Current Research Projects
2018-2021
Efficacy of conservation actions for native fishes in tributaries of the Colorado River, Grand Canyon
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2018-2021
Assessing the impact of nutrient enrichment in the Henry’s Fork River Headwaters
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2018-2020
Ecological effects and fishery conservation implications of a quasi-natural fish barrier on the Lower San Juan River, Utah
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2015-2018
Improving estimates of vital rates of endangered fishes on the San Juan River using novel applications of PIT-tag technology.
Join the Lab
MS Graduate Research Assistantship
Many opportunities to join the Fish Ecology Lab are available to Graduate School candidates, Post-Doctoral Fellow candidates, Technicians, and Internship candidates.
I am currently seeking motivated and independent post-doctoral candidates who are interested in drafting a NSF post-doctoral fellowship proposal to work on fish population dynamics and climate change associated with the Toolik NSF LTER program, located in northern Alaska.
Post-doctoral Research Fellow Position
We seek a quantitative post-doctoral or post graduate (MS) research scientist to assist with three food web modeling experiments and a variety of other analytical projects for data sets describing two large and important lake ecosystems, Pyramid Lake, NV and Utah Lake, UT. In addition, the successful candidate will supervise the summer collection of fisheries field data on beautiful Bear Lake, UT/ID.
News & Announcements
Humpback Chub Successfully Released In Bright Angel Creek In Grand Canyon National Park
On May 14, biologists with the National Park Service, U.S. Fish and Wildlife Service, and Bureau of Reclamation released endangered humpback chub into Bright Angel Creek in Grand Canyon National Park.
Evaluating the Conservation Potential of Tributaries for Native Fishes in the Upper Colorado River Basin
Modeling the fish community population dynamics and forecasting the eradication success of an exotic fish from an alpine stream
Management actions aimed at eradicating exotic fish species from riverine ecosystems can be better informed byforecasting abilities of mechanistic models.
