Attained confidence level as a measure of success in meeting fire management objectives

Abstract Evaluating long-term monitoring data for fire and resource management applications often differs from traditional experimental research designs. A major challenge is that management objectives are typically specified as a range of acceptable values, bounded by a lower and upper acceptable limit (e.g., to reduce fuel loading by 50 to 65%), a so-called range objective . Traditional hypothesis tests, however, do not work for range objectives. In this paper, we introduce the “Attained Confidence Level” (ACL), defined as the proportion of the distribution of a test statistic that falls within the stated objective, allowing managers to quantify confidence in meeting range objectives. The ACL is also applicable to management goals defined by a single bound, a so-called threshold objective (e.g., maintain shrub cover ≥ 50%). The flexibility of the ACL offers clear benefits for Adaptive Management over the binary decision framework of traditional hypothesis tests, wherein one must reject or fail to reject a null hypothesis at a predetermined fixed significance level. We describe the conceptual development of the ACL and demonstrate its utility and benefits using three empirical fire management case studies. By providing a continuous measure of confidence, the ACL acts as a bridge between statistical rigor and the practical tradeoffs of fire management, allowing for transparent decision-making in the face of ecological and operational uncertainty.