环境变量的维度减少对物种分布模型的性能产生了重大影响
Hao-Tian Zhang1, Wen-Yong Guo2,3, Wen-Ting Wang1
1School of Mathematics and Computer Science Northwest Minzu University Lanzhou China.
Ecology and evolution
|November 29, 2023
概括
减小尺寸技术 (DRT) 可以改善物种分布模型 (SDM). 线性DRT,特别是主要组件分析 (PCA),比非线性方法更能提高SDM预测性能,特别是在复杂的模型或大型数据集的情况下.
科学领域:
- 生态学和进化生物学
- 计算生物学 计算生物学
- 环境科学 环境科学
背景情况:
- 物种分布模型 (SDM) 需要有效的方法来从巨大的环境变量中提取低维数据.
- 高维环境数据可能会给SDM的准确性和效率带来挑战.
研究的目的:
- 调查减小维度技术 (DRT) 是否提高了物种分布模型 (SDM) 的预测性能.
- 为了比较线性和非线性DRT在增强SDM预测方面的有效性.
- 评估DRT,模型复杂性和样本大小对SDM性能的影响.
主要方法:
- 应用了四种DRT:主要组件分析 (PCA),独立组件分析 (ICA),内核主要组件分析 (KPCA) 和统一的多重近似和投影 (UMAP).
- 为23种真实植物和9种虚拟植物物种开发了5个SDM,使用缩小维度的环境变量.
- 使用选定变量 (皮尔森相关系数 - PCC) 与SDM进行预测性能比较.
主要成果:
- 除KPCA外的DRT,与PCC相比,总体上提高了SDM预测性能.
- 线性DRT (PCA,ICA) 的表现优于非线性DRT (KPCA,UMAP).
- 与PCC相比,PCA显示了最显著的改善 (2.55%与复杂模型相比,2.68%与中等样本大小相比).
结论:
- DRT显著影响SDM预测性能.
- 线性DRT,特别是PCA,在改善SDM预测方面非常有效.
- 在复杂的模型条件下和更大的样本大小下,PCA显示出特别的好处.
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