Data from: Using fuzzy logic to determine the vulnerability of marine species to climate change

• Main Authors: Jones, Miranda C., Cheung, William W. L.
• Other Authors: Federated Research Data Repository, Dépôt fédéré de données de recherche
• Format: Dataset
• Language: unknown
• Published: Scholars Portal Dataverse 2021
• Subjects: Life sciences; medicine and health care; Fuzzy logic; Marine; Fishes; Risk of impact; vulnerability; Invertebrates; ocean acidification; climate change; Global; Other; envir; geo; Ocean acidification
• Online Access: https://doi.org/10.5683/sp2/tgdqio; https://doi.org/10.5061/dryad.9dc21; https://doi.org/10.14288/1.0397932

Dryad version number: 1 Version status: submitted Dryad curation status: Published Sharing link: https://datadryad.org/stash/share/8t6D89aHWEVNmleQnsSoYfgPfLkJ0FllsA4Nlzwlwx8 Storage size: 67944 Visibility: public Usage notes Estimated vulnerability and risk of impacts Data table that contains species name, the estimated vulnerability index and risk of impact index under the RCP 8.5 scenario. Jones_Cheung_SDATA.csv Abstract Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species-specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the ...