Steinhorst et al.: Estimates of abundance for the run composition of salmonids 
3 
parentage determined genetically (Steele et al., 2013), 
or by a ventral-fin clip. Genotyping procedures for par- 
entage were conducted after the trapping season and 
gives accuracy rates approaching 100% (Steele et al., 
2013). Fish not determined to be of hatchery origin 
were treated as wild fish. 
set containing sex, age, and stock for a subsample of 
wild fish that were trapped (for compositional data). 
These 3 data sets were used to produce estimates and 
CIs for the number of wild fish by sex or age or stock. 
Estimator and confidence intervals 
Subsampling of trapped wild steelhead Scale and tissue 
samples were then taken from a systematic subsample 
of trapped fish deemed wild. Percentages of the wild 
steelhead that were subsampled averaged around 50% 
during this study. Scale samples were used to deter- 
mine age on the basis of visual examination of scale 
annuli. Age data collected at LGD were used to assign 
returning adults back to a brood year (BY, the year in 
which their parents spawned). 
Tissue samples from the anal fin were used to deter- 
mine sex and the stock of origin. Stock composition was 
determined by using individual assignment, a method 
of genetic stock identification (Pella and Milner, 1987; 
Shaklee et al., 1999) based on single nucleotide poly- 
morphisms (SNPs). Adults were screened at 187 SNPs 
and with a sex-specific allelic discrimination assay 
(Campbell et al., 2012). Only individuals that were 
genotyped at >90% of SNPs were included. We used 
the maximum likelihood framework implemented in 
the program gsi_sim (Anderson et al., 2008; Anderson, 
2010) to assign individuals to a stock. Each fish was 
assigned to the stock in which the probability of its 
genotype occurring was greatest by using the allocate- 
sum procedure (Wood et al., 1987). We did not attempt 
to identify out-of-basin strays. For this study, we as- 
sumed that the stock was determined without error (a 
future study will examine uncertainty in genetic as- 
signments). In essence, we treated the genetic data in 
the same way as we did for the age data. 
Ackerman et al.^ defined 10 genetically determined 
stocks used for assignments at LGD. The locations of 
these stocks included 1) the upper Salmon River (UPS); 
2) Middle Fork Salmon River (including Chamberlain 
and Bargamin creeks) (MFS); 3) South Fork Salmon 
River (SFS); 4) lower Salmon River (LOS); 5) upper 
Clearwater River (Lochsa and Selway rivers) (UPC) ; 6) 
South Fork Clearwater River (including Clear Creek) 
(SFC); 7) lower Clearwater River (LOC); 8) Imnaha 
River (IMN); 9) Grande Ronde River (GRR); and 10) 
Tucannon River, Asotin Creek, and other tributaries to 
the Snake River downstream of the Clearwater River 
confluence (LSN). 
The sampling design produced 3 data sets: 1) a cen- 
sus of numbers of fish returning to and migrating past 
the dam (window counts), 2) a hatchery-versus-wild 
data set for all trapped fish (trap data), and 3) a data 
2 Ackerman, M. W., N. V. Vu, J. McCane, C. A. Steele, M. 
R. Campbell, A. P. Matala, J. E. Hess, and S. R. Narum. 
2014. Chinook and steelhead genotyping for genetic stock 
identification at Lower Granite Dam. Project progress report. 
2013 annual report. Idaho Dep. Fish Game, IDFG Rep. 14- 
01, 60 p. [Available at website] 
Abundance of wild steelhead The window-count data 
provided the abundance of adult steelhead migrating 
past LGD, but our focus was on wild fish; therefore, we 
first had to partition the overall abundance estimate 
into a wild-versus-hatchery abundance estimate. The 
proportions of wild and hatchery steelhead changed 
over the season, but within each weekly or monthly 
stratum, proportions were assumed to be relatively con- 
stant. Given the window counts by strata, C^, C 2 ,...,Cs, 
the number of wild steelhead (W) was estimated with 
the following equation: 
( 1 ) 
where Pi,P 2 ,---,Ps = estimates of the proportion of wild 
steelhead by stratum from the trap 
^ data; and 
Ps = NJt, (or denoted p, for a pooled 
estimate). 
These and all subsequent notations are defined in Ta- 
ble 1. Given the fixed numbers of adults counted at the 
dam for each stratum, we found a Cl for the number 
of wild fish by using either an asymptotically normal 
interval or by a parametric bootstrap. The asymptoti- 
cally normal interval is given as 
<w<w + z^^,s^, 
( 2 ) 
where S~ = , C 
1 -^ 
a 
AlluAl. 
L-l 
(3) 
and Z ^2 = the (100a/2)th percentile of a standard nor- 
mal distribution. 
For the parametric bootstrap, we assumed the boot- 
strap number of wild fish has a binomial distribution, 
AT*- binomial (f 5 ,pj^g) and p* = N*lt^. We produced 500 
sets (B) of (p*,p*,...,pp, yielding W*, W*,..., W*. The 
lOOa/2 and (1 — §) percentiles of W*,W’*,...,W’* gave us 
the 100(1 - a)% Cl for the true number of wild steelhead 
that passed LGD for the year. In this study, we used 
90% CIs as an acceptable tradeoff of the type-I error 
rate with the power to discern important differences. 
Composition of wild steelhead When we had estimates 
of the number of wild fish migrating past LGD, we 
partitioned them into groups of interest for population 
assessments. There were 2 competing approaches for 
estimating numbers of wild fish by sex, age, or genetic 
origin: pooled or stratified. The compositional data set 
was about a tenth of the size of the hatchery-versus- 
wild data set (most fish were hatchery-origin; wild fish 
were subsampled at approximately 50%). There was 
ample data in the trap data set to estimate proportion 
of wild steelhead by stratum but not enough data for 
