Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling

Remote sensing techniques offer an opportunity to improve biodiversity
modeling and prediction worldwide. Yet, to date, the weather-station based
WorldClim dataset has been the primary source of temperature and
precipitation information used in correlative species distribution models.
WorldClim consists of grids interpolated from in situ station data
recorded primarily from 1960 to 1990. Those datasets suffer from uneven
geographic coverage, with many areas of Earth poorly represented. Here, we
compare two remote sensing data sources for the purposes of biodiversity
prediction: MERRA climate reanalysis data and AMSR-E, a pure remote
sensing data source. We use these data to generate novel temperature-based
bioclimatic information and to model the distributions of 20 species of
vertebrates endemic to four regions of South America: Amazonia, the
Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic
datasets derived from MERRA and AMSR-E information with in situ station
data, and contrast species distribution models based on these two products
to models built with WorldClim. Surface temperature estimates provided by
MERRA and AMSR-E showed warm temperature biases relative to the in situ
data fields, but the reliability of these datasets varied in geographic
space. Species distribution models derived from the MERRA data performed
equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic
Forest) than models built with the WorldClim data. In contrast, the
performance of models constructed with the AMSR-E data was similar to
(Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of
models built with WorldClim data. Whereas this initial comparison assessed
only temperature fields, efforts to estimate precipitation from remote
sensing information hold great promise; furthermore, other environmental
datasets with higher spatial and temporal fidelity may improve upon these
results.