Decisions made on our behalf by managers of the public trust are inherently complex. Tradeoffs are inevitable and choices are required despite pervasive uncertainty. First, focusing narrowly on the issue of scientific uncertainty at the species population level – the micro-scale, I consider data limitations and analytical constraints that make scientific inferences about population biology and demographic processes difficult. More specifically, I developed a novel integrated population model for American alligators (Alligator mississippiensis) to improve agency understanding of alligator population dynamics despite multiple interacting sources of uncertainty. I then verified model behavior using simulation. Second, zooming out to consider how science informs management - the meso scale, I address technical challenges limiting direct application of available research and monitoring data in policy deliberations. In my second empirical chapter, I implemented a genetic algorithm to identify an alligator harvest policy that sets annual harvest quotas to optimize long-term management objectives based on data from existing monitoring programs. The forward-looking approach accounts for multiple sources of uncertainty and can be tailored to other species with complex life histories and different agency contexts. Lastly, at the broadest level – the meta scale, I examined collaborative applications of structured decision making (SDM) in wildlife conservation and management through the lens of relational power. In my third and final empirical chapter, I present an autoethnography to illuminate how individual and institutional relationships shape SDM practice and its portrayal in the peer-reviewed literature. My dissertation demonstrates robust and flexible analytical tools that can help resource managers translate scientific research and monitoring data into actionable knowledge. It also speaks to the insufficiency of focusing exclusively on the technical dimensions of making science useful to managers.
