Conservation and natural-resource management rely on incomplete and imperfect information about dynamic systems. Uncertainty arises from partial understanding of socio-ecological systems, creating risk for decision makers. In a decision-making context, uncertainty can take many forms. Commonly encountered uncertainties include observational, structural, and implementation uncertainty. In a decision analysis, each of these types of uncertainties can be represented within quantitative operating models of managed systems. Socio-ecological systems are dynamic across space and time, such that spatial and temporal variability are frequently encountered in both social and ecological data, adding complexity to a decision analysis. It is possible to partition variability into source components using probability distributions in a quantitative mixed-modeling framework. While some sources of natural variability are largely irreducible over decision-relevant timeframes or relatively insensitive to the various decision alternatives available to managers, the magnitude and structure of variability can influence management outcomes. We discuss approaches for quantitative consideration of uncertainty and variability within a decision analysis, ways in which uncertainty translates to risk for decision makers, and the potential importance of viewing variability as informative to risk assessment rather than treating it as "noise".
Quantitative models can be valuable decision-support tools within a structured decision-making process. Typically, the role of a quantitative model within a decision-analytic process is to predict outcomes and calculate associated performance attributes that relate to decision objectives. If uncertainty is incorporated into the modeling, then scenario forecasting will produce distributions of potential outcomes for alternative management strategies. We present examples to demonstrate how viewing uncertainty and variability through probabilistic measures can add value to information presented to decision makers. More specifically, we show how visualizing varying variance can be important to a formal assessment of alternative options. For decision makers, the perceived risk associated with making a choice can frequently be considered as a combination of the probability of experiencing an outcome and the severity of an outcome. Probabilistic consequence distributions can also be an effective method of comparing decision options and assessing relative risk tolerances. Quantitatively accounting for uncertainty and variability is important to decision analyses that attempt to connect value-based objectives and empirical-based information, and thus these approaches have immediate relevance to modern-day conservation.