Contrasted patterns in climate change risk for Mediterranean fisheries
Pita, I.; Mouillot, D.; Moullec, F.; Shin, Y.-J. (2021). Contrasted patterns in climate change risk for Mediterranean fisheries. Glob. Chang. Biol. 27(22): 5920-5933. https://dx.doi.org/10.1111/gcb.15814 In: Global Change Biology. Blackwell Publishers: Oxford. ISSN 1354-1013; e-ISSN 1365-2486, more |   |
| Keyword | | | Author keywords | climate change; climate vulnerability assessment; exploited marine species; fisheries; Mediterranean Sea |
| Authors | | Top | - Pita, I.
- Mouillot, D.
- Moullec, F., more
- Shin, Y.-J.
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| Abstract | Climate change is rapidly becoming one of the biggest threats to marine life, and its impacts have the potential to strongly affect fisheries upon which millions of people rely. This is particularly crucial for the Mediterranean Sea, which is one of the world's biodiversity hotspots, one of the world's most overfished regions, and where temperatures are rising 25% faster than in the rest of the ocean on average. In this study, we calculated a vulnerability index for 100 species that compose 95% of the Mediterranean catches, through a trait-based approach. The Climate Risk Assessment (CRA) methodology was subsequently used to assess the risks due to climate change of Mediterranean fisheries. We found that the northern Mediterranean fisheries target more vulnerable species than their southern counterparts. However, when combining this catch-based vulnerability with a suite of socio-economic parameters, north African countries stand out as the most vulnerable to climate change impacts. Indeed, considering countries’ exposure of the fisheries sector and their vulnerability to climate change, a sharp contrast between northern and southern Mediterranean appears, with Egypt and Tunisia scoring the highest risk. By integrating a trait-based approach on targeted marine species with socio-economic features, our analysis helps to better understand the ramifications of climate change consequences on Mediterranean fisheries and highlights the regions that could potentially be particularly affected. |
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