Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

The variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new package - - to calculate diversity indices based on remotely sensed data, by discussing the theory behind the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and biochemical), taxonomic, phylogenetic and genetic diversity. Making use of the package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix

Species-area relationships have long been used to assess patterns of species diversity across scales. Here, this concept is extended to spectral diversity using hyperspectral data collected by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over western Michigan. This mixture of mesic forest and agricultural lands offers two end-points on the local-scale diversity continuum; one set of well-mixed forest patches and one set of highly homogeneous agricultural patches. Using the sum of the first three principal component values and the principal components' convex hull volume, spectral diversity was compared within and among these plots and to null expectations for perfectly random and perfectly patchy landscapes. Overall, the spectral diversity-area relationship confirms the patterns that would be expected for this landscape, but this application suggests that this approach could be extended to less well-understood landscapes and could reveal key insights about the relative importance of different drivers of community assembly, even in the absence of additional data about plant functional traits or species' identities.