Trinity River Restoration Program Juvenile Salmonid Outmigrant Monitoring Evaluation, Phase II Final Technical Memorandum December 21, 2009 Prepared for: Trinity River Restoration Program 1313 S. Main Street P.O. Box 1300 Weaverville, California 96093 Prepared by: ESSA Technologies, Ltd. 1765 W 8th Avenue Vancouver, British Columbia V6J 5C6 Simon Fraser University Department of Statistics and Actuarial Science 8888 University Drive Burnaby, British Columbia V5A 1S6 North State Resources, Inc. 5000 Bechelli Lane, Suite 203 Redding, California 96002 (530) 222-5347 USBR TASK ORDER 06A0204097G NSR# 10116-02 © 2009 Trinity River Restoration Program Citation: Schwarz, C.J., D. Pickard, K. Marine and S.J. Bonner. 2009. Juvenile Salmonid Outmigrant Monitoring Evaluation, Phase II– December 2009. Final Technical Memorandum for the Trinity River Restoration Program, Weaverville, CA. 155 pp. + appendices. Executive Summary The Trinity River Restoration Program (hereafter called the Program) was initiated in 1984 to restore and maintain the fish and wildlife stocks of the Trinity River Basin to levels that existed just prior to construction of the CVP Trinity River Division. Using an adaptive management framework the Program hopes to determine the most effective restoration strategies to restore the Trinity River’s natural riverine processes and enhance fish and wildlife populations. The Program’s Integrated Assessment Plan (IAP) identifies key assessments that can be used to evaluate long-term progress toward achieving Program goals and objectives and/or provide short-term feedback to improve Program management actions by testing key hypotheses and reducing management uncertainties. This report addresses juvenile salmonid outmigrant monitoring (a key component of the IAP), evaluating tradeoffs between alternative monitoring methods for assessing smolt abundance, run timing, and condition. In addition, we assess the ability of the monitoring data to inform Program restoration goals for affected salmon species. Abundance and run timing We propose a new Bayesian spline-based methodology to estimate salmon abundance and run timing which provides several compelling advantages over the more traditional pooled or stratified Peterson estimator. In particular, the ability to share data among weeks within the Bayesian approach allows greater flexibility in terms of handling missing data. This feature may enable the Program to reduce the frequency of required mark-recapture events during periods with fewer smolt outmigrants without affecting estimates of precision. Flow-based methods using the fraction of discharge sampled appear to capture the general shape of the outgoing migration pattern quite well. However, estimated capture-efficiencies based on sampled flow volumes underestimate actual screw trap efficiencies as measured by mark-recapture methods. This implies that the flow-based methods may underestimate the actual number of outgoing migrants. Unfortunately, the relationship between the flow-based and mark-recapture efficiencies may vary considerably across years, even within the same study. Further work is needed to identify underlying reasons for such wide variation before flow-based estimates can reliably be applied to years lacking a supporting mark-recapture study. Our results suggest that a hybrid approach may be most suitable for application in future years, as the relationship appears to remain fairly consistent across weeks within a year. This suggests that undertaking flow measurements over the entire season supplemented with mark-recapture experiments in a few weeks to calibrate screw traps and establish the relationship should work quite well, particularly in cases where continuous electronic flow monitoring is possible. Estimates of smolt run timing can be obtained fairly easily from the spline-based methods based on the estimate of the population passing the screw-trap in each week. The method implicitly assumes that smolt passage is uniform within the week which is clearly not the case. Error introduced by this assumption is small, however, relative to sampling errors. A key requirement to ensure that estimates of run times and abundance are sensible, is that the study covers the entire smolt migration period (or at least that the number of fish moving outside the study window is negligible). It is possible to use the spline-based methods to interpolate outside the study’s temporal boundaries. Given that the tail-end of the study usually has few fish, interpolation after the study period is unlikely to be problematic. However, for several of the datasets large numbers of fish were ESSA Technologies. Ltd. Trinity River Restoration Program Simon Fraser University i Juvenile Salmonid Outmigrant Monitoring Evaluation North State Resources, Inc. Phase II already passing the screw traps when the study began so that any interpolation prior to study initiation would be highly problematic. Condition Several potential fish size and condition metrics were initially considered for use in long-term evaluations of the program. Fork length has the longest time series available and was the only condition-related metric selected for detailed evaluation in this report. Assumptions around sampling fork length are not as rigorous as those for abundance and run timing. For example varying the sampling windows across years does not affect fork length estimates as much as it does the abundance and run timing estimates. There are substantial data for all three species of interest (i.e., coho, steelhead, and Chinook salmon); in fact fork length is the only dataset with sufficient data to complete any analyses for coho. A limitation in use of fork length data is that it is unclear how fork length would be expected to change in response to the restoration activities. We explored several hypotheses for the purpose of this report, but more effort should be invested to clarify specific hypotheses and associated metrics. The metrics should consider run timing in relation to fork length (e.g., is it more important to know the size of the early outmigrants or the late outmigrants?). We recommend using a smoothed metric to remove the day-to-day variability in the fork length data, rather than simply using the raw data. Outmigrant data as a program tool The key challenge in evaluating across-year trends in outmigrants is the small time series available for most measures. The limiting factor in detecting longer term trends is the process error. The sampling error can be controlled by adjusting effort within each year, but in many cases, sampling error is small relative to process error. Unless additional covariates can be obtained to remove some of the process error, this large variation apparent in the response measures between years results in studies with low power. Power can be increased appreciably where these studies can be continued for ten or more years. Some of the variability across years, especially in terms of log(abundance) and run timing, results from the particular survey timing each year (e.g., the sampling window varies and in some years may miss the beginning of the run). Certain metrics are easier to measure and less sensitive to study design. For example, fish condition as measured by fork length is relatively easy and inexpensive to measure over the course of a year. However, it is uncertain how condition of outmigrating smolts relates to improvements in fish rearing due to habitat improvements. Abundance would seem to be a more direct measure of overall improvement, but has high process variation and is expensive to measure. Trinity River Restoration Program ii ESSA Technologies. Ltd. Juvenile Salmonid Outmigrant Monitoring Evaluation Simon Fraser University Phase II North State Resources, Inc. Table of Contents Executive Summary......................................................................................................................................................i Table of Contents....................................................................................................................................................... iii List of Figures ..............................................................................................................................................................v List of Tables............................................................................................................................................................. vii Appendices ..................................................................................................................................................................ix Acknowledgements ......................................................................................................................................................x Acknowledgements ......................................................................................................................................................x 1. Introduction..............................................................................................................................................................1 1.1 Background..................................................................................................................................................1 1.2 Objectives ....................................................................................................................................................1 1.2.1 Data extraction and synthesis (Section