Small scale fisheries in a warming ocean
Bibliographic record
Abstract
Global warming, caused by the increase of greenhouse gas (GHG) emissions through human activities, has a strong impact on our oceans including changes to oceano-graphic characteristics, as well as to abundance and distribution of marine life. Moreover, it also has severe socio-economic impacts on people living at and from the sea. In order to predict and evaluate the impacts of global warming (and sub-sequently to find suitable adaptation strategies), scientific computer models are utilized. These climate change models predict the effects of global heating on marine life and associated fisheries on a global scale, but often with a high level of uncertainty and low geographic resolution. This makes it difficult to determine effective adaptation measures for fisheries on a local level. The development of adaptation and mitigation strategies is especially urgent in small-scale fisheries that contribute about half of global fish catches and make an important contribution to nutrition, food security, sustainable livelihoods and poverty alleviation, especially in developing countries. This study used a comprehensive conceptual framework that integrates different formats of knowledge, and an interdisciplinary research approach illustrated by the integration of both, the natural and the social sciences traditions. Our study aimed to explore local adaptation measures of fishers and fishing communities by complementing fine-grained scientific climate model predictions with insights based on the perceptions, knowledge, and practices local fishers have about climate change. This combined approach represents an innovative lens to understand climate change and human adaptation since it merges both predictive (computer models) and social sciences (traditional and local knowledge of fishers). We believe it will enhance our ability to promote and strengthen the natural capacity of adaptation of fishers and fishing communities with the aim to promote and support adaptation strategies of small-scale fishers. First, the modelling aimed to predict the climate change impacts on commercial fish species and their distribution in three case countries (Ecuador; mainland and Galapagos Islands, South Africa and the Philippines). These models were based on multitemporal data sets for the areas where the study took place, designed by using outputs of the IPCC scenarios and risk analysis methods. This allowed us to identify some of the anticipated impacts of climate change on the currently exploited fish stocks in those countries. The second part of the study aimed to i) explore local perceptions by fishers, of the effects of climate change on small-scale fisheries, ii) describe how well prepared the small-scale fishing sector is in front of climate change, and iii) illustrate the adaptation measures, capabilities, challenges, and actions, carried on by fishers, to cope with climate change. We organized four workshops (in the same three case countries) involving varied and relevant sectors and actors, within the small-scale fisheries sector. The workshops were attended by fishers, researchers and managers and exhibited diverse formats, based on the location’s and fisheries sector characteristics.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".