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WILD JUVENILE STEELHEAD AND CHINOOK SALMON ABUNDANCE AND COMPOSITION AT LOWER GRANITE
DAM, MIGRATORY YEARS 2010-2017
CUMULATIVE PROGRESS REPORT
Photo: IDFG
Prepared by:
Carlos A. Camacho, Fisheries Biologist
Kristin K. Wright, Supervisory Fisheries Biologist John Powell, Fishery Biologist 2
William C. Schrader, Principal Fisheries Biologist Timothy Copeland, Fisheries Program Coordinator
Michael W. Ackerman, Fishery Biologist 2 Marika E. Dobos, Fisheries Biologist
Micah Davison, Supervisory Fisheries Biologist Matthew P. Corsi, Principal Fisheries Biologist
Matthew R. Campbell, Fisheries Program Coordinator
IDFG Report Number 18-05 March 2018
Wild Juvenile Steelhead and Chinook Salmon Abundance and Composition at Lower Granite Dam, Migratory Years
2010-2017
Cumulative Report 2010 through 2017
By
Carlos A. Camacho Kristin K. Wright
John Powell William C. Schrader Timothy Copeland
Michael W. Ackerman Marika E. Dobos Micah Davison
Matthew P. Corsi Matthew R. Campbell
Idaho Department of Fish and Game 600 South Walnut Street
P.O. Box 25 Boise, ID 83707
To
U.S. Department of Energy Bonneville Power Administration
Division of Fish and Wildlife P.O. Box 3621
Portland, OR 97283-3621
IDFG Report Number 18-05 March 2018
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ACKNOWLEDGEMENTS
Report Authors: Carlos A. Camacho (IDFG) Kristin K. Wright (IDFG / PSMFC) John Powell (IDFG / PSMFC) William C. Schrader (IDFG) Timothy Copeland (IDFG)
Michael W. Ackerman (IDFG / PSMFC) Marika E. Dobos (IDFG) Micah Davison (IDFG / PSMFC) Matthew P. Corsi (IDFG) Matthew R. Campbell (IDFG)
Report Contributors: Data, reviews, and other assistance (alphabetical) IDFG • Miranda Adams • Randy Bartels • Grant Bruner • Alan Byrne • John Cook • Gary Fitzgerald • Karen Gregory • Sam Hagen • Pete Hassemer • Lance Hebdon • Brian Hoffman • Brian Leth • Charleen Neal • Charlie Petrosky • Scott Putnam • Lynn Schrader • Carl Stiefel • Chris Sullivan • Chuck Warren IDFG / PSMFC • Paul Bunn • Tyler Gross • Kala Hernandez • Cliff Hohman • Tyler Johnson • Lisa Kautzi • Dylan Kovis • Jesse McCane • Rachel Neuenhoff • Laura Redfield • Leslie Reinhardt • Ron Roberts
• Daniel Trovillion • Thea Wickersham • Lynn Schrader • Craig Steele • Ninh Vu • Tyler Zumwalt University of Idaho • Kirk Steinhorst Columbia River Inter-Tribal Fish Commission • Jon Hess • Andrew Matala • Shawn Narum NMFS Northwest Fisheries Science Center
• Vicky Brenner • Shane Collier • Jack Lyman • Doug Marsh • Ken McIntyre • Darren Ogden • Neil Paasch • Steve Smith • Ken Thomas
Quantitative Consultants, Inc. • Allen Bartels • Jermey Lueck • Jody White Fish Passage Center • Brandon Chockley
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ACKNOWLEDGEMENTS (CONTINUED)
Project Administration: Funding and other assistance (alphabetical) • Bonneville Power Administration (BPA); projects:
1990-055-00 Idaho Steelhead Monitoring and Evaluation Studies 1991-073-00 Idaho Natural Production Monitoring and Evaluation Program 2010-026-00 Chinook and Steelhead Genotyping for Genetic Stock Identification (GSI) at Lower Granite Dam
• Idaho Office of Species Conservation (IOSC) • Pacific States Marine Fisheries Commission (PSMFC) • U. S. Fish and Wildlife Service, Lower Snake River Compensation Program (LSRCP) Suggested citation: Camacho, C. A., K. K. Wright, J. Powell, W. C. Schrader, T. Copeland, M. W. Ackerman, M. E.
Dobos, M. Davison, M. P. Corsi, and M. R. Campbell. 2018. Wild juvenile steelhead and Chinook Salmon abundance and composition at Lower Granite Dam, migratory years 2010 and 2017. Idaho Department of Fish and Game Report 18-05. Cumulative Report, BPA Projects 1990-055-00, 1991-073-00, 2010-026-00.
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ABBREVIATIONS AND ACRONYMS
BPA Bonneville Power Administration
BY Brood Year
CI Confidence Interval
COE U. S. Army Corps of Engineers
CWT Coded Wire Tag
DPS Distinct Population Segment
ESA Endangered Species Act
ESU Evolutionarily Significant Unit
F Female
FL Fork Length
GSI Genetic Stock Identification
IA Individual Assignment
ICBTRT Interior Columbia Basin Technical Recovery Team
IDFG Idaho Department of Fish and Game
IOSC Idaho Office of Species Conservation
LGR Lower Granite Dam
LSRCP Lower Snake River Compensation Plan
M Male
MCMC Markov Chain Monte Carlo
MPG Major Population Group
MSA Mixed Stock Analysis
NMFS National Marine Fisheries Service
PBT Parentage Based Tagging
PIT Passive Integrated Transponder
PSMFC Pacific States Marine Fisheries Commission
QCI Quantitative Consultants, Inc.
SNP Single Nucleotide Polymorphism
SY Spawn Year
TAC Technical Advisory Committee, US v. Oregon
VSP Viable Salmonid Population
WDFW Washington Department of Fish and Wildlife
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TABLE OF CONTENTS Page
ACKNOWLEDGEMENTS ............................................................................................................ i ACKNOWLEDGEMENTS (continued) ......................................................................................... ii ABBREVIATIONS AND ACRONYMS ........................................................................................ iii ABSTRACT ................................................................................................................................. 1 INTRODUCTION ........................................................................................................................ 3 METHODS .................................................................................................................................. 4
Juvenile Trap Operations at Lower Granite Dam ...................................................................... 4 Scale and Tissue (Genetic) Processing and Analysis............................................................... 5 Emigration by Origin, Age, Sex, and Genetic Stock ................................................................. 7 Wild/natural Spring-Summer Chinook Salmon Stock-Recruitment Relationship in the
Snake River Basin ............................................................................................................ 10 RESULTS ................................................................................................................................. 11
Steelhead Emigration ............................................................................................................. 11 Wild Steelhead Stock, MPG, Sex, and Age Composition .................................................... 11
Chinook Salmon Yearling Emigration ..................................................................................... 12 Wild Chinook Salmon Yearling Stock, MPG, Sex, and Age Composition............................. 12
Chinook Salmon Subyearling Emigration ............................................................................... 13 Wild Chinook Salmon Subyearling Stock, MPG, Sex, and Age Composition ....................... 13
Chinook Salmon Females Available for Natural Reproduction ............................................... 13 Chinook Salmon Stock-Recruit Model .................................................................................... 14
DISCUSSION............................................................................................................................ 14 LITERATURE CITED ................................................................................................................ 18 TABLES .................................................................................................................................... 25 FIGURES .................................................................................................................................. 29 APPENDICES ........................................................................................................................... 48
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LIST OF TABLES Page
Table 1. Major population groups and independent populations within the Snake
River steelhead distinct population segment (DPS) and spring/summer Chinook Salmon evolutionary significant unit (ESU; ICBTRT 2003, 2005; Ford et al. 2010; NMFS 2011). ........................................................................... 26
Table 2. Abundance of Snake River spring-summer Chinook Salmon females available for natural reproduction (FANR), and the number of wild juveniles estimated at Lower Granite Dam by brood year and smolt year used to construct the Beverton-Holt stock-recruit model. ................................................ 28
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LIST OF FIGURES Figure 1. Genetic stocks and baseline collections used for steelhead mixed stock
analysis at Lower Granite Dam, spawn years 2009-2016 (Vu et al. 2015). The Hells Canyon Tributaries major population group (shaded gray) does not support independent populations and is considered extirpated (NMFS 2011). See text for genetic stock abbreviations. ................................................. 30
Figure 2. Genetic stocks and baseline collections used for Chinook Salmon mixed stock analysis at Lower Granite Dam, spawn years 2009-2016 (Vu et al. 2015). Reintroduced fish exist in functionally extirpated TRT populations as mapped. See text for genetic stock abbreviations. ............................................. 31
Figure 3. Schematic of the juvenile companion (SCRAPI) to the Salmonid Compositional Bootstrap Intervals (SCOBI) Lower Granite Dam decomposition model. ........................................................................................ 32
Figure 4. Wild steelhead, Chinook Salmon yearling, and Chinook Salmon subyearling juvenile emigrant estimates at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. Genetic sampling of Chinook Salmon subyearlings ceased after migration year 2014. ...................... 33
Figure 5. Wild juvenile steelhead emigration estimates by genetic stock at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .......................................................................................................................... 34
Figure 6. Wild juvenile steelhead emigration estimates by sex at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. ........................... 35
Figure 7. Wild juvenile steelhead emigration estimates by genetic stock and sex at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .............................................................................................................. 36
Figure 8. Wild juvenile steelhead emigration estimates by freshwater age at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .......................................................................................................................... 37
Figure 9. Wild juvenile steelhead emigration estimates by genetic stock and freshwater age at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. ....................................................................... 38
Figure 10. Wild steelhead juvenile per female parent productivity at Lower Granite Dam, brood years 2009-2010. Emigrating juvenile abundance and escapement of parent spawners were estimated at LGR. .................................. 39
Figure 11. Wild steelhead juvenile per female parent productivity by genetic stock at Lower Granite Dam, brood years 2009-2010. Emigrating juvenile abundance and escapement of parent spawners were estimated at LGR. ......... 40
Figure 12. Wild juvenile Chinook Salmon yearling emigration estimates by genetic stock at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .......................................................................................... 41
Figure 13. Wild juvenile Chinook Salmon yearling emigration estimates by at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .......................................................................................................................... 42
Figure 14. Wild juvenile Chinook Salmon yearling emigration by genetic stock and sex at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. ........................................................................................................ 43
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Figure 15. Wild juvenile Chinook Salmon subyearling emigration estimates by genetic stock at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .......................................................................................... 44
Figure 16. Wild juvenile Chinook Salmon yearling emigration estimates sex at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. .......................................................................................................................... 45
Figure 17. Wild juvenile Chinook Salmon yearling emigration estimates by genetic stock and sex at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. ....................................................................... 46
Figure 18. Comparison of observed wild yearling Chinook Salmon juvenile abundance at Lower Granite Dam and females available for natural reproduction for brood years 1990-2015. The Beverton-Holt stock-recruitment model’s prediction of juvenile production for brood years 2016 and 2017 were included for comparison. The shaded area represents the 95% confidence interval for the fitted Beverton-Holt model. ......................................................... 47
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LIST OF APPENDICES Page
Appendix A: Annual Lower Granite Dam juvenile trapping operations, 2010-2017. ................ 49 Appendix A-1. Daily number of unclipped (hatchery and wild combined) steelhead
juveniles trapped at the Lower Granite Dam juvenile fish facility while the trap was open March 26-July 31, migratory years 2010-2017. The expanded annual estimate of unclipped steelhead juveniles passing LGR is listed in each panel. Horizontal bar (gray) indicates when biological samples were not taken while the trap was open. .............................................. 50
Appendix A-2. Weekly Fish Passage Center trap collection and valid trap samples of juvenile steelhead at Lower Granite Dam, migration years 2010-2014. .............. 51
Appendix A-3. Daily number of unclipped Chinook Salmon yearling juveniles trapped at the Lower Granite Dam juvenile fish facility while the trap was open March 26-July 31, migratory years 2010-2017. Horizontal bar (gray) indicates when biological samples were not taken while the trap was open. ..................... 55
Appendix A-4. Weekly Fish Passage Center trap collection and valid trap samples of yearling Chinook Salmon at Lower Granite Dam, migration years 2010-2014. .................................................................................................................. 56
Appendix A-5. Daily number of unclipped Chinook Salmon subyearling juveniles trapped at the Lower Granite Dam juvenile fish facility while the trap was open March 26-July 31, migratory years 2010-2017. Horizontal bar (gray) indicates when biological samples were not taken while the trap was open. .......................................................................................................................... 60
Appendix A-6. Weekly Fish Passage Center trap collection and valid trap samples of subyearling Chinook Salmon at Lower Granite Dam, migration years 2010-2014. .................................................................................................................. 61
Appendix B: Wild steelhead juveniles at Lower Granite Dam, migratory years 2010-2017. .................................................................................................................. 63
Appendix B-1. Wild juvenile steelhead emigration estimates by sex for each genetic stock at Lower Granite Dam, spawn years 2010-2017. L = lower bound and U = upper bound of 90% confidence intervals. See text for stock abbreviations. .......................................................................................................................... 64
Appendix B-2. Genetic stock composition of wild steelhead juvenile emigrants at Lower Granite Dam, migration years 2010-2017. See text for genetic stock abbreviations. .................................................................................................... 68
Appendix C: Wild Chinook Salmon yearling juveniles at Lower Granite Dam, migratory years 2010-2017. ............................................................................................... 69
Appendix C-1. Wild juvenile Chinook Salmon yearling emigration estimates by sex for each genetic stock at Lower Granite Dam, spawn years 2010-2017. L = lower bound and U = upper bound of 90% confidence intervals. See text for stock abbreviations. ...................................................................................... 70
Appendix C-2. Genetic stock composition of wild Chinook Salmon yearling juvenile emigrants at Lower Granite Dam, migration years 2010-2017. See text for genetic stock abbreviations. ............................................................................... 73
Appendix D: Wild Chinook Salmon subyearling juveniles at Lower Granite Dam, migratory years 2010-2017. (March-July). .......................................................... 74
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Appendix D-1. Wild juvenile Chinook Salmon subyearling emigration estimates by sex for each genetic stock at Lower Granite Dam, spawn years 2010-2014. L = lower bound and U = upper bound of 90% confidence intervals. See text for stock abbreviations. ...................................................................................... 75
Appendix D-2. Genetic stock composition of wild Chinook Salmon subyearling juvenile emigrants at Lower Granite Dam, migration years 2010-2014. See text for genetic stock abbreviations. ............................................................................... 77
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ABSTRACT
This report summarizes the abundance, composition, and productivity of wild juvenile steelhead and yearling and subyearling Chinook Salmon emigrating past Lower Granite Dam, migratory years 2010-2017. We used systematic biological samples from the Juvenile Fish Facility to decompose each species by origin, age, sex, and genetic stock.
Steelhead juvenile emigration averaged 830,679 and ranged from 672,883 to 928,701
fish. Genetic stock abundance followed a general pattern with the Grande Ronde having the highest abundance (17-24%) in all years followed by the Upper Salmon (14-22%). The South Fork Salmon (2-7%) and Lower Salmon (2-5%) were the least abundant and the remaining genetic stocks (Middle Fork Salmon, Upper Clearwater, South Fork Clearwater, Lower Clearwater, Imnaha, and Lower Snake) fluctuated between years. Depending on the year, 4 to 5 freshwater age classes were observed with the vast majority of emigrants comprised of freshwater age-2 and age-3 fish. Genetic stock typically having large (≥78 cm) returning adults had higher proportions of freshwater age-3 juveniles than other genetic stocks. Sex ratios were female biased (54-67%) for the aggregate juvenile emigration. However, sex ratios by genetic stock were not biased towards either sex in most years, but were skewed towards female when biased. Juvenile per parent productivity was complete for the aggregate and individual genetic stocks for brood years 2009-2012 and ranged from 13-47 juveniles per parent. Brood year 2009 had the highest productivity and brood year 2011 had the lowest.
Yearling Chinook Salmon juvenile emigration averaged 1,202,436 and ranged from
601,722 to 1,712,102 fish. Genetic stock abundance followed a general pattern of the Hells Canyon having the highest abundance (32-40%) in all years, while Fall (0-2%) and Tucannon (0-1%) were the least abundant. The remaining genetic stocks (Upper Salmon, Middle Fork Salmon, South Fork Salmon, and Chamberlain) fluctuated in proportion between years. Sex ratios were female biased (54-56%) for the aggregate juvenile emigration in all years except 2011 and 2015. However, sex ratios were not statistically biased in most years for each genetic stock. Juvenile per parent productivity was estimated for brood years 2010-2015 and updated the time series starting in brood year 1990. The aggregate juvenile productivity ranged from 28-403 juveniles per female. The most recent complete brood years were on the lower end of the observed range. A Beverton-Holt stock-recruit model computed intrinsic productivity to be 405 juveniles per female and asymptotic production was 1.40 million natural juveniles.
Subyearling Chinook Salmon juvenile emigration was analyzed for migration years 2010-
2014. Emigration estimates for all subyearling Chinook averaged 2,376,104 fish and ranged from 1,324,581 to 3,390,860 fish. The Fall genetic stock comprised 87-97% of the overall emigration abundance of subyearling Chinook. Sex ratios were not biased towards either sex. Sampling for subyearling Chinook Salmon was discontinued after migration year 2014 due to the lack of the target spring/summer Chinook in the sample and logistics. Productivity was not calculated for subyearling Chinook Salmon.
This report is the second attempt at a stock assessment using genetic stock identification,
but the first using a hierarchical compositional analysis (SCRAPI) for natural juvenile emigration from the Snake River steelhead DPS and spring/summer Chinook ESU. Estimates within this report supersede previous estimates covering the same migration years. The ultimate goal of this program is to develop productivity relationships at various population levels. Data provided here are essential to understanding productivity and diversity of the emigrating life stages.
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Authors: Carlos A. Camacho, Fisheries Biologist Kristin K. Wright, Supervisory Fisheries Biologist John Powell, Fisheries Biologist 2 William C. Schrader, Principal Fisheries Biologist Timothy Copeland, Fisheries Program Coordinator Michael W. Ackerman, Fisheries Biologist 2 Marika E. Dobos, Fisheries Biologist Micah Davison, Supervisory Fisheries Biologist Matthew P. Corsi, Principal Fisheries Biologist Matthew R. Campbell, Fisheries Program Coordinator
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INTRODUCTION
Populations of steelhead trout Oncorhynchus mykiss and Chinook Salmon O. tshawytscha in the Snake River basin declined substantially following the construction of hydroelectric dams in the Snake and Columbia rivers. Raymond (1988) documented a decrease in survival of emigrating steelhead trout and Chinook Salmon from the Snake River following the construction of dams on the lower Snake River during the late 1960s and early 1970s. Abundance rebounded slightly in the early 1980s, but then adult escapement over Lower Granite Dam (LGR) into the Snake River basin declined again (Busby et al. 1996). In recent years, abundances in the Snake River basin have slightly increased. However, the increase has been dominated by hatchery fish, while returns of naturally produced steelhead and Chinook Salmon remain critically low.
Snake River steelhead were classified as threatened under the Endangered Species Act
(ESA) in 1997. Within the Snake River steelhead distinct population segment (DPS), there are six major population groups (MPGs): Lower Snake River, Grande Ronde River, Imnaha River, Clearwater River, Salmon River, and Hells Canyon Tributaries (Table 1; ICBTRT 2003, 2005; NMFS 2011). The Hells Canyon MPG is considered to be extirpated. Twenty-four extant demographically independent populations have been identified within the DPS.
Snake River spring/summer Chinook Salmon (hereafter Chinook Salmon) were classified
as threatened in 1992 under the ESA. Within the Snake River spring/summer Chinook Salmon evolutionarily significant unit (ESU), there are five major population groups: Lower Snake River, Grande Ronde/Imnaha rivers, South Fork Salmon River, Middle Fork Salmon River, and Upper Salmon River (Table 1). Twenty-nine extant demographically independent populations have been identified within the ESU.
Anadromous fish management programs in the Snake River basin include large-scale
hatchery programs intended to mitigate for the impacts of hydroelectric dam construction and operation and recovery planning and implementation efforts aimed at recovering ESA-listed wild steelhead and salmon stocks. The long-range goal of Idaho Department of Fish and Game’s anadromous fish program, consistent with basinwide mitigation and recovery programs, is to preserve Idaho’s salmon and steelhead runs and recover them to provide benefit to all users (IDFG 2013). Management to achieve these goals requires an understanding of how salmonid populations function as well as regular status assessments (McElhany et al. 2000). However, specific data on Snake River steelhead and Chinook Salmon populations are lacking, particularly key parameters such as population abundance, age composition, genetic diversity, recruits per spawner, and survival rates (ICBTRT 2003).
Idaho Department of Fish and Game provides research, monitoring, and evaluation of the
status of the state’s populations of anadromous salmon and steelhead. Recommendations for monitoring populations across the Columbia River basin include: 1) annual estimation of juvenile emigrant abundance across major populations, and 2) estimation of the adult-to-juvenile productivity of emigrants (Crawford and Rumsey 2011). These are two of several critical metrics necessary to assess overall trends in abundance and productivity within freshwater habitat.
The aggregate emigration of juveniles from Snake River steelhead and spring-summer
Chinook Salmon populations is measured at LGR, with the exception of the Tucannon River (Washington) populations. Some wild fish originate from Washington or Oregon tributaries, but the majority are from Idaho. Age, sex, and genetic stock composition data obtained at the LGR juvenile fish trap facilitates estimation of productivity and survival metrics that are important for monitoring recovery of wild fish for both species. Productivity is the generational replacement rate,
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defined as the number of progeny surviving to adulthood per parent (i.e. recruits per spawner). Conversion of progeny to adulthood (smolt-to-adult return rate [SAR]) is an important survival metric to monitor. Estimates of wild juvenile abundance and composition will be combined with similar information for adults (e.g. Schrader et al. 2012 and Camacho et al. 2017), enabling us to estimate adult-to-juvenile, juvenile-to-adult, and survival metrics (e.g., SARs) for each component of the aggregate populations sampled at LGR.
This report focuses on four main objectives to summarize wild steelhead and
spring/summer Chinook Salmon emigrating past LGR during migratory years (MY) 2010 through 2017. The first objective is to estimate wild steelhead and spring/summer Chinook Salmon juvenile abundance for the aggregate and stocks passing Lower Granite Dam. The second objective is to estimate freshwater productivity rates through brood year cohort analysis for each stock. The third objective is to estimate juvenile production potential using juvenile-to-female stock recruitment models for the aggregate and stocks. The fourth objective is to evaluate spring-summer Chinook Salmon subyearlings crossing LGR since some spring/summer Chinook populations produce subyearling juveniles and contribute to overall ESU juvenile production (Connor et al. 2001; Copeland and Venditti 2009).
Because of the collaborative nature of the work at LGR, this report is a product of several
Bonneville Power Administration (BPA) projects: Idaho Steelhead Monitoring and Evaluation Studies (1990-055-00), Idaho Natural Production Monitoring and Evaluation Program (1991-073-00), Chinook and Steelhead Genotyping for Genetic Stock Identification at Lower Granite Dam (2010-026-00), Parental Based Tagging (2010-031-00), and Smolt Monitoring Project (1987-127-00).
METHODS
Juvenile Trap Operations at Lower Granite Dam
Samples of steelhead and Chinook Salmon juveniles passing LGR were collected during daily operation of the Juvenile Fish Facility by Washington Department of Fish and Wildlife (WDFW; DeHart 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017). The juvenile trap is located on the LGR juvenile bypass system. The trap captures a systematic sample of fish passing through the Juvenile Fish Facility by operating two trap gates according to a predetermined sample rate. The sample rate determines how long the trap gates remain open, up to six times per hour. The trap is operational 24 hours per day and fish are processed every morning. Additional details on the juvenile trap can be found in Mensik et al. (2010). Sample rate is predetermined daily to collect 250-750 fish per day (all species combined) and is based on the expected number of fish entrained in the bypass system that day.
Standard methods were used by WDFW and Idaho Department of Fish and Game (IDFG)
staff to process juvenile fish (see Mensik et al. 2010). All fish captured were anesthetized; identified to species; examined for external marks, tags, and injuries; and scanned for an internal coded wire tag (CWT) or passive integrated transponder (PIT) tag. All fish were classified by origin (wild or hatchery) and the presence (hereafter unclipped) or absence (hereafter clipped) of the adipose fin. Wild fish have an unclipped adipose fin. The majority of hatchery-origin steelhead and Chinook Salmon have a clipped adipose fin, some are released with an unclipped adipose fin for population supplementation purposes. For unclipped steelhead, hatchery origin was additionally determined by the presence of dorsal or ventral fin erosion, which is assumed to occur only in hatchery-reared fish (Latremouille 2003). Captured fish determined to be potentially wild
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were subsampled for tissue (both species) and scales (steelhead only). The trap sample was sorted and processed by WDFW personnel and the subsample passed to IDFG technicians. Fish bearing PIT tags and/or diseased or injured fish were omitted from the subsample, as were Chinook deemed to be yearling fall Chinook based on external morphology (Tiffan et al. 2000). Target sample sizes were 2,000 per species for steelhead and yearling Chinook and 500 subyearling Chinook for the trapping season. All subsampled fish were measured for fork length (FL, to the nearest millimeter). After processing, all fish were returned to the bypass system to resume downstream migration.
Scale samples were taken from steelhead juveniles above the lateral line and posterior to
the dorsal fin. Scales were stored in coin envelopes for transport to the IDFG ageing laboratory in Nampa, Idaho. Tissue samples were taken from a small clip of the caudal fin from both species. Tissues were stored in a vial with 200-proof non-denatured ethyl alcohol or on Whatman sheets for transport to the IDFG Eagle Fish Genetics Laboratory (EFGL) in Eagle, Idaho.
Scale and Tissue (Genetic) Processing and Analysis
Technicians processed scale samples in the IDFG Nampa Research Anadromous Ageing Laboratory according to protocols detailed in Wright et al. (2015). Briefly, 6-10 non-regenerated scales were cleaned and mounted between two glass microscope slides. The best scales were imaged at 40x magnification on a computer video monitor using a Leica DM4000B microscope and a Leica DC500 digital camera. Two technicians independently viewed each image to assign ages without reference to fish length. If there was no age consensus among the readers, a third reader viewed the image and all readers collectively examined the image to resolve their differences before a final age was assigned. If a consensus age was not attained, the sample was excluded from further analysis. In this report, total age equals freshwater age, so we do not use the ageing designations developed for anadromous salmonids which also account for time spent in the ocean and instead report age as an integer.
Detailed methods for extraction of genomic DNA from tissue samples, DNA amplification,
and SNP genotyping are described in Vu et al. (2015). For MY2009-2015, all individuals from both species were genotyped using 95 to 191 single-nucleotide polymorphisms (SNPs) and a sex-specific genetic assay. The 191 steelhead SNPs include three SNPs used to identify putative O. mykiss x O. clarkii hybrids. SNP amplification was performed using Fluidigm 96.96 Dynamic Array IFCs (chips). Chips were imaged on a Fluidigm EP1TM system and analyzed and scored using the Fluidigm SNP Genotyping Analysis Software. Samples were processed at either the EFGL, or the Columbia River Inter-Tribal Fish Commission’s genetics laboratory in Hagerman, Idaho (BPA project 2010-026-00).
For MY2016-2017, the EFGL transitioned to a new type of SNP genotyping method
referred to as “Genotyping-in-Thousands by sequencing” (GT-seq). This methodology was developed by scientists at the Columbia River Inter-Tribal Fish Commission and involves the genotyping of SNP genetic markers panels (50-500) on thousands of pooled individuals at a time (Campbell et al. 2015). These pooled reactions are made possible through the use of sample-specific barcodes that are incorporated during the polymerase chain reaction (PCR) process. Following PCR, SNP amplicons are sequenced on an Illumina HiSeq DNA sequencer. Detailed methods for GT-seq library preparation and genotyping are provided in Campbell et al. (2015). The transition to this new genotyping method has allowed the EFGL to incorporate additional SNP markers in GSI panels for both species, at reduced consumable costs. For steelhead, 77 additional SNP markers were added to the panel, bringing the total to 268. For Chinook Salmon, 106 additional SNP markers were added to the panel, bringing the total to 298.
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Since 2008, fin tissue has been sampled from nearly all adult steelhead and spring-
summer Chinook Salmon broodstock spawned at Snake River hatcheries in Idaho, Oregon, and Washington (Steele et al. 2016). For steelhead in 2008, some Dworshak Hatchery early-arriving broodstock, most Lyons Ferry Hatchery broodstock, and all Oregon hatcheries broodstock were not sampled. The PBT project genotypes the broodstock tissue samples at 95 SNPs (within the 191 described above for both species) and creates a parental database of the SNP genotypes. The genotyping of broodstock essentially “tags” all steelhead and spring-summer Chinook Salmon juveniles released in the Snake River basin. This allows researchers to identify the origin and total age (brood year) of their offspring using parentage analysis (Steele et al. 2013). Beginning in MY2012, parentage analysis was conducted on juveniles captured and sampled at the LGR trap using a parental database of broodstock spawned from 2008 onward to identify hatchery fish that were phenotypically wild. Parentage assignment using SNP genotypes was performed using the program SNPPIT (Anderson 2010a).
Genetic stock identification is a form of mixed stock analysis that uses genetic data to
estimate the stock of origin of individuals (or groups of individuals). Individual assignment (IA) uses allele frequency estimates from baseline populations as reference information to characterize potentially contributing stocks. Individual assignment methods assign each individual to the stock in which the probability of its genotype occurring is the greatest. The proportion of a particular stock can then be estimated by summing all of the individual assignments to that stock and dividing by the total sample size.
For both GSI methods, a genetic baseline is first established by sampling fish from discrete
“reference” populations (i.e. wild Snake River spawning aggregations) that potentially contribute to the mixed population (i.e. aggregate wild escapement at LGR). Fish captured at LGR were genotyped and assigned wholly (IA) back to their stock of origin (Pella and Milner 1987, Shaklee et al. 1999). Vu et al. (2015) provide a detailed description of the Snake River genetic baselines used for both steelhead and Chinook Salmon GSI analyses (also see Figures 1 and 2). Snake River stocks used for IA at LGR were defined by Ackerman et al. (2012). Reporting groups (referred to here as genetic stocks) are assemblages of reference (baseline) populations grouped primarily by genetic and geographic similarities and secondarily by political boundaries and management units (Ackerman et al. 2012).
Assignment of individuals to genetic stocks from MY2010-2016 was accomplished using
the program gsi_sim (Anderson et al. 2008, Anderson 2010b) by first calculating the probability that an individual’s genotype was produced from each of the populations in the baseline. This probability is estimated by scaling the multinomial likelihood of the observed genotype arising from each population (calculated following Paetkau et al. [1995] as reported in Satterthwaite et al. 2014) to sum to one. Beginning in MY2017 individuals were assigned to genetic stocks using the maximum likelihood estimation method of Smouse et al. (1990) as implemented in the program gsi_sim (Anderson et al. 2008, Anderson 2010b). The probability of membership to each population is summed within reporting units (allocate-sum procedure; Wood et al. 1987), and an individual’s genetic stock is assigned as the reporting unit with the maximum probability of membership.
Ten wild steelhead genetic stocks were used. The genetic stocks include: 1) UPSALM:
upper Salmon River (including North Fork Salmon River and upstream); 2) MFSALM: Middle Fork Salmon River (including Chamberlain and Bargamin creeks); 3) SFSALM: South Fork Salmon River; 4) LOSALM: Little Salmon River and tributaries of the lower Salmon River; 5) UPCLWR: upper Clearwater River (Lochsa and Selway rivers); 6) SFCLWR: South Fork Clearwater River
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(including Clear Creek); 7) LOCLWR: lower Clearwater River; 8) IMNAHA: Imnaha River; 9) GRROND: Grande Ronde River; and 10) LSNAKE: tributaries of the lower Snake River both above (e.g., Alpowa and Asotin creeks) and below (primarily Tucannon River) LGR. Some Tucannon River steelhead ascend LGR dam and either stay upriver to spawn or fall back and spawn downriver. Results from some genetic stocks are aggregated to report by Snake River steelhead MPGs (Table 1).
Seven wild Chinook Salmon genetic stocks were used. The genetic stocks include: 1)
UPSALM: upper Salmon River (including North Fork Salmon River and upstream); 2) MFSALM: Middle Fork Salmon River; 3) CHMBLN: Chamberlain Creek; 4) SFSALM: South Fork Salmon River; 5) HELLSC: Hells Canyon stock, an aggregate genetic stock that includes the Clearwater, Little Salmon, lower Salmon, Grande Ronde, Imnaha, and lower Snake rivers; 6) TUCANO: Tucannon River; and 7) FALL: Snake River fall Chinook Salmon. Chinook Salmon populations in TUCANO can be distinguished from HELLSC in GSI analyses because they exhibit low levels of introgression with fall Chinook Salmon (Narum et al. 2010). The TUCANO genetic stock was included in the baseline to represent fish that originated below LGR, but ascend the dam and either stay upriver to spawn or fall back and spawn downriver. Except for fall Chinook Salmon, these genetic stocks largely correspond to Snake River spring-summer Chinook Salmon MPGs (Table 1). The MFSALM and CHMBLN genetic stock results were aggregated to report the Middle Fork Salmon River MPG. Three collections of Snake River fall Chinook Salmon (Clearwater River, Nez Perce Tribal Hatchery, and Lyons Ferry Hatchery) were included in the baseline to distinguish fall Chinook Salmon trapped prior to August 18 from spring-summer Chinook Salmon using genetic data (Ackerman et al. 2014).
The resolution of the Snake River genetic baselines used to perform IA analyses was
evaluated in Vu et al. (2015) as part of BPA project 2010-026-00. The GSI project will continue to update the genetic baselines periodically in an effort to improve resolution. Further, the GSI project will continue to develop methods and evaluate available tools to assess and improve the accuracy and precision of genetic stock proportion and abundance estimates in the future; these efforts will be reported in the annual progress reports for BPA project 2010-026-00.
The accuracy of the sex-specific genetic assays is evaluated in Steele et al. (2016). Sex
was not and generally cannot be reliably determined by personnel at the LGR trap; thus, a direct comparison was not attempted. Campbell et al. (2012) and references therein describe in more detail the methods of sex-determination using genetic assays.
Emigration by Origin, Age, Sex, and Genetic Stock
Estimates were made using methods modified from Steinhorst et al. (2017). Those methods were modified to incorporate a hierarchical structure (Figure 3) and to allow variance in the initial point estimate. Juvenile emigration abundance was estimated using 1) daily counts of putative wild juveniles collected in the LGR juvenile fish trap, 2) the trap sample rate (the proportion of time the trap is open), and 3) estimated daily collection efficiencies (probability of entrainment in the juvenile bypass system at the dam). The daily counts of all steelhead and Chinook juveniles at LGR during March-July as well as the trap sample rates are obtained from the FPC. DeHart (2015) describes juvenile collection procedures at LGR (also see http://www.fpc.org/documents/metadata/FPC_SMP_Metadata.html). The estimated daily juvenile collection (guidance) efficiencies are obtained from the NWFSC (Steve Smith, personal communication). Collection efficiencies for steelhead, yearling Chinook, and subyearling Chinook are estimated using procedures detailed in Sandford and Smith (2002).
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First, an estimate of the total number of ad-intact juveniles emigrating past LGR is found by expanding the number of ad-intact juveniles caught in the trap each day by the probability that a juvenile is sampled. The total number of ad-intact juveniles are estimated as:
𝑁𝑁𝑠𝑠 = ∑ 𝑐𝑐𝑠𝑠𝑠𝑠
𝑡𝑡𝑠𝑠 × 𝑒𝑒𝑠𝑠𝑠𝑠𝐷𝐷𝑑𝑑=1 = ∑ 𝑁𝑁𝑠𝑠𝑑𝑑𝐷𝐷
𝑑𝑑=1 where s is species (steelhead, yearling Chinook), Ns is abundance by species, d is day of the year, csd is the daily count in the trap by species, td is the daily trapping rate, and esd is the estimated daily collection efficiency for each species. Total abundance for each species during a migratory year (MY) is then estimated from the sums of daily estimates beginning at initiation of trapping until the date of last recorded passage. Note that the realized population sampling rate is the product of td and esd and changes almost daily.
The total estimate of unmarked, untagged juveniles must then be decomposed into estimates of W and ad-intact hatchery (HNC) juveniles. The current sampling protocol at the juvenile fish facility currently excludes steelhead juveniles with eroded dorsal fins, so abundance estimates of steelhead HNC juveniles represents only a partial estimate. The estimate of W (or HNC) juveniles is calculated by grouping the daily passage estimates, Nsd and the daily juvenile data into strata consisting of T “statistical weeks” formed by combining adjacent weeks (if needed to obtain sufficient sample sizes). The weekly juvenile estimates are (𝑁𝑁𝑠𝑠1,𝑁𝑁𝑠𝑠2, … ,𝑁𝑁𝑠𝑠𝑠𝑠) where 𝑁𝑁𝑠𝑠𝑡𝑡 = ∑ 𝑁𝑁𝑠𝑠𝑑𝑑𝑑𝑑∈𝑡𝑡 .
The rearing data are
𝑅𝑅𝑠𝑠1,𝑅𝑅𝑠𝑠2, … ,𝑅𝑅𝑠𝑠𝑠𝑠 𝑡𝑡1𝑒𝑒𝑠𝑠1, 𝑡𝑡2𝑒𝑒𝑠𝑠2, … , 𝑡𝑡𝑠𝑠𝑒𝑒𝑠𝑠𝑠𝑠
where 𝑅𝑅𝑠𝑠𝑠𝑠 is HNC or W and 𝑀𝑀 is the number of juveniles trapped. Note that 𝑒𝑒𝑠𝑠𝑠𝑠 = 𝑒𝑒𝑠𝑠𝑑𝑑 for all juveniles trapped on day d. We divide the data into strata and obtain the expanded number of HNC and W juveniles by dividing each juvenile by its probability being sampled, 𝑡𝑡𝑠𝑠𝑒𝑒𝑠𝑠𝑠𝑠. For stratum t, the expanded number of juveniles that are HNC and W for stratum t is
𝑓𝑓𝑠𝑠𝑠𝑠𝑡𝑡 = �𝛿𝛿𝑠𝑠𝑠𝑠𝑡𝑡𝑠𝑠𝑒𝑒𝑠𝑠𝑠𝑠𝑠𝑠∈𝑡𝑡
where 𝛿𝛿𝑠𝑠𝑠𝑠 is 1 if R is HNC (or W) and 0 otherwise giving the table Rear/Stratum 1 2 … T HNC
1sHNCf 2sHNCf … sHNCTf #HNC
W 1sWf 2sWf
sWTf #W Column total #1 #2 … #T #
Dividing by the marginal column totals, #1, #2,…,#T yields proportions HNC and W by strata, 𝑝𝑝𝑠𝑠𝑠𝑠𝑡𝑡. If we want a pooled proportion, then we would use #HNC/# and #W/#.
We then multiply the proportion W (or HNC) for each stratum by the stratum estimate of total juveniles, 𝑁𝑁𝑠𝑠𝑡𝑡 and sum over strata yielding,
𝑊𝑊 = ∑ 𝑝𝑝𝑠𝑠𝑠𝑠𝑡𝑡𝑁𝑁𝑠𝑠𝑡𝑡 𝑠𝑠𝑡𝑡=1 and 𝐻𝐻𝑁𝑁𝐻𝐻 = ∑ 𝑝𝑝𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑡𝑡𝑠𝑠
𝑡𝑡=1 𝑁𝑁𝑠𝑠𝑡𝑡
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This method to estimate total W and HNC emigration is preferred if proportion W (or HNC) varies over the season AND there are sufficient samples to get stable estimates of proportion W (or HNC) by strata. Otherwise, one might use the pooled estimates of proportion W and HNC.
The wild emigration estimates by strata (𝑊𝑊𝑠𝑠1,𝑊𝑊𝑠𝑠2, … ,𝑊𝑊𝑠𝑠𝑠𝑠) are then decomposed by a PRIMARY category (usually genetic stock). Genetic stock proportions may change throughout the trapping season, but we assume the genetic stock proportions are roughly equal within a stratum. We would like to know the true proportions, e.g., of genetic stocks A to E (𝜋𝜋𝐴𝐴𝑡𝑡 ,𝜋𝜋𝐵𝐵𝑡𝑡 ,𝜋𝜋𝑠𝑠𝑡𝑡,𝜋𝜋𝐷𝐷𝑡𝑡,𝜋𝜋𝐸𝐸𝑡𝑡) for t = 1,…,T where T is the number of strata. If all wild trapped juveniles were analyzed, then we could compute estimates of the proportions (𝜋𝜋𝐴𝐴𝑡𝑡 ,𝜋𝜋𝐵𝐵𝑡𝑡,𝜋𝜋𝑠𝑠𝑡𝑡,𝜋𝜋𝐷𝐷𝑡𝑡,𝜋𝜋𝐸𝐸𝑡𝑡) as above for W and HNC. However, not all trapped juveniles are analyzed every day so the realized capture rate of a group of fish analyzed on day d is 𝑡𝑡𝑑𝑑 × 𝑒𝑒𝑠𝑠𝑑𝑑 × 𝑎𝑎𝑠𝑠𝑠𝑠
𝑐𝑐𝑠𝑠𝑠𝑠 where 𝑎𝑎𝑠𝑠𝑑𝑑 is the number of
juveniles analyzed on day d. If 𝑎𝑎𝑗𝑗𝑑𝑑 is the number of wild fish of group j on day d, we get an estimate of the number of wild juveniles of each group for stratum t as
𝐴𝐴𝑗𝑗𝑡𝑡 = �𝑎𝑎𝑗𝑗𝑑𝑑
𝑡𝑡𝑑𝑑 × 𝑒𝑒𝑠𝑠𝑑𝑑 × 𝑎𝑎𝑑𝑑𝑐𝑐𝑠𝑠𝑑𝑑= � �
𝛿𝛿𝑗𝑗𝑡𝑡𝑠𝑠 × 𝑒𝑒𝑠𝑠𝑠𝑠 × 𝑎𝑎𝑑𝑑𝑐𝑐𝑠𝑠𝑑𝑑𝑓𝑓𝑓𝑓𝑠𝑠ℎ 𝑠𝑠 𝑜𝑜𝑜𝑜 𝑑𝑑𝑎𝑎𝑑𝑑 𝑑𝑑𝑑𝑑 𝑓𝑓𝑜𝑜 𝑡𝑡𝑑𝑑 𝑓𝑓𝑜𝑜 𝑡𝑡
where 𝛿𝛿𝑗𝑗 = 0 except for fish of group 𝐴𝐴𝑗𝑗 when 𝛿𝛿𝑗𝑗 = 1. Note that we assume that all fish analyzed on a given day have the same trapping rate and collection efficiency so 𝑡𝑡𝑠𝑠 × 𝑒𝑒𝑠𝑠𝑠𝑠 = 𝑡𝑡𝑑𝑑 × 𝑒𝑒𝑠𝑠𝑑𝑑 and the second summation on the right sums to 𝑎𝑎𝑗𝑗𝑑𝑑/(𝑡𝑡𝑑𝑑 × 𝑒𝑒𝑠𝑠𝑑𝑑 × 𝑎𝑎𝑠𝑠
𝑐𝑐𝑠𝑠𝑠𝑠) as indicated.
Except for the addition of the subsampling rate, 𝑎𝑎𝑠𝑠
𝑐𝑐𝑠𝑠𝑠𝑠, we obtain the expanded group by
stratum table as above for W and HNC, Group/Stratum 1 2 … T
A 11A 12A …
1TA B
21A 22A … 2TA
… … … … … E
51A 52A 5TA
Column total #1 #2 … #T Dividing by the column totals, we obtain estimates of the group proportions, 𝜋𝜋𝑗𝑗𝑡𝑡, by
stratum. The strata proportions are applied to the estimated total wild emigration estimate for each week. Summing over strata provides an estimate of the numbers of wild fish for each group (𝐴𝐴𝐴𝐴, … ,𝐴𝐴𝐸𝐸). Note that strata were formed to try to provide a minimum sample size of approximately 100 analyzed fish per stratum. If there is a SECONDARY classification variable, then the 𝐴𝐴𝑗𝑗𝑡𝑡 expanded frequency table above is 3-dimensional where 𝐴𝐴𝑓𝑓𝑗𝑗𝑡𝑡 is calculated as 𝐴𝐴𝑗𝑗𝑡𝑡 above with SECONDARY dimension i. For a given stratum and primary category, there may be no data for the secondary category. In this case the expanded frequencies are pooled over strata and a pooled estimate of the SECONDARY proportions is computed for each level of the PRIMARY variable.
Confidence intervals for all point estimates are computed using a bootstrapping algorithm
(Manly 1997). There are three sources of sampling error in the decomposed emigration estimates: variance in the estimated number of fish trapped each day, variance in the proportion wild by
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stratum, and variance in estimates of age, sex, and genetic stock proportions. To account for these sources of variability when estimating abundance by age, sex, and genetic stock, we use a compound bootstrap routine: a parametric bootstrap by stratum of the W/HNC data, and a weighted nonparametric bootstrap by stratum of the biological sample data (age, sex, and genetic stock). The number of juveniles trapped per day is considered a series of Bernoulli trials, where 𝑁𝑁𝑑𝑑 is the true number of juveniles passing the trap for day d and each juvenile is trapped with probability 𝑡𝑡𝑑𝑑𝑒𝑒𝑠𝑠𝑑𝑑. A bootstrap value of trap catch c for day d is generated by taking a random value from 𝑐𝑐𝑑𝑑∗~𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑎𝑎𝐵𝐵(𝑁𝑁𝑠𝑠𝑑𝑑 ,𝑝𝑝𝑠𝑠𝑑𝑑), where 𝑝𝑝𝑠𝑠𝑑𝑑 = 𝑡𝑡𝑑𝑑 × 𝑒𝑒𝑠𝑠𝑑𝑑 as above. Given a bootstrap value for the number of juveniles trapped on day d, we get a bootstrap value for the number of juveniles arriving for day d as 𝑁𝑁𝑠𝑠𝑑𝑑∗ = 𝑐𝑐𝑠𝑠𝑑𝑑∗ /𝑝𝑝𝑠𝑠𝑑𝑑. Summing over days in a stratum, we get a bootstrap value for the total number of juveniles emigrating during each stratum (𝑁𝑁𝑠𝑠1∗ ,𝑁𝑁𝑠𝑠2∗ , … ,𝑁𝑁𝑠𝑠𝑠𝑠∗ ). For each stratum, we get a bootstrap estimate of the proportion wild in the stratum using a weighted nonparametric bootstrap for the W/HNC data for that stratum with weights 𝑝𝑝𝑠𝑠 where k indexes fish in stratum t. Note that 𝑝𝑝𝑠𝑠 = 𝑝𝑝𝑑𝑑 for all trapped fish on day d. This leads to bootstrap values 𝑓𝑓𝑠𝑠𝑠𝑠𝑡𝑡
∗ and hence bootstrap proportion wild for each stratum. Multiplying these proportions by the bootstrap estimates of total juveniles emigrating during each stratum yields bootstrap estimates of number wild by stratum. Summing over strata we get bootstrap estimates of total wild. We produce many bootstrap values and order them, then the 100(1-α)% confidence interval is found by moving in α/2 proportion of the way from either extreme of the ordered bootstrap estimates.
Given the bootstrap values for total wild juveniles, we can get bootstrap values for the
numbers of juveniles of each age, sex, and genetic stock if we can get bootstrap values for the proportions of juveniles in each respective group for each stratum. We do this by producing bootstrap values 𝐴𝐴𝑗𝑗𝑡𝑡∗ (or 𝐴𝐴𝑓𝑓𝑗𝑗𝑡𝑡∗ ) via a weighted bootstrap of the age, sex, or genetic stock data for that stratum with weights 𝑡𝑡𝑠𝑠 × 𝑒𝑒𝑠𝑠𝑠𝑠 × 𝑎𝑎𝑠𝑠
𝑐𝑐𝑠𝑠𝑠𝑠. These are converted to proportions and bootstrap
estimates of numbers of wild fish by age or sex or genetic stock by stratum are found by multiplying these proportions by the bootstrap estimates of wild juveniles for the stratum. We then sum over strata to obtain season long bootstrap estimates of numbers of juveniles by age or sex or genetic stock. We conduct the compound bootstrap procedure 5,000 times. For each iteration, the numbers of wild fish of various ages, sex, or stock are computed. The one-at-a-time bootstrap intervals are found by finding the 5th and 95th percentiles of the 5,000 ordered bootstrap values for each group (i.e., α = 0.10). The algorithm was written and implemented in the R programming environment (R Development Core Team 2015) by Kirk Steinhorst (University of Idaho) and can be found within the SCOBI package (https://github.com/mackerman44/SCOBI) as the SCRAPI function.
Wild/natural Spring-Summer Chinook Salmon Stock-Recruitment Relationship in the Snake River Basin
Productivity was estimated using a stock-recruit model for the aggregate population of Chinook Salmon by relating the abundance of emigrating juveniles at LGR to the number of female parents available for natural reproduction. The number of Chinook Salmon females available for natural reproduction (FANR) upstream of LGR was estimated by subtracting harvest and broodstock removals from the adult count crossing LGR for the spring and summer run types. The estimated number of adults per run type (excluding jacks) passing LGR during 2014 was obtained directly from the Fish Passage Center (www.fpc.org, accessed March 2018). COE designates jack Chinook Salmon as fish between 30 and 56 cm in length at Columbia River dams counting windows. Adult Chinook Salmon that pass LGR between March 3 and June 17 are defined as “spring run,” and those passing LGR between June 18 and August 17 are defined as “summer run.” The total number of adult Chinook Salmon (excluding jacks) captured at hatchery
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traps and the number of females taken into hatcheries was obtained from the Pacific States Marine Fish Commission Fish Inventory System (FINS) hatchery database, Oregon Department of Fish & Wildlife (ODFW; Joseph Feldhaus, personal communication), and the Shoshone-Bannock Tribes (Jonathan Ebel, personal communication). McCall and Pahsimeroi hatchery fish were considered summer run and all other hatchery stocks were considered spring run. The percentage of females, by run type, was estimated for all adult Chinook Salmon identified to sex at hatchery weirs. The estimated percentage of females was applied to the aggregate LGD counts for each run type to estimate the total number of female Chinook Salmon passing LGD. The total harvest estimates upstream of LGD were obtained from IDFG (Chris Sullivan, personal communication). Female harvest was estimated by multiplying run-specific total harvest by the respective sex ratio. To estimate the FANR, the adjusted hatchery female number and the adjusted number of females harvested upstream of LGR were subtracted from the estimated number of females passing LGD. Spring and summer FANR estimates were combined to estimate total FANR.
A Beverton-Holt function was used to update the stock-recruitment model last published
in Stiefel et al. 2015 for BYs 1990-2014. Juvenile estimates generated from SCRAPI were used for BYs 2008-2017 and supersede previously reported estimates for BYs 2008-2012. The stock-recruitment model used the following formula (Beverton and Holt 1957).
𝑅𝑅 = 𝛼𝛼𝛼𝛼
1 + 𝛽𝛽𝛼𝛼
where 𝛼𝛼 = parent year spawning escapement (i.e. FANR), 𝑅𝑅 = recruits (juveniles) produced by parent year spawning escapement (𝛼𝛼), and 𝛼𝛼 and 𝛽𝛽 are fitted parameters. In this formulation, 𝛼𝛼 is the density independent parameter and β is a density-dependent parameter. The Beverton-Holt model assumes that juvenile mortality increases as juvenile competition for finite resources of food and space increases (Wootton 1990). The result is asymptotic maximum juvenile recruitment at high spawning stock abundances (Beverton and Holt 1957). Model parameters were estimated using the stock-recruitment scripts developed by Ogle (2016) implemented in the statistical program R (R Development Core Team 2015).
RESULTS
Steelhead Emigration
Migration year juvenile emigration estimates of unclipped steelhead ranged from 672,883 fish in 2015 to 928,701 fish in 2017 (Appendix A-1). Unclipped steelhead were captured on the first day of trapping in all MYs and the latest capture was on July 27, 2011. The range of valid fish captured for analysis ranged from 666 in MY2015 to 2,175 in MY2011 (Appendix A-2). Unclipped steelhead juveniles were predominantly wild fish consisting of 93-99%. Wild steelhead juvenile estimates ranged from 669,442 (624,364-716,896 90% C.I.) in 2015 to 911,602 (877,415-945,279 90% C.I.) in 2011 (Figure 4; Appendix B-1).
Wild Steelhead Stock, MPG, Sex, and Age Composition
Steelhead genetic stock abundance generally followed similar pattern from MY2010- 2017. The GRROND had the highest proportion of the overall emigration in all years followed by the UPSALM (Figure 5, Appendix B-2). The SFSALM and LOSALM were the least abundant in all years, except MY2015. The remaining genetic stocks (MFSALM, UPCLWR, SFCLWR,
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LOCLWR, IMNAHA, and LSNAKE) fluctuated in proportion among MYs. Within each genetic stock, abundances were not significantly different from the previous MY, except MY2017. MY2017 abundances for the GRROND and UPSALM were significantly higher than the previous year and the MFSALM, SFSALM, and LOSALM were significantly less than the previous year. Aggregating genetic stocks into MPGs resulted in the SALMON MPG having the highest proportion of the emigration in all MYs, except 2013. The CLRWTR MPG had the second highest proportion of the run, except 2013 when it had the highest. The IMNAHA and LSNAKE MPG varied from year to year for the MPG with the lowest proportion of the emigration.
Females were statistically more abundant than males throughout all MYs and ranged from
54% female in 2012 to 67% female in 2016 (Figure 6, Appendix B-1). Abundances by sex for each genetic stock were not statistically different in most years. However, when statistical differences were present, females were more abundant than males (Figure 7; Appendix B-1).
We observed a range of 4 to 5 different freshwater age classes from fish that we were able
to assign an age. For all MYs, freshwater age-2 wild steelhead were the most numerous migrating passed LGR, except in 2010, 2013, and 2015 (Figure 8). In general, genetics stocks were dominated by either freshwater age-2 or age-3 juveniles. Furthermore, the SFSALM, MFSALM, and UPCLWR were dominated by freshwater age-3 juveniles, while the rest were more likely to be dominated by freshwater age-2 juveniles (Figure 9).
For all wild steelhead emigrating passed LGR, juveniles per female parent productivity
was complete for brood years 2009-2012. Brood year 2009 produced 991,556 juveniles from 16,037 female parents resulting in the highest productivity estimate of 62 juveniles per female parent. Brood year 2011 produced 729,937 juveniles from 29,542 female parents resulting in the lowest productivity with 25 juveniles per female parent (Figure 10). For genetic stocks, BY2009 has the highest juvenile to female productivity and ranged from 78 juveniles per female parent for the IMNAHA to 51 juveniles per female parent for the MFSALM. The lowest juveniles per female parent productivity did not occur in the same brood year across all genetic stocks. The lowest productivity measured was 15 juveniles per female parent for BY2011 in the LSNAKE (Figure 11).
Chinook Salmon Yearling Emigration
Migration year juvenile emigration estimates of unclipped Chinook Salmon yearlings ranged from 601,722 in 2015 to 1,712,102 in 2012 (Appendix A-3). Unclipped Chinook Salmon yearlings were captured on the first day of trapping in all MYs, except 2010 when the first Chinook Salmon yearling was captured on the second day of trapping. Chinook Salmon yearlings are captured throughout the trapping season; however, <6% of all yearlings captured from MY2010-2017 were encountered during the months of June and July. The range of valid fish captured for analysis ranged from 612 in MY2015 to 2,768 in MY2012 (Appendix A-4). Wild fish consisted of 84-100% of the annual unclipped Chinook Salmon yearlings emigration over LGR. Wild Chinook Salmon yearling juvenile migration year estimates ranged from 533,570 (499,841-569,171 90% C.I.) in 2015 to 1,670,979 (1,627,599-1,715,732 90% C.I.) in 2012 (Figure 5; Appendix C-1).
Wild Chinook Salmon Yearling Stock, MPG, Sex, and Age Composition
Yearling Chinook Salmon genetic stock abundance generally followed similar patterns across migration years 2010-2017. The HELLSC had the highest proportion of the overall emigration in all years, while FALL and TUCANO were the least abundant (Figure 12, Appendix C-2). The remaining genetic stocks (UPSALM, MFSALM, SFSALM, and CHMBLN) fluctuated slightly in proportion among MYs. Within each genetic stock, significant increases in abundance
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from the previous MY were observed in 2012 and 2014 for the HELLSC, MFSALM, and SFSALM. Abundances in 2015 were significantly lower than all other MYs for the HELLSC, UPSALM, and MFSALM genetic stocks. Aggregating genetic stocks into MPGs resulted in similar patterns and trends as the genetic stocks.
Females were statistically more abundant than males throughout all MYs, except 2011
and 2015 when abundances were not significantly different (Figure 13, Appendix C-1). When more abundant, females consisted of 54% in 2013 to 56% in 2017 of the emigrating fish. Abundances by sex for each genetic stock were not statistically different in most years. However, when statistical differences were present, females were more abundant than males (Figure 14; Appendix C-1).
Chinook Salmon Subyearling Emigration
Sampling for subyearlings coincided with emigration timing from spring-summer Chinook Salmon stocks that have exhibited the subyearling emigrant life history (i.e. Pahsimeroi River). Estimates presented here are incomplete and will be dependent on when sampling occurred and run timing for subyearlings.
Migration year juvenile emigration estimates of unclipped Chinook Salmon subyearlings
ranged from 1,324,581 in MY2014 to 3,390,860 in MY2010 (Appendix A-5). The majority of unclipped Chinook Salmon subyearlings were captured in late May through the end of sampling. The earliest capture was on April 4, 2013. The range of valid fish captured for analysis ranged from 500 in MY2010 to 2,662 in MY2012 (Appendix A-6). Unclipped Chinook Salmon subyearling juveniles were predominantly wild fish consisting of 99-100%. Wild Chinook Salmon subyearling juvenile migration year estimates ranged from 1,320,539 (1,281,117-1,360,123 90% C.I.) in 2014 to 3,307,043 (3,307,043-3,476,774 90% C.I.) in 2010 (Figure 5; Appendix D-1).
Wild Chinook Salmon Subyearling Stock, MPG, Sex, and Age Composition
Chinook Salmon subyearling genetic stock abundance generally followed similar pattern from migration years 2010-2014. The FALL genetic stock comprised >87% of the overall emigration in all years followed by HELLSC with ≤9% (Figure 15, Appendix D-2). The remaining genetic stocks (UPSALM, CHMBLN, MFSALM, and SFSALM) were never >2% of the total emigration in any MY. The FALL genetic stock abundance was significantly different among all years. Abundance was highest in 2010 and lowest in 2014. Aggregating genetic stocks into MPGs resulted in similar patterns and trends as the genetic stocks.
The sex ratio was not biased to either sex in all MYs (Figure 16, Appendix D-1). Sex ratios
for each genetic stock were not biased to either sex (Figure 17; Appendix D-1). Small sample sizes for the CHMBLN, SFSALM, TUCANO, MFSALM, and SFSALM in some MYs resulted in abundances for only one sex.
Chinook Salmon Females Available for Natural Reproduction
Of the 119,831 hatchery and wild adult spring/summer Chinook Salmon counted at the LGR window in 2015, an estimated 62,868 (52%) were female. Angler harvest, including estimated incidental mortality, removed 19,529 females and hatchery take removed 7,401 females from above LGR. The remaining 35,938 female Chinook Salmon were available for natural reproduction upstream of LGR (Table 2).
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Of the 74,160 adult hatchery and wild adult spring/summer Chinook Salmon counted at the LGR window in 2016, an estimated 39,382 (53%) were female. Angler harvest, including estimated incidental mortality, removed 10,875 females and hatchery take removed 5,206 females from above LGR. The remaining 23,301 female Chinook Salmon were available for natural reproduction upstream of LGR (Table 2).
Of the 36,309 adult hatchery and wild adult spring/summer Chinook Salmon counted at
the LGR window in 2017, an estimated 17,643 (49%) were female. Angler harvest, including estimated incidental mortality, removed 2,875 females and hatchery take removed 6,277 females from above LGR. The remaining 8,491 female Chinook Salmon were available for natural reproduction upstream of LGR (Table 2).
Chinook Salmon Stock-Recruit Model
For BYs 1990-2015, estimated intrinsic productivity (𝛼𝛼) was 405 juveniles per female and asymptotic production was 1.40 million natural juveniles from the Snake River ESU (Figure 18). The estimated number of yearling juveniles emigrating from the Snake River ESU past LGR during juvenile migration year 2015 (brood year 2013) was 1,171,926 Chinook Salmon (Table 2). This completes the data set for BYs 1990-2013. The Beverton-Holt model predicted juvenile production as 1,235,216 for BY2016 and 1,025,553 for BY2017. These predicted values will be updated with observed values in subsequent reports.
DISCUSSION
This report is the second attempt at a stock assessment using genetic stock identification for natural juvenile emigration from the Snake River steelhead DPS and spring/summer Chinook ESU, exclusive of the Tucannon River. However, this is the first attempt using a hierarchical compositional analysis. Previous stock assessments were done either on the aggregate juvenile emigration without identifying the component units (e.g., Raymond 1979, 1988; Copeland et al. 2009) or without the implementation of PBT to distinguish unclipped hatchery from wild fish (e.g. Copeland et al. 2013). The ultimate goal of this program is to develop productivity relationships at the aggregate, genetic stock, and MPG level, requiring stock-specific information relative to sex and age. In the following discussion, we focus primarily on caveats for the interpretation of the present results.
In this analysis, it was assumed that the realized sampling rate adequately describes the
probability that any fish passing LGR can be sampled. There are three possible exceptions to this assumption. First, we assume there is no size bias in collection (i.e., larger juveniles are less likely to enter the juvenile by-pass system). Second, PIT-tagged fish were not sampled; therefore, populations in which there is a high rate of tagging may be underrepresented in the analysis. Lastly, yearling fall Chinook Salmon that were phenotypically distinguishable from spring-summer Chinook Salmon juveniles (Tiffan et al. 2000) were not biosampled at the trap for the first two years of sampling. Consequently, our estimates of the abundance of yearling fall Chinook are biased low for MY2010 and 2011. However, starting in MY2012 these fish were included in the subsample to be identified using GSI and for more accurate subsequent estimates.
Genetic and scale sampling did not completely coincide with the entirety of juvenile
trapping at the LGR Juvenile Fish Facility. Juvenile trapping typically starts on March 26 and ends on October 31. Genetic and scale sampling occurred from start of juvenile trapping to end of July. Previous PIT tag work showed that spring-summer Chinook Salmon subyearlings typically arrive
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at LGR prior to the end of July. Sampling from the start of juvenile trapping to the end of July ensured genetics and scales were taken from the vast majority of steelhead juveniles and spring-summer Chinook Salmon yearlings and subyearlings while reducing sampling of fall Chinook Salmon. Fall Chinook Salmon subyearling emigration is more protracted than spring-summer Chinook Salmon or steelhead and was not sampled for genetics and scales after July 31. Fall Chinook Salmon estimates presented in this report do not represent total emigration and caution should be used when interpreting the data.
Beginning in MY2010 for steelhead and MY2011 for Chinook Salmon, PBT was used to
further refine the total wild juvenile emigration estimate (Steele et al. 2012). PBT identifies unclipped hatchery fish that are otherwise indistinguishable from wild fish. Beginning with BY2009, all adult steelhead and spring-summer Chinook Salmon hatchery stocks in the Snake River basin were PBT-tagged (Steele et al. 2011). By incorporating the PBT analysis, hatchery reared juveniles were better identified and removed from the unclipped fish emigration analysis resulting in a more accurate estimate of wild fish.
The vast majority of Snake River stocks spawn above Lower Granite Dam with a notable
exception. The juvenile emigration estimates for both yearling and subyearling Chinook Salmon from the Tucannon River genetic stock are reported. These estimates represent the number of offspring that were produced from adults originating from the Tucannon River and successfully strayed and reproduced above LGR. Tucannon River populations demonstrate slight evidence for introgression (5.3%) with fall Chinook (Narum et al. 2010) and are generally highly identifiable (Ackerman et al. 2012). The estimates are not intended to be used to calculate productivity metrics for Tucannon River populations, given that the majority of this group spawns downstream from LGR. Similarly, some wild steelhead belonging to the LSNAKE (Lower Snake River) genetic stock spawn in Asotin Creek (61 rkm upstream from LGR) and many steelhead from the Tucannon River stray extensively above LGR (Bumgarner and Dedloff 2011). Accounting of production by steelhead and Chinook Salmon populations that do not spawn exclusively above LGR will be incomplete in this report but they must be identified for accurate assessment of the other Snake River stocks.
Steelhead and Chinook Salmon exhibit multiple life history strategies through a diversity
of age at emigration and sex based anadromy, which provides an added measure of resiliency when specific life stages or year classes endure abnormally high mortality (Quinn 2005; Copeland et al. 2017). Snake River steelhead juveniles within a year class can have a wide range of freshwater ages, which spreads a single cohort across many migration years. By spreading the potential risk across multiple environmental conditions, the likelihood of the entire cohort experiencing catastrophic climatic conditions is reduced. This strategy builds in a factor of resiliency when conditions are poor, but can inhibit fast population growth when conditions are optimal. Conversely, spring-summer Chinook Salmon cohorts typically emigrate in a one- to two-year time span and are more susceptible to adverse conditions, but are also more likely to respond to optimal conditions. Additionally, the sex of a fish can influence if a fish emigrates. Theory suggests females benefit from anadromy by attaining larger adult body size and higher fecundity, while males can successfully mature and reproductively compete in a non-anadromous form (Hendry et al. 2004); therefore, we may expect a female bias in emigrating juveniles. This appears true for steelhead juveniles and Chinook Salmon yearlings. The fitness of a particular life history strategy may vary with density, frequency of other types, and condition of individuals (Hendry et al. 2004), all of which change with time and location. Certainly, sex ratios from juveniles varies among stocks, which may be influenced by the habitats in which they rear. For example, some tributaries can produce more female emigrants than others even within the same watershed (Mills et al. 2012). Life history and demographic patterns in data presented here may suggest smaller-
16
scale investigations to determine causes and they also provide the means to test predictions at the emigrating juvenile stage, which is a convenient and meaningful stage to consider recruitment (Solomon 1985).
Emigrating juvenile recruitment is an important productivity metric useful for understanding
the governing processes during the freshwater life stages. The relationship between the parental abundance and the subsequent recruitment can describe the intrinsic productivity of a population, but varies widely among cohorts as measured by the number of juveniles per female, especially for Chinook Salmon. However, a pattern of density dependence has been observed in juvenile production of Snake River spring-summer Chinook Salmon that does not seem biologically intuitive given historical accounts of adult and juvenile abundances in the Snake River basin. Potential explanatory hypotheses include:
- a lack/reduction of marine derived nutrients from returning adult carcasses has
reduced the productivity of infertile spawning streams thus reducing juvenile carrying capacity (Naiman et al. 2002),
- current spawners home to relatively small patches of core spawning areas effectively maintaining localized high densities even in low spawner abundances (Thurow 2000; Isaak and Thurow 2006; Hamann and Kennedy 2012),
- introduced species and hatchery-produced fish compete with and prey on young wild salmon (Levin et al. 2002; Weber and Fausch 2003),
- naturally spawning hatchery fish do not spawn as effectively as natural fish, and strays or supplementation fish may increase localized density dependence (Fleming and Gross 1993),
- reduction of off-channel habitat in spawning and rearing areas (Pollock et al. 2004), - temperature stress related to global warming, and loss of tree cover via forest fires
and grazing raise water levels at critical times (Flebbe 1997; Schoennagel et al. 2005), - high adult escapements are coincidental with drought, but associated low stream flow
is critical to juvenile survival in the interior Columbia basin (Arthaud et al. 2010), - loss of life history diversity and local adaptations and temporal variation in movement
lead to a reduction in occupied habitat and regional productivity (Adkison 1995; Lichatowich and Mobrand 1995), and
- lack of historically high adult abundances to accurately assess the full range of juvenile production and true carrying capacity.
Accurately quantifying a juvenile steelhead cohort is difficult due to their life history
diversity. Observations of freshwater ages at juvenile emigration range up to 5 years in this study, but have been observed up to age 6 from adult scales (Camacho et al. 2017). Long freshwater rearing times elongate the timeframe to consider a juvenile estimate complete for a single brood year. Additionally, an unknown portion of steelhead residualize in their natal stream and are never quantified, which effectively reduces the estimate of progeny created from a single cohort. Furthermore, productivity is not only affected by the abundance of progeny, but also the abundance of parents available for reproduction. The analysis applied for steelhead did not account for naturally spawning hatchery fish in the estimate of the spawning stock, nor was the spawning stock reduced to only females. Both, unaccounted resident juveniles and unaccounted naturally spawning females, will affect the true stock-recruitment relationship. Future analysis will explore methods to reduce the spawning stock to the females available for natural reproduction similar to those applied to Chinook Salmon. Unfortunately, methods of assessing the rate of residualization for steelhead in the Snake River basin are lacking.
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In conclusion, the juvenile estimates reported here will be used to evaluate the abundance,
performance, and status of wild steelhead and spring-summer Chinook Salmon across the Snake River basin. Because of the vast area in which steelhead and salmon inhabit, it is not possible to sample all juvenile habitats. The work presented in this report is an attempt to measure juvenile emigration at a course scale and should be considered complimentary to the intensive small-scale rotary screw trap work (e.g. Apperson et al. 2017) occurring in the Snake River basin. In addition, the combination of juvenile compositional analyses at LGR and similar adult data (Camacho et al. 2017, 2018) provide opportunities to evaluate the status and productivity performance of particular reporting groups throughout the lifecycle. In the future, estimates within this report will be combined with other datasets to explore relationships leading to a better understanding of how Snake River steelhead and spring-summer Chinook Salmon populations function.
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wilderness: implications for persistence. Pages 143-151 In: McCool, S. F.; Cole, D. N.; Borrie, W. T.; O’Loughlin, J. Wilderness science in a time of change conference-Volume 3: Wilderness as a place for scientific inquiry, May 23-27, 1999, Missoula, MT. U.S. Forest Service, Proceedings, RMRS-P-15-VOL-3.
Tiffan, K. F., D. W. Rondorf, R. D. Garland, and P. A. Verhey. 2000. Identification of fall versus
spring Chinook Salmon migrating through the lower Snake River based on body morphology. Transactions of the American Fisheries Society 129:1389-1395.
Vu, N., M. W. Ackerman, K. K. Wright, J. McCane, M. R. Campbell, J. E. Hess, and S. R. Narum.
2015. Chinook and steelhead genotyping for genetic stock identification at Lower Granite Dam. Idaho Department of Fish and Game Report 15-02. Annual Report, BPA Project 2010-026-00.
Weber, E. D., and K. D. Fausch. 2003. Interactions between hatchery and wild salmonids in
streams: differences in biology and evidence for competition. Canadian Journal of Fisheries and Aquatic Sciences 60:1018-1036.
Wood, C. C., S. McKinnell, T. J. Mulligan, and D. A. Fournier. 1987. Stock identification with the
maximum likelihood method: sensitivity analysis and application to complex problems. Canadian Journal of Fisheries and Aquatic Sciences 44:866-881.
Wootton, R. J. 1990. Ecology of Teleost Fishes. Fish and Fisheries Series 1. Chapman and Hall.
24
Wright, K., W. Schrader, L. Reinhardt, K. Hernandez, C. Hohman, and T. Copeland. 2015. Process and methods for assigning ages to anadromous salmonids from scale samples. Idaho Department of Fish and Game Report 15-03. https://collaboration.idfg.idaho.gov/FisheriesTechnicalReports/Forms/AllItems.aspx.
25
TABLES
26
Table 1. Major population groups and independent populations within the Snake River steelhead distinct population segment (DPS) and spring/summer Chinook Salmon evolutionary significant unit (ESU; ICBTRT 2003, 2005; Ford et al. 2010; NMFS 2011).
Snake River steelhead DPS
Major population group Population name
Lower Snake River 1. Tucannon River 2. Asotin Creek
Grande Ronde River
3. Lower Grande Ronde River 4. Joseph Creek 5. Wallowa River 6. Upper Grande Ronde River
Imnaha River 7. Imnaha River
Clearwater River
8. Lower Clearwater River 9. North Fork Clearwater River (extirpated) 10. Lolo Creek 11. Lochsa River 12. Selway River 13. South Fork Clearwater River
Salmon River
14. Little Salmon River 15. Chamberlain Creek 16. South Fork Salmon River 17. Secesh River 18. Panther Creek 19. Lower Middle Fork Salmon River 20. Upper Middle Fork Salmon River 21. North Fork Salmon River 22. Lemhi River 23. Pahsimeroi River 24. East Fork Salmon River 25. Upper Salmon River
Hells Canyon Tributaries (extirpated)
27
Table 1. Continued.
Snake River spring-summer Chinook Salmon ESU Major population group Population name
Lower Snake River 1. Tucannon River 2. Asotin Creek (extirpated) a
Grande Ronde/Imnaha Rivers
3. Wenaha River 4. Lostine River 5. Minam River 6. Catherine Creek 7. Upper Grande Ronde River 8. Imnaha River 9. Big Sheep Creek (extirpated) a 10. Lookinglass Creek (extirpated) a
South Fork Salmon River
11. Little Salmon River 12. South Fork Salmon River 13. Secesh River 14. East Fork South Fork Salmon River
Middle Fork Salmon River
15. Chamberlain Creek 16. Lower Middle Fork Salmon River 17. Big Creek 18. Camas Creek 19. Loon Creek 20. Upper Middle Fork Salmon River 21. Sulphur Creek 22. Bear Valley Creek 23. Marsh Creek
Upper Salmon River
24. North Fork Salmon River 25. Lemhi River 26. Upper Salmon River Lower Mainstem 27. Pahsimeroi River 28. East Fork Salmon River 29. Yankee Fork Salmon River 30. Valley Creek 31. Upper Salmon River Upper Mainstem 32. Panther Creek (extirpated) a
Dry Clearwater River (extirpated) a
33. Potlatch River (extirpated) a 34. Lapwai Creek (extirpated) a 35. Lawyer Creek (extirpated) a 36. Upper South Fork Clearwater River (extirpated) a
Wet Clearwater River (extirpated) a
37. Lower North Fork Clearwater River (extirpated) 38. Upper North Fork Clearwater River (extirpated) 39. Lolo Creek (extirpated) a 40. Lochsa River (extirpated) a 41. Meadow Creek (extirpated) a 42. Moose Creek (extirpated) a 43. Upper Selway River (extirpated) a
a Reintroduced fish exist in extirpated areas except the North Fork Clearwater River.
28
Table 2. Abundance of Snake River spring-summer Chinook Salmon females available for natural reproduction (FANR), and the number of wild juveniles estimated at Lower Granite Dam by brood year and smolt year used to construct the Beverton-Holt stock-recruit model.
Brood Year Smolt Year FANR Juveniles Juveniles/Female
1990 1992 4,976 527,000 106 1991 1993 2,916 627,037 215 1992 1994 6,826 627,942 92 1993 1995 8,514 1,558,786 183 1994 1996 1,043 419,826 403 1995 1997 497 161,157 324 1996 1998 1,556 599,159 385 1997 1999 11,885 1,560,298 131 1998 2000 3,726 1,344,382 361 1999 2001 1,630 490,534 301 2000 2002 8,733 1,128,582 129 2001 2003 51,902 1,455,786 28 2002 2004 31,415 1,517,951 48 2003 2005 26,126 1,734,464 66 2004 2006 28,374 1,227,474 43 2005 2007 10,899 787,150 72 2006 2008 9,253 856,556 93 2007 2009 8,562 894,629 104 2008 2010 22,942 1,268,656 56 2009 2011 17,314 1,150,789 69 2010 2012 36,348 1,670,979 47 2011 2013 30,755 993,503 37 2012 2014 22,783 1,406,033 61 2013 2015 11,718 533,570 46 2014 2016 31,076 1,424,036 46 2015 2017 35,938 1,171,926 33 2016 2018 23,301 1,235,216* 53* 2017 2019 8,491 1,025,553* 121*
* Predicted values based on the Beverton-Holt model
29
FIGURES
30
Figure 1. Genetic stocks and baseline collections used for steelhead mixed stock analysis
at Lower Granite Dam, spawn years 2009-2016 (Vu et al. 2015). The Hells Canyon Tributaries major population group (shaded gray) does not support independent populations and is considered extirpated (NMFS 2011). See text for genetic stock abbreviations.
31
Figure 2. Genetic stocks and baseline collections used for Chinook Salmon mixed stock
analysis at Lower Granite Dam, spawn years 2009-2016 (Vu et al. 2015). Reintroduced fish exist in functionally extirpated TRT populations as mapped. See text for genetic stock abbreviations.
32
Figure 3. Schematic of the juvenile companion (SCRAPI) to the Salmonid Compositional
Bootstrap Intervals (SCOBI) Lower Granite Dam decomposition model.
33
Figure 4. Wild steelhead, Chinook Salmon yearling, and Chinook Salmon subyearling
juvenile emigrant estimates at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%. Genetic sampling of Chinook Salmon subyearlings ceased after migration year 2014.
34
Figure 5. Wild juvenile steelhead emigration estimates by genetic stock at Lower Granite
Dam, migration years 2010-2017. Confidence intervals are at 90%.
35
Figure 6. Wild juvenile steelhead emigration estimates by sex at Lower Granite Dam,
migration years 2010-2017. Confidence intervals are at 90%.
36
Figure 7. Wild juvenile steelhead emigration estimates by genetic stock and sex at Lower
Granite Dam, migration years 2010-2017. Confidence intervals are at 90%.
37
Figure 8. Wild juvenile steelhead emigration estimates by freshwater age at Lower Granite
Dam, migration years 2010-2017. Confidence intervals are at 90%.
38
Figure 9. Wild juvenile steelhead emigration estimates by genetic stock and freshwater age
at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%.
39
Figure 10. Wild steelhead juvenile per female parent productivity at Lower Granite Dam,
brood years 2009-2010. Emigrating juvenile abundance and escapement of parent spawners were estimated at LGR.
40
Figure 11. Wild steelhead juvenile per female parent productivity by genetic stock at Lower
Granite Dam, brood years 2009-2010. Emigrating juvenile abundance and escapement of parent spawners were estimated at LGR.
41
Figure 12. Wild juvenile Chinook Salmon yearling emigration estimates by genetic stock at
Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%.
42
Figure 13. Wild juvenile Chinook Salmon yearling emigration estimates by at Lower Granite
Dam, migration years 2010-2017. Confidence intervals are at 90%.
43
Figure 14. Wild juvenile Chinook Salmon yearling emigration by genetic stock and sex at
Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%.
44
Figure 15. Wild juvenile Chinook Salmon subyearling emigration estimates by genetic stock
at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%.
45
Figure 16. Wild juvenile Chinook Salmon yearling emigration estimates sex at Lower Granite
Dam, migration years 2010-2017. Confidence intervals are at 90%.
46
Figure 17. Wild juvenile Chinook Salmon yearling emigration estimates by genetic stock and
sex at Lower Granite Dam, migration years 2010-2017. Confidence intervals are at 90%.
47
Figure 18. Comparison of observed wild yearling Chinook Salmon juvenile abundance at
Lower Granite Dam and females available for natural reproduction for brood years 1990-2015. The Beverton-Holt stock-recruitment model’s prediction of juvenile production for brood years 2016 and 2017 were included for comparison. The shaded area represents the 95% confidence interval for the fitted Beverton-Holt model.
48
APPENDICES
49
Appendix A: Annual Lower Granite Dam juvenile trapping operations, 2010-2017.
50
Appendix A-1. Daily number of unclipped (hatchery and wild combined) steelhead juveniles trapped at the Lower Granite Dam juvenile fish facility while the trap was open March 26-July 31, migratory years 2010-2017. The expanded annual estimate of unclipped steelhead juveniles passing LGR is listed in each panel. Horizontal bar (gray) indicates when biological samples were not taken while the trap was open.
Total Unclipped: 912,390
Total Unclipped: 927,191
Total Unclipped: 924,791
Total Unclipped: 813,437
Total Unclipped: 824,303
Total Unclipped: 672,883
Total Unclipped: 827,082
Total Unclipped: 928,701
51
Appendix A-2. Weekly Fish Passage Center trap collection and valid trap samples of juvenile steelhead at Lower Granite Dam, migration years 2010-2014.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis Total Fish Trapped
Wild Fish Trapped
Genetic Stock Sex Age
Migration year 2010
1 13 - 18a 3/27 - 5/2b 37 330,437 319,094 197 191 180 170 176 2 19 5/3 - 5/9 7 118,033 111,142 141 133 133 123 132 3 20 5/10 - 5/16 7 92,372 86,717 176 165 164 160 163 4 21 5/17 - 5/23 7 207,698 190,307 355 326 316 292 310 5 22 5/24 - 5/30 7 71,171 61,067 188 165 164 163 162 6 23 5/31 - 6/6 7 68,817 62,228 179 163 161 153 156 7 24 - 31a 6/7 - 7/31b 55 23,862 20,926 129 117 115 111 110
Total: 127 912,390 851,481 1,365 1,260 1,233 1,172 1,209
Migration year 2011
1 13 - 14a 3/26 - 4/3b 9 20,393 19,163 114 110 108 100 102 2 15 4/4 - 4/10 7 55,732 54,923 149 147 146 138 144 3 16 - 17a 4/11 - 4/24 14 71,987 71,054 190 186 182 176 177 4 18 4/25 - 5/1 7 47,395 46,101 111 108 108 104 106 5 19 5/2 - 5/8 7 72,825 70,037 152 146 146 139 143 6 20 5/9 - 5/15 7 322,885 319,059 470 464 462 441 449 7 21 5/16 - 5/22 7 175,379 174,374 316 314 307 268 304 8 22 5/23 - 5/29 7 88,343 85,739 311 302 302 295 298 9 23 5/30 - 6/5 7 43,473 43,201 150 149 148 135 146
10 24 - 31a 6/6 - 7/31 56 28,779 27,950 212 206 206 192 202 Total: 128 927,191 911,601 2,175 2,132 2,115 1,988 2,071
52
Appendix A-2 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis Total Fish Trapped
Wild Fish Trapped
Genetic Stock Sex Age
Migration year 2012
1 14 - 15a 3/26 - 4/8 14 36,806 36,806 208 208 206 206 193 2 16 - 17a 4/9 - 4/22 14 87,316 83,870 121 111 110 109 105 3 18 - 19a 4/23 - 5/6 14 430,550 419,704 191 186 183 172 171 4 20 5/7 - 5/13 7 109,740 106,065 126 121 121 121 114 5 21 5/14 - 5/20 7 161,541 152,904 237 224 224 224 214 6 22 5/21 - 5/27 7 72,080 65,241 240 213 210 207 204 7 23 - 32a 5/28 - 7/31b 65 26,759 26,075 151 147 146 146 144
Total: 128 924,792 890,665 1,274 1,210 1,200 1,185 1,145
Migration year 2013
1 13 - 15a 3/26 - 4/14b 20 60,053 60,053 156 156 156 156 155 2 16 - 17a 4/15 - 4/28 14 72,884 64,031 115 101 100 99 96 3 18 4/29 - 5/5 7 147,202 143,600 137 134 134 133 126 4 19 5/6 - 5/12 7 243,239 240,583 258 255 255 252 246 5 20 5/13 - 5/19 7 213,067 208,831 493 483 483 481 478 6 21 5/20 - 5/26 7 35,042 34,385 223 219 217 216 208 7 22 5/27 - 6/2 7 25,148 24,320 304 295 295 294 286 8 23 - 31a 6/3 - 7/31b 59 16,802 16,234 121 116 115 115 107
Total: 128 813,437 792,037 1,807 1,759 1,755 1,746 1,702
53
Appendix A-2 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis Total Fish Trapped
Wild Fish Trapped
Genetic Stock Sex Age
Migration year 2014
1 13 - 14a 3/26 - 4/6b 12 18,462 18,325 191 189 187 186 176 2 15 - 16a 4/7 - 4/20 14 85,908 85,908 164 164 164 163 159 3 17 - 18a 4/21 - 5/4 14 209,102 205,983 211 208 208 207 193 4 19 5/5 - 5/11 7 205,346 205,346 318 318 316 313 292 5 20 - 21a 5/12 - 5/25 14 240,970 236,812 332 326 326 317 304 6 22 - 31a 5/26 - 7/31b 67 64,516 63,845 171 170 170 164 161
Total: 128 824,304 816,219 1,387 1,375 1,371 1,350 1,285
Migration year 2015
1 13 - 16a 3/26 - 4/19b 25 50,901 48,887 118 117 115 114 103 2 17 - 19a 4/20 - 5/10 21 304,821 304,820 137 137 134 134 124 3 20 5/11 - 5/17 7 100,152 100,152 87 87 84 84 83 4 21 5/18 - 5/24 7 144,031 143,746 200 199 197 197 185 5 22 - 31a 5/25 - 7/31b 68 72,979 71,837 124 122 119 119 113
Total: 128 672,884 669,442 666 662 649 648 608
Migration year 2016
1 13 - 16a 3/26 - 4/17b 23 154,985 148,921 299 288 283 283 272
2 17 4/18 - 4/24 7 128,366 127,338 116 115 115 115 115 3 18 4/25 - 5/1 7 125,413 121,494 244 237 235 235 231 4 19 5/2 - 5/8 7 141,835 135,466 91 87 86 86 85 5 20 5/9 - 5/15 7 159,071 157,787 229 227 224 224 223 6 21 5/16 - 5/22 7 58,539 57,271 110 108 108 108 105 7 22 - 31a 5/23 - 7/31 70 58,873 57,155 153 149 149 149 142
Total: 128 827,082 805,432 1,242 1,211 1,200 1,200 1,173
54
Appendix A-2 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis Total Fish Trapped
Wild Fish Trapped
Genetic Stock Sex Age
Migration year 2017
1 13 - 16a 3/26 - 4/16b 22 82,860 82,696 175 174 166 163 158 2 17 - 19a 4/17 - 5/7 21 277,120 262,437 216 205 199 194 197
3 20 5/8 - 5/14 7 347,146 342,210 345 340 336 333 330 4 21 5/15 - 5/21 7 77,357 77,108 279 277 270 265 262 5 22 5/22 - 5/28 7 92,588 92,588 298 298 295 289 287 6 23 - 32a 5/29 - 7/31b 64 51,630 51,517 184 183 183 182 177
Total: 128 928,701 908,556 1,497 1,477 1,449 1,426 1,411 a Statistical weeks are grouped to try to provide a minimum sample size of 100 valid fish with a genotype
and age. b Includes a partial week.
55
Appendix A-3. Daily number of unclipped Chinook Salmon yearling juveniles trapped at the Lower Granite Dam juvenile fish facility while the trap was open March 26-July 31, migratory years 2010-2017. Horizontal bar (gray) indicates when biological samples were not taken while the trap was open.
Total Unclipped: 1,542,044
Total Unclipped: 1,373,747
Total Unclipped: 601,722
Total Unclipped: 1,177,075
Total Unclipped: 1,453,162
Total Unclipped: 1,712,102
Total Unclipped: 1,268,656
Total Unclipped: 1,176,266
56
Appendix A-4. Weekly Fish Passage Center trap collection and valid trap samples of yearling Chinook Salmon at Lower Granite Dam, migration years 2010-2014.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis
Total Fish
Trapped Wild Fish Trapped Genetic Stock Sex
Migration year 2010
1 13-18a 3/27 - 5/2b 37 676,578 676,578 267 267 265 246 2 19 5/3 - 5/9 7 162,054 162,054 208 208 208 197 3 20 5/10 - 5/16 7 133,866 133,866 256 256 246 231 4 21 5/17 - 5/23 7 180,003 180,003 403 403 396 375 5 22 5/24 - 5/30 7 71,657 71,657 177 177 176 168 6 23-31a 5/31 - 7/31b 62 44,498 44,498 126 126 125 114
Total: 127 1,268,656 1,268,656 1,437 1,437 1,416 1,331
Migration year 2011
1 13 - 14a 3/26 - 4/3b 9 31,065 30,271 158 153 87 84 2 15 4/4 - 4/10 7 107,227 106,257 287 284 167 163 3 16 4/11 - 4/17 7 97,734 96,550 333 329 194 189 4 17 4/18 - 4/24 7 87,587 84,638 172 166 97 91 5 18 4/25 - 5/1 7 163,289 161,959 348 345 206 202 6 19 5/2 - 5/8 7 189,527 184,526 376 366 215 202 7 20 5/9 - 5/15 7 264,136 255,863 380 368 214 205 8 21 5/16 - 5/22 7 138,722 134,254 204 197 115 104 9 22 5/23 - 5/29 7 61,767 61,466 183 182 110 97
10 23 - 31a 5/30 - 7/31 63 35,211 35,005 176 174 103 99 Total: 128 1,176,265 1,150,789 2,617 2,564 1,508 1,436
57
Appendix A-4 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis
Total Fish
Trapped Wild Fish Trapped Genetic Stock Sex
Migration year 2012
1 14 3/26 - 4/1 7 75,553 74,188 824 804 472 471 2 15 4/2 - 4/8 7 83,581 80,931 344 333 193 188 3 16 4/9 - 4/15 7 79,248 77,860 190 186 109 107 4 17 4/16 - 4/22 7 263,428 260,477 179 177 105 102 5 18 4/23 - 4/29 7 655,229 644,361 253 249 147 145 6 19 - 20a 4/30 - 5/13 14 262,086 245,547 204 192 107 105 7 21 5/14 - 5/20 7 162,369 158,253 278 271 158 156 8 22 5/21 - 5/27 7 99,691 98,640 336 331 195 193 9 23 - 32a 5/28 - 7/31b 65 30,917 30,722 160 159 94 88
Total: 128 1,712,102 1,670,979 2,768 2,702 1,580 1,555
Migration year 2013
1 13 3/26 - 3/31b 6 7,531 7,059 100 93 91 89 2 14 4/1 - 4/7 7 22,340 19,918 198 177 170 170 3 15 4/8 - 4/14 7 73,507 60,196 128 104 102 98 4 16 4/15 - 4/21 7 148,581 135,536 191 175 168 166 5 17 - 18a 4/22 - 5/5 14 444,024 365,598 220 184 182 181 6 19 - 20a 5/6 - 5/19 14 424,157 354,686 189 160 158 158 7 21 5/20 - 5/26 7 29,829 27,857 197 184 181 178 8 22 5/27 - 6/2 7 19,959 15,882 125 104 102 102 9 23 - 31a 6/3 - 7/31b 59 7,147 6,771 116 111 109 109
Total: 128 1,177,075 993,503 1,464 1,292 1,263 1,251
58
Appendix A-4 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis
Total Fish
Trapped Wild Fish Trapped Genetic Stock Sex
Migration year 2014
1 13 3/26 - 3/30b 5 26,157 24,600 313 294 174 174 2 14 3/31 - 4/6 7 68,088 66,922 452 444 274 274 3 15 4/7 - 4/13 7 81,058 80,024 314 310 197 197 4 16 4/14 - 4/20 7 290,806 283,775 353 347 215 215 5 17 - 19a 4/21 - 5/11 21 703,979 678,810 281 271 175 175 6 20 - 21a 5/12 - 5/25 14 187,675 183,444 204 200 130 130 7 22 - 31a 5/26 - 7/31b 67 95,399 88,458 266 251 161 161
Total: 128 1,453,162 1,406,033 2,183 2,117 1,326 1,326
Migration year 2015
1 13 - 14a 3/26 - 4/5b 11 45,284 38,446 172 138 138 138 2 15 - 18a 4/6 - 5/3 28 342,445 312,738 151 133 133 133 3 19 - 20a 5/4 - 5/17 14 154,997 137,315 156 139 138 137 4 21 - 31a 5/18 - 7/31b 75 58,996 45,071 133 102 100 100
Total: 128 601,722 533,570 612 512 509 508
Migration year 2016
1 13 - 14a 3/26 - 4/3b 9 40,728 31,271 303 233 228 228 2 15 4/4 - 4/10 7 65,572 57,007 213 185 178 178 3 16 4/11 - 4/17 7 536,070 514,576 355 341 330 329 4 17 4/18 - 4/24 7 277,246 258,668 183 171 169 169 5 18 - 19a 4/25 - 5/8 14 397,795 357,530 127 114 111 111 6 20 - 21a 5/9 - 5/22 14 180,912 166,893 179 164 159 159 7 22 - 31a 5/23 - 7/31 70 43,721 38,091 154 135 128 128
Total: 128 1,542,044 1,424,036 1,514 1,343 1,303 1,302
59
Appendix A-4 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate
Number of Valid Fish Samples Used In SCRAPI Analysis
Total Fish
Trapped Wild Fish Trapped Genetic Stock Sex
Migration year 2017
1 13 - 15a 3/26 - 4/9b 15 120,536 104,758 276 243 243 242 2 16 4/10 - 4/16 7 280,850 262,714 339 317 315 315 3 17 4/17 - 4/23 7 322,308 272,381 299 253 252 252 4 18 4/24 - 4/30 7 236,500 184,923 277 217 215 215 5 19 5/1 - 5/7 7 103,114 72,937 121 87 87 87 6 20 5/8 - 5/14 7 188,476 170,869 175 158 155 155 7 21 - 22a 5/15 - 5/28 14 79,186 68,336 230 201 194 194 8 23 - 32a 5/29 - 7/31b 64 42,777 35,008 93 80 80 80
Total: 128 1,373,747 1,171,926 1,810 1,556 1,541 1,540 a Statistical weeks are grouped to try to provide a minimum sample size of 100 valid fish with a genotype and age. b Includes a partial week.
60
Appendix A-5. Daily number of unclipped Chinook Salmon subyearling juveniles trapped at the Lower Granite Dam juvenile fish facility while the trap was open March 26-July 31, migratory years 2010-2017. Horizontal bar (gray) indicates when biological samples were not taken while the trap was open.
Total Unclipped: 1,324,581
Total Unclipped: 1,853,750
Total Unclipped: 2,753,366
Total Unclipped: 3,390,860
Total Unclipped: 2,572,283
61
Appendix A-6. Weekly Fish Passage Center trap collection and valid trap samples of subyearling Chinook Salmon at Lower Granite Dam, migration years 2010-2014.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate Number of Valid Fish Samples Used In SCRAPI
Analysis Total Fish Trapped
Wild Fish Trapped
Genetic Stock Sex
Migration year 2010
1 13 - 24a 3/27 - 6/13b 79 2,019,892 2,019,892 114 114 114 108 2 25 6/14 - 6/20 7 304,570 304,570 95 95 95 92 3 26 6/21 - 6/27 7 291,667 291,667 117 117 116 113 4 27 - 31a 6/28 - 7/31b 34 774,731 774,731 174 174 173 149
Total: 127 3,390,860 3,390,860 500 500 498 462
Migration year 2011
1 13 - 24a 3/26 - 6/12b 79 1,435,826 1,435,826 425 425 141 130 2 25 6/13 - 6/19 7 228,937 228,084 317 316 104 93 3 26 6/20 - 6/26 7 245,276 245,276 360 360 119 109 4 27 - 31a 6/27 - 7/31 35 662,244 656,224 698 691 223 213
Total: 128 2,572,283 2,565,410 1,800 1,792 587 545
Migration year 2012
1 14 - 24a 3/26 - 6/10 77 745,102 745,102 727 727 144 138 2 25 - 26a 6/11 - 6/24 14 576,858 576,858 773 773 153 151 3 27 6/25 - 7/1 7 224,966 224,966 565 565 110 106 4 28 - 32a 7/2 - 7/31b 30 306,824 306,200 597 596 119 116
Total: 128 1,853,750 1,853,126 2,662 2,661 526 511
62
Appendix A-6 Continued.
SCOBI Strata
Statistical Week
Sampling Period
Days Trap Open
Total Unclipped Estimate
Wild Estimate Number of Valid Fish Samples Used In SCRAPI
Analysis Total Fish Trapped
Wild Fish Trapped
Genetic Stock Sex
Migration year 2013
1 13 - 22a 3/26 - 6/2b 69 674,718 674,718 428 428 141 138 2 23 - 24a 6/3 - 6/16 14 1,093,803 1,091,023 461 460 153 151 3 25 - 26a 6/17 - 6/30 14 391,831 391,831 509 509 168 168 4 27 - 31a 7/1 - 7/31b 31 593,014 593,014 262 262 87 87
Total: 128 2,753,366 2,750,586 1,660 1,659 549 544
Migration year 2014
1 13 - 23a 3/26 - 6/8b 75 664,516 661,677 330 328 159 159 2 24 - 25a 6/9 - 6/22 14 343,817 342,615 297 296 144 144 3 26 - 31a 6/23 - 7/31b 39 316,247 316,247 280 280 136 136
Total: 128 1,324,580 1,320,539 907 904 439 439 a Statistical weeks are grouped to try to provide a minimum sample size of 100 valid fish with a genotype and
age. b Includes a partial week.
63
Appendix B: Wild steelhead juveniles at Lower Granite Dam, migratory years 2010-2017.
64
Appendix B-1. Wild juvenile steelhead emigration estimates by sex for each genetic stock at Lower Granite Dam, spawn years 2010-2017. L = lower bound and U = upper bound of 90% confidence intervals. See text for stock abbreviations.
Estimated number of wild steelhead juveniles at Lower Granite Dam Migration Year & Genetic Stock
Female Male Total Wild Estimate L U Estimate L U Estimate L U
2010 Total 458,976 430,834 495,864 392,505 358,522 421,336 851,481 812,790 889,987 UPSALM 65,818 54,618 81,779 64,423 50,427 77,815 130,241 113,762 150,041 MFSALM 55,005 40,110 69,090 28,143 19,825 40,495 83,148 66,954 101,096 SFSALM 17,901 9,877 24,594 19,193 10,908 27,840 37,094 25,035 47,027 LOSALM 15,586 12,077 24,955 13,759 8,264 22,209 29,345 23,847 43,150 UPCLWR 31,413 24,494 46,016 36,372 27,159 50,250 67,785 58,131 89,252 SFCLWR 53,863 40,551 65,216 51,964 40,380 63,903 105,827 88,093 120,676 LOCLWR 42,845 31,160 53,375 35,163 25,640 45,983 78,008 62,541 92,358 IMNAHA 33,882 24,007 43,576 42,727 28,228 51,183 76,609 58,487 87,317
GRROND 98,013 84,932 115,290 65,088 50,049 76,532 163,101 142,897 182,650 LSNAKE 44,650 33,100 55,757 35,673 25,545 44,135 80,323 64,707 92,890
2011 Total 500,330 475,758 527,628 411,272 385,746 434,123 911,602 877,415 945,279
UPSALM 81,746 74,547 96,391 66,198 55,377 75,721 147,944 136,143 165,284 MFSALM 39,921 31,014 47,304 25,238 19,346 32,759 65,159 54,352 75,441 SFSALM 21,754 15,167 27,040 21,058 14,709 26,372 42,812 33,388 49,548 LOSALM 24,444 18,420 30,769 17,683 12,092 22,737 42,127 33,972 49,705 UPCLWR 46,767 37,091 54,553 29,649 23,669 37,385 76,416 65,349 87,163 SFCLWR 47,085 38,859 56,690 46,330 36,212 53,414 93,415 80,691 103,981 LOCLWR 45,173 36,371 52,664 36,104 30,328 44,766 81,277 71,084 92,746 IMNAHA 46,727 36,621 53,116 44,152 35,678 51,556 90,879 77,120 99,359
GRROND 102,424 92,954 116,421 85,405 74,563 97,445 187,829 174,430 206,850 LSNAKE 44,289 37,601 53,722 39,455 32,302 47,338 83,744 74,297 95,500
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Appendix B-1. Continued. Estimated number of wild steelhead juveniles at Lower Granite Dam Migration Year & Genetic Stock
Female Male Total Wild Estimate L U Estimate L U Estimate L U
2012 Total 481,431 450,555 524,179 409,234 368,613 440,798 890,665 859,480 923,869 UPSALM 57,520 54,037 81,308 64,733 49,791 85,081 122,253 112,529 156,241 MFSALM 59,090 38,455 74,047 49,822 33,056 67,034 108,912 81,768 128,514 SFSALM 26,464 13,559 36,277 19,196 9,105 29,268 45,660 28,053 58,168 LOSALM 24,977 16,150 36,598 22,066 9,628 32,636 47,043 31,332 60,828 UPCLWR 55,012 34,334 68,740 45,389 23,822 57,819 100,401 69,573 114,901 SFCLWR 29,266 20,600 39,031 37,970 28,574 54,277 67,236 55,404 86,384 LOCLWR 30,779 26,422 46,968 27,631 19,958 38,945 58,410 51,676 78,730 IMNAHA 49,516 37,285 67,317 28,422 18,246 38,777 77,938 62,221 97,706
GRROND 100,893 79,759 116,604 79,744 62,488 98,008 180,637 152,483 202,088 LSNAKE 47,914 35,815 65,349 34,261 20,736 44,304 82,175 63,722 101,627
2013 Total 475,513 453,316 508,504 316,524 288,228 335,001 792,037 761,703 823,413
UPSALM 74,197 65,507 90,151 47,588 38,396 57,454 121,785 110,080 140,574 MFSALM 52,198 41,119 62,483 22,110 14,481 28,287 74,308 60,000 85,266 SFSALM 21,007 13,679 27,431 4,551 1,384 7,213 25,558 17,046 32,278 LOSALM 17,278 12,578 23,898 16,427 7,077 19,262 33,705 22,925 39,640 UPCLWR 79,029 60,020 88,775 49,767 37,797 60,250 128,796 105,507 140,828 SFCLWR 45,681 38,443 58,507 43,466 31,158 51,737 89,147 75,430 103,697 LOCLWR 46,532 35,278 55,672 29,911 23,464 39,091 76,443 63,929 88,974 IMNAHA 33,853 25,514 41,410 17,631 12,203 23,255 51,484 41,198 60,463
GRROND 74,169 67,893 90,470 57,002 47,379 69,648 131,171 120,798 152,242 LSNAKE 31,569 26,835 42,692 28,071 22,428 37,743 59,640 53,369 75,382
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Appendix B-1. Continued.
Estimated number of wild steelhead juveniles at Lower Granite Dam Migration Year & Genetic Stock
Female Male Total Wild
Estimate L U Estimate L U Estimate L U
2014 Total 520,010 489,224 547,757 296,209 274,812 321,694 816,219 781,995 849,252 UPSALM 74,981 63,957 87,955 54,337 44,868 66,421 129,318 115,512 147,260 MFSALM 61,524 48,023 70,709 30,179 21,421 37,270 91,703 74,642 102,510 SFSALM 27,735 21,583 36,747 13,641 8,553 19,095 41,376 33,633 51,630 LOSALM 26,437 18,346 32,747 3,389 1,417 6,650 29,826 22,047 37,116 UPCLWR 72,403 55,898 79,982 43,720 31,574 51,219 116,123 93,799 123,990 SFCLWR 44,242 33,671 53,276 35,161 24,638 42,001 79,403 63,407 89,038 LOCLWR 34,881 28,322 45,033 29,816 20,990 36,623 64,697 53,937 76,856 IMNAHA 39,629 30,442 48,476 22,582 17,265 31,368 62,211 52,532 74,734
GRROND 96,407 86,998 115,031 45,204 39,732 58,726 141,611 133,441 166,137 LSNAKE 41,771 33,411 51,379 18,180 14,236 26,283 59,951 51,406 73,202
2015 Total 441,615 399,943 483,854 227,827 197,208 261,969 669,442 624,364 716,896
UPSALM 63,745 48,959 83,104 32,473 21,728 46,778 96,218 78,410 119,716 MFSALM 46,016 25,423 57,549 20,742 12,956 33,479 66,758 44,735 82,827 SFSALM 34,444 17,556 47,687 12,282 2,534 19,540 46,726 25,682 59,339 LOSALM 13,781 7,197 23,466 15,649 2,014 21,011 29,430 13,054 37,948 UPCLWR 61,591 38,233 78,362 29,045 17,470 46,235 90,636 64,694 111,907 SFCLWR 42,623 28,394 61,478 12,754 7,621 23,315 55,377 41,775 78,304 LOCLWR 35,593 29,590 56,042 22,587 12,405 33,548 58,180 47,980 82,088 IMNAHA 27,804 26,056 50,917 10,624 5,667 17,222 38,428 35,305 63,126
GRROND 90,845 59,879 99,241 52,861 37,125 69,215 143,706 105,974 156,378 LSNAKE 25,173 20,463 41,641 18,810 12,700 32,544 43,983 38,478 67,448
67
Appendix B-1. Continued. Estimated number of wild steelhead juveniles at Lower Granite Dam Migration Year & Genetic Stock
Female Male Total Wild
Estimate L U Estimate L U Estimate L U 2016 Total 537,859 502,567 564,154 267,574 249,177 296,623 805,433 772,536 840,101
UPSALM 65,670 56,002 80,855 48,645 39,327 59,331 114,315 102,076 133,500 MFSALM 53,880 41,354 63,930 29,607 18,946 36,469 83,487 66,211 94,535 SFSALM 36,394 26,133 45,134 17,843 12,143 24,871 54,237 42,336 64,739 LOSALM 23,061 17,173 33,026 11,852 6,943 17,362 34,913 27,323 45,866 UPCLWR 79,494 62,783 91,144 28,386 19,104 35,743 107,880 87,851 120,860 SFCLWR 33,490 26,321 44,121 24,410 18,134 34,720 57,900 49,152 73,287 LOCLWR 46,548 36,898 58,072 16,347 12,132 23,833 62,895 53,057 77,722 IMNAHA 51,847 37,808 59,774 19,488 14,322 27,428 71,335 56,559 82,458
GRROND 101,489 82,900 113,574 38,947 34,244 52,881 140,436 123,106 159,249 LSNAKE 45,986 36,810 58,688 32,049 23,314 40,589 78,035 65,707 92,336
2017 Total 553,109 524,987 591,599 355,447 322,733 379,966 908,556 869,866 947,377
UPSALM 119,124 104,522 138,206 80,935 66,859 95,220 200,059 180,680 223,989 MFSALM 31,512 22,460 41,076 17,164 9,562 23,374 48,676 36,295 58,844 SFSALM 12,886 7,272 20,197 8,753 3,285 12,919 21,639 13,508 29,459 LOSALM 11,104 5,467 16,088 5,288 629 7,410 16,392 8,092 20,790 UPCLWR 58,472 47,849 73,971 50,262 34,883 59,539 108,734 90,283 125,407 SFCLWR 51,549 39,891 62,930 25,981 19,155 36,450 77,530 64,134 92,987 LOCLWR 30,083 25,305 42,015 29,734 23,891 41,295 59,817 53,912 78,077 IMNAHA 42,561 31,836 52,163 15,114 9,764 21,256 57,675 45,391 68,340
GRROND 141,497 120,755 157,500 78,516 62,667 91,372 220,013 193,097 238,920 LSNAKE 54,321 44,520 68,769 43,700 33,547 55,565 98,021 85,545 117,037
68
Appendix B-2. Genetic stock composition of wild steelhead juvenile emigrants at Lower Granite Dam, migration years 2010-2017. See text for genetic stock abbreviations.
69
Appendix C: Wild Chinook Salmon yearling juveniles at Lower Granite Dam, migratory years 2010-2017.
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Appendix C-1. Wild juvenile Chinook Salmon yearling emigration estimates by sex for each genetic stock at Lower Granite Dam, spawn years 2010-2017. L = lower bound and U = upper bound of 90% confidence intervals. See text for stock abbreviations.
Migration Year & Genetic Stock
Estimated number of wild Chinook Salmon yearling juveniles at Lower Granite Dam Female Male Total Wild
Estimate L U Estimate L U Estimate L U 2010 Total 706,813 683,104 787,436 561,843 485,398 581,941 1,268,656 1,216,121 1,322,691
UPSALM 188,542 163,376 227,447 152,792 119,125 175,013 341,334 301,018 383,504 MFSALM 163,477 140,956 201,299 109,190 89,532 138,938 272,667 246,775 321,433 CHMBLN 17,990 7,680 30,416 32,325 6,678 36,704 50,315 20,589 56,155 SFSALM 83,404 67,186 111,771 59,970 39,733 78,186 143,374 119,034 175,167 HELLSC 246,489 225,237 285,597 189,789 151,192 212,249 436,278 396,035 476,799
TUCANO 4,031 1,796 7,222 11,208 1,099 19,650 15,239 4,706 23,612 FALL 2,880 1,872 6,229 6,569 4,105 10,906 9,449 7,439 15,606
2011 Total 554,559 522,432 588,253 596,230 562,200 629,393 1,150,789 1,114,434 1,186,955 UPSALM 122,922 106,420 141,203 153,954 129,219 168,391 276,876 248,168 296,771 MFSALM 96,717 83,170 114,711 137,036 117,980 154,379 233,753 212,072 257,951 CHMBLN 18,019 11,446 25,601 25,469 16,562 33,148 43,488 32,461 53,932 SFSALM 96,179 82,268 112,901 82,227 69,146 97,633 178,406 160,819 201,057 HELLSC 212,158 187,530 230,162 186,346 172,981 213,522 398,504 373,291 429,645
TUCANO 2,334 0 5,528 3,721 1,340 6,642 6,055 2,663 10,480 FALL 6,230 2,797 9,987 7,477 3,865 10,585 13,707 8,645 18,411
2012 Total 922,300 856,197 979,466 748,679 692,913 810,955 1,670,979 1,627,599 1,715,732 UPSALM 163,736 134,413 200,245 128,314 100,921 161,561 292,050 254,390 340,965 MFSALM 236,313 187,832 273,961 215,008 164,212 242,831 451,321 381,578 487,895 CHMBLN 33,649 14,849 49,681 21,512 9,205 39,338 55,161 32,047 77,750 SFSALM 175,598 138,377 213,195 124,011 90,805 148,496 299,609 249,838 337,592 HELLSC 300,957 262,903 346,154 250,066 227,985 304,137 551,023 518,093 623,079
TUCANO 8,634 0 19,825 2,735 0 8,321 11,369 2,935 23,679 FALL 3,413 2,350 7,106 7,033 6,105 12,980 10,446 9,870 18,351
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Appendix C-1. Continued. Migration Year & Genetic Stock
Estimated number of wild Chinook Salmon yearling juveniles at Lower Granite Dam Female Male Total Wild
Estimate L U Estimate L U Estimate L U 2013 Total 558,625 516,933 599,214 434,878 395,917 476,165 993,503 960,068 1,026,526
UPSALM 136,283 124,561 178,407 107,547 78,888 126,375 243,830 219,846 287,590 MFSALM 88,008 71,869 117,266 102,129 81,490 130,650 190,137 167,762 231,556 CHMBLN 10,670 1,825 17,882 7,102 2,668 16,539 17,772 7,759 28,566 SFSALM 73,994 49,156 90,259 44,384 33,043 62,689 118,378 91,914 141,749 HELLSC 236,664 186,263 251,716 163,341 131,456 186,269 400,005 338,406 416,257
TUCANO 5,856 78 12,448 44 0 189 5,900 129 12,650 FALL 7,150 6,750 16,053 10,331 10,772 19,232 17,481 19,585 32,378
2014 Total 761,797 717,324 835,214 644,236 572,952 687,276 1,406,033 1,369,025 1,443,681 UPSALM 178,130 149,624 229,160 120,360 86,882 153,237 298,490 257,081 357,051 MFSALM 217,752 141,170 230,625 132,177 110,843 180,057 349,929 276,982 382,943 CHMBLN 14,185 8,565 36,895 14,481 6,219 27,003 28,666 21,137 56,044 SFSALM 81,156 69,590 118,457 99,104 75,509 131,556 180,260 160,759 233,726 HELLSC 253,740 228,192 303,345 262,599 195,775 279,184 516,339 448,408 553,443
TUCANO 6,604 2,841 16,451 775 160 1,468 7,379 3,567 17,487 FALL 10,230 8,475 22,108 14,740 5,789 21,960 24,970 18,196 38,632
2015 Total 246,252 211,027 281,669 287,318 250,412 324,551 533,570 499,841 569,171 UPSALM 46,909 25,309 63,894 60,205 37,716 81,448 107,114 75,430 130,716 MFSALM 43,118 26,103 59,993 73,079 35,974 83,750 116,197 72,941 128,191 CHMBLN 7,958 2,763 16,358 2,043 524 4,982 10,001 4,635 19,087 SFSALM 46,833 20,306 58,484 45,057 28,925 65,461 91,890 58,950 110,697 HELLSC 97,844 88,748 135,476 100,935 90,350 141,991 198,779 193,549 260,822
TUCANO 1,209 509 5,822 3,217 0 4,247 4,426 1,035 8,868 FALL 2,381 471 4,099 2,782 2,143 11,873 5,163 3,758 14,027
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Appendix C-1. Continued. Migration Year & Genetic Stock
Estimated number of wild Chinook Salmon yearling juveniles at Lower Granite Dam Female Male Total Wild
Estimate L U Estimate L U Estimate L U 2016 Total 773,949 721,463 827,588 650,087 598,487 700,867 1,424,036 1,387,015 1,461,450
UPSALM 173,811 127,948 194,033 134,608 100,202 161,236 308,419 247,531 331,915 MFSALM 179,943 142,955 207,086 151,963 108,252 169,282 331,906 270,645 354,978 CHMBLN 28,575 14,887 37,713 8,470 6,161 19,469 37,045 25,865 51,967 SFSALM 132,227 91,708 149,543 139,664 118,914 180,885 271,891 229,517 309,020 HELLSC 249,961 248,596 323,364 212,383 184,384 251,189 462,344 456,329 550,186
TUCANO 3,908 0 11,300 486 0 1,173 4,394 318 11,614 FALL 5,524 4,474 10,908 2,513 2,100 5,397 8,037 7,332 15,399
2017 Total 634,581 596,527 664,570 537,345 509,823 574,102 1,171,926 1,134,270 1,209,543 UPSALM 123,863 106,810 141,347 111,072 90,722 123,927 234,935 207,543 254,614 MFSALM 92,604 76,522 106,735 99,996 86,536 118,329 192,600 172,144 215,406 CHMBLN 3,104 800 6,567 7,349 2,143 11,431 10,453 4,725 15,581 SFSALM 118,883 100,688 134,739 101,668 88,621 120,340 220,551 198,201 242,890 HELLSC 293,590 267,066 318,162 215,845 198,989 243,244 509,435 482,218 546,207
FALL 2,537 330 4,675 1,415 211 3,588 3,952 1,312 6,651
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Appendix C-2. Genetic stock composition of wild Chinook Salmon yearling juvenile emigrants at Lower Granite Dam, migration years 2010-2017. See text for genetic stock abbreviations.
74
Appendix D: Wild Chinook Salmon subyearling juveniles at Lower Granite Dam, migratory years 2010-2017. (March-July).
75
Appendix D-1. Wild juvenile Chinook Salmon subyearling emigration estimates by sex for each genetic stock at Lower Granite Dam, spawn years 2010-2014. L = lower bound and U = upper bound of 90% confidence intervals. See text for stock abbreviations.
Migration Year & Genetic Stock
Estimated number of wild Chinook Salmon subyearling juveniles at Lower Granite Dam Female Male Total Wild
Estimate L U Estimate L U Estimate L U 2010 Total 1,722,978 1,537,860 1,965,057 1,667,882 1,431,654 1,858,712 3,390,860 3,307,043 3,476,774
UPSALM 27,334 5,269 72,815 24,439 1,685 73,451 51,773 11,673 112,091 MFSALM 0 0 0 8,502 0 18,625 8,502 0 18,807 CHMBLN 0 0 0 0 0 0 0 0 0 SFSALM 4,553 0 11,468 0 0 0 4,553 0 11,228 HELLSC 85,029 45,955 162,484 44,550 20,030 62,265 129,579 80,929 204,664
TUCANO 0 0 0 0 0 0 0 0 0 FALL 1,606,062 1,405,955 1,831,090 1,590,391 1,362,844 1,787,226 3,196,453 3,070,475 3,303,102
2011 Total 1,066,696 1,017,417 1,332,128 1,498,714 1,232,515 1,548,981 2,565,410 2,506,784 2,626,591 UPSALM 16,288 7,559 32,684 39,158 20,797 91,177 55,446 37,734 112,663 MFSALM 11,056 0 50,909 12,860 8,696 61,779 23,916 16,502 86,262 CHMBLN 4,913 0 28,164 6,298 0 43,775 11,211 11,154 60,499 SFSALM 7,002 0 15,819 3,684 0 10,074 10,686 0 19,799 HELLSC 103,194 53,700 187,108 131,693 30,123 129,797 234,887 111,054 302,002
TUCANO 1,578 0 7,349 0 0 0 1,578 0 7,380 FALL 922,665 864,469 1,165,240 1,305,021 1,056,316 1,373,967 2,227,686 2,091,159 2,329,510
2012 Total 931,932 859,611 1,028,444 921,194 824,260 991,599 1,853,126 1,821,404 1,886,318 UPSALM 21,642 5,654 43,458 16,203 2,281 32,428 37,845 13,662 63,601 MFSALM 13,785 5,261 32,257 13,687 0 21,156 27,472 6,247 46,150 CHMBLN 0 0 0 0 0 0 0 0 0 SFSALM 2,628 0 13,024 5,083 5,599 24,082 7,711 8,423 32,474 HELLSC 50,262 39,657 97,392 59,192 34,439 93,589 109,454 89,815 171,693
TUCANO 0 0 0 0 0 0 0 0 0 FALL 843,615 750,172 916,590 827,029 729,607 898,816 1,670,644 1,585,986 1,703,582
76
Appendix D-1. Continued. Migration Year & Genetic Stock
Estimated number of wild Chinook Salmon subyearling juveniles at Lower Granite Dam Female Male Total Wild
Estimate L U Estimate L U Estimate L U 2013 Total 1,339,308 1,239,696 1,497,441 1,411,278 1,258,376 1,510,794 2,750,586 2,698,515 2,803,794
UPSALM 913 0 11,096 9,496 0 24,189 10,409 1,989 30,187 MFSALM 0 0 0 869 0 10,754 869 0 10,866 CHMBLN 0 0 0 0 0 0 0 0 0 SFSALM 9,127 0 30,387 2,963 0 15,780 12,090 0 36,431 HELLSC 32,525 21,376 81,622 30,742 4,421 57,003 63,267 38,141 115,257
TUCANO 0 0 0 0 0 0 0 0 0 FALL 1,296,743 1,176,286 1,432,102 1,367,208 1,215,487 1,471,855 2,663,951 2,578,744 2,713,536
2014 Total 691,396 632,341 756,353 629,143 567,994 689,685 1,320,539 1,281,117 1,360,123 UPSALM 17,461 3,729 32,865 10,498 0 21,303 27,959 7,843 45,064 MFSALM 17,979 7,284 34,903 6,565 0 15,319 24,544 10,795 42,983 CHMBLN 0 0 0 3,582 0 11,914 3,582 0 11,901 SFSALM 0 0 0 0 0 0 0 0 0 HELLSC 34,080 17,697 52,023 13,469 7,096 27,290 47,549 31,037 71,560
TUCANO 0 0 0 0 0 0 0 0 0 FALL 621,876 562,309 683,456 595,029 533,606 652,703 1,216,905 1,167,188 1,264,447
77
Appendix D-2. Genetic stock composition of wild Chinook Salmon subyearling juvenile emigrants at Lower Granite Dam, migration years 2010-2014. See text for genetic stock abbreviations.
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Prepared by: Approved by: IDAHO DEPARTMENT OF FISH AND GAME Carlos A. Camacho J. Lance Hebdon Fisheries Biologist Anadromous Fisheries Manager Kristin K. Wright James P. Fredericks, Chief Supervisory Fisheries Biologist Bureau of Fisheries John Powell Fisheries Biologist 2 William C. Schrader Principal Fisheries Biologist Timothy Copeland Fisheries Program Coordinator Michael W. Ackerman Fisheries Biologist 2 Marika E. Dobos Fisheries Biologist Micah Davison Supervisory Fisheries Biologist Matthew P. Corsi Principal Fisheries Biologist Matthew R. Campbell Fisheries Program Coordinator
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