University of Wisconsin–Madison
Spatial Analysis For Conservation and Sustainability

Untangling multiple species richness hypothesis globally using remote sensing habitat indices

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Remotely sensed data can estimate terrestrial productivity more consistently and comprehensively across large
areas than field observations. However, questions remain how species richness and abundances are related to
terrestrial productivity in different biogeographic realms. The Dynamic Habitat Indices (DHIs) are a set of three
remote sensing indices each related to a key biodiversity productivity hypothesis (i.e., available energy proxied by
the annual cumulative productivity, environmental stress proxied by the minimum productivity throughout the
year, and environmental stability proxied by the annual coefficient of variation in productivity). Here, we quantify
the relevance of each hypothesis globally and for different biogeographic realms using models of species richness
for three taxa (amphibians, birds, and mammals) derived from IUCN species range maps. Using parameterized
generalized additive models (GAM’s) we found that the available energy hypothesis was the best individual
index explain 37–43% of the variation in species richness globally with the best models for amphibians and
worst for mammal richness. Examining the residuals of these GAMS indicated that adding the environmental
stress hypothesis explained 0–22% additional variance, especially in the Nearctic where large amounts of snow
and ice are prevalent and environmental conditions deteriorate during winter. The addition of the environmental
stability hypothesis generally explained more variance than the environmental stress hypothesis, especially in
the Neartic and Paleartic and for birds however, in certain cases, the environmental stress hypothesis explains
more variance at the realm scale.