Turnover, or change in the composition of species over space and time, is one of the primary ways to define beta diversity. Inferring what factors impact beta diversity is not only important for understanding biodiversity processes but also for conservation planning. At present, a popular approach to understanding the drivers of compositional turnover is through generalized dissimilarity modelling (GDM). We argue that the current GDM approach suffers several limitations and provide an alternative modelling approach that remedies these issues. We propose using generative spatial random effects models implemented in a Bayesian framework. We offer hierarchical specifications to yield full regression and spatial predictive inference, both with associated full uncertainties. The approach is illustrated by examining dissimilarity in three datasets: tree survey data from Panama's Barro Colorado Island (BCI), plant occurrence data from southwest Australia and plant abundance surveys from the Greater Cape Floristic Region (GCFR) of South Africa. We select a best model using out‐of‐sample predictive performance. We find that the form of the best model differs across the three datasets, but our models provide performance ranging from comparable to significant improvement over GDMs. Within the GCFR, the spatial random effects play a more important role in the modelling than all the environmental variables. We have proposed a model that provides several improvements to the current GDM framework. This includes advantages such as a flexible spatially varying mean function, spatial random effects that capture dependence unaccounted for by explanatory variables, and spatially heterogeneous variance structure. All these features are offered in a model that can adequately handle a large incidence of
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Abstract total dissimilarity through ‘one‐inflation’, as would be expected from highly biodiverse areas with steep turnover gradients. -
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Abstract Aim With plant biodiversity under global threat, there is an urgent need to monitor the spatial distribution of multiple axes of biodiversity. Remote sensing is a critical tool in this endeavour. One remote sensing approach for detecting biodiversity is based on the hypothesis that the spectral diversity of plant communities is a surrogate of multiple dimensions of biodiversity. We investigated the generality of this ‘surrogacy’ for spectral, species, functional and phylogenetic diversity across 1,267 plots in the Greater Cape Floristic Region (GCFR), a hyper‐diverse region comprising several biomes and two adjacent global biodiversity hotspots.
Location The GCFR centred in south‐western and western South Africa.
Time period All data were collected between 1978–2014.
Major taxa studied Vascular plants within the GCFR.
Methods Spectral diversity was calculated using leaf reflectance spectra (450–950 nm) and was related to other dimensions of biodiversity via linear models. The accuracy of different spectral diversity metrics was compared using 10‐fold cross‐validation.
Results We found that a distance‐based spectral diversity metric was a robust predictor of species, functional and phylogenetic biodiversity. This result serves as a proof‐of‐concept that spectral diversity is a potential surrogate of biodiversity across a hyper‐diverse biogeographic region. While our results support the generality of spectral diversity as a biodiversity surrogate, we also find that relationships vary between different geographic subregions and biomes, suggesting that differences in broad‐scale community composition can affect these relationships.
Main conclusions Spectral diversity was shown to be a robust surrogate of multiple dimensions of biodiversity across biomes and a widely varying biogeographic region. We also extend these surrogacy relationships to ecological redundancy to demonstrate the potential for additional insights into community structure based on spectral reflectance.
