Prediction of phylogeographic endemism in an environmentally complex biome (1)

 

Phylogeographic endemism, the degree to which the history of recently evolved lineages is spatially restricted, reflects fundamental evolutionary processes such as cryptic divergence, adaptation and biological responses to environmental heterogeneity. We identify two divergent bioclimatic domains within the forest and high turnover around the Rio Doce. Independent modelling of these domains demonstrates that endemism patterns are subject to different climatic drivers. These results accord with recent speleothem and fossil pollen studies, suggesting that climatic variability through the last 250 kyr impacted the northern and the southern forests differently.

 

All data available on Dryad

 

(1) Carnaval AC, Waltari E, Rodrigues MT, Rosauer D, VanDerWal J, Damasceno R, Prates I, Strangas M, Spanos Z, Rivera D, Pie MR, Firkowski CR, Bornschein MR, Ribeiro LF, Moritz C (2014) Data from: Prediction of phylogeographic endemism in an environmentally complex biome. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.8kc1v

 

 

Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling (2)

 

 

  • 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.

  • 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.

 

All data available on Dryad

 

(2)Waltari E, Schroeder R, McDonald K, Anderson RP, Carnaval A. (2014). Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling. Methods in Ecology and Evolution, online in advance of print. http://dx.doi.org/10.1111/2041-210X.12264.