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

were combined with the social vulnerability indicator scores for adjacent provinces–the Gulf and the Maritime management areas’ scores were applied to more than one province (NB- gulf, NS-gulf, PEI, and Que; and NB-mar, NS-mar; respectively) while the Newfoundland and Labrador management area scores were only applied to NL (Fig 3). IE;A;Y;r ¼ EA;Y;r MinðEÞ � MaxðEÞ MinðEÞ ð Þ Eq 3 Min-Max approach for normalising exposure indicator scores [43]. Where IE,A,Y,r is the nor- malised index score for the exposure score EA,Y,r as calculated in Eq 2. Min(E) and Max(E) indicate the respective minimum and maximum exposure scores obtained. The normalisation sets the maximum score to be 1 and the minimum score to be 0. Social vulnerability. Vulnerability represents a province’s reliance on fisheries (sensitiv- ity) as well as the community’s broader ability to respond to, and absorb, changes (adaptive capacity). Vulnerability was assessed at the provincial level as this was the finest level of politi- cal organization for which most relevant data were consistently available across the region. To align the social data with the exposure data, the year 2000 was used as the baseline where possi- ble. However, some of the data (e.g., crew size) was drawn from reports which are not pub- lished annually, and consequently these data are from the nearest year available (in all instances this was within 10 years of the year 2000). The social data was centered on the year 2000 baseline to align with the biophysical data.

Potential changes in the socioeconomic structures for the region were not considered. While it is recognised that in reality social systems will certainly respond and adapt in response to changing resource availability. The intent of this assessment was to establish the relative importance of the resources in the baseline year in order to identify which provinces are more likely to be impacted by, and require responses to, changes in resources. Sensitivity of each province was based on the importance of the assessed fisheries to provin- cial economies and social structures. Compared to other Canadian provinces, fisheries in Atlantic Canada represent a much more important economic sector as well as a source of live- lihood in rural communities. Each province was scored on two indicators: a) the economic value derived from the seven species relative to total provincial GDP, and b) the employment directly associated with harvesting the selected species (Fig 4). Given the particularly high value of some fisheries in the region (e.g., lobster) the economic value of the fisheries provides insight on the relative importance of the industry to the provinces. Whereas the amount of employment that is tied to fisheries better reflects the sensitivity of communities. In reality these two indicators may not be entirely independent from each other, however given the pro- vincial economies and the distinct management structures of the separate fisheries (e.g., two large vessels harvest nearly all of the surf clam in NS-mar, whereas the lobster fishery is domi-

nated by far more numerous but smaller vessels with fewer than five crew members) they were treated as representing these two distinct aspects of the provincial socioeconomic structure. The first indicator was assessed using year 2000 total annual landed value (averaged from 1991–2010) of the assessed species as a fraction of total provincial GDP (averaged between 1997 and 2003). To make the dollar values comparable across years all dollar values were nor- malised to year 2000 dollars [26,44]. Scaling the fisheries value relative to provincial GDP allowed the indicator scores to be relatable between the provinces, which have a broad range of economic scales. It is difficult to obtain realistic employment estimates for individual fisheries because of the seasonal nature of the sector. This type of data is further convoluted by different methods of reporting employment statistics. For example, employment data are often presented as full- OA and climate change impacts on Atlantic Canadian fisheries PLOS ONE | https://doi.org/10.1371/journal.pone.0226544 January 10, 2020 9 / 29