NASA Helps Forecast Reptile Distributions In Madagascar

The discovery suggests for poorly explored regions, NASA satellite data and data from museum collections can help identify promising places to survey for new species of life, while locating areas likely to be of conservation importance.

The results of this NASA-funded study, led by American Museum of Natural History biologist, Christopher J. Raxworthy, Associate Curator, division of Vertebrate Zoology, and six colleagues, demonstrated existing museum collections and satellite measurements of Earth's surface and climate hold great promise for the accurate prediction of species distributions.

The findings, published in the latest issue of the journal Nature, demonstrate an approach for speeding up the process of regional species inventories, especially in poorly known tropical environments with diverse habitats and climates. The research also shows, both historical and modern field data can be extremely useful for predicting chameleon species distribution in Madagascar, although contemporary field data used in concert with satellite data provides more accurate biogeographic distribution predictions.

This study is the first to successfully predict the distribution of any species in Madagascar using satellite imagery and information from museum specimens. It is also the first to evaluate the predictive usefulness of historical museum specimens in collections (dating back to the 1800s) versus recently collected field data from Madagascar.

The island nation, with a terrain of narrow coastal plain, high plateau and central mountains, is home to an extraordinary diverse group of species, making it an excellent location for this type modeling.

This work is consistent with a new element of the NASA Earth Science applications program focusing on ecological forecasting. The program uses Earth observation data and models to forecast species distributions and how environmental change might affect them.

This new chameleon prediction study tested the accuracy of several distribution models. The models, based on information gathered from historical museum specimens collected prior to 1978 and on modern data from specimens collected after 1988, were compared against other locality data that was set aside for testing purposes, and against recent inventories of 11 sites where chameleons were also surveyed.

All of the models rely on environmental data collected by several NASA satellites, a Space Shuttle radar mapping mission, U.S. Geological Survey and National Oceanic and Atmospheric Administration data sets. Environmental data included land cover (as viewed from space), rainfall, cloud cover, average and seasonal temperatures, and topographic data, which were input into the Genetic Algorithm for Rule-set Prediction (GARP), a software package for biodiversity and ecology research that allows users to predict species distributions.

The intriguing result that ended up predicting where to locate chameleon species previously unknown to science arose unintentionally. When the researchers examined the models for four species, they found overlapping areas of error about where the models predicted that the species lived.

Examining their field data collected in two of these regions, they realized these areas actually contained seven other closely related species that are new to science. The areas that initially seemed to represent "error" in the models pointed to regions that are of importance, because they provide habitats for locally confined species that had been previously unrecognized. Through careful evaluation of their model, the researchers made this serendipitous discovery.

"Our results show that distribution models can help scientists and those who make conservation decisions determine areas with potential unrecognized biodiversity," Raxworthy said.

The research was funded in part by NASA's Earth Science Enterprise, a long-term research effort dedicated to protecting and understanding of our home planet, while inspiring the next generation of explorers.