Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Sci Total Environ. 2024 Dec 1;954:176213. doi: 10.1016/j.scitotenv.2024.176213. Epub 2024 Sep 18.
Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vittata in China and pinpointing the key drivers that influence its distribution can facilitate the identification of optimal areas for using P. vittata as a remediation tool for As-polluted soils. In this study, a comparative analysis was conducted using ten machine learning models to assess the habitat suitability of P. vittata based on 744 specimen records and 20 environmental factors. The key drivers affecting the distribution of P. vittata were also investigated based on the optimal model. The results indicate that the XGBOOST model was the most reliable and stable, achieving a coefficient of determination of 0.95. Approximately 24.47 % of China's land area was identified as suitable for P. vittata. Particularly, it was predominantly found in Hainan (45.9 %), Guangxi (92.96 %), Guangdong (91.68 %), Hunan (91.26 %), Guizhou (90.83 %), Chongqing (88.17 %), Fujian (85.70 %), Yunnan (77.44 %), Jiangxi (73.99 %) and Zhejiang (57.05 %). Furthermore, this study pinpointed the lowest temperature, annual temperature range, and mining density as key drivers, contributing 45.9 %, 31.9 %, and 7.2 %, respectively. Spatial correlation analysis revealed a significant correlation between mining density and the habitat distribution of P. vittata (Moran' I = 0.519). This study confirmed that both natural factors and anthropogenic activities affect the distribution of P. vittata and provided valuable insights for optimizing the application of P. vittata in soil phytoremediation and reclamation.
凤尾蕨（P. vittata）具有修复砷（As）污染土壤的巨大潜力。了解凤尾蕨在中国的栖息地适宜性，并确定影响其分布的关键驱动因素，可以促进识别使用凤尾蕨作为修复砷污染土壤的最佳区域。在这项研究中，使用十种机器学习模型对凤尾蕨的栖息地适宜性进行了比较分析，该模型基于 744 个标本记录和 20 个环境因素。还根据最优模型研究了影响凤尾蕨分布的关键驱动因素。结果表明，XGBOOST 模型是最可靠和稳定的，决定系数为 0.95。中国约有 24.47%的土地面积适宜凤尾蕨生长。特别是，海南（45.9%）、广西（92.96%）、广东（91.68%）、湖南（91.26%）、贵州（90.83%）、重庆（88.17%）、福建（85.70%）、云南（77.44%）、江西（73.99%）和浙江（57.05%）是凤尾蕨的主要分布区。此外，本研究确定了最低温度、年温差和采矿密度是关键驱动因素，分别贡献了 45.9%、31.9%和 7.2%。空间相关分析表明，采矿密度与凤尾蕨的栖息地分布呈显著相关（Moran' I = 0.519）。本研究证实，自然因素和人为活动都会影响凤尾蕨的分布，为优化凤尾蕨在土壤植物修复和复垦中的应用提供了有价值的见解。
