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A state-and-transition simulation modeling approach for estimating the historical range of variability

机译:一种状态和过渡仿真建模方法,用于估计变化的历史范围

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Reference ecological conditions offer important context for land managers as they assess the condition of their landscapes and provide benchmarks for desired future conditions. State-and-transition simulation models (STSMs) are commonly used to estimate reference conditions that can be used to evaluate current ecosystem conditions and to guide land management decisions and activities. The LANDFIRE program created more than 1,000 STSMs and used them to assess departure from a mean reference value for ecosystems in the United States. While the mean provides a useful benchmark, land managers and researchers are often interested in the range of variability around the mean. This range, frequently referred to as the historical range of variability (HRV), offers model users improved understanding of ecosystem function, more information with which to evaluate ecosystem change and potentially greater flexibility in management options. We developed a method for using LANDFIRE STSMs to estimate the HRV around the mean reference condition for each model state in ecosystems by varying the fire probabilities. The approach is flexible and can be adapted for use in a variety of ecosystems. HRV analysis can be combined with other information to help guide complex land management decisions.
机译:参考生态条件为土地管理者提供了重要的背景信息,因为他们可以评估其景观状况并为未来的期望条件提供基准。状态转换模拟模型(STSM)通常用于估计参考条件,这些参考条件可用于评估当前的生态系统条件并指导土地管理决策和活动。 LANDFIRE程序创建了1000多个STSM,并使用它们评估了美国生态系统偏离平均参考值的情况。尽管均值提供了有用的基准,但土地经理和研究人员通常对均值周围的变化范围感兴趣。该范围通常称为变异性(HRV)的历史范围,可为模型用户提供对生态系统功能的更深入的了解,可用于评估生态系统变化的更多信息以及潜在的更大管理灵活性。我们开发了一种方法,该方法使用LANDFIRE STSM通过改变着火概率来估计生态系统中每个模型状态在平均参考条件附近的HRV。该方法是灵活的,可以适用于各种生态系统。 HRV分析可以与其他信息结合使用,以帮助指导复杂的土地管理决策。

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