Adaptive Management of Bull Trout Populations in the Lemhi Basin

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.

Adaptive management: where are we now?

“…Long lag times mean that the outcomes of management can only be assessed in the long term; yet with such time scales distinguishing the effects of management from those of other factors may be challenging. Management implemented at small scales, but influenced by a backdrop of change occurring at larger spatial and longer temporal scales, may similarly lead to changes in the system occurring before learning about dynamics can take place Tyre et al 2011). Moreover, AM includes an assumption of dynamic stability in the underlying environment and the structural features of the resource system (Williams 2011b).…”

Using the Value of Information to improve conservation decision making

“…Another eight (27%) papers used a unitless value that reflected a weighted response across multiple objectives (Runge et al ., ; Williams et al ., ; Smith et al ., ; Johnson et al ., , b , ; Thorne et al ., ; Williams & Johnson, ). Other papers used a range of performance metrics, namely cost ratio (Sahlin et al ., ), probability of survival of different age classes (Canessa et al ., ), population growth rate in per cent (Cohen et al ., ), species retention rate at the end of a 20‐year simulation period (Grantham et al ., ), increase in gas extraction while maintaining brook trout ( Salvelinus fontinalis ) populations (Smith et al ., ), probability of population persisting for 256 years (Tyre et al ., ), utility function reflecting both yield (kilotons) and risk of falling below critical spawning mass (Kuikka et al ., ), and proportion of maximum phylogenetic diversity retained (Hartmann & Andre, ).…”

Category count models for adaptive management of metapopulations: Case study of an imperiled salamander

“…However, optimizing a spatially explicit management policy is often computationally intractable for problems of realistic size (Fackler, ; Nicol & Chadès, ; Schapaugh & Tyre, ). Category count models are useful alternatives that ignore direct interactions between sites (Fackler, ; Tyre et al, ); each site is assigned to a category (here, S/O, S/N, U/O, or U/N) at time step t , and the site's future category ( t + 1) depends on its current category, the action applied at time t and, possibly, other factors that are common across all sites. When multiple sites are managed, we define the system state S t as a vector of length 4, where S t ( i ) represents the number of sites in category i at time t (i.e., {#S/O, #S/N, #U/O, #U/N}).…”