Using a Vegetation Model and Stakeholder Input to Assess the Climate Change Vulnerability of Tribally Important Ecosystem Services

We demonstrate a generalizable approach for assessing climate change effects on tribally important ecosystem goods and services. Indigenous peoples may be highly vulnerable to the impacts of climate change because they rely on ecosystem goods and services, such as traditional foods, hunting, timber production, nontimber forest resources, and cultural resources. However, there are few assessments that have examined the potential impact of climate change on these goods and services and even less that examine ecological, socio-economic, and cultural resources in the Pacific Northwest, USA. Our approach uses four basic steps: (1) identify 78 tribally important ecosystem services (species and resources), (2) relate those ecosystem services with biologically relevant vegetation projections from a dynamic global vegetation model, (3) identify appropriate timeframes and future climate scenarios, and (4) assess future changes for vegetation types and ecosystem services. We then highlight how model uncertainty can be explored to better inform resilience building and adaptation planning. We found that more than half of the species and resources analyzed may be vulnerable to climate change due to loss of potential habitat, including aridland species and grazing quality. We further highlight our findings for tribally important species, huckleberries (genus Vaccinium) and bitterbrush (Purshia tridentate (Pursh) DC.), and show how this information can be applied to help inform resource management and adaptation planning. We have demonstrated a generalizable approach that identified tribally important ecosystem services and related them with biologically relevant vegetation projections from a Dynamic Global Vegetation Model. Although our assessment is focused in the Pacific Northwest, our approach can be applied in other regions for which model data is available. We recognize that there is some inherent uncertainty associated with using model output for future scenario planning; however, if that uncertainty is addressed and applied as demonstrated by our approach, it then can be explored to help inform resource management and adaptation planning.