CSMEP Salmon Viability Monitoring Model

Exploring the effect of uncertainty in monitoring data on salmonid population viability assessments.

The Salmon Viability Monitoring Model (SVMM) was developed as part of CBFWA’s Collaborative Systemwide Monitoring and Evaluation Project (CSMEP) to explore how alternative monitoring strategies might affect population viability assessments. The model allows the user to manipulate the uncertainty surrounding each data type to reflect alternative on-the-ground methodologies (e.g. redd counts, weirs, snorkel counts). The user is also able to manipulate the test datasets to see if the effect of uncertainty on the viability assessments is dependent on the actual underlying viability of the populations. The viability assessments within the SVMM are based on the Interior Columbia Technical Recovery Team IC-TRT viability criteria. Like all NOAA Technical Recovery Teams, the IC-TRT viability criteria require information about: abundance, productivity, spatial structure, and diversity. Figure 1 illustrates generally how the SVMM works.

Figure 1. An underlying test scenario is input by the user describing the ‘true’ abundance, age-structure, spatial-structure, and diversity. The user can then manipulate the monitoring design inputs to test many alternative strategies. The SVMM simulates observed data by taking the true data inputs and adding randomly generated monitoring ‘noise’ according to the monitoring design specified. The SVMM then compares the true viability with the observed viability and reports the frequency with which correct decisions are made.

The SVMM can be used in combination with the Integrated Costs Database Tool (ICDT) to compare the tradeoff between accuracy of viability assessments and the cost of a given monitoring design. The SVMM is a tool that can be used to:

While the SVMM can provide much useful information, it relies heavily on the user input information and cannot replace common sense or make decisions for you. The SVMM was built using the IC-TRT viability criteria as a foundation. The general strategy of using a simulation model to assess the effect of uncertainty in monitoring data could be applied to any of the eight TRT rule sets. Each of the TRT rule sets uses the same basic information: abundance, productivity, spatial structure, and diversity, and so the SVMM or another model could be adapted for any of the TRT rule sets. However, at this time the SVMM is specific to the IC-TRT rule set. The SVMM calculates productivity by a simple comparison of spawner to spawner abundance. The number of spawners resulting from each cohort is calculated based on a user-defined age-structure. Because the current model assumes a single reproductive event it may not be appropriate for application to steelhead ESUs at this time.

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Developed by Darcy Pickard and Ken MacDonald.