Snake River White Sturgeon
Genetic Management Plan
Final Report to Idaho Power Company
Prepared by
Andrea Schreier, Scott Brandl,
and Bernie May
Genomic Variation Lab
University of California, Davis
Davis, California
September 26, 2013
TABLE OF CONTENTS
DOCUMENT OBJECTIVES/EXECUTIVE SUMMARY .......................................................3
SECTION 1. REVIEW OF GENETIC CONCEPTS ................................................................4
SECTION 2. GENETIC DIVERSITY AND POPULATION STRUCTURE OF SNAKE RIVER
WHITE STURGEON ..............................................................................................................10
SECTION 3. CONSIDERATIONS FOR SNAKE RIVER WHITE STURGEON GENETIC
MANAGEMENT .....................................................................................................................13
SECTION 4. RECOMMENDATIONS FOR SNAKE RIVER WHITE STURGEON GENETIC
MANAGEMENT .....................................................................................................................25
APPENDIX I. POPULATION GENETIC ANALYSIS METHODS ....................................41
APPENDIX II. ESTIMATING THE NUMBER OF WHITE STURGEON SPAWNERS
CONTRIBUTING TO THE 2006 AND 2011 BLISS YEAR CLASSES ...............................44
REFERENCES ........................................................................................................................53
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DOCUMENT OBJECTIVES/EXECUTIVE SUMMARY
White sturgeon is a critically imperiled species in the Snake River. Its imperiled status
results from anthropogenic threats including a modified hydrograph, habitat loss and
fragmentation, poor water quality, and historical overharvest. State, federal, tribal, and
hydropower companies collaboratively manage and/or monitor white sturgeon in the Snake
River to increase abundance of the species and preserve its genetic diversity. This document is
designed to guide the adaptive management of Snake River white sturgeon from a genetic
perspective using the most up to date genetic analyses available.
Genetic diversity analyses, population structure analyses, and population genetic theory
are used to provide recommendations for genetic diversity preservation, conservation
aquaculture, translocation, and genetic monitoring of white sturgeon. The first section of this
document reviews relevant genetic concepts that will be discussed in the context of Snake River
white sturgeon management. Section 2 describes the results of a recent analysis of Snake River
white sturgeon genetic diversity and population structure using thirteen polysomic microsatellite
markers. Section 3 presents genetic considerations for Snake River white sturgeon management,
and Section 4 closes with specific recommendations for genetic management of Snake River
white sturgeon. Two appendices providing supplementary information are attached to this
document. Appendix I provides a detailed description of the methods used to evaluate genetic
diversity and population structure in Snake River white sturgeon. Appendix II reports the results
of a progeny array reconstruction analysis conducted to estimate the number of spawners
contributing to the 2006 and 2011 year classes in the Bliss to CJ Strike Reach of the Middle
Snake River.
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SECTION 1. REVIEW OF GENETIC CONCEPTS
Defining genetic diversity
Population genetic diversity refers to the totality of all gene variants, at all genes, from all
individuals of a gene pool. Alleles are the possible variants at a particular locus in the genome
and it is not uncommon for a locus to have many variants. Large populations are more likely to
possess a larger number of alleles relative to small populations. A private allele is an allele that
is found in only one population. Allelic richness, a common metric for measuring genetic
diversity, is the average number of different alleles per locus normalized for sample size. A
second metric used to quantify genetic diversity is heterozygosity. A heterozygous individual
possesses more than one allele at a particular locus and in population genetics, heterozygosity is
quantified within an individual or for a population. Heterozygosity may be used as a metric for
conservation or management. Frankham et al. (2002) suggests that a suitable conservation goal is
to retain 90% of the expected heterozygosity within a population of interest over 100 years.
Heterozygosity loss is a by-product of genetic drift (see below), thus smaller populations lose
heterozygosity faster than larger ones.
The origin of genetic diversity and forces that affect it
Mutation is the source of all new genetic diversity. The accumulation of mutations occurs
on an evolutionary time scale, which is why genetic diversity must be actively maintained by
managers and meaningful variation cannot be accumulated in a small number of generations.
Mutations can be beneficial, neutral, or deleterious. Directional selection causes beneficial alleles
to increase in frequency and deleterious alleles to decrease in frequency in accordance with the
relative fitness gain or loss conferred under particular environmental conditions. Balancing
selection increases genetic diversity by conferring a reproductive advantage for heterozygotes,
while diversifying selection favors homozygous genotypes. Neutral alleles, or alleles under little
or no selection pressure, evolve through random neutral processes known as genetic drift.
Genetic drift refers to changes in allele frequencies due to the stochastic nature of alleles being
passed to progeny. A neutral mutation will eventually become fixed or lost from the population
entirely. The rate at which genetic drift can change the frequency of alleles is relative to the size
of the population and the frequency of the allele. In small populations, the chance a mutation
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randomly will be lost or become fixed is much greater than in a large population due to the
stochastic nature of the passing of alleles between generations in small populations. Likewise, an
allele at a frequency of 0.1% is more likely to be eliminated from a population than an allele that
exists in 30% of individuals.
Advantages of high genetic diversity
An important principle of management and conservation of wild populations is the
maintenance of genetic diversity. Genetic diversity is the raw material on which evolutionary
forces operate. If a population possesses genetic diversity, it is more likely to possess one or
more genotypes suited to novel conditions in a changing environment. Genotypes that confer a
reproductive advantage under a new selective regime will increase in frequency in a population,
allowing it to adapt. An ideal population should have individuals that are locally adapted to their
habitat and spawning conditions--expressing a phenotype that provides them with the most
reproductive success--while maintaining sufficient genetic diversity to respond to changes in
selection pressure. The positive correlation between genetic diversity and average fitness has
been demonstrated in many taxa (Reed & Frankham 2003; Leimu et al. 2006).
Problems associated with low genetic diversity
A population with low genetic diversity that is exposed to a changing environment may
lack the alleles necessary to adapt and go extinct. Small populations are particularly prone to
genetic diversity loss due to genetic drift, especially when they are small for a number of
generations. However, a population with a large census size may also exhibit low genetic
diversity. An example is provided by the Tasmanian devil, a mesopredator endemic to Tasmania.
Certain Tasmanian devil populations have declined 80% since the mid-nineties primarily due to
their susceptibility to Tasmanian devil facial tumor disease (DFTD). Before the DFTD outbreak,
Tasmanian devils were common across Tasmania but exhibited low genetic diversity because
populations originated from a small number of founders from mainland Australia and several
population bottlenecks occurred in the 20th century (Jones et al. 2004). The seemingly robust
devil populations have low genetic diversity at the major histocompatibility complex, a gene
family associated with immune response in mammals (Loh et al. 2006; McCallum et al. 2007).
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Inability to adapt to this disease challenge may drive Tasmanian devils to extinction (McCallum
et al. 2007).
Inbreeding
Inbreeding occurs in small populations that contain more related individuals relative to
larger populations. When relatives mate, their offspring may inherit alleles from each parent that
are identical by descent, or originate from the same copy in a common ancestor. Problems
associated with inbreeding occur when deleterious recessive alleles found in the homozygous
state are expressed, which is more likely in an inbred population. Reduced fitness from
inbreeding, known as inbreeding depression, can manifest in a number of ways, including
reduced fecundity, sperm quality and quantity, development time, competitive ability, immune
response and disease resistance (Reid et al. 2003; Spielman et al. 2004; Whiteman et al. 2006).
There is clear, irrefutable evidence of inbreeding depression across many taxa (Frankham 2005),
yet inbreeding depression may not be observed in a small population in the short term (O’Grady
et al. 2006), particularly if the inbred population is well adapted to the current conditions. A
well-adapted inbred population can reach carrying capacity of a habitat, giving the appearance of
a healthy population. However, the reduced diversity in the inbred population will make it
susceptible to changes in its environment.
When a population has very low genetic diversity intervention from resource managers
may be warranted. “Genetic rescue” (Tallmon et al. 2004) of dangerously inbred populations
has been successful in lab and agricultural species by bringing in individuals from a larger
population or separate inbred population, providing new alleles and reducing the risk that
deleterious recessives will be expressed. Genetic rescue has been implemented in a number of
wild species as well, including the Florida panther, Mexican wolves and desert topminnow
(Vrijenhoek 1994; Hedrick & Fredrickson 2010). However, this is considered a last resort as
translocating individuals poses disease and outbreeding depression risks.
Population structure/outbreeding depression
A genetically structured population is one in which certain groups of individuals mate
more frequently with each other than with other groups of individuals. If these populations are
partially or fully reproductively isolated from each other for enough generations they may
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become locally adapted. Population structure analysis can often show structure where it was
previously thought there was none, such as in Sacramento splittail (Baerwald et al. 2007). It may
show gene flow or migration between populations that were thought to be isolated, as observed
in the hooded seal (Coltman et al. 2007). How population genetic structure should be taken into
consideration when defining groups for management varies with circumstances.
If migration and interbreeding occurs between previously isolated communities, they may
become less fit for their environment, an outcome known as outbreeding depression.
Outbreeding depression is the result of two different phenomena: fitness loss due to introgression
of non-adaptive alleles and the disruption of co-adapted gene complexes (Gilk et al. 2004). The
most common kind of outbreeding depression occurs if a locally adapted population receives
gene flow from an outside population and the resulting offspring of an intermediate phenotype
are by definition less fit. Outbreeding depression may also occur when individuals from
disparate populations mate and co-adapted gene complexes are disrupted. Co-adapted gene
complexes are specific alleles of different genes that are adapted to work together within
individuals to increase fitness. When mating between genetically divergent individuals breaks up
these genes that were adapted to work together their offspring exhibit reduced fitness.
Effective population size
Many management or conservation plans contain goals to achieve a target population
census size. However, the census size does not always accurately reflect evolutionary processes.
The concept of effective population size, or Ne, is used by population geneticists to describe
how a population responds to neutral evolutionary forces. Ne is the size of an ideal population
(one with constant population size, equal sex ratio, and no immigration, mutation, or selection)
that would experience genetic drift or the effects of inbreeding at a rate equal to that of the
observed population. In other words, Ne tells us how a population will be affected by genetic
drift and inbreeding and is usually smaller than the census size. Small populations are affected
more dramatically by genetic drift and inbreeding than large populations, and large populations
with low allelic diversity (and thus low Ne) are in essence responding to environmental
challenges as though they were smaller than their census size.
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The effects of low Ne can be compounded by the demographic effects of small population
size such as Allee effects or demographic stochasticity. The combined effects of demographic
and environmental stochasticity with increased inbreeding and genetic diversity loss can force a
small population into an ‘extinction vortex’ (Shaffer 1981; Gilpin & Soulé 1986). The minimum
viable population size (MVP) is the minimum Ne a population must maintain to avoid falling into
an extinction vortex (Holsinger 2000). The concept of MVP was used as the basis for the much
cited 50/500 rule (Franklin 1980). This rule provides a guideline for managing small populations
to prevent extinction due to genetic concerns. The 50/500 rule states that a species or population
with an Ne of less than 50 may be at high risk of extinction. To ensure the long term viability of a
species, including reasonable assurance that genetic diversity is maintained, a population should
have an effective size of 500. The 50/500 rule is a good starting point in making policy decisions
but each species circumstances are unique and non-genetic concerns must be taken into
consideration as well.
Neutral genetic markers and genetic management
Although the preservation of adaptive potential and protection of genetic integrity of wild
populations is the focus of genetic management, we use neutral genetic markers instead of
adaptive markers (e.g. genes) to develop management recommendations. There are several
reasons why neutral markers may be preferable to adaptive markers for developing genetic
management recommendations. Neutral markers are the most appropriate tool for analyzing
population genetic structure because they are not under selection (Holderegger et al. 2006).
Neutral microsatellite markers, such as those used in this report, are particularly appropriate for
examining population structure as they mutate more rapidly than many adaptive loci and
therefore exhibit higher polymorphism. Highly polymorphic markers have greater power to
detect genetic differences between populations.
Although neutral genetic diversity is not a proxy for adaptive genetic diversity, which is
the basis for a population’s adaptive potential, data from neutral markers can be used to evaluate
measures such as inbreeding, Ne, or spawner number that strongly influence population viability.
Neutral markers can be used to detect demographic events that may result in loss of adaptive
genetic diversity, such as a bottleneck (Marsden et al. 2012). In small populations, slightly
beneficial alleles that may be important for future adaptation are functionally neutral because
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genetic drift is a more powerful evolutionary force in small populations than selection (Hare et
al. 2011). Genetic diversity loss in small populations revealed by neutral markers may indicate
loss of slightly beneficial alleles in small populations.
Applying genetic concepts to the polyploid white sturgeon
This brief review of genetic concepts that applies generally to fisheries management but
the polyploid white sturgeon provides challenges to population genetic analysis. White sturgeon
are ancestral octoploids that possess ~240 chromosomes (Hedrick et al. 1991; Fontana 1994;
Van Eenennaam et al. 1998). Each white sturgeon individual inherits four copies of each
chromosome from its mother and four copies from its father, for a total of eight genome copies.
Although a number of microsatellites have been developed for white sturgeon (Rodzen & May
2002; Börk et al. 2008), it is difficult to assay the duplicated white sturgeon genome because we
are unable to determine gene dosage, or the number of copies of each allele that an individual
possesses. Therefore, we are limited in the number of genetic diversity and genetic distance
metrics we can estimate.
We can examine genetic diversity in white sturgeon by calculating the number of alleles
found within populations and using a rarefaction technique to account for differences in sample
sizes (e.g. Drauch. Because we can’t resolve gene dosage, we are unable to examine
heterozygosity levels in a population or an individual. The inability to measure heterozygosity
prevents us from using heterozygosity-based measures of population structure, such as FIS and
FST. We use Phi-PT (Peakall et al. 1995) as an FST analog to examine population structure
within and among white sturgeon populations but are unable to evaluate FIS, which provides a
measure of inbreeding.
Effective population size cannot be measured in white sturgeon, not only because of its
polyploid genome but also due to the species’ life history. Ne estimators require allele frequency
and/or heterozygosity data which cannot be estimated from the white sturgeon genome using
currently available genetic markers. Models for effective population size estimators assume
populations are semelparous and the longevity of white sturgeon creates overlapping generations
within populations. Estimation of the number of spawners in a single year class provides a more
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appropriate measure to examine the maintenance of genetic diversity in populations of a longlived polyploid (Whiteley et al. 2012).
SECTION 2. GENETIC DIVERSITY AND POPULATION STRUCTURE OF SNAKE RIVER
WHITE STURGEON
We conducted population genetic analyses on white sturgeon samples collected from
throughout the Snake River to provide recommendations for the species’ management based
upon genetic criteria. In this report, Snake River reaches (river segments between dams) will be
referenced by the upstream dam, as is the convention in WSTAC documents (Figure 1). White
sturgeon are indigenous to the Snake River from the confluence of the Columbia and Snake
Rivers upstream to the base of Shoshone Falls (Cochnauer et al. 1985; Figure 1). Tissue samples
for genetic analysis were collected from nearly all Snake River reaches by fisheries researchers,
excluding the river reaches immediately upstream and downstream of Ice Harbor Dam, the
Oxbow reach, and Brownlee reach (Table 1). Few sturgeon inhabit the Oxbow and Brownlee
reaches and therefore tissue samples are difficult to obtain. A detailed description of the
methods used to evaluate white sturgeon genetic diversity and population structure within and
among Snake River reaches can be found in Appendix I. This section will focus on interpreting
the results of the genetic analyses in the context of informing management of Snake River white
sturgeon.
Genetic diversity analyses
Reaches of the Snake River downstream of the Hells Canyon Dam (hereafter the Lower
Snake) possessed more alleles and more private alleles than reaches upstream of Brownlee Dam
(hereafter, the Middle Snake; Figure 2A, B). Although we examined more samples in the Lower
Snake relative to the Middle Snake, we still detected more alleles in Lower Snake reaches when
rarefaction was conducted to account for differences in sample size. However, the disparity in
genetic diversity levels between the Lower and Middle Snake was lessened when rarefaction was
conducted (Figure 2B). Higher genetic diversity levels in the Lower Snake relative to the Middle
Snake may be due to gene flow between the Columbia River and Lower Snake before
impoundment of these rivers (Schreier 2012).
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Although we cannot estimate heterozygosity in white sturgeon due to their duplicated
genome, we can calculate the number of alleles possessed by each individual across 13 loci.
This is a proxy for heterozygosity as individuals with higher levels of heterozygosity will have a
greater number of alleles relative to more homozygous individuals. Lower Snake reaches
possess a greater mean number of alleles per individual than Middle Snake reaches (Figure 3).
Differences in genetic diversity levels among reaches within the Lower Snake and within
the Middle Snake were small relative to differences between the Lower Snake and Middle
Snake, despite a wide range in abundance and population status among reaches within each
region. Homogeneity of genetic diversity levels in the Middle Snake may be due to stocking and
translocation activities that have moved juveniles or adults among reaches (Idaho Power
Company 2005). Movement of individuals among Middle Snake reaches would increase genetic
diversity in isolated reaches with small population sizes and low recruitment.
Population structure analyses
An AMOVA analysis produced a global genetic differentiation estimate, Phi-PT, of 0.032
(P = 0.0001) which indicated that there was a significant amount of population genetic structure
in the Snake River. However, this estimate also indicates that only ~3% of genetic variability is
partitioned among Snake River reaches while the majority of genetic variability is found within
reaches. In other words, the genetic diversity within Snake River reaches is high relative to the
amount of genetic differentiation among them. A second AMOVA analysis treating the Lower
Snake and Middle Snake as the units of population delineation still showed that only a small
proportion of genetic diversity was partitioned among regions (Phi-PT = 0.035; P = 0.0001)
while most genetic diversity was found within regions.
Both the Structure analyses (mean LnP(K) = -34491; ΔK=2) and pairwise Phi-PT
analyses (Table 2) revealed significant genetic differentiation among the Lower Snake and
Middle Snake reaches. Although the Lower Snake and the Middle Snake were identified as
distinct populations, the Structure analysis suggested that individuals inhabiting the Lower Snake
showed mixed ancestry in the Middle Snake and a second white sturgeon population (Figure 4).
The putatively admixed individuals from the Lower Snake possessed genomes that originated
largely from the Middle Snake but showed influence from a second white sturgeon population
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(Figure 4). Previous Structure analysis of the whole Columbia-Snake drainage indicated that the
Lower Snake was very genetically similar to the Middle and Upper Columbia River (McNary –
Transboundary Reach), providing additional evidence for historical gene flow between the
Columbia and Snake Rivers (Figure 5; Schreier 2012). Only three individuals in the Middle
Snake show any ancestry in the Columbia River, suggesting natural barriers to gene flow existed
between the Columbia/Lower Snake and Middle Snake before impoundment (Figure 5).
Within the Lower Snake, pairwise Phi-PT analyses revealed high genetic similarity
among all three reaches, suggesting that individuals in the Lower Snake reaches possessed
similar levels of admixture. No genetic differentiation was detected between the Little Goose to
Lower Granite and Lower Granite to Hells Canyon reaches (Table 2), possibly due to movement
of white sturgeon through Lower Granite Dam into the Little Goose pool (G. Mendel,
Washington Department of Fish and Wildlife, pers. comm.). The genetic similarity among all
Lower Snake reaches is illustrated in a PCO plot, which clusters these reaches into a single
quadrant (Figure 6).
Although samples from just upstream and downstream of Ice Harbor Dam were not
included in this analysis, a range-wide population structure analysis showed high genetic
similarity between reaches of the Columbia-Snake flanking this region (McNary, Little Goose;
Drauch Schreier et al. 2013). Therefore it is likely that the areas just upstream and downstream
of Ice Harbor Dam are genetically similar to Lower Snake population reaches and it should be
included in the Lower Snake population. Future sampling and data collection will be necessary
to confirm this hypothesis. In contrast, the Oxbow and Brownlee reaches are located between
two genetically differentiated populations (Lower Snake, Middle Snake). Because the historical
isolation mechanism between the Lower Snake and Middle Snake populations is unknown, it is
more difficult to speculate about the to which the Oxbow and Brownlee reaches belong.
Additional sampling and genetic analysis will be required to determine whether Oxbow and
Brownlee white sturgeon should be included in the Lower Snake or Middle Snake population.
Within the Middle Snake, the Shoshone reach was the most genetically distinct from all
other reaches (Table 2). The Shoshone reach represents the upstream-most extent of the white
sturgeon range in the Snake River and it is now the most isolated as impoundment has impeded
all natural upstream migration in the Snake. The only reach from which the Shoshone reach was
12
not significantly differentiated was the Upper Salmon Falls reach, which is populated perhaps
entirely by downstream migrants from the Shoshone reach (Idaho Power Company 2005).
Several comparisons between other Middle Snake reaches are significant but pairwise Phi-PT
values are generally lower within the Middle Snake relative to comparisons between the Middle
and Lower Snake (Table 2). The PCO analysis separates Middle Snake reaches linearly along
coordinate 2, with the exception of the Lower Salmon and Upper Salmon reaches (Figure 6).
The homogenous genetic composition among the Middle Snake reaches may be furthered
by contemporary downstream migration and anthropogenic activities, although it may be
historical in nature. Net downstream entrainment of tagged individuals has been documented in
several reaches of the Middle Snake (Idaho Power Company 2005). Downstream migration of
~2% of Bliss white sturgeon into the CJ Strike reach annually and human assisted translocation
of individuals between CJ Strike and Bliss would increase similarity between those reaches
(Idaho Power Company 2005; Bates & Lepla 2009; Lepla & Bates 2011). The Lower Salmon
and Upper Salmon reaches exhibit no genetic differentiation, likely due to a common history of
stocking with hatchery-reared individuals from the Bliss reach to mitigate for recruitment failure
and low abundance (Idaho Power Company 2005; Bentz & Lepla 2009; Bentz 2010). The fact
we cannot detect either downstream migrants or translocated or stocked individuals with a
Structure analysis suggests that genetic similarities existed among reaches before these
management activities were conducted. An alternative explanation is that our molecular markers
are not power enough to detect fine scale genetic differentiation in this region.
SECTION 3. CONSIDERATIONS FOR SNAKE RIVER WHITE STURGEON GENETIC
MANAGEMENT
Conservation of genetic diversity in Snake River reaches
Preserving genetic diversity, including rare alleles, in Snake River white sturgeon is a
goal of the proposed White Sturgeon Conservation Plan (WSCP; Idaho Power Company 2005).
Our analyses suggest that this may be achieved by protecting the reaches identified by the state
of Idaho as Core Conservation populations: the Hells Canyon and Bliss reaches (Dillon &
Grunder 2008). Both Core Conservation populations possess high levels of genetic diversity
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relative to levels detected in other reaches within their respective populations (Lower Snake and
Middle Snake; Figure 2A, B). The protection of the Hells Canyon and Bliss reaches ensures that
genetic diversity unique to the Lower Snake population (reaches downstream of Hells Canyon
Dam) and that unique to the Middle Snake population (reaches upstream of Brownlee Dam) will
be preserved.
Relative to the Hells Canyon Core population, the Little Goose and Lower Granite
reaches in the Lower Snake River population exhibit fairly high levels of genetic diversity
(Figure 2A, B). Limited spawning has been documented downstream of Ice Harbor, Lower
Monumental, Little Goose, and Lower Granite dams by Parsley & Kappenman (2000), indicating
that recruitment may occur at least periodically in those reaches. Thus, Lower Snake reaches
may not require management intervention to enhance genetic diversity levels.
In contrast, Middle Snake reaches possess less genetic diversity than Lower Snake
reaches, particularly those reaches at the upstream-most extent of the species’ range (Figure 2A,
B). Lower genetic diversity levels in these reaches are due to low abundance and isolation from
gene flow. Genetic diversity levels in the Shoshone, Upper Salmon, and Lower Salmon reaches
were likely increased by hatchery stocking into the Shoshone and Lower Salmon reaches from
1989 – 2000 using broodstock originating from the genetically diverse Bliss reach (Idaho Power
Company 2005). Conservation aquaculture may be considered to increase or protect existing
levels of genetic diversity in reaches with low abundance and little to no recruitment. However,
conservation aquaculture programs should be designed to reduce possible negative genetic and
ecological impacts of hatchery supplementation on source and recipient reaches.
Design of conservation aquaculture programs
The WSCP recommends that conservation aquaculture be used to enhance white
sturgeon abundance in the Shoshone and Swan Falls reaches (Idaho Power Company 2005).
Conservation aquaculture is a “stopgap” measure to preserve the phenotypic, behavioral, and
genetic characteristics of an imperiled population while ecological restoration improves habitat
to support natural reproduction and population recovery (Anders 1998; Ireland et al. 2002).
Conservation aquaculture is an essential management tool for white sturgeon populations
14
experiencing no natural recruitment such as the Kootenai River, Transboundary Reach, and
Nechako River (Ireland et al. 2002; Schreier & May 2012; Drauch Schreier et al. 2012). Despite
the necessity of such programs, caution must be taken to minimize the deleterious genetic and
ecological effects of hatchery supplementation (Waples 1999; Einum & Fleming 2001).
Negative genetic consequences of conservation aquaculture programs include genetic diversity
loss, reduction in Ne, inbreeding and outbreeding depression, and domestication selection (Naish
et al. 2008).
Conservation aquaculture programs can be designed to reduce the negative genetic
consequences arising from artificial spawning and hatchery rearing. Sampling eggs or larvae
from natural spawning events to rear in a hatchery increases genetic diversity and minimizes
some aspects of domestication selection. Using many parents to create hatchery year classes
and conducting multi-year stocking reduces genetic diversity loss. Selecting an appropriate
broodstock source preserves genetic diversity while minimizing the risk of outbreeding
depression. Below we review genetic considerations surrounding the design of sturgeon
conservation aquaculture programs. Two sturgeon conservation aquaculture strategies will be
discussed: egg/larval sampling and broodstock capture.
Egg/Larval Sampling
In traditional sturgeon conservation aquaculture, wild broodstock are captured,
transported to a hatchery, artificially spawned, released, and their offspring are stocked into a
wild (recipient) population. This approach may lead to negative genetic consequences. Often
physical or temporal limitations reduce the number of broodstock that can be used within and
among years which leads to genetic diversity loss. Overrepresentation of a few hatchery families
relative to wild families in the recipient population can lead to a Ryman-Laikre effect (Ryman &
Laikre 1991), which reduces the Ne of the recipient population. Non-random selection of
broodstock is a form of domestication selection that can lower the fitness of hatchery produced
progeny. However, direct collection of eggs or larvae from natural spawning events is an
alternative approach to conservation aquaculture that minimizes some negative genetic effects.
15
Capturing naturally spawned eggs or larvae instead of broodstock reduces the time,
person-power, and space required to conduct sturgeon conservation aquaculture. The egg/larval
collection approach may represent a larger number of parents than traditional broodstock
capture, which would reduce the likelihood of a Ryman Laikre effect on the recipient population.
Natural mate choice behaviors of spawning adults are maintained through this method,
eliminating one stage at which domestication selection may occur.
Egg/larval collection methods have been validated by conservation aquaculture programs
for several species. Hydraulic sampling was used to successfully collect surviving eggs from
naturally spawned steelhead and Chinook redds for conservation aquaculture (Berejikian et al.
2011). The Washington Department of Fish and Wildlife (WDFW) white sturgeon conservation
aquaculture program captures naturally spawned larvae during their passive drift stage
downstream from a known spawning site for rearing in a hatchery (J. McClellan, Colville Tribes,
pers. comm.). Larval collection represents considerably more genetic diversity in a single year
than the wild broodstock based method in the Upper Columbia program (Schreier & May 2012).
Due to its greater ability to preserve genetic diversity and minimize domestication
selection, egg/larval collection is a preferred method of sturgeon conservation aquaculture.
However, we recognize that logistical constraints make this method impractical in some river
systems. This approach requires exact knowledge of spawning sites and timing of spawning
events. The location or timing of white sturgeon spawning in the Snake River may make
collection of eggs and/or larvae infeasible. The following section will discuss genetic
considerations for the broodstock based conservation aquaculture in case this option is selected
for sturgeon conservation aquaculture in the Snake River. These considerations include 1)
selection of an appropriate broodstock source, 2) maximization of Ne and maintenance of genetic
diversity, 3) prevention of inbreeding, and 4) minimization of domestication selection.
Broodstock Capture
Selection of an Appropriate Broodstock Source
Broodstock for conservation aquaculture ideally should be collected from the recipient
population to maintain natural patterns of population structure and preserve unique genetic
diversity. However, collecting broodstock from Snake River reaches with few sexually mature
16
adults or low genetic diversity levels is undesirable. When a recipient reach is not a good
candidate for broodstock collection, another reach may be selected as broodstock source as long
as it meets several demographic and genetic criteria.
Optimal broodstock sources for conservation aquaculture should possess 1) relatively
high abundance, 2) high levels of genetic diversity and 3) genetic similarity to the recipient
population (Drauch et al. 2007). Abundant populations are more likely to possess favorable
genetic characteristics such as high genetic diversity, high effective population size (Ne), and low
levels of inbreeding. High abundance also ensures that the removal of some natural reproduction
from the source population will not significantly harm its viability. However, care should be
taken to avoid removing production from so many sexually mature adults that recruitment in the
source population is negatively impacted.
An influx of genetic diversity from a highly diverse broodstock source may minimize the
effects of inbreeding depression in a recipient population and improve its ability to adapt to
changing selective pressures (Section 1). Releasing genetically diverse progeny increases the
likelihood that some individuals will be well adapted to the novel environment, which may
reduce post-stocking mortality. For example, a common toad reintroduction utilizing a
genetically divergent but highly diverse source population was more successful than
reintroductions attempts using a local source population with low genetic diversity (Zeisset &
Beebee 2012). Individuals from the divergent but diverse populations quickly adapted to local
conditions while individuals from the lower diversity source failed to contribute to the
reintroduced (Zeisset & Beebee 2012).
The use of genetically similar broodstock sources increases the likelihood that stocked
individuals will survive in the recipient reach and reduces the chance that outbreeding depression
will occur (Section 1). Genetic similarity between two populations implies they share recent coancestry or maintain high levels of contemporary gene flow. Genetically similar populations are
more likely to share adaptive alleles that would allow individuals from the source population to
survive in the recipient population’s environment. In addition, mating between individuals from
genetically similar populations is less likely to break up co-adapted gene complexes and
therefore presents minimal risk of outbreeding depression.
17
Maximization of Ne and Maintenance of Genetic Diversity
As genetic diversity and its interactions with the environment underlie much of the basis
for phenotypic and behavioral characteristics that conservation aquaculture seeks to protect, it is
important that conservation aquaculture programs preserve as much genetic diversity as possible.
The magnitude of genetic diversity loss in a small population can best be predicted by its
effective population size, Ne (Section 1). Populations with a large Ne will be able to maintain
high levels of genetic diversity that are needed for adaptation over time. Populations with a
small Ne are at risk for genetic diversity loss that could threaten their long term viability.
The Ne of a conservation aquaculture program can be decreased by several factors
including an unequal sex ratio and variance in individual reproductive success (Futuyma 2005).
Conservation aquaculture programs may be designed to minimize these effects. To avoid Ne
reduction due to an unequal sex ratio, similar numbers of male and female parents may be used
to create each year class. Several strategies might be taken to reduce variance in reproductive
success. Pooling of gametes, where gametes of multiple individuals are mixed in a single batch
for fertilization, is associated with high variance in reproductive success because of strong sperm
competition. Therefore the partitioning of eggs from each female into separate batches for
fertilization by different males is recommended (Fiumera et al. 2004; McLean et al. 2007). In
monogamous crosses (one female mated with a single male), the fitness of the male is entirely
dependent on that of the female and vice versa. Complete loss of reproductive fitness due to
pairing with an unfit mate can lead to large variance in reproductive success.
Partial factorial or factorial mating schemes (Figure 7) increase the likelihood that an
individual will be able to produce successful offspring by “spreading the risk” of reproductive
failure over matings with multiple individuals. A simulation study comparing different mating
schemes in a robust redhorse conservation aquaculture program predicted that factorial mating
would produce the highest Ne (Fiumera et al. 2004). However, full factorial crosses may be
infeasible for conservation aquaculture programs using wild broodstock that may reach
reproductive readiness at different times. Available hatchery space may also limit the number of
crosses that are possible in a particular program. Partial factorial crosses, which spawn together
subsets of the male and female broodstock pool, may be nearly as effective as full factorial
crosses for maximizing the effective number of breeders (Busack & Knudsen 2007).
18
Another strategy to reduce variance in reproductive success and increase Ne is to equalize
family sizes, or release the same number of offspring from each family. This strategy involves
culling individuals from highly successful families to avoid the effect of “swamping” the
recipient population with the genes of a few families. Limiting the number of individuals
stocked also reduces the Ryman-Laikre effect (Ryman & Laikre 1991), although this concern is
less relevant in a reach with no natural reproduction. Although equalizing family size or
otherwise limiting the number of individuals stocked maximizes Ne, it also may reduce the rate
at which the recipient population increases in abundance. In the Snake River, a modeling study
found that equalization of family sizes in conservation aquaculture would result in smaller
increases in abundance than if all offspring were released (Jager 2005).
Other ways to maximize Ne and the amount of genetic diversity preserved in conservation
aquaculture are to utilize a large number of parents in captive breeding and to stock over multiple
years. The Kootenai Tribe of Idaho (KTOI) Kootenai River white sturgeon conservation
aquaculture program strives to represent at least 12 females annually (Lewandowski 2011) and
from 2002 – 2012, has used between 10 and 28 unique broodstock (4 – 16 females) each year (C.
Lewandowski, KTOI, pers. comm.). The Upper Columbia River white sturgeon conservation
aquaculture program, operated by WDFW, used 95 broodstock from 2001 - 2010. Both
programs have been highly successful at preserving genetic diversity of their recipient
populations. The KTOI and WDFW programs have each represented 96% of microsatellite
alleles in the Kootenai and Upper Columbia River in ten and nine years of conservation
aquaculture, respectively (Schreier & May 2012; Drauch Schreier, et al. 2012).
Inbreeding Prevention
Conservation aquaculture programs can introduce inbreeding (Section 1) into a recipient
population in two ways. First, different mating schemes in the hatchery may increase overall
relatedness within a recipient population. Partial factorial and factorial mating schemes produce
large numbers of half sibling families which theoretically may increase the level of relatedness in
the recipient population, although this effect is lessened if these schemes are implemented across
multiple years of a hatchery program (Dupont-Nivet et al. 2006; Busack & Knudsen 2007).
19
The second way in which conservation aquaculture can introduce inbreeding in a
recipient population is through the unintentional mating of close relatives in a hatchery program.
The resultant offspring of closely related parents may exhibit inbreeding depression (Section 1),
or a lowering of fitness due to the expression of recessive deleterious alleles. The likelihood of
crossing close relatives in a hatchery environment depends upon the size of the broodstock
source population and whether or not the source population has a history of supplementation.
The likelihood of capturing two sexually mature adults that are close relatives is higher in a small
population than in a large population. Populations into which extensive stocking has been
conducted also would contain a higher proportion of full siblings and half siblings relative to a
wild population of similar size. In the long-lived white sturgeon, parent-offspring crosses are
also a possibility.
Inbreeding may be prevented in both in conservation aquaculture and recipient
populations through several measures. Multi-year stocking programs using unique wild
broodstock each year can alleviate concerns about increasing relatedness in the recipient
population due to partial factorial and factorial mating schemes (Dupont-Nivet et al. 2006;
Busack & Knudsen 2007). Marking all fish handled in the conservation aquaculture program
will allow hatchery personnel to identify and avoid using broodstock from previous years and
also avoid crosses between hatchery-reared individuals which may be close relatives. If marking
hatchery-reared individuals is infeasible due to small size at release or other constraints, a
broodstock genotype archive may assist hatchery personnel in avoiding crosses between close
relatives. The KTOI white sturgeon conservation aquaculture program maintains a tissue archive
in collaboration with the Genomic Variation Lab at UC Davis to identify familial relationships
between hatchery-reared offspring (Drauch Schreier et al. 2012). In the future, when hatcheryreared offspring in the Kootenai River have reached sexual maturity, we can use parentage
analysis to identify close relatives in the potential broodstock pool.
Prevention of Domestication Selection
In conservation aquaculture, organisms are bred in a human-controlled environment
which can lead to domestication selection, or artificial selection pressures that may lead to
genetic changes in a population (Waples 1999). One way domestication selection may occur is
through the intentional or unintentional selection by hatchery personnel for particular phenotypes
20
in broodstock or offspring. The selection of early returning individuals as broodstock in
hatchery programs for coho and Chinook salmon in Washington have resulted in earlier run
times in recipient populations (Quinn et al. 2002). Similarly, the selection by hatchery personnel
of Russian and stellate sturgeon females in advanced stages of maturity early in their spawning
runs have led to a shortening of the spawning period for both species in the Kuban River
(Chebanov et al. 2002).
Modified selection pressures need not be directly anthropogenic in origin. The unnatural
conditions experienced by offspring reared in hatcheries, such as abundant food and unnaturally
high densities may select for traits that are not conducive for survival in the wild. The absence
of natural selective pressures may be considered another form of artificial selection. In addition,
artificial spawning protocols in the hatchery may de-couple the correlation between traits
conferring high fitness in the wild and reproductive success in the hatchery. Individuals with
maladaptive traits may experience high reproductive success in the hatchery relative to
individuals with phenotypes optimal for fitness in the wild (McLean et al. 2007). Maladapted
offspring may survive in that hatchery in the absence of environmental stressors but exhibit high
post-stocking mortality or low reproductive success in the wild (Waples 1999).
Reducing the number of generations in captivity may minimize the potential for
domestication selection (Frankham 2008), although even a single generation of selection in a
hatchery may reduce an individual’s reproductive success in the wild (Christie et al. 2012).
Using many randomly collected unique wild adults as broodstock each year reduces the potential
for domestication selection relative to a program with a captive broodstock (McLean et al. 2007).
Capturing eggs or larvae from natural spawning events would eliminate unnatural selective
pressures on the parental generation, although rearing in a captive environment would still
impose artificial selective pressures on hatchery-reared offspring.
Theoretically, equalizing family sizes may minimize domestication selection as it reduces
competition among families (some of which might be better adapted to captivity) and increases
competition within families (Frankham 2008). Although this approach does reduce genetic
adaptation to the captive environment, empirical data do not support that equalization of family
sizes positively affects fitness of captive bred populations after reintroduction into the wild
(Frankham et al. 2000; Frankham 2008). Because post-stocking mortality of hatchery-reared
21
fishes is often high and likely to differ among families, the positive effects of equalization of
family sizes may be erased by the time stocked individuals reach sexual maturity (Waples 1999).
Translocation
Translocation involves the movement of individuals from a source population into a
recipient population or uninhabited region (reintroduction). Selection of an appropriate source
population for translocation should follow the guidelines for source selection in conservation
aquaculture programs. Source populations for translocation should be 1) abundant, 2) possess
high levels of genetic diversity and 3) be genetically similar to the source population. See
Selection of an Appropriate Broodstock Source for a thorough treatment of the genetic criteria
for source population selection.
Like conservation aquaculture, translocation should be seen as a “stopgap” measure to
support declining reaches until natural recruitment can be re-established. One advantage of
translocation over conservation aquaculture is that translocation uses only wild-bred individuals
and therefore is less likely to introduce inbreeding or domestication selection. Reintroductions
conducted through translocation of wild individuals tend to be more successful than
reintroductions conducted through translocation of captive-bred individuals (Frankham 2008).
Adult and juvenile white sturgeon are currently being translocated in the Middle Snake
River. Reproductive adults from the CJ Strike reach are being moved in the Bliss reach and
juveniles from the Bliss reach are being returned into the CJ Strike reach to mitigate for
recruitment failure in the CJ Strike reach (Bates & Lepla 2009; Lepla & Bates 2011). The Bliss
reach is an ideal source for translocation in the Middle Snake based on the criteria of abundance,
high levels of genetic diversity, and genetic similarity to other reaches of the Middle Snake
(Section 2). Because no spawning habitat is available in the CJ Strike Reach (Idaho Power
Company 2005), the translocation of reproductive adults will have no effect on reproductive
success in the reach. However, the continued movement of sexually mature adults may
eventually reduce abundance in the CJ Strike reach if an adequate number of juveniles are not
translocated from Bliss to CJ Strike. If translocation is to be conducted from Bliss into other
reaches of the Middle Snake, the movement of individuals from multiple year classes (juveniles
and adults) or translocation over multiple years would maximize the amount of genetic diversity
22
introduced into the recipient population (Young 1999). Care must be taken not to deplete the
pool of reproductive adults in the Bliss reach to support abundance in reaches with little or no
potential to re-establish natural reproduction.
Genetic monitoring
Genetic monitoring provides a means to examine population genetic trends in managed
populations over time. Standardized genetic monitoring protocols will allow managers to
identify genetic changes in populations, such as genetic diversity loss, that may warrant
management intervention. Genetic monitoring of supplemented populations is particularly
important due to the potential of conservation aquaculture programs to reduce Ne and threaten
the long term viability of recipient populations.
The first step in establishing a genetic monitoring program is to develop a genetic
baseline to which all future genetic assessments can be compared (Stephens et al. 2013). The
population genetic analyses described in this report provide a sufficient baseline for future
genetic monitoring of most Snake River reaches. If monitoring is to be conducted immediately
above and below the Ice Harbor Dam, or in the Oxbow or Brownlee reaches, sufficient numbers
of samples (minimally N = 50) from those reaches must be collected and analyzed to establish a
genetic baseline.
To be directly comparable to the genetic baseline, population genetic parameters should
be measured using the same 13 microsatellite loci used to develop the baseline. Metrics such as
number of alleles, numbers of alleles/individual/locus (proxy for heterozygosity), and pairwise
Phi-PT analyses should be estimated and compared between time intervals. Monitoring in a
conservation aquaculture program may compare the amount of genetic diversity represented in a
year class to the total amount of genetic diversity in a recipient reach (Schreier & May 2012;
Drauch Schreier et al. 2012). Future technological advances may lead to the development of
more powerful genetic markers for Snake River white sturgeon genetic monitoring. New genetic
baselines must be developed if new genetic markers are to be used in future genetic monitoring
of Snake River white sturgeon.
The time intervals at which a population should be assessed as part of genetic monitoring
varies with the life history schedule of a particular species. In a short lived or semelparous
23
species, annual or biennial genetic assessments may be appropriate. Populations under imminent
threat from introgression or other genetic or demographic stressor at frequent intervals as well
(e.g. Stephens et al. 2013). In long lived organisms that are under no imminent genetic or
demographic threat, genetic monitoring may be conducted on a more protracted schedule.
The White Sturgeon Conservation Plan recommends that population monitoring be
conducted on Snake River reaches at five (Shoshone, Upper Salmon, Bliss, CJ Strike, Swan
Falls) or ten (Hells Canyon) year intervals (Idaho Power Company 2005). Tissue samples for
genetic monitoring should be collected when these population monitoring activities occur.
However, as long-lived species can retain genetic diversity over extended time periods (Quattro
et al. 2002; Kuo & Janzen 2004; Lawrence et al. 2008), genetic diversity changes due to
declining abundance or inbreeding may not be detectable until long after demographic
parameters begin to change. In reaches not involved in supplementation or translocation (as a
source or recipient reach), genetic monitoring should be conducted at ten year intervals. Reaches
involved in supplementation or translocation should be monitored at five year intervals.
Managers may increase the frequency of monitoring at their discretion if there is reason to
suspect a significant demographic change (e.g. bottleneck) may be imminently threatening a
reach’s genetic diversity levels. Reaches with particularly low abundance (Oxbow, Brownlee)
may be monitored at a more frequent interval. We recommend that managers collect a minimum
of 50 tissue samples from each reach at the appropriate genetic monitoring interval. They may
be stored in 95% ethanol and archived at room temperature until DNA can be extracted.
We recommend special monitoring efforts for Snake River conservation aquaculture
programs to determine whether the program is maximizing Ne and representing adequate
amounts of genetic diversity. Direct Ne estimation is not possible in white sturgeon due to the
species’ complex genome and life history. Examining genetic diversity levels of broodstock or
progeny provides feedback to managers about whether or not their program design adequately
meets genetic diversity targets.
If the broodstock collection approach to conservation aquaculture is used, we recommend
that genetic monitoring involve a genome size monitoring component. Recent work has shown
that spontaneous autopolyploids are unintentionally produced in aquaculture programs for white
sturgeon, bester, Siberian sturgeon, and Sakhalin sturgeon (Omoto et al. 2005; Drauch Schreier
24
et al. 2011; Pšenička et al. 2011; Zhou et al. 2011; Schreier et al. 2013). The spontaneous
autopolyploids possess a genome size 1.5x the size of the normal species genome size. White
sturgeon spontaneous autopolyploids are viable and appear to be fertile but the fitness of their
offspring is uncertain (Drauch Schreier et al. 2011; Schreier et al. 2013). Flow cytometry
analysis, whereby DNA in blood cells of broodstock and offspring can be dyed and measured,
can identify whether spontaneous autopolyploids are being produced in Snake River white
sturgeon conservation aquaculture.
SECTION 4. RECOMMENDATIONS FOR SNAKE RIVER WHITE STURGEON
MANAGEMENT
This section provides recommendations for genetic management of Snake River white
sturgeon, organized by genetic consideration discussed in Section 3. Bullet points provide
specific recommendations and where necessary, accompanying text provides further information.
Conservation of genetic diversity in Snake River reaches
Continue to protect Core Conservation populations.
Protecting the Hells Canyon and Bliss reaches as Core Conservation populations will preserve
genetic diversity unique to the Lower Snake and Middle Snake populations, respectively. The
Bliss reach, in particular, possesses genetic diversity unique to the Middle Snake that may be
drawn upon to supplement genetic diversity in small, recruitment-limited reaches upstream.
No management action is needed to supplement genetic diversity in the Lower Snake.
As periodic recruitment continues to support Lower Snake reaches and genetic diversity levels
are relatively high, no management intervention to supplement genetic diversity is currently
necessary in the Lower Snake.
25
Management action may be warranted to prevent continued genetic diversity loss in the
Middle Snake.
Small, recruitment-limited reaches in the Middle Snake will continue to lose genetic diversity if
natural recruitment is not re-established. Conservation aquaculture or translocations provide
the means to preserve or supplement recruitment-limited reaches with unique Middle Snake
genetic diversity.
Design of conservation aquaculture program for Middle Snake River
We recommend collection and rearing fertilized eggs or down-migrating larvae instead of
collecting broodstock to conduct conservation aquaculture in the Middle Snake River.
Sampling naturally spawned eggs or larvae lessens some negative genetic effects of conservation
aquaculture, including genetic diversity loss, inbreeding, and domestication selection. Sampling
at multiple sites and at multiple times in the spawning season represents a maximum number of
parents and minimizes selection for maturity rate or spawning time.
The Bliss Reach represents an appropriate source for egg/larval collection for stocking in the
Middle Snake population. However, occasional collection of eggs and/or larvae within the
reach into which supplementation is to occur is desirable.
The Bliss reach represents a good source for larval collection due to its high levels of genetic
diversity and genetic similarity to other reaches in the Middle Snake population. However,
occasional sampling of eggs and/or larvae within the reach into which supplementation is to
occur will increase the total number of white sturgeon parents represented in the Middle Snake.
Increasing the number of parents represented will reduce inbreeding in the Middle Snake
population. If egg/larval sampling in recipient reaches is not feasible and all larval sampling
will be restricted to the Bliss reach, relatedness within the Middle Snake may also be reduced by
translocating adult white sturgeon from downstream reaches (CJ Strike, Swan Falls) into the
Bliss reach. Translocation of sexually mature individuals from CJ Strike or Swan Falls reaches
26
will increase the number of unique parents that can be represented in Middle Snake conservation
aquaculture.
If the egg or larval sampling strategy for sturgeon conservation aquaculture is deemed infeasible
in the Middle Snake River, broodstock collection for conservation aquaculture may be conducted
following these recommendations:
Only wild broodstock should be used for Snake River conservation aquaculture.
Spawning an often limited number of captive broodstock reduces genetic diversity and increases
the potential for domestication selection in conservation aquaculture.
Broodstock should be collected from the recipient reach, if possible, to maximize the number
of unique parents represented in Snake River conservation aquaculture.
Previous stocking in the Middle Snake River was conducted with a small number of parents from
the Bliss reach (Patterson et al. 1992). Including adults originating from other areas in the
Middle Snake, such as the reaches into which stocking will occur, would increase the genetic
diversity and reduce the level of relatedness in the Middle Snake population.
If the recipient reach is not deemed an appropriate broodstock source, the following may be
considered:
The broodstock source for conservation aquaculture should be from within the population to
which the recipient reach belongs.
Conservation aquaculture in Middle Snake River should only use broodstock originating from a
Middle Snake reach. Similarly, if conservation aquaculture were conducted in the Lower Snake
River, only broodstock from a Lower Snake reach should be used.
27
Only Snake River reaches with relatively high abundance, high genetic diversity, and regular
recruitment should be considered as possible broodstock sources for conservation
aquaculture.
The Bliss reach possesses relatively high abundance, high genetic diversity, and high genetic
similarity to other Middle Snake reaches and therefore represents an appropriate broodstock
source for Middle Snake conservation aquaculture. If conservation aquaculture were to be
conducted in the Lower Snake, the Hells Canyon reach possesses relatively high abundance,
high genetic diversity, and high genetic similarity to other Lower Snake reaches and therefore
represents an appropriate broodstock source.
Capture a sufficient number of randomly collected broodstock to represent high levels of
genetic diversity.
Previous conservation aquaculture activities in the Middle Snake River used only 3 (one female
and two males) and 4 (two females, two males) broodstock in 1988 and 1990, respectively. An
appropriate broodstock target for genetic diversity preservation would be 10 broodstock of each
sex each year, assuming that a factorial or partial factorial mating scheme is used (see below).
Random selection of broodstock reduces the potential for domestication selection.
Stock multiple year classes to maximize Ne and genetic diversity in recipient reaches.
Stocking over multiple year classes increases the number of parents represented in recipient
reaches and therefore increases genetic diversity.
Natural reproduction in source populations should not be compromised by broodstock
selection.
The number of broodstock selected for conservation aquaculture each year should not be great
enough to appreciably reduce the reproductive capacity of source population. Appendix II
28
provides information about the number of broodstock that produce surviving offspring each year
in the Bliss reach.
Implement partial factorial or factorial mating schemes with similar numbers of male and
female parents to maximize Ne in each year class.
Factorial mating schemes maximize Ne but may not be practical due to variability in broodstock
availability or ovulation times. Partial factorial mating schemes provide more flexibility in
making crosses while providing similar benefits to Ne as full factorial crosses.
Managers must consider tradeoffs between Ne and genetic diversity preservation and rate of
increase in recipient population abundance when deciding whether or not to equalize family
sizes within a year class.
Equalizing family sizes will maximize Ne and genetic diversity in the recipient reaches while
reducing the rate at which the white sturgeon in the recipient reach increase in abundance.
Choice of whether or not to equalize family sizes will depend upon management goals for a
particular reach.
Mark broodstock and hatchery-reared individuals before release so they can be identified in
later years of the conservation aquaculture program.
The ability to identify former broodstock reduces the likelihood that individuals will be included
more than once in conservation aquaculture. The ability to identify hatchery-reared offspring
and their year class or family of origin reduces the likelihood that close relatives will be crossed
in future years of a conservation aquaculture program.
Translocation recommendations
29
If translocation is continued as a means to mitigate for reduced recruitment in the Snake River,
the following is recommended:
The Bliss reach should continue to be used as a source for translocation in the Middle Snake.
If translocations were to be conducted in the Lower Snake, the Hells Canyon reach would be
an appropriate source.
Translocation events should be comprised of multiple year classes and/or should be
conducted over multiple years to maximize genetic diversity transfer to the recipient
population.
Representing multiple year classes in a translocation event and/or translocating individuals over
multiple years will maximize the number of parents represented in the recipient population. The
CJ Strike to Bliss reach adult translocation program is an exception to this recommendation, as
it is designed specifically to relocate CJ Strike reach reproduction upstream into an area of
appropriate habitat.
Genetic monitoring recommendations
Collect a minimum of 50 samples from each reach to be monitored at either five- (Shoshone,
Upper Salmon, Bliss, CJ Strike, Swan Falls) or ten-year (Hells Canyon) intervals. Tissue
may be stored in 95% ethanol until genetic analysis.
Metrics including number of alleles, number of alleles/individual, and pairwise Phi-PT
should be estimated for each reach with the same genetic markers used to establish genetic
baselines.
Reaches affected by conservation aquaculture or translocation (either as source or recipient
reaches) should be monitored at 5 year intervals.
30
Reaches not affected by conservation aquaculture or translocation should be monitored at 10
year intervals.
Managers should use discretion to increase monitoring frequency for unsupplemented
reaches if there is reason to suspect the population is in imminent threat of genetic diversity
loss due to observed demographic events.
Conservation aquaculture programs should be monitored to determine whether the program
design adequately meets genetic diversity targets.
Representation of 80% of a reach’s genetic diversity within single years and 90% of a reach’s
genetic diversity across 5 years is an adequate target.
In conservation aquaculture programs using a broodstock collection approach, genome size
monitoring should be conducted annually in the first 2-3 years of the program to determine
whether spontaneous autopolyploids are being produced.
Blood should be collected from juvenile white sturgeon produced using a broodstock collection
approach before they are released. Flow cytometry analysis conducted on blood cells can
determine whether an individual has an abnormally duplicated genome.
ACKNOWLEDGEMENTS
Funding for the development of the white sturgeon genetic management plan was
provided by Idaho Power Corporation (contract # 201121683-0). Alison Muller, Emily
Ringelman, and Alisha Goodbla assisted with data collection. Ken Lepla and Phil Bates
provided helpful information and Mandi Finger provided comments that improved earlier drafts
of the plan.
31
Table 1. Samples used in Snake River white sturgeon population genetic analysis. N refers to
sample size.
Location
Lower Monumental to Little Goose Reach (LGLM)
Little Goose Reach to Lower Granite Reach (LGrLG)
Lower Granite to Hells Canyon Reach (HCLGr)
Brownlee to Swan Falls Reach (SFBr)
Swan Falls to CJ Strike Reach (CJSF)
CJ Strike to Bliss Reach (BLCJ)
Bliss to Lower Salmon Falls (LSBL)
Lower Salmon Falls to Upper Salmon Falls (USLS)
Upper Salmon Falls to Shoshone Falls (SHUS)
Total
32
N
47
49
97
28
47
50
74
21
50
463
Tissue
Fin clip
Fin clip
Fin clip
Blood
Fin clip
Fin clip
Fin clip
Fin clip
Fin clip
Table 2. Pairwise Phi-PT values (below diagonal) show levels of genetic differentiation among Snake River reaches. P-values are
above the diagonal. A sequential Bonferroni correction was conducted to account for multiple comparisons. See Table 1 for reach
abbreviations.
Reaches LGLM LGrLG
HCLGr SFBr
CJSF
BLCJ
LSBL
USLS
LGLM
0.0026
0.0025
0.0015 0.0001 0.0001
0.0001 0.0010
LGrLG
0.016
0.4702
0.0001 0.0001 0.0001
0.0001 0.0001
HCLGr
0.013
0.000
0.0004 0.0001 0.0001
0.0001 0.0002
SFBr
0.029* 0.022*
0.018*
0.0050 0.0025
0.0003 0.0019
CJSF
0.052* 0.031*
0.032*
0.016
0.0113
0.0001 0.0136
BLCJ
0.049* 0.029*
0.030*
0.011
0.010
0.0029 0.101
LSBL
0.064* 0.049*
0.046*
0.029* 0.031* 0.012
0.419
USLS
0.047* 0.033*
0.029*
0.028
0.018
0.008
0.000
SHUS
0.073* 0.056*
0.049*
0.050* 0.042* 0.024*
0.020* 0.020
* Indicates a significant difference after sequential Bonferroni correction (α = 0.05)
33
SHUS
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0010
0.0256
Figure 1. Range of Snake River white sturgeon relative to the species’ range-wide distribution.
Each red dash denotes an impoundment and each blue dash denotes a natural barrier. Snake
River dams are as follows: a) Ice Harbor, b) Lower Monumental, c) Little Goose, d) Lower
Granite, e) Hells Canyon, f) Oxbow, g) Brownlee, h) Swan Falls, i) CJ Strike, j) Bliss, k) Lower
Salmon Falls, l) Upper Salmon Falls, m) Shoshone Falls. Note that Shoshone Falls is a natural
barrier to white sturgeon upstream movement.
34
Figure 2. Genetic diversity levels are highest in the Snake River downstream of the Hells
Canyon complex in analyses (A) ignoring and (B) using rarefaction to account for differences in
sample size (N=21).
35
50
No. Alleles/Individual
49
48
47
46
45
44
43
42
41
40
Figure 3. Number of alleles/individual in each Snake River reach. See Table 1 for reach
abbreviations.
36
Figure 4. A STRUCTURE bar histogram shows a distinction between white sturgeon inhabiting
the Snake River below Hells Canyon Dam and above Brownlee Dam. Each bar represents a
different individual and each color is a genetic cluster detected by the program STRUCTURE.
The proportion of color in each bar represents an individual’s proportional assignment to a
different genetic cluster. See Table 1 for reach abbreviations.
37
Figure 5. A STRUCTURE bar histogram shows an isolation by distance pattern of population
structure in the Columbia-Snake drainage system. Each bar represents a different individual and
each color is a genetic cluster detected by the program STRUCTURE. The proportion of color
in each bar represents an individual’s proportional assignment to a different genetic cluster.
CRE = Columbia River estuary, JDDD = John Day to Dalles reach, MNJD = McNary to John
Day reach, PRMN = Priest Rapids to McNary reach, WPPR = Wanapum to Priest Rapids reach,
RIWP = Rock Island to Wanapum reach, GC - RI = Grand Coulee to Chief Joseph, Chief Joseph
to Wells, Wells to Rocky Reach, and Rocky Reach to Rock Island reaches, TR = Transboundary
Reach, KT = Kootenai River, LGLM = Little Goose to Lower Monumental reach, LGrLG =
Lower Granite to Little Goose reach, HCLGr = Hells Canyon to Lower Granite reach, SFBR =
Swan Falls to Brownlee reach, CJSF = CJ Strike to Swan Falls reach, BLCJ = Bliss to CJ Strike
reach, USLS = Upper Salmon to Lower Salmon Falls reach, SHUS = Shoshone to Upper Salmon
Falls reach. Figure modified from Schreier (2012).
38
SHUS
Coord. 2
LGLM
LSBL
HCLGr
LGrLG
USLS
BLCJ
SFBr
CJSF
Coord. 1
Figure 6. A principal coordinate analysis (PCO) of pairwise Phi-PT values illustrates genetic
divergence between Lower Snake and Middle Snake reaches.
39
Figure 7. Illustration of A) monogamous, B) partial factorial, and C) factorial mating schemes
that may be used in conservation aquaculture.
40
APPENDIX I. POPULATION GENETIC ANALYSIS METHODS
Molecular methods
DNA was extracted from blood and fin tissue collected from white sturgeon sampled
from most reaches of the Snake River known to contain white sturgeon (N=463; Table 1). A
Qiagen DNeasy blood and tissue kit was used to extract DNA from blood samples while a
PureGene DNA extraction kit was used for fin clip tissue. DNA was quantified on a Fujifilm
FLA 5100 fluorimager and diluted to 20 ng. Thirteen microsatellites were used to measure
genetic diversity levels and examine population structure within and among reaches of the Snake
River. Polymerase chain reaction (PCR) conditions and primer sequences for microsatellites
used in this study (AciG 2, AciG 35, AciG 52, AciG 53, AciG 110, AciG 140, As015, Atr 105,
Atr 107, Atr 109, Atr 117, Atr 1101, Atr 1173) are published elsewhere (Rodzen & May 2002;
Zhu et al. 2005; Börk et al. 2008; Drauch Schreier et al. 2012). After amplification, PCR
products were separated by size on a 3730xl Genetic Analyzer (Life Technologies) using the Rox
400 HD size standard. The program GeneMapper v. 4.0 (Life Technologies) was used to score
alleles. Because white sturgeon are evolutionary octoploids (genome present in eight copies;
Drauch Schreier et al. 2011), microsatellite alleles could not be scored as codominant loci.
Instead, each microsatellite allele was treated as a present/absent dominant locus (Rodzen & May
2002; Cordeiro et al. 2003; Israel et al. 2009; Pfeiffer et al. 2009).
Data analysis
Genetic Diversity
Genetic diversity of Snake River white sturgeon was assessed in two ways. First, we
calculated the number of alleles and number of private alleles, or alleles unique to a particular
reach, found in each reach of the Snake River in the program GenAlEx (Peakall & Smouse
2006). The first calculation of number of alleles and private alleles was conducted without
accounting for differences in sample size among reaches. However, differences in sample size
can bias comparisons of genetic diversity as larger samples are expected to contain more alleles
simply because they contain more individuals. A second calculation of allele number was
conducted using a rarefaction method to account for differences in sample size among reaches.
41
This method, implemented in the R program Raresampler, took a subsample equal to the smallest
sample size in the dataset (N=21; USLS) 100 times from each Snake River reach. The number
of alleles was averaged across the 100 iterations and means were graphed for comparison.
Next, we calculated the mean number of alleles per individual as a proxy for
heterozygosity, a parameter that cannot be measured using a dominant dataset. The number of
alleles possessed by each individual across 13 microsatellite loci was calculated in Excel and
averaged for each reach. Individuals with missing data for ≥3 microsatellites were excluded
from calculations to avoid a downward bias.
Population Structure
We examined population structure within and among reaches of the Snake River in four
ways. First, we conducted an AMOVA in the program GenAlEx to provide a global Phi-PT
estimate of how genetic differentiation was partitioned within and among the Snake River
reaches (9999 permutations to assess significance). A second AMOVA analysis was conducted
to determine how much genetic variation was partitioned within and among the Lower and
Middle Snake. Next, we calculated pairwise Phi-PT values in GenAlEx to examine how much
genetic differentiation existed among pairs of Snake River reaches (9999 permutations to assess
significance). A sequential Bonferroni correction was made when interpreting pairwise Phi-PT
values to account for multiple comparisons. Finally, we used the Bayesian population
assignment program STRUCTURE (Pritchard et al. 2000; Falush et al. 2007; Hubisz et al. 2009)
to estimate the number of K populations that exist in the Snake River. STRUCTURE is a genetic
clustering program that groups individuals into populations so that Hardy Weinberg
disequilibrium and linkage disequilibrium are reduced. In other words, it uses a Bayesian
approach to cluster genetically similar individuals. We implemented the correlated allele
frequency model and admixture model in STRUCTURE simulations, as recommended when
gene flow may occur among sampling locations. The LOCPRIOR model was utilized, which
incorporates geographic sampling information to fine tune STRUCTURE analyses when this
prior is found to be informative by the program (Hubisz et al. 2009). The LOCPRIOR model
can be particularly useful in situations where genetic differentiation among samples is expected
42
to be low, as was expected in the Snake River based on previous genetic analysis of the
Columbia-Snake River system (Schreier 2012). Six replicates were conducted for each K, or
possible number of populations, tested in STRUCTURE. We initially conducted exploratory
analyses (50,000 burn-in; 100,000 iterations) of a wide range of K values to determine the subset
of K values most likely in the Snake River. A more extensive analysis (500,000 burn-in;
1,000,000 iterations) was conducted to test the likelihood of this subset of K values, in this case
K=1 through K=4. The program Structure Harvester (Earl & vonHoldt 2011) was used to plot
the mean posterior probabilities (Ln Pr(X|K)) of K=1 to K=4. The K with the highest likelihood
was interpreted to be the number of populations in the Snake River. As examining likelihood
values may overestimate K, particularly when dominant data are used for analysis, the metric ΔK
(Evanno et al. 2005) was also evaluated in Structure Harvester. The program CLUMPP was
used to compile results among all STRUCTURE replicates (Jakobsson & Rosenberg 2007), and
DISTRUCT (Rosenberg 2004) was used to visualize individual assignments.
43
APPENDIX II. ESTIMATING THE NUMBER OF WHITE STURGEON SPAWNERS
CONTRIBUTING TO THE 2006 AND 2011 BLISS YEAR CLASSES
Introduction
Effective population size, Ne, can be used by fisheries biologists to understand how
managed populations evolve and maintain genetic diversity. Maintaining genetic diversity
allows a population to adapt to environmental changes over time and therefore is a crucial
process for population viability. In many long lived organisms, including sturgeon, Ne is
difficult to measure because most Ne estimators are developed for species with discrete
generations (Hare et al. 2011). Estimating the number of breeders in single cohorts may provide
an alternative way to evaluate reproductive trends that ultimately determine Ne in long-lived,
iteroparous organisms (Whiteley et al. 2012).
For some non-model species, the number of spawners may be estimated directly through
parentage-based tagging (PBT; Anderson & Garza 2006), where all parents are genotyped
(“tagged”) and a sample of offspring are genotyped and assigned to parents through parentage
analysis. However, PBT is only useful in species in which a majority of parents can be
genotyped. Sturgeon are cryptic, iteroparous spawners making thorough parental sampling
difficult.
An alternative approach to estimate spawner number in wild populations is through the
reconstruction of family relationships using relatedness analyses, known as progeny array
reconstruction. In progeny array reconstruction, individuals of unknown parentage are either
parsed into relationship categories (e.g. Blouin et al. 1996) or maximum likelihood methods are
used to reconstruct the most likely pedigree in a sample of wild individuals (e.g. Jones & Wang
2010). The number of spawners can be inferred from the number of full sibling and half sibling
clusters or through the structure of the pedigree. A pair of full siblings share two unique parents
while two half siblings share three unique parents. Two unrelated individuals, sharing no
parents, would represent four unique spawners.
Several published papers have used relatedness-based approaches to estimate spawner
number in sturgeon year classes. Rodzen et al. (2004) used the Lynch & Milligan (1994)
relatedness estimator for dominant data to reconstruct family relationships among individuals
44
from a white sturgeon caviar farm. In this approach, a UPGMA tree was used to cluster
individuals into full sibling groups. Israel & May (2010) utilized a similar approach to
reconstruct families among wild down-migrating green sturgeon larvae in the Sacramento River.
They used the Hardy (2003) relatedness coefficient to create relatedness distributions from
known families and develop relatedness threshold values that would distinguish between
relationship types among wild samples. Drauch Schreier et al. (2009) combined the approaches
of Rodzen et al. (2004) and Israel & May (2010) to estimate that 82 parents contributed
surviving offspring to the 2006 white sturgeon year class in the Bliss to CJ Strike Reach of the
Snake River. As in the Israel & May (2010) study, Drauch Schreier et al. (2009) were unable to
reliably resolve half-sibling relationships. As white sturgeon are thought to be polygynadrous,
the presence of half siblings in the dataset is likely and non-detection of half siblings could bias
spawner number estimates.
Here we apply a maximum likelihood analysis to reconstruct familial relationships
among wild white sturgeon juveniles from the 2006 and 2011 Bliss to CJ Strike reach year
classes. First, we report preliminary validation analyses conducted on known white sturgeon
families from a California caviar farm population. Second, we apply the analysis to reconstruct
pedigrees from wild individuals from the 2006 and 2011 year classes and develop estimates of
spawner number for each year. These data may be applied to better understand reproductive
dynamics in white sturgeon inhabiting Bliss reach, a Core Conservation population in the Middle
Snake River.
Methods
Sample collection and DNA extraction
Fin tissue was collected from 193 individuals in the Bliss to CJ Strike Reach from 20112012 as part of juvenile indexing conducted by Idaho Power Company (IPC) biologists.
Juveniles were sampled with an otter trawl (2011, 2012) or a small mesh gill net (2012). IPC
biologists used individual length and a length at age relationship developed for white sturgeon
inhabiting the Bliss to CJ Strike Reach to ensure that all individuals sampled belonged to the
2011 year class (Bates 2013). All tissues samples were stored in 95% ethanol for genetic
45
analysis. We extracted DNA from each tissue sample using a Universal BioRobot (Qiagen),
quantified on a Fujifilm FLA 5100 fluorimager, and diluted to 20 ng.
Microsatellite Genotyping
We genotyped all individuals at thirteen microsatellite loci (AciG 2, AciG 35, AciG 52,
AciG 53, AciG 110, AciG 140, As015, Atr 105, Atr 107, Atr 109, Atr 117, Atr 1101, Atr 1173).
Polymerase chain reaction (PCR) conditions and thermal profiles for these loci are published
elsewhere (Rodzen & May 2002; Zhu et al. 2005; Börk et al. 2008; Drauch Schreier et al. 2012).
PCRs were diluted and 1.0 μl of product was combined with 8.85 μl of highly deionized
formamide (The Gel Company) and 0.15 μl of 400 HD Rox size standard (Life Technologies).
We conducted fragment analysis on a 3730xl Genetic Analyzer (Life Technologies) and
performed allele calling with GeneMapper v.4.0 software (Life Technologies). White sturgeon
possess complex polyploid genomes (Drauch Schreier et al. 2011) and we could not resolve
dosage of microsatellite alleles. Therefore, we treated each microsatellite allele as a dominant
present/absent locus (Rodzen & May 2002; Israel et al. 2009; Drauch Schreier et al. 2012)
Genotype data previously collected for the 2006 year class in Drauch Schreier et al. (2009) were
used for pedigree reconstruction analysis.
Data Analysis
We took a different approach to estimate spawner number in the 2006 and 2011 Bliss to
CJ Strike year classes than that taken in Drauch Schreier et al. (2009). Previously we used a
simulation approach to identify relatedness threshold values for distinguishing between white
sturgeon relationship classes. Specifically, we simulated full sibling, half sibling, and unrelated
individuals from the Bliss to CJ Strike reach using genotype data from randomly sampled adults
from that reach. We used pairwise relatedness values calculated from simulated progeny to
develop the relatedness threshold values that we used to assort wild individual pairs into
relationship categories (full-sibling, half-sibling, unrelated). The approach taken in this study, as
implemented in the program Colony (Jones & Wang 2010), uses maximum likelihood analysis to
reconstruct an entire pedigree from genotype data of wild individuals of unknown parentage.
This maximum likelihood approach was previously unavailable to us as early versions of Colony
did not accept dominant data. Construction of an entire pedigree is superior to the simulation
46
approach because it estimates an entire family configuration simultaneously and provides
measures of confidence for each pedigree node.
Before applying Colony to reconstruct family structure in young wild juveniles, we
validated the ability of the maximum likelihood approach to resolve family relationships in white
sturgeon using dominant microsatellite data. We used genotype data from fifteen half sibling
families of known parentage from a California caviar farm to determine whether Colony could
accurately reconstruct the pedigree. Our first analyses used a small and balanced dataset (SBL;
N=45; 3 individuals/family) to determine what program parameters provided the most accurate
and precise results within a reasonable computational time (≤ 1 week/analysis). A “medium,”
“full likelihood” run with “high” precision performed well and completed in the desired
timeframe. As recommended by (Wang 2008), a complexity prior was applied to minimize
spurious connections between individuals that may occur in the complex pedigree resulting from
mating in a polygamous species. Locus-specific allelic dropout rates and a global genotyping
error rate of 0.01 were used. Additional validation analyses included a larger balanced dataset
(LBL; N=150; 10 individuals/family) and a small, unbalanced dataset (SUBL; N=54; variable
number from each family).
After validation analyses were completed, we used Colony to reconstruct pedigrees for
the 2006 and 2011 year classes from the Bliss to CJ Strike reach. We conducted medium length,
full likelihood, high precision analyses with locus-specific allelic dropout rates, a genotyping
error rate of 0.01, and the complexity prior applied. For all maximum likelihood analyses, three
replicates were conducted to ensure that the most likely pedigree given the data had been
reconstructed. The 2006 year class genotype data were analyzed in two ways. First, all
individuals (N=192) were analyzed together to provide an overall estimate of spawner number.
Second, mitochondrial control region haplotype data were used to parse individuals from the
2006 year class into five maternal lineage groups for analysis (Table A1; Drauch Schreier et al.
2009). Haplotype data were available from 159 individuals from the 2006 year class. The
purpose of the second analysis was to see if partitioning the dataset decreased the likelihood that
full sibling families would be split into multiple half sibling families, a tendency observed in
validation analyses with large datasets (see Results). Maternal lineages E, F, G, and H were not
analyzed separately because they contained only a single individual each. We summed the
47
number of spawners estimated for each of the five maternal lineage groups to develop a spawner
number estimate for the 2006 year class. For the 2011 year class, all individuals were analyzed
together as no mitochondrial control region haplotype data were collected.
Results
Validation analysis
Colony successfully reconstructed all half sibling and full sibling relationships in the SBL
validation with 100% accuracy (8 parents inferred; Table A2). The program’s performance
declined somewhat as the sample size increased. Thirteen parents were inferred for the LBL
validation analysis. Most maternal half sibling families were correctly resolved, with the
exception of families of female 065 for which two dams were inferred (Table A2). However,
more than one father was inferred for full sibling families sired by male 7219. A similar
phenomenon was observed in the SUBL validation analysis. Seven of fifteen full sibling
families were reconstructed correctly, while nine full sibling families were split into two or more
half sibling families (Table A2). Two individuals were placed in full sibling families when they
were paternal half siblings to all other individuals in the family. In total, 19 parents were
inferred for the SUBL dataset. In all three validation analyses, all unrelated individuals were
correctly identified as unrelated.
2006 Year Class
In the analysis of the full 2006 year class dataset (N=192), a total of 147 full sibling
families was detected and the number of spawners was estimated to be 92 individuals. Each full
sibling family contained between one and five individuals (mean 1.3 individuals/family). When
the 2006 year class data was split into different maternal lineages, the number of full sibling
families detected in each lineage ranged from 1 (E-H) to 62 (A). Each family contained one to
four individuals (mean 1.2 individuals/family). Spawner number estimate ranges from
mitochondrial haplotype lineages are presented in Table A1.
The analysis of separate
mitochondrial lineages estimated that the number of spawners contributing surviving offspring in
2006 was 162 individuals.
48
2011 Year Class
A total of 168 full sibling families were reconstructed in the 2011 year class (N=193).
Each family contained one to three individuals (mean 1.1 individuals/family). The spawner
number estimate for the Bliss to CJ Strike 2011 year class was 86 individuals.
Discussion
Progeny reconstruction analyses of both the full 2006 and 2011 Bliss to CJ Strike year
class datasets suggest that similar numbers of adult white sturgeon reproduce successfully in the
Bliss Reach during years with appropriate environmental conditions. These estimates are similar
to that predicted by the relatedness threshold approach (82 spawners; Drauch Schreier et al.
2009). However, analysis of separate haplotype groups in the Bliss 2006 year class provided a
greater estimate of spawner number. We expected that reconstructing separate pedigrees for each
maternal lineage in the 2006 year class would prevent upward biasing of spawner number
estimates due to splitting of full sibling families into multiple half sibling families, as observed in
the LBL and SUBL validation datasets. A comparison of the maternal lineage analyses and the
full dataset analysis revealed that the full analysis indeed classified 11 full sibling groups (as
identified in the lineage analysis) as half siblings. However, separate analysis of haplotype
groups also prevented the detection of paternal half siblings as these individuals may possess
different mtDNA haplotypes. The number of spawners in the 2006 year class likely falls
somewhere in between the two estimates, from 92-162 individuals.
The number of spawners detected in the Bliss to CJ Strike reach was greater than the
number of green sturgeon spawners detected by progeny array reconstruction in the Sacramento
River. Only 5-14 full sibling families (10-28 spawners) were detected across five years in the
Sacramento River, with the majority of families containing more than one individual (Israel &
May 2010). In contrast, we find that most parent pairs in the Bliss to CJ Strike reach produce a
single offspring. We expect the Bliss reach to possess more spawners than the federally
threatened Southern Distinct Population Segment green sturgeon which is projected to have
lower abundance than the Bliss Reach (Moyle 2002).
49
As reported in Idaho Power Company (2005), 14% of females >180 cm in the Bliss reach
are estimated to spawn in a given year. Assuming a white sturgeon sex ratio of 1:1
(Beamesderfer et al. 1995; Chapman et al. 1996; Idaho Power Company 2011) and a recent Bliss
population estimate of 4,025 individuals (Bentz & Lepla 2011), ~166 females should spawn in
years where environmental conditions are appropriate. Our estimates suggest that 43-81 females
spawn successfully in good years, which is less than the number of spawners predicted by field
studies. The discrepancy between the field data and our spawner number estimates may be due
to unsuccessful spawning by some individuals. Previous reproductive experience and body size
have been shown to influence spawning success in other fishes (Trippel 1998; Garant et al. 2001;
Berkeley et al. 2004) and it is possible that small, inexperienced white sturgeon females produce
few surviving offspring. Females that mate with unfit males may also produce few or no
surviving offspring. Some female parents may not have been detected in our analysis due to
limited sample sizes (N = 159-193). The non-random distribution of related juveniles may also
bias spawner number estimates (Whiteley et al. 2012) but this is unlikely in the Bliss dataset as
the majority of individuals examined were found to be unrelated.
The spawner number estimates reported here for the 2006 and 2011 year classes provide
a basis for genetic monitoring. An estimate of spawner number does not directly translate to an
Ne estimate and therefore cannot be used to make inferences about evolutionary dynamics. A Ne
estimate can be made using spawner number estimates from single year classes separated by
several generations (Waples & Yokota 2007) but this approach is not practical in the long-lived
white sturgeon. However, examining the trend in spawner number over time may alert managers
to reductions in reproductive success that may eventually reduce Ne.
Acknowledgements
Funding for this study was provided by the Idaho Power Company (contract #
201121683-0). Alison Muller, Emily Ringelman, and Alisha Goodbla assisted with data
collection. Ken Lepla and Phil Bates provided details regarding sample collection and the
Genomic Variation Lab provided feedback during the development of this report.
50
Table A1. Sample sizes for mitochondrial lineages analyzed separately in the 2006 year class.
Haplotypes E – H were analyzed together because they contained a single individual each. Ns is
the number of spawners estimated for each lineage.
Haplotype
A
B
C
D
E-H
N
82
38
12
23
4
Ns
54
40
22
38
8
51
Table A2. Results of Colony validation analyses using individuals of known parentage from a
California caviar farm. Nfemale refers to the number of female parents inferred from the
reconstructed pedigree, while Nmale refers to the number of male parents inferred. The sex of the
inferred parents can be determined because their identity was known a priori. Ns is the total
number of parents inferred in the dataset. The true numbers of female and male parents were
five and three, respectively (Ns = 8).
Dataset
N
Nfemale
Nmale
Ns
SBL
45
5
3
8
LBL
150
6
7
13
SUBL
54
9*
10
19
*Five female parents were inferred for offspring of female O62
52
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