Steelhead (Oncorhynchus mykiss expressing an anadromous life history) in the Sacramento and San Joaquin rivers and their tributaries in California’s Central Valley (CCV) belong to a Distinct Population Segment (DPS) that is listed as threatened under the US Endangered Species Act. Although contemporary management and recovery plans include numerous planned and ongoing efforts seeking to aid in DPS recovery—such as gravel augmentation, manipulation of spring flows, and restoration of rearing and spawning habitat—a paucity of data precludes the possibility of evaluating the effect of these actions on populations of Steelhead in CCV streams. Knowledge gaps relating to historic and current abundance, population-specific ratios of resident and anadromous life-history expression, and the influence of hatchery-reared fish remain largely unaddressed. This is partly a result of aspects of Steelhead biology that make them difficult to monitor, including the multitude of factors that contribute to the expression of anadromy, polymorphic populations, and migration periods that coincide with challenging field conditions. However, these gaps in understanding are also partly the result of an institutional focus on Chinook Salmon (Oncorhynchus tshawytscha) and a pervasive notion that actions benefiting Chinook populations will also benefit Steelhead populations. To evaluate these gaps and to suggest approaches for assessing DPS recovery actions, we review available data and existing monitoring efforts, and consider the actions necessary to inform the development of targeted O. mykiss monitoring programs. Current management and recovery goals focus on abundance estimates of Steelhead only, yet current monitoring is insufficient for reliable estimates. We argue that a reallocation of monitoring resources to better understand the interaction between resident O. mykiss and Steelhead would provide better data to estimate the vital rates needed to evaluate the effects of recovery actions.

Conservation and management of culturally and economically important species rely on monitoring programs to provide accurate and robust estimates of population size. Rotary screw traps (RSTs) are often used to monitor populations of anadromous fish, including fall-run Chinook Salmon (Oncorhynchus tshawytscha) in California’s Central Valley. Abundance estimates from RST data depend on estimating a trap's efficiency via mark-recapture releases. Because efficiency estimates are highly variable and influenced by many factors, abundance estimates can be highly uncertain. An additional complication is the multiple accepted methods for how to apply a limited number of trap efficiency estimates, each from discrete time-periods, to a population’s downstream migration, which can span months. Yet, few studies have evaluated these different methods, particularly with long-term monitoring programs. We used 21 years of mark-recapture data and RST catch of juvenile fall-run Chinook Salmon on the Stanislaus River, California, to investigate factors associated with trap efficiency variability across years and mark-recapture releases. We compared annual abundance estimates across five methods that differed in treatment of trap efficiency (stratified versus modeled) and statistical approach (frequentist versus Bayesian) to assess the variability of estimates across methods, and to evaluate whether method affected trends in estimated abundance. Consistent with short-term studies, we observed negative associations between estimated trap efficiency and river discharge as well as fish size. Abundance estimates were robust across all methods, frequently having overlapping confidence intervals. Abundance trends, for the number of increases and decreases from year to year, did not differ across methods. Estimated juvenile abundances were significantly related to adult escapement counts, and the relationship did not depend on estimation method. Understanding the sources of uncertainty related to abundance estimates is necessary to ensure that high-quality estimates are used in life cycle and stock-recruitment modeling.