Mapping beta diversity from space: Sparse Generalized Dissimilarity Modelling (SGDM) for analysing high-dimensional data

1. Spatial patterns of community composition turnover (beta diversity) may be mapped through Generalised Dissimilarity Modelling (GDM). While remote sensing data are adequate to describe these patterns, the often high-dimensional nature of these data poses some analytical challenges, potentially resulting in loss of generality. This may hinder the use of such data for mapping and monitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional datasets, and secondly fitting the transformed data with GDM. The SCCA penalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance of a GDM fit on the r...

1. 群落组成更替（β多样性，beta diversity）的空间格局可通过广义相异性建模（Generalised Dissimilarity Modelling, GDM）进行制图。尽管遥感数据足以刻画此类格局，但这类数据常具备高维特性，会带来诸多分析层面的挑战，可能导致模型普适性受损，进而阻碍其应用于β多样性格局的制图与监测。
2. 本研究提出稀疏广义相异性建模（Sparse Generalised Dissimilarity Modelling, SGDM）这一方法学框架，旨在优化高维数据通过GDM预测群落更替的应用效果。SGDM采用两阶段流程：首先利用稀疏典型相关分析（Sparse Canonical Correlation Analysis, SCCA）对环境数据进行变换，以处理高维数据集；随后将变换后的数据通过GDM进行拟合。研究通过网格搜索流程选择SCCA的惩罚参数，以优化GDM拟合模型在r……上的预测性能。