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<p>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 <i>range objective</i>. 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 <i>threshold</i> 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.</p>

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

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  • 发表日期：

    2026年7月1日

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  • Calvin A. Farris,
  • Kenneth G. Gerow

摘要

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.