Bioclimatic variables derived from remote sensing: assessment and application for species distribution modelling. Issue 10 (17th October 2014) - Waltari, Eric; Schroeder, Ronny; McDonald, Kyle; Anderson, Robert P.; Carnaval, Ana; Freckleton, Robert
Summary Remote sensing techniques offer an opportunity to improve biodiversity modelling and prediction world‐wide. Yet, to date, the weather station‐based WorldClim data set 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 data sets 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 data sets 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 data sets 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 data sets with higher spatial and temporal fidelity may improve upon these results.