Best available science has long been the standard for using science to inform environmental and natural resource policy. This study examines the selection of data from federal, state or local, and nongovernmental sources for use in making ground-level natural resource policy, or biodiversity management decisions. The authors argue that aspects of neo-institutional theory are explanatory of data selection within a natural resource agency. They empirically test their theory by analyzing original data collected from a 2007 survey of U.S. Fish and Wildlife Service field offices, which attained a response rate of 36.6% (204 of 557 field offices). The authors find that data selection cannot merely be explained by the discussion of best available science. Rather, neo-institutional theory tenets of normative isomorphism and path dependency are explanatory of how science is selected for use in Article 214Administration & Society 45(2) making biodiversity management decisions. However, coercive isomorphism does not possess the same explanatory ability with regard to U.S. Fish and Wildlife Service field office data selection.

This study extends a previous project which examined the salience of neo-institutional theory in explaining how data are selected for use in making and implementing biodiversity management decisions. Our prior findings prompted us to examine the selection of data from federal, state or local, and nongovernmental sources using aspects of diffusion theory. We argue that diffusion theory also possesses explanatory value regarding the identification and selection of data within a natural resource agency. We empirically test our theory by analyzing original data collected from a 2007 survey of U.S. Fish and Wildlife Service field offices. We find that drivers of diffusion identified by previous research are explanatory of how data are selected. Specifically, perceptions of other field offices' data selection procedures and collaboration with interest or advocacy groups aid in explaining field office data selection. The results enhance our understanding of the science-natural resource policy relationship.