通过机器学习模型,确定中国Pteris vittata的息地适宜性以及相关的关键驱动因素
Shiqi Chen1, Guanghui Guo1, Mei Lei1
1Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
The Science of the total environment
|September 20, 2024
概括
维塔塔 (P. vittata) 可以修复污染的土壤. 这项研究在中国确定了合适的息地,发现温度和采矿密度是影响其分布的关键因素,以实现有效的植物修复.
科学领域:
- 环境科学 环境科学
- 生态生态学 生态生态学
- 生物修复是一种生物修复.
背景情况:
- Pteris vittata (P. vittata) 在 (As) 土壤整治方面表现有前途.
- 了解P. vittata在中国的息地适合性对于优化其在As污染土壤修复中的使用至关重要.
研究的目的:
- 通过机器学习评估P. vittata在中国的息地适宜性.
- 确定影响P. vittata分布的关键环境和人为驱动因素.
主要方法:
- 利用了744个样本记录和20个环境因素.
- 我们比较了10个机器学习模型,选择了XGBOOST作为最佳模型.
- 进行了空间相关性分析,以评估驱动因素和分销之间的关系.
主要成果:
- XGBOOST模型显示出高可靠性 (R2 = 0.95).
- 大约24.47%的中国土地面积适合P. vittata.
- 最低的温度,年均温度范围和采矿密度被确定为主要的分布驱动因素.
结论:
- 自然条件和人类活动都对P. vittata的分布产生了重大影响.
- 结果为在土壤植物修复和回收项目中战略部署P. vittata提供了洞察力.
- 最佳的P. vittata息地集中在中国南部和中部.
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