Data from: 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 resulting components. The proposed method was illustrated on a
case study with a clear environmental gradient of shrub encroachment
following cropland abandonment, and subsequent turnover in the bird
communities. Bird community data, collected on 115 plots located along the
described gradient, were used to fit composition dissimilarity as a
function of several remote sensing datasets, including a time series of
Landsat data as well as simulated EnMAP hyperspectral data. 3. The
proposed approach always outperformed GDM models when fit on
high-dimensional datasets. Its usage on low-dimensional data was not
consistently advantageous. Models using high-dimensional data, on the
other hand, always outperformed those using low-dimensional data, such as
single date multispectral imagery. 4. This approach improved the direct
use of high-dimensional remote sensing data, such as time series or
hyperspectral imagery, for community dissimilarity modelling, resulting in
better performing models. The good performance of models using
high-dimensional datasets further highlights the relevance of dense time
series and data coming from new and forthcoming satellite sensors for
ecological applications such as mapping species beta diversity.