Remotely sensed data can help to identify both suitable habitat for individual species, and environmental conditions
that foster species richness, which is important when predicting how biodiversity will respond to global change. The
question is how to summarize remotely sensed data so that they are most relevant for biodiversity analyses, and the
Dynamic Habitat Indices are three metrics designed for this. Our goals here were to a) derive, for the first time, the
Dynamic Habitat Indices (DHIs) globally, and b) use these to evaluate three hypotheses (available energy, environmental
stress, and environmental stability) that attempt to explain global variation in species richness of
amphibians, birds, and mammals. The three DHIs summarize three key measures of vegetative productivity: a)
annual cumulative productivity, which we used to evaluate the available energy hypothesis that more energy is
associate with higher species richness; b) minimum productivity throughout the year, which we used to evaluate the
environmental stress hypothesis that higher minima cause higher species richness, and c) seasonality, expressed as
the annual coefficient of variation in productivity, which we used to evaluate the environmental stability hypothesis
that less intra-annual variability causes higher species richness. We calculated the DHIs globally at 1-km resolution
from MODIS vegetation products (NDVI, EVI, LAI, fPAR, and GPP), based on the median of the good observations of
all years from the entire MODIS record for each of the 23 or 46 possible dates (8- vs. 16-day composites) during the
year, and calculated species richness for three taxa (amphibians, birds, and mammals) at 110-km resolution from
species range maps from the IUCN Red List. We found marked global patterns of the DHIs, and strong support for all
three hypotheses. The three DHIs for a given vegetation product were well correlated (Spearman rank correlations
ranging from −0.6 (cumulative vs. variation DHIs) to −0.93 (variation vs. minimum DHI)). Similarly, DHI components
derived from different MODIS vegetation products were well correlated (0.8–0.9), and correlations of the
DHIs with temperature and precipitation were moderate and strong respectively. All three DHIs were well correlated
with species richness, showing in ranked order positive correlations for cumulative DHI based on GPP (Spearman
rank correlations of 0.75, 0.63, and 0.67 for amphibians, resident birds, and mammals respectively) and minimum
DHI (0.73, 0.83, and 0.62), and negative for variation DHI (−0.69, −0.83, and −0.59). Multiple linear models of all
three DHIs explained 67%, 65%, and 61% of the variability in species richness of amphibians, resident birds, and
mammals, respectively. The DHIs, which are closely related to well-established ecological hypotheses of biodiversity,
can predict species richness well, and are promising for application in biodiversity science and conservation.
The Dynamic Habitat Indices (DHIs) from MODIS and global biodiversity
Download Radeloff_Global_DHIs_RSE_2019