Abstract
Global biodiversity change creates a need for standardized monitoring methods. Modelling and mapping spatial patterns of community composition using high-dimensional remotely sensed data requires adapted methods adequate to such datasets. Sparse generalized dissimilarity modelling is designed to deal with high dimensional datasets, such as time series or hyperspectral remote sensing data. In this manuscript we present sgdm, an R package for performing sparse generalized dissimilarity modelling (SGDM). The package includes some general tools that add functionality to both generalized dissimilarity modelling and sparse generalized dissimilarity modelling. It also includes an exemplary dataset that allows for the application of SGDM for mapping the spatial patterns of tree communities in a region of natural vegetation in the Brazilian Cerrado.
Highlights
Global biodiversity change may lead to declines and changes in ecosystem functioning and provisioning of services [1], which presents the need for standardized methods capable of extracting useful information from in situ biological observations for the use of global biodiversity monitoring [2,3]
The dissimilarity predictions generated by Generalized dissimilarity modelling (GDM) can be used to visualize the spatial pattern in community compositional change through a subsequent non-linear ordination
A methodological enhancement was developed that allows for fitting high-dimensional data in GDM
Summary
Global biodiversity change may lead to declines and changes in ecosystem functioning and provisioning of services [1], which presents the need for standardized methods capable of extracting useful information from in situ biological observations for the use of global biodiversity monitoring [2,3]. Generalized dissimilarity modelling (GDM) is a well-established dissimilarity-based statistical technique for analysing and predicting biological variation as a function of environment [10] It relates dissimilarity in the composition of a biological community (e.g., differences in species or traits) between pairs of sites with the respective environmental difference as described by the predictors. A methodological enhancement was developed that allows for fitting high-dimensional data in GDM This enhancement is called sparse generalized dissimilarity modelling (SGDM) [21] and is a two-stage approach that consists of initially reducing the environmental data (i.e., predictor variables) by means of a sparse canonical correlation analysis (SCCA) [22], and fitting the resulting transformed environmental space with a GDM model. We aim to fill this gap and present sgdm, an R package for performing sparse generalized dissimilarity modelling, including some additional tools suitable for both SGDM and GDM
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