Uncertainty is a fundamental feature of ecological systems and their management: knowledge of the system state and its dynamics is imperfect, actions have uncertain consequences, and factors like climate change will alter the environment in ways that can be hard to predict. My dissertation represents a range of approaches to uncertainty in ecology and conservation, from improving inference about complex biological systems to optimizing management actions that alter stochastic dynamics:
Reducing uncertainty about the system state can be accomplished through improved models, including hierarchical and state-space frameworks that distinguish process dynamics from imperfect measurements. Yet analyzing populations impacted by both process noise and observation error remains a difficult computational and statistical task, particularly in scenarios where measurement error is large and demography is complex.
Often uncertainty cannot be reduced, so decisions must be made in the face of uncertainty. These decisions are challenging, as biological systems are stochastic, nonlinear, and often non-stationary, yet inappropriate strategies can lead to ecologically, economically, and socially unacceptable outcomes.
Uncertainty can also be generated through data disparities that reflect legacies of social and political inequity. How this uncertainty propagates in decision-making to produce poor decision outcomes remains largely unexplored.
As a researcher, I aim to change the way policy makers and practitioners think about conservation decisions and to provide insight into how we can infer complex ecological dynamics from imperfect observations. Another focus of my research is to narrow the research-implementation gap by developing decision-support tools and software that bring advanced analytical capabilities to a wider audience.