Gomez and Sullivan’s Betsy O’Malley presented on “The effect of annual variability on passage standards set through population modeling” at the 151st American Fisheries Society Annual Meeting in November 2021. The presentation highlighted the fact that balancing fish population management goals with the benefits of hydropower production will continue to be a contentious issue. A recent focus on watershed-scale management has led to the use of population models that allow managers to explore how passage at individual dams changes overall population recovery and helps managers set passage efficiency standards at individual hydropower projects. As models are only a representation of reality, it is necessary to understand how year-to-year differences (or variation) in model inputs can influence the development of passage standards that are attainable for dam owners while also being capable of meeting resource management goals.
To investigate this issue, we performed a simple modeling exercise using an existing alewife population model and compared the result of the model using different levels of variability. The model previously used average values as inputs, meaning there was no allowance for year-to-year variation. We wanted to see how year-to-year variation in the number of juvenile alewives produced per unit of habitat affected the probability of meeting a specific adult abundance goal (235 returning spawners/acre) for a given time period (e.g., 5 years, 10 years, etc.). We tested two levels of juvenile production and two levels of year-to-year variation. Results indicate that high levels of year-to-year variation in the number of juveniles produced decreased the probability of meeting the abundance goal compared to low levels of variation. This suggests that the decision regarding the amount of year-to-year variation to use in the model affects the probability of successfully meeting the abundance goal, meaning that it is important to either have information on or discussions around the effects of different levels of variability when setting a management goal. Model results also suggested that the time period used to set management goals affects the probability of meeting that goal due to year-to-year variation. For example, if the management goal was to meet an abundance target every year for 20 years (100% of the time), we saw a very low probability of success. However, if the management goal was to meet an abundance target 16 out of 20 years (80% of the time), there was a much higher probability of success, even though the number of adult fish, on average, was above the management goal in both scenarios. This suggests that when setting management goals, it may be beneficial to use criteria that balance how frequently the abundance goal is met with the probability of it being met over a given duration of time.
