Reliably Determining the Status of Listed Salmonids: Valuable Insights for AM Practitioners

Endangered species represent a major challenge to adaptive management, both because of the constraints on management actions posed by vulnerable populations, and because of the M&E challenges in evaluating the response of these populations to implemented actions. Overestimating population status entails conservation risk while underestimating their status can in some cases pose unnecessary economic costs associated with the constraints resulting from Endangered Species Act regulations.

We developed a simulation model (the Salmon Viability Monitoring Model or SVMM) that describes the effect of uncertainty in monitoring data on the ability to assess the status of endangered Snake River Spring/Summer Chinook. The model was built with the cooperation of the Interior Columbia Technical Recovery Team (TRT), who provided the decision framework for our model, and was part of the Collaborative, Systemwide, Monitoring and Evaluation Project, a collaborative effort to improve monitoring programs for salmonids in the Columbia basin. This tool yields insights that are helpful at the Design, Monitoring, Evaluation and Adjust steps of the AM cycle.

Using the model, monitoring data and resultant status assessments can be simulated for different types of monitoring programs under various scenarios of salmon abundance, productivity, spatial distribution and diversity. SVMM can be used to verify biological criteria used to make decisions; evaluate the sensitivity of a decision to the quality of monitoring data; test the influence of specific types of monitoring data on decisions; compare relative effects of uncertainty in measurement (“measurement error”) versus natural variability over time (“process error”); help determine the accuracy needed to make an acceptable number of correct decisions for a given data input scenario; and provide a relatively simple framework for communicating information about uncertainty to decision makers.

Using SVMM, we evaluated four alternatives to monitoring 32 Snake Basin chinook populations: Low ($0.2M/yr); Medium ($0.7M/yr), Status Quo ($1.3M/yr), and High ($2.1M/yr). Through Monte Carlo simulations we determined the proportion of cases in which these four M&E options led to correct assessments of viability: 41% (L), 73% (M), 60% (SQ), and 84% (H). Incorrect assessments under the Status Quo and Low alternative were 4 and 12 times more likely (respectively) to be underestimates of viability than overestimates; this reflects the design of decision rules by the TRT.

This study demonstrates the benefit of simulating the Monitoring, Evaluation and Adjust steps of the AM cycle, including the decision rules (either pre-determined or hypothetical) to be used to adjust management.