Abstract
Ocean acidification (OA) has the potential to restructure ecosystems due to variation in species sensitivity to the projected changes in ocean carbon chemistry. Ecological models can be forced with scenarios of OA to help scientists, managers, and other stakeholders understand how ecosystems might change. We present a novel methodology for developing estimates of species sensitivity to OA that are regionally specific, and applied the method to the California Current ecosystem. To do so, we built a database of all published literature on the sensitivity of temperate species to decreased pH. This database contains 393 papers on 285 species and 89 multi-species groups from temperate waters around the world. Research on urchins and oysters and on adult life stages dominates the literature. Almost a third of the temperate species studied to date occur in the California Current. However, most laboratory experiments use control pH conditions that are too high to represent average current chemistry conditions in the portion of the California Current water column where the majority of the species live. We developed estimates of sensitivity to OA for functional groups in the ecosystem, which can represent single species or taxonomically diverse groups of hundreds of species. We based these estimates on the amount of available evidence derived from published studies on species sensitivity, how well this evidence could inform species sensitivity in the California Current ecosystem, and the agreement of the available evidence for a species/species group. This approach is similar to that taken by the Intergovernmental Panel on Climate Change to characterize certainty when summarizing scientific findings. Most functional groups (26 of 34) responded negatively to OA conditions, but when uncertainty in sensitivity was considered, only 11 groups had relationships that were consistently negative. Thus, incorporating certainty about the sensitivity of species and functional groups to OA is an important part of developing robust scenarios for ecosystem projections.