A Cost-Optimization Framework for Planning Applied Environmental Science

Natural resource managers need information about the status and structure of complex environmental systems to meet society's dual demand for natural resource use and conservation. Applied scientists supply this information through monitoring and research efforts that can be expensive. The relationship between increasing investment in information and the attendant decline in probability of making bad decisions can be optimized to ensure that applied environmental research is cost effective. In the present article, I use straightforward analytical and numerical methods to explore conditions in which the value of information is less than the cost of obtaining it. As pressures on Earth's natural resources grow, managers will be called on increasingly to do more with less, which can be achieved with formal optimization. Implementing such optimizations will help ensure that formal decision-making tools are used to translate science into decisions, which will often require collaboration between biological scientists and social scientists.