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Ocean acidification is an emerging consequence of anthropogenic carbon dioxide emissions. The full extent of the biological impacts are currently not entirely defined. However, it is expected that invertebrate species that rely on the mineral calcium carbonate will be directly affected. Despite the limited understanding of the full extent of potential impacts and responses there is a need to identify potential pathways for human societies to be affected by ocean acidification. Research on these social implications is a small but developing field. This research contributes to this field by using an impact assessment framework, informed by a biophysical model of future species distributions, to investigate potential impacts facing Atlantic Canadian society from potential changes in shellfish fisheries driven by ocean acidification and climate change. New Brunswick and Nova Scotia are expected to see declines in resource accessibility but are relatively socially insulated from these changes. Conversely, Prince Edward Island, along with Newfoundland and Labrador are more socially vulnerable to potential losses in fisheries, but are expected to experience relatively minor net changes in access. [ABSTRACT FROM AUTHOR]

Ocean acidification (OA) driven by anthropogenic CO 2 emissions affects marine ecosystems, fisheries and aquaculture. Assessing the impacts of OA using projection models facilitates the development of future scenarios and potential solutions. Here, we explored various ways to incorporate OA impacts into a multi-stressor dynamic bioclimatic envelope model to project biogeographic changes of ten commercially exploited invertebrate species. We examine three dimensions of uncertainties in modelling biophysical OA effects: model structure, parameterization, and scenario uncertainty. Our results show that projected OA impacts are most sensitive to the choice of structural relationship between OA and biological responses, followed by the choice of climate change emission scenarios and parameterizations of the size of OA effects. Species generally showed negative effects to OA but sensitivity to the various sources of uncertainty were not consistent across or within species. For example, some species showed higher sensitivity to structural uncertainty and very low sensitivity to parameter uncertainty, while others showed greatest sensitivity to parameter uncertainty. This variability is largely due to geographic variability and difference in life history traits used to parameterize model simulations. Our model highlights the variability across the sources of uncertainty and contributes to the development of integrating OA impacts in species distribution models. We further stress the importance of defining the limitations and assumptions, as well as exploring the range of uncertainties associated with modelling OA impacts. [ABSTRACT FROM AUTHOR]