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Rainfall and river networks prove accurate predictors of fish biodiversity
Date:5/9/2008

Princeton researchers have invented a method for turning simple data about rainfall and river networks into accurate assessments of fish biodiversity, allowing better prediction of the effects of climate change and the ecological impact of man-made structures like dams.

The mathematics behind the new method also can be used to model and predict a wide range of other questions, from the transmission of waterborne illnesses to vegetation patterns on land adjacent to rivers.

The researchers, who published a report in the May 8 issue of Nature, have created a computer simulation that allows them to predict -- based on rainfall measurements and the structure of river networks -- how many species of fish will occupy any given region.

It is an extremely simple model but it predicts absolutely fantastically well all of the characteristics of biodiversity that we were interested in, said Ignacio Rodrguez-Iturbe, the James S. McDonnell Distinguished University Professor of Civil and Environmental Engineering and the leader of the research group that published the report in Nature.

Our model implies that water dynamics have a commanding effect on biodiversity in river basins.

Paolo D'Odorico, associate professor of environmental sciences at the University of Virginia, called the research exquisitely original and thought-provoking.

It is the first study I am aware of that provides a real quantitative framework for the study of river biogeography, D'Odorico said.

In their research, the authors merged different sets of existing data from the Mississippi-Missouri river basin, an extremely large region that covers more than half of the United States. This network of rivers springs from the Mississippi River, which cuts down the middle of the country. The triangle-shaped basin stretches from Minnesota to Louisiana and from Montana to New York.

Using one set of data, the researchers were ab
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Contact: Teresa Riordan
triordan@princeton.edu
609-258-9754
Princeton University, Engineering School
Source:Eurekalert  

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