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Species and environmental dataThis compiled (zip) file consists of 7 matrices of data: one species data matrix, with abundance observations per visited plot; and 6 environmental data matrices, consisting of land cover classification (Class), simulated EnMAP and Landsat data (April and August), and a 6 time-step Landsat time series (January, March, May, June, July and September). All data is compiled to the 125m radius plots, as described in the paper.Leitaoetal_Mapping beta diversity from space_Data.zip,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 resulting components. The proposed method was illustrated on a case study with a clear environmental gradient of shrub encroachment …