Fuzzy logic modeling: fish at risk

Published 2017-10-10

Photo: Australian Museum

A new study published in the journal Global Change Biology, uses a modeling approach to identify fish and shellfish species that are most susceptible to the impacts of climate change. Because there is limited data on the biological ecological attributes in many marine fishes, the authors of this study used a “fuzzy logic approach” to infer the vulnerability levels of species to changes in the environment. This modeling approach accommodates for the inherent variability in the data available and uncertainties that are associated with climate projections and models. Using this approach, the authors generate a vulnerability index based on the species temperature range, restrictions on geographic range, generation times and reproductive cycles, and specific habitat requirements.

This study assesses the vulnerability of 1,074 species to climate change impacts, including rising temperature and ocean acidification under the “business-as-usual” greenhouse gas emission scenario. They found that species most at risk include the Eastern Australian salmon, yellowbar angelfish, toli shad, sohal surgeonfish, and spotted grouper. The Eastern Australian salmon, for example, is particularly susceptible due to its limited distribution and habitat exposed to large changed in ocean conditions. In Canadian waters, the sockeye salmon, Pacific bonito, thresher sharks, and porbeagle sharks are also at high risk of climate change impacts. In general, species that are the most vulnerable tend to be large-bodied species that are endemic to specific habitats.

On the other hand, Pacific sandcrab, blue crab, and the Pacific sandlance inhabit areas that are at a lower risk of climate change impacts and make the list of the least vulnerable species in the study. These large data set modeling approaches provide important and predictive information on how important marine species will be impacted by future changes in the environment.

Journal Reference:
1. Miranda C. Jones, William W. L. Cheung. Using fuzzy logic to determine the vulnerability of marine species to climate change. Global Change Biology, 2017; DOI: 10.1111/gcb.13869