Functional unsupervised classification of spatial biodiversity

The recognition of spatial heterogeneity as well as of areas of low and high biodiversity through spatial techniques is essential to guide decision-making regarding the conservation and management of natural areas. In this context, reliable maps of biodiversity across sampling sites can be useful tools. Many ecological studies, which have dealt with a spatial approach for biodiversity, have focused only on one specific biodiversity aspect at a time, such as species richness or species evenness, yielding a partial overview of this complex concept. To solve this issue, we propose a spatial functional data analysis approach to diversity profiles for assessing spatial biodiversity and identifying groups of sampling sites which are similar in spatial patterns. Specifically, the functional distance-based LISA algorithm has been extended to the case of diversity profiles in lattice, after smoothing the discretized curves and specifying a suitable distance measure. The proposed spatial clustering algorithm has been applied to a real data set involving tree species diversity in a fully censured plot in the Harvard Forest, New England region. Our approach provides a useful method for identifying areas of low and high biodiversity, with the potential to address the monitoring of environmental policies. Indeed, we think that a classification of diversity profiles, which takes into account the spatial dependence, would permit a more homogeneous partition of sampling stations with a substantial noise reduction in supporting conservation planning.