Drivers and value tradeoffs of regional-scale adaptation in rural landscapes of central Europe
Coupled human and natural systems exhibit complex interactions (e.g. feedback-loops) that are often poorly understood. Decision-makers from regional (e.g., state or provincial) scale environmental stewardship programs to international policy makers are often faced with uncertainties about future climatic and sociopolitical conditions (henceforth, system change) when supporting livelihoods and ecosystem services derived from lands and waters they oversee. Understanding how these system changes interact with adaptive decision-making processes toward stewardship of ecosystem services represents a considerable gap in knowledge. Adaptation, or iterative adjustment of management practices in response to or anticipation of system change, has been forwarded as a means of effective ecosystem stewardship. Furthermore, lack of clarity about value tradeoffs among competing program objectives (e.g., economics and aesthetics) often precludes development and implementation of adaptation. Although there have been several qualitative studies on regional to national adaptation, lacking is an empirical understanding of how the drivers and value tradeoffs associated with adaptation differ among regions and between related sectors spanning multiple countries. Diverse cultural heritages and political structures among regions of central Europe offer great opportunities for examining spatial patterns of limitations to regional-scale adaptation in forest and agricultural sectors. This project will develop a quantitative index of adaptation for examining hypotheses about patterns of rural adaptation within regions of nine countries in central Europe. Alternative hypotheses describe contrasting assumptions regarding geographic variation in the relative importance among drivers and objectives associated with adaptation. Predictions derived from these hypotheses will be examined through a survey instrument that gathers information from programs focused on rural stewardship. Survey data will be analyzed using a hierarchical Bayesian approach ...