Potential socioeconomic impacts from ocean acidification and climate change effects on Atlantic Canadian fisheries

of adaptive capacity in their assessment of vulnerability to OA in the United States [14]. How- ever, many of the commercial harvesters in Atlantic Canada already target multiple species [23]. While this may ultimately indicate that the provinces are less sensitive to changes than this assessment implies, it also means that this aspect will be difficult to improve upon as a method to reduce vulnerability for the region. Developing other industries and factors sup- porting provincial economies could reduce the relative importance of fisheries in the region and thereby reduce sensitivity related to changing fisheries. A finer scale assessment is also potentially highly relevant for addressing potential impacts in Atlantic Canada. Much of the region is made up of small communities with much more localised economic and social factors than is represented by the provincial scale statistics [23,24]. In some counties, employment in shellfish fisheries is much higher than the provincial rates imply–especially when additional steps of the supply chain are included. For example, lobster fishing area (LFA) 34 is by far the most productive area for American lobster in the Maritime management area [66]. Following an approximation that counties rely most heavily on adjacent management areas, this implies that counties such as Digby, Yarmouth and Shel- burne could be much more sensitive to changes in that fishery than other NS counties. More- over, LFA 34 corresponds to the area with some of the most substantial DBEM projected

losses for lobster landings (S1E–S1H Fig). Future assessments at a finer social scale might be necessary to identify these potentially more at-risk communities. Following the conclusions of this study, NB and NS would be strong candidates for future fine-scale assessment, as these provinces were most exposed to changes in fisheries, thus highly vulnerable communities in these provinces could be among the most at risk in the region. Relatedly, future assessments could include more detailed economic models to better account for how markets may respond changes in the industry. More broadly, future research into socioeconomic implications of OA and climate change will benefit substantially from better understanding species and ecosystem responses to OA and other climate change stressors, especially including how responses to these stressors will interact. Conclusions could also be improved with more detailed biophysical models. To this end improving the resolution and incorporating finer scale ocean currents and processes into models, such as the DBEM used here, would allow for more nuanced estimates of social impacts and more specific predictions of where and how fisheries will potentially be most affected. Consideration of additional changes to ecosystem functions, such as increased rates of harmful algal blooms, will also benefit future estimates of potential impacts on fisheries. Additionally, the OA impact level in the iteration of the DBEM used in this research was based on the mean effect from two meta-analyses [3,4]. The OA driven losses could be much

more severe than anticipated by the model, potentially to the point where OA might not be entirely overwhelmed by temperature driven patterns in abundance. Conversely, the OA impacts could also be much less severe and the benefits for many species in the region from thermal loading could be more pronounced. Recent efforts have explicitly tested different impact levels if OA in the DBEM [34]. Thus future socioeconomic assessments using DBEM projections may be able to examine a broader range of OA impact levels. Similarly, the OA effects in this assessment assume a linear impact from OA (i.e., as pH changes there was a lin- ear relationship to the affected life-history traits–growth and survival). This treatment does not allow for adaptation, where species might be able to limit the effects of OA through biolog- ical mechanisms such as acclimatisation (including behavioural responses), parental effects (i.e., epigenetic effects) or evolution [e.g., 67]. However, including a simulation of adaptation in the DBEM (where the pH level is applied in relation to the previous time-step rather than the baseline level) appears to lead to negligible effects from OA in the model outputs [34]. OA and climate change impacts on Atlantic Canadian fisheries PLOS ONE | https://doi.org/10.1371/journal.pone.0226544 January 10, 2020 24 / 29