Adaptive Management of Bull Trout Populations in the Lemhi Basin

Authors

Andrew J. Tyre, University of Nebraska-LincolnFollow
James T. Peterson, University of Georgia
Sarah J. Converse, Patuxent Wildlife Research Center
Tiffany Bogich, University of Cambridge
Damien Miller, U.S. Fish and Wildlife Service
Max Post van der Burg, University of Nebraska-Lincoln
Carmen Thomas, U.S. Fish and Wildlife Service
Ralph Thompson, U.S. Fish and Wildlife Service
Jeri Wood, U.S. Fish and Wildlife Service
Donna C. Brewer, National Conservation Training Center
Michael C. Runge, USGS Patuxent Wildlife Research Center

Date of this Version

12-2011

Citation

Tyre AJ, Peterson JT, Converse SJ, Bogich T, Miller D, Post van der Burg M, Thomas C, Thompson R, Wood J, Brewer DC, Runge MC. 2011. Adaptive management of bull trout populations in the Lemhi Basin. Journal of Fish and Wildlife Management 2(2):262–281; e1944-687X. doi: 10.3996/022011-JFWM-012

Abstract

The bull trout Salvelinus confluentus, a stream-living salmonid distributed in drainages of the northwestern United States, is listed as threatened under the Endangered Species Act because of rangewide declines. One proposed recovery action is the reconnection of tributaries in the Lemhi Basin. Past water use policies in this core area disconnected headwater spawning sites from downstream habitat and have led to the loss of migratory life history forms. We developed an adaptive management framework to analyze which types of streams should be prioritized for reconnection under a proposed Habitat Conservation Plan. We developed a Stochastic Dynamic Program that identified optimal policies over time under four different assumptions about the nature of the migratory behavior and the effects of brook trout Salvelinus fontinalis on subpopulations of bull trout. In general, given the current state of the system and the uncertainties about the dynamics, the optimal policy would be to connect streams that are currently occupied by bull trout. We also estimated the value of information as the difference between absolute certainty about which of our four assumptions were correct, and a model averaged optimization assuming no knowledge. Overall there is little to be gained by learning about the dynamics of the system in its current state, although in other parts of the state space reducing uncertainties about the system would be very valuable. We also conducted a sensitivity analysis; the optimal decision at the current state does not change even when parameter values are changed up to 75% of the baseline values. Overall, the exercise demonstrates that it is possible to apply adaptive management principles to threatened and endangered species, but logistical and data availability constraints make detailed analyses difficult.