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气候智能林业是否受到稀薄强度和树种组成的影响? 在中欧的长期实验场地进行的案例研究
Diana Alfieri1, Astor Toraño Caicoya2, Roberto Tognetti3
1Department of Biosciences and Territory, University of Molise, Italy.
Journal of environmental management
|October 11, 2025
概括
气候智能林业 (CSF) 通过混合物种和密集稀释,增强森林适应能力和社会经济效益. 这种方法提高了森林的弹性和可持续性.
科学领域:
- 林业科学 林业科学
- 适应气候变化 适应气候变化
- 可持续的森林管理 可持续的森林管理
背景情况:
- 气候智能林业 (CSF) 整合了气候缓解,适应和社会经济目标.
- 可持续的森林管理需要评估生态和社会经济因素之间的复杂相互作用.
研究的目的:
- 评估树木物种组成和稀释强度对气候智能林业 (CSF) 绩效的影响.
- 确定驱动CSF缓解,适应和社会经济支柱的关键指标.
- 将气候智能林业综合指数应用于德国巴伐利亚的长期实验用地.
主要方法:
- 开发和应用一个复合指数的气候智能林业 (CSF).
- 9个结构和功能指标的选择和加权.
- 使用通用添加模型对时间动态的分析.
- 使用线性混合模型评估指标对CSF支柱的影响.
主要成果:
- 与针叶单一种植相比,混合和宽叶森林表现出更好的适应性.
- 强化稀释通过增加结构多样性和减少脆弱性,提高了CSF的绩效.
- 森林损害,生长种群和苗条系数是CSF所有支柱的关键指标.
- 圆木生产对CSF的社会经济层面产生了重大影响.
结论:
- 长期监测地块对于了解森林对气候变化的动态反应至关重要.
- 基于指标的方法支持适应性森林管理战略,以实现气候和可持续性目标.
- 需要进一步的研究和数字工具来量化社会经济方面,并协调跨尺度的数据.
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