as well as gathering information on biological, economic, and social components of this ecosystem and its fisheries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".