Computer Models Used to Improve Efficiency
Reduce Costs for Species Preservation



March 26, 1998
Contact: Chris Curran
(513) 556-1806 (O)
chris.curran@uc.edu

Cincinnati -- A University of Cincinnati computer model already used by major corporations to plan efficient distribution networks is now being used by conservation researchers to study the most cost-effective plans for species preservation.

In a paper to be published in Science Friday, March 27, University of Cincinnati researcher Jeffrey Camm and his collaborators will report on the use of optimization techniques to select the right tracts of lands for nature reserves, taking into account both species distributions and land values.

The co-authors are: Amy Ando with Resources for the Future in Washington, D.C.; Stephen Polasky, a resource economist at Oregon State University; and Andrew Solow of the Woods Hole Oceanographic Institution. Camm is head of the quantitative analysis and operations management department in UC's College of Business Administration.

The team's work is an extension of the research done by Andrew Dobson et al. on efficient conservation, which was published in Science last year (Vol. 275, p.550). Dobson reported that conservation could be made more efficient by identifying species "hot spots" where large numbers of endangered species are located. However, Camm's group discovered that species "hot spots" are often real estate hot spots, dramatically increasing the cost of conservation efforts.

So, Ando developed a data base of land values and estimated land values for 2,822 key counties across the United States. Camm factored those values into a computer optimization model, and the results provide a more flexible approach as well as a more economical approach.

"We found that you can get the same number of species using more land and locations at a much cheaper cost than just looking at the species distribution," said Camm. "If you factor in the land values, you might have to buy more locations, but they're cheaper to cover the same species."

That's a significant advantage over previous methods for planning the development of nature reserves. "What people have been using up until the last couple of years have been just rules of thumb, not true mathematical optimization."

Camm's computer model can be adjusted to handle both species distribution and land type. It can take into account various budgetary limits, and it can produce multiple solutions for politicians and planners to consider.

"Say there are 50 best solutions, we can find all of those. But if there's only one unique best solution, we can generate the top 50 based on cost. So, you can add these to the computer model and provide alternatives."

The model can even be adapted worldwide. "If they want to do this in Europe, the basic model is there," said Camm. "What would change is how you define a piece of land. It might not be counties. It might be some other designation."

"You simply have to have a way of breaking a map down into discrete pieces. Once you have that, and you have the data on land values and on species existing there, the basic model can be applied."

Camm is quick to point out that his optimization model does not cover all the variables. For example, it doesn't take into account the potential impact of isolating small populations of endangered species across many reserves compared with a large area where species can interbreed. These larger, ecosystem effects are more difficult to measure and involve a degree of uncertainty.

Camm, Polasky and Solow, along with colleagues Raymond O'Connor at the University of Maine and Blair Csuti at Oregon State University are studying the impact of uncertainty on nature reserve site selection under a $270,000 Environmental Protection Agency grant.

The EPA project is expected to be completed later this year.

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chris.curran@uc.edu
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