What actually confers adaptive capacity? Insights from agro-climatic vulnerability of Australian wheat.

Vulnerability assessments have often invoked sustainable livelihoods theory to support the quantification of adaptive capacity based on the availability of capital--social, human, physical, natural, and financial. However, the assumption that increased availability of these capitals confers greater adaptive capacity remains largely untested. We quantified the relationship between commonly used capital indicators and an empirical index of adaptive capacity (ACI) in the context of vulnerability of Australian wheat production to climate variability and change. We calculated ACI by comparing actual yields from farm survey data to climate-driven expected yields estimated by a crop model for 12 regions in Australia's wheat-sheep zone from 1991-2010. We then compiled data for 24 typical indicators used in vulnerability analyses, spanning the five capitals. We analyzed the ACI and used regression techniques to identify related capital indicators. Between regions, mean ACI was not significantly different but variance over time was. ACI was higher in dry years and lower in wet years suggesting that farm adaptive strategies are geared towards mitigating losses rather than capitalizing on opportunity. Only six of the 24 capital indicators were significantly related to adaptive capacity in a way predicted by theory. Another four indicators were significantly related to adaptive capacity but of the opposite sign, countering our theory-driven expectation. We conclude that the deductive, theory-based use of capitals to define adaptive capacity and vulnerability should be more circumspect. Assessments need to be more evidence-based, first testing the relevance and influence of capital metrics on adaptive capacity for the specific system of interest. This will more effectively direct policy and targeting of investment to mitigate agro-climatic vulnerability.

Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park.

Resource managers at parks and other protected areas are increasingly expected to factor climate change explicitly into their decision making frameworks. However, most protected areas are small relative to the geographic ranges of species being managed, so forecasts need to consider local adaptation and community dynamics that are correlated with climate and affect distributions inside protected area boundaries. Additionally, niche theory suggests that species' physiological capacities to respond to climate change may be underestimated when forecasts fail to consider the full breadth of climates occupied by the species rangewide. Here, using correlative species distribution models that contrast estimates of climatic sensitivity inferred from the two spatial extents, we quantify the response of limber pine (Pinus flexilis) to climate change in Rocky Mountain National Park (Colorado, USA). Models are trained locally within the park where limber pine is the community dominant tree species, a distinct structural-compositional vegetation class of interest to managers, and also rangewide, as suggested by niche theory. Model forecasts through 2100 under two representative concentration pathways (RCP 4.5 and 8.5 W/m(2)) show that the distribution of limber pine in the park is expected to move upslope in elevation, but changes in total and core patch area remain highly uncertain. Most of this uncertainty is biological, as magnitudes of projected change are considerably more variable between the two spatial extents used in model training than they are between RCPs, and novel future climates only affect local model predictions associated with RCP 8.5 after 2091. Combined, these results illustrate the importance of accounting for unknowns in species' climatic sensitivities when forecasting distributional scenarios that are used to inform management decisions. We discuss how our results for limber pine may be interpreted in the context of climate change vulnerability and used to help guide adaptive management.