改善森林动态预测中的不确定性量化:森林变化的动态模型
Malcolm S Itter1, Andrew O Finley2,3
1Department of Environmental Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
概括
动态时空模型 (DSTM) 通过预测全球变化下的森林动态来改善森林管理. 库存数据的不确定性对这些预测有重大影响,强调需要精细的模型来指导适应性保护策略.
科学领域:
- 生态生态学 生态生态学
- 林业林业 林业 林业 林业
- 计算生物学 计算生物学
背景情况:
- 森林动态模型对于预测全球变化影响至关重要.
- 目前的模型由于未来条件,人口统计和数据的不确定性而表现出很高的变化.
- 量化和整合不确定性对于适应性森林管理至关重要.
研究的目的:
- 为森林动态开发一个可扩展的,标准级的动态时空模型 (DSTM).
- 将一个规模结构化的人口动态模型集成到贝叶斯层次的DSTM中.
- 提供森林物种人口统计和尺寸分布变化的概率预测.
主要方法:
- 将矩阵投影模型 (麦肯德里克 - ·福斯特方程) 集成到贝叶斯层次的DSTM中.
- 利用森林库存数据进行模型参数化和验证.
- 应用该模型来预测缅因州佩诺布斯科特实验森林的长期 (60年以上) 森林动态.
主要成果:
- 开发的DSTM提供了对特定物种的人口统计率的概率预测.
- 模型成功地预测了随着时间的推移大小-物种分布的变化.
- 来自异质库存的库存观测的可变性是预测不确定性的关键驱动因素.
结论:
- 该研究提出了一种新的,可扩展的DSTM,用于预测树林动态.
- 库存数据的不确定性对森林应对全球变化的预测有重大影响.
- 可以改进DSTM框架,以更好地反映森林动态和适应性管理.
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