Zhang Hao-Tian, Guo Wen-Yong, Wang Wen-Ting
School of Mathematics and Computer Science Northwest Minzu University Lanzhou China.
Research Center for Global Change and Complex Ecosystems, School of Ecological and Environmental Sciences East China Normal University Shanghai China.
Ecol Evol. 2023 Nov 20;13(11):e10747. doi: 10.1002/ece3.10747. eCollection 2023 Nov.
How to effectively obtain species-related low-dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high-dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.
如何从海量环境变量中有效获取与物种相关的低维数据，已成为物种分布模型（SDMs）亟待解决的问题。在本研究中，我们将探讨对环境变量进行降维是否能提高物种分布模型的预测性能。我们首先使用两种线性（即主成分分析（PCA）和独立成分分析）和两种非线性（即核主成分分析（KPCA）和均匀流形逼近与投影）降维技术（DRTs）对高维环境数据进行降维。然后，我们基于23种真实植物物种和9种虚拟物种的降维环境变量建立了5个物种分布模型，并通过皮尔逊相关系数（PCC）将其预测性能与基于所选环境变量的物种分布模型进行比较。此外，我们研究了降维技术、模型复杂度和样本量对物种分布模型预测性能的影响。除KPCA外，其他降维技术下物种分布模型的预测性能优于使用PCC的情况。并且使用线性降维技术的物种分布模型的预测性能优于使用非线性降维技术的情况。此外，使用降维技术处理环境变量对物种分布模型预测性能的影响不亚于模型复杂度和样本量。当模型复杂度处于复杂水平时，与PCC相比，PCA能最大程度提高物种分布模型的预测性能2.55%。在样本量处于中等水平时，与PCC相比，PCA将物种分布模型的预测性能提高了2.68%。我们的研究表明，降维技术对物种分布模型的预测性能有显著影响。具体而言，线性降维技术，尤其是PCA，在相对复杂的模型复杂度或大样本量下，对提高模型预测性能更有效。
