News Release

USU biologist Will Pearse receives NSF Macrosystems Biology and Early NEON Science Award

Award will support ecological research at regional to continental scales

Grant and Award Announcement

Utah State University

Utah State University Biologist Will Pearse

image: Will Pearse, assistant professor in USU's Department of Biology and the USU Ecology Center, is a 2018 recipient of a National Science Foundation MacroSystems Biology and Early NEON Science Award. view more 

Credit: Mary-Ann Muffoletto, USU

LOGAN, UTAH, USA -- As wildfires continue to rage across the American West and the resulting smoky haze obscures mountain views outside his office window, Utah State University scientist Will Pearse ponders preventive steps land managers could have taken, if they'd had a glimpse into the future.

"If we'd been able, years ago, to predict the spread of invasive species like cheatgrass, we might be dealing with a very different situation," says Pearse, assistant professor in USU's Department of Biology and the USU Ecology Center.

He views the National Science Foundation's massive National Ecological Observatory Network, known as "NEON," as an opportunity to provide better information for future generations. The continental-scale observatory, which includes field sites throughout the nation collecting high-quality, standardized air, ground and water data, is an "ecologist's dream," he says.

"The volume and accuracy of this effort provides incredibly powerful data," says Pearse, who is the recipient of a recently announced $300,000 NSF MacroSystems Biology and Early NEON Science award. "The information NEON participants are collecting provides a kind of early warning system,"

One of nine scientists selected nationwide for the prestigious grant, Pearse's project, "Phylogenetically-informed Modeling of the Regional Context of Community Assembly," will use evolutionary history to address practical ecological challenges.

The project will enable Pearse to engage both graduate and undergraduate students in real-world research projects.

"Having information about specific species' evolutionary history gives context," he says. "With this information, we can model such phenomena as proliferation of disease vectors such as ticks that spread Lyme disease and pest beetles, whose outbreaks affect forestry yields."

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