Understanding the dynamics of complex ecological and environmental systems and designing policies to promote their sustainability is a formidable challenge that requires analytical and measurement savvy. Both the practitioner and policymaker must be able to evaluate scientific research, recognizing fundamental pitfalls in research design data interpretation, and contextual relevance. Computational modeling tools have allowed for more dynamic and accurate predictions of complex environmental and ecological systems, though simulation output is only as valid as the quality of the input data. Analyzing the integrity of measurement scenarios is critical; what omissions and limitations might bias an outcome, and how might human behavioral interactions cause scenario modeling to differ from quantitative predictions? To learn these skills, students enrolled in this course conduct practical exercises illustrating a range of measurement and modeling techniques, including statistical analysis of ecological and environmental data and system dynamics modeling. Building on these methods, skill development also includes scientific writing, critiquing primary research literature, negotiating environmental resource rights, and accurately communicating environmental science in non-technical language. Course activities are rooted in core issues of environmental and sustainability sciences — climate change, human population dynamics, population viability analysis of endangered species, economic appraisal of projects that impact natural resources, impacts of built and natural environments on human health, and climate justice. Quantitative techniques are taught at an introductory level; some data analysis and simulation modeling are conducted using Excel spreadsheets.
