Computational Ecology: You Heard It Here First (Well, Maybe)

You heard it hear first -- "computational ecology." Okay, maybe you didn't hear it here first. Maybe the term has been around for a number of years and I'm just now running across it. Sigh. In any event, it doesn't seem to be referenced in Wikipedia, so I must be ahead of the curve, at least a little bit.

In a nutshell, computational ecology is:

an interdisciplinary field devoted to the quantitative description and analysis of ecological systems using empirical data, mathematical models (including statistical models), and computational technology [Ref].

In short, it is about applying computer-based mathematical modeling to environmental issues. What makes computational ecology a possible these days is the availability of satellite-based remote sensing, computers that can handle large databases, high-speed communication networks, and small radio transmitters and sensors. All the usual stuff we now take for granted, in other words.

What can you do with all this? Well, researchers at the University of California, Santa Barbara (UCSB) are harnessing supercomputers and electronic circuit theory to help save wildlife from shrinking habitat. The project is run by the university's National Center for Ecological Analysis and Synthesis (NCEAS), and NCEAS scientists are applying electronic circuit theory to model wildlife migration and gene flow across fragmented landscapes.

Due to the massive volume of landscape data and the application of algorithms from circuit theory, NCEAS is speeding up its code using sparse linear solvers, graph computations, vector,ization and parallelization via Interactive Supercomputing's (ISC) Star-P software platform. Star-P is a client-server parallel-computing platform that’s been designed to work with multiple Very High Level Language (VHLL) client applications such as MATLAB, Python, or R, and has built-in tools to expand VHLL computing capability through addition of libraries and hardware-based accelerators.

"It turns out that circuit theory shares a surprising number of properties with ecological theory describing animal movements and connectivity," said Brad McRae, NCEAS project leader. "We can now represent landscapes as conductive surfaces -- with features like forests and highways having different resistance to movement -- and analyze connectivity across them using powerful circuit algorithms. Unlike standard conservation planning tools, these algorithms simultaneously incorporate all possible pathways when predicting how corridors, barriers, and other features affect movement and gene flow over large areas."

"The combination of vectorization with Star-P's graph toolbox and efficient sparse linear solvers has allowed scientists to take full advantage of their 8-processor server (with 32 gigabytes of memory) to run their models," says ISC's Steve Reinhardt. "The result: scientists can now model larger maps with much finer grids, while cutting computing time from three days to about 15 minutes for typical problems."

Posted by Jon Erickson at 04:49 PM  Permalink