红树林的全球潜在分布:考虑到沿度梯度的盐沼相互作用
Lina Cui1, Donald L DeAngelis2, Uta Berger3
1Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing, China.
Journal of environmental management
|January 4, 2024
概括
这项研究引入了一种结合环境数据和盐沼互动的新方法,以预测红树林分布. 包括盐沼互动在内的模型准确性略有提高,有助于红树林保护工作.
科学领域:
- 生态生态学 生态生态学
- 生物地理学 生物地理学
- 保护生物学 保护生物学
背景情况:
- 红树林生态系统具有高度的生产力和敏感性,其发展受到多种因素的影响.
- 物种分布模型 (SDM) 传统上使用环境因素,但最近的研究强调需要包括生物相互作用,以全面理解.
- 准确的红树林分布绘制对于有效的保护和恢复战略至关重要.
研究的目的:
- 通过将环境因素与盐沼互动相结合,开发一种用于预测全球红树林分布的新方法.
- 用盐沼互动指数 (SII) 来量化盐沼互动对红树林分布的影响.
- 通过结合生物相互作用来提高物种分布模型 (SDM) 的准确性.
主要方法:
- 利用MaxEnt模型来预测红树林分布.
- 对比了两组预测变量:仅环境因素和环境因素加盐沼互动指数 (SII).
- 在全球和度区域层面分析了潜在的红树林分布.
主要成果:
- 单纯环境和环境+SII模型都显示出良好的预测能力,SII提供了轻微的改善.
- 潜在的红树林分布随着度的增加而减少,并受到温度,降水和风速的影响.
- 盐沼互动指数 (SII) 在全球 (5.9%的贡献) 显著,在10-30°S和20-30°N区域特别重要,特别是在沿海生态环境中.
结论:
- 盐沼互动,包括促进和竞争,显著影响红树林的分布,特别是在沿海生态和特定的度区域.
- 开发的盐沼互动指数 (SII) 为这种生物影响提供了可量化的衡量标准,提高了红树林分布图的准确性.
- 正如SII所示,将生物相互作用纳入SDM对于推进生态建模和为有效的红树林管理和恢复项目提供信息至关重要.
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