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Designing Wildlife Habitats for Wide-ranging Species

New programming approach enables dramatic modeling improvements

Steve Reinhardt

Due to massive volumes of landscape data and the novel application of algorithms from circuit theory, researchers are working to speed code using state-of-the-art sparse linear solvers, graph computations, vectorization and parallelization of the code. The result is a dramatic reduction in computing time from days to minutes on an eight-core server.

Ecologists are modeling wildlife migration and gene flows across fragmented landscapes using electrical network theory. Corridors are areas that connect important habitats in human-altered landscapes. They provide natural avenues along which animals can travel, plants can propagate, genetic interchange can occur, species can move in response to environmental changes and natural disasters, and threatened populations can be replenished from other areas.

A good example is “Y2Y,” or the Yellowstone to Yukon corridor, where U.S. and Canadian conservation organizations are trying to identify which habitats to conserve to protect species from harmful decline or extinction. Effective modeling can be instrumental in smart conservation planning, helping organizations decide which lands to preserve or restore and where to best invest their tight conservation budgets in order to preserve habitat and connectivity for wildlife populations. Circuitscape,¹ a tool written in Python and M, uses several knowledge discovery algorithms to map known characteristics of the target species to the topography and thereby model the species’ behavior and identify likely corridors.

Traditionally, code may have taken three days to process a landscape with a million raster cells on a desktop workstation. Improvements resulting from using a suite of algorithms known as the Knowledge Discovery Suite² (KDS), which defines algorithms at a higher level with efficient parallel implementations, combined with vectorization and parallelization with Star-P, enable problems 10 times bigger to be processed within minutes on a HPC. Sequential performance is also increased dramatically. With this fast response time, ecologists can use these techniques as part of their routine workflow, rather than an esoteric tool only to be used in specialized situations.

1. www.circuitscape.org
2. Brad H. McRae, Brett G. Dickson, Timothy H. Keitt, and Viral B. Shah, "Using circuit theory to model connectivity in ecology, evolution, and conservation", Ecology, Vol. 89, No. 10, pp. 2712-2724.

Steve Reinhardt is Vice President of Joint Research at Interactive Supercomputing. He may be contacted at editor@ScientificComputing.com.