Evaluating the Accuracy and Precision of Multiple Abundance Estimators Using State-Space Models: A Case Study for a Threatened Population of Chinook Salmon in Johnson Creek, Idaho

Abstract

Over the last century, Chinook Salmon Oncorhynchus tshawytscha populations in the Pacific Northwest have experienced dramatic declines, leading to many of them being listed as threatened under the Endangered Species Act. The abundance of these threatened populations relative to the thresholds for delisting remains the primary metric for assessing recovery, yet determining the true population abundances from multiple survey types with unknown levels of accuracy and precision remains difficult. The abundance of the spring–summer Chinook Salmon population in Johnson Creek, Idaho, has been measured using a mark–recapture survey and three different redd count surveys (RCSs) that vary temporally and spatially. Using a state-space model, we determined the accuracy and precision of each survey type by decoupling the observation error of the survey from the process error describing the annual variability in the true population abundance. We then extended the results of the model to determine the risk of managers’ incorrectly delisting the population (a type I error) or incorrectly keeping it listed (a type II error). Finally, we show that salmon managers with data-limited populations (primarily those with only single-pass index RCSs) might use the results of our risk analysis to determine whether expanding survey efforts to minimize management risks is appropriate when they are confronted with dwindling financial resources. For example, we determined that although both the multiple-pass extended RCS (CV = 0.06) and mark–recapture surveys (CV = 0.14) provide unbiased estimates of salmon abundance in Johnson Creek, the mark–recapture study can have annual costs that are 30–100 times greater. Managers may determine that directing research funds toward acquiring information unique to weir-based mark–recapture surveys (i.e., migration timing, good genetics samples, etc.) may not be justified for all populations.

Received October 7, 2013; accepted March 5, 2014

ACKNOWLEDGMENTS

We would like to thank Eric Buhle and Paul Kucera for their comments, which contributed greatly to the final version of this article. We would also like to thank the two reviewers whose edits led to considerable improvements to the initial draft. We thank Idaho Fish and Game and Doug Nelson for collecting and providing data and the Bonneville Power Administration for funding major portions of the Johnson Creek research.