Determining genetic impacts of two different hatchery management strategies in Chinook
salmon (Onchorhynchus tshawytscha) using restriction-site associated DNA-sequencing
(RAD)
Jennifer Gardner
Abstract
Captive breeding programs can be used to supplement declining wild populations. For
declining salmon populations these programs take the form of hatcheries which can be managed
in a number of ways. Two main strategies are segregation: keeping the hatchery population
completely separate from the wild population, and integration: keeping the hatchery population
as close to the wild population as possible by allowing interbreeding. Next generation restrictionsite associated DNA-sequencing (RAD) can be used to generate genetic data in order to
determine population structure among hatchery fish and wild populations. This study used
Chinook salmon (Onchorhynchus tshawytscha) from a hatchery developing both integrated and
segregated line. DNA was extracted and RAD sequencing was used to analyze 200 SNP
polymorphic loci. These analyses revealed that there are some detectable differences between
populations, with all hatchery populations being different from the wild population but with
integrated lines being the closest of any to the wild population. This study supports the theory
behind integrated hatchery lines, revealing that after three generations the integrated line is still
fairly similar to the wild population. It also shows that segregated populations can change
drastically in three generations so unless segregation from the wild population is certain,
integration seems the better strategy for minimizing impacts of hatcheries on wild populations.
Keywords
Hatchery, RAD sequencing, Chinook salmon,
Introduction
As increasing numbers of species and populations of wild populations become endangered
efforts are being made to maintain them (Ruckelshaus et al. 2002)Programs that help increase
population numbers are sometimes put in place (Laikre et al. 2010). These programs can take
different forms, including, transplanting individuals from other populations, using captive
breeding and release to increase numbers for harvest, using captive breeding to supplement the
wild population, and maintaining an isolated captive breeding population (Laikre et al. 2010).
Depending on the organism in question, different programs are more applicable. However, there
are problems associated with each type of program (Laikre et al. 2010). A major concern is the
genetic risks associated with captive breeding. Genetic changes can be due to captivity resulting
in reduced selection on deleterious alleles (Lynch and Hely 2001), or intentional domestication
selection being different from selection in wild environments (Snyder et al. 1996). Additionally,
loss of genetic diversity due to founder effects of captive populations and overrepresentation of
family sizes (Small et al. 2009), genetic drift or inbreeding in a small population size can also
happen in captive populations (Naish et al. 2013, Fraser 2008, Lynch and Hely, 2001).
Pacific salmon (Onchorhynchus spp.) have been declining in the Pacific Northwest due to over
harvesting, dam construction, and other anthropogenic causes (Ruckelshaus et al. 2002).
Hatchery programs support nearly all the fisheries for Pacific salmon in this area (Mobrand et al.
2005). These hatcheries are now carefully managed and directed to use best available science to
determine the best ways to minimize the impact of hatcheries on wild populations (Mobrand et
al. 2005). Hatcheries are meant to supplement wild populations to prevent extinction and allow
continued harvest, but without changing the overall structure of the wild population they are
supplementing (Fraser 2008). A measure commonly used to determine changes in populations is
measuring genetic changes (Hale et al. 2013). Within hatchery management there are two main
lines of thinking about how to minimize genetic changes in the wild population (Mobrand et al.
2005). The first is to raise hatchery fish and never let them interbreed with wild populations, thus
any genetic changes that occur in the hatchery population will not get in to the wild population
(Mobrand et al. 2005). The second is to only expose the hatchery fish to captivity for as little
time as possible and let them interbreed with wild populations to keep the hatchery population as
similar to the wild population as possible (Mobrand et al. 2005).
These two strategies are called: segregated hatchery lines and integrated hatchery lines (Mobrand
et al. 2005, Seamons et al. 2012). Segregated lines are created when the fish being spawned in
the hatchery (broodstock) only comes from fish previously produced in the hatchery (Mobrand et
al. 2005). Segregation attempts to minimize interbreeding between hatchery and wild
populations. It has also been shown that maintaining segregated hatchery lines that do not
interbreed with the wild population is difficult (Seamons et al. 2012). Integrated lines are created
when hatchery broodstock are taken from the wild population each generation. Fish that
previously spawned in the hatchery are avoided as broodstock in subsequent generations and can
usually be identified by internal tags as well as having the adipose fin clipped (Seamons et al.
2012). This strategy attempts to minimize domestication selection by never allowing more than
one generation to be under domesticating influence (Mobrand et al 2005). Some research has
shown that integrated lines can have minimal influence on the wild population (Christie et al.
2011, Hess et al. 2012, Christie et al. 2013). However, other work has suggested that offspring of
captive bred parents have very reduced reproductive success compared to offspring of wild
matings and that integrated lines can have large influences on the wild population (Araki et al.
2009, Buhle 2009). Segregated lines are created when hatchery broodstock only comes from fish
previously produced in the hatchery (Mobrand et al. 2005). Segregation attempts to minimize
interbreeding between hatchery and wild populations. It has also been shown that maintaining
segregated hatchery lines that do not interbreed with the wild population is difficult (Seamons et
al. 2012). However, other instances show that it may be possible to keep lines segregated and
maintain genetic diversity (Heggenes et al. 2011)
Genetic sequencing can be used to determine the effectiveness of these hatchery strategies (
Estoup et al. 1998, Hale et al. 2013, Christie et al. 2013 ). Genetic sequences can be compared
across hatchery and wild populations. Ideally, the integrated line will be exactly like the wild
population genetically because that would indicate that no genetic changes occur in one
generation of captivity (Mobrand et al. 2005). Also segregated could be very different
genetically from the wild population but if the line is truly segregated and never interbreeds with
the wild population, in a perfect world, these changes will never appear in the wild population
(Mobrand et al. 2005). In order to determine subtle differences between populations, it becomes
increasingly necessary to look at genetic sequence data on a close to genome wide scale (Lamaze
et al. 2012, )
As next-generation DNA sequencing methods become more readily accessible, large-scale
analysis of hatchery systems becomes more plausible. While full genome analysis is still
expensive there are genome-wide survey techniques that are just as useful for analysis (citation).
Restriction-site associated DNA-sequencing (RAD) allows for the whole genome to be randomly
sampled for single nucleotide polymorphisms (Baird et al. 2008, Davey and Baxter 2010). This
technique increases the number of molecular markers being used in analysis. Increase in number
of markers allows for increased resolution of differences between populations (Allendorf et al.
2010). RAD sequencing techniques have been shown to increase the resolution of analysis of
differences within populations (Wagner et al. 2013, Catchen et al. 2013, Hale et al. 2013).
The Cle Elum Supplementation and Research Facility is a Chinook salmon (O. tshawytscha)
hatchery that has set up both integrated and segregated hatchery lines from 1998-2010. Each
individual used by the hatchery had DNA samples taken and phenotypic data recorded. This
study system is novel in that it allows a cross-generational analysis of hatchery populations. It
also allows direct comparisons of integrated and segregated lines as they both came from the
same population and are occurring simultaneously so are thus experiencing the same
environment during their time in the wild. Previous studies have been restricted to looking only
at single generations and individual management strategies. This study system will allow more
direct comparisons between strategies as well as across generations.
This study will explore the rate of divergence arising from different hatchery management
strategies, namely the amount of divergence between integrated hatchery lines and segregated
hatchery lines when compared to the wild population used to found those lines. Using three
generations of samples from a segregated line and three generations from an integrated line it
will be possible to see how genetic changes accumulate. By comparing hatchery populations to
the wild population they came from this study will be able to determine if the management
strategies are working to mitigate genetic changes. Therefore, we use next-generation,
restriction-site associated DNA-sequencing (RAD) to determine if there are significant
differences between these hatchery Chinook salmon lines and the wild population they came
from. The results of this study have direct application to advising further hatchery management
in order to mitigate genetic effects of hatchery work. By answering the question “How much
genetic change is there in hatchery lines over three generations?” this study will be able to
inform further hatchery management strategies as well as further research to continue to improve
hatchery management and prevent changes to wild populations.
Materials and Methods
Sampling and DNA Extraction
Samples were collected from the Cle Elum Supplementation and Research Facility
(CESRF) in Cle Elum, Washington. Each fish used by the facility had DNA collected in a fin
clip and phenotypic data recorded. Samples come from segregated and integrated hatchery lines..
The segregated line includes one generation of wild founders from 1998 and three generations of
hatchery adults from 2002-2010 (Figure 1). The integrated hatchery includes three generations of
natural origin adults from 2002-2010. The 2006 and 2010 generations have possible hatchery
influence (Figure 1). Each line had 50 fish each generation for a total of 350 fish over five
generations between the two lines.
DNA was extracted from fin clips. 1 mm2 segments of fin were used for extraction. DNA
was extracted using Qiagen DNeasy kits and following standard protocol.
RAD Library Construction and Sequencing
Restriction site-associated DNA libraries were prepared following protocols outlined in
(give citation). Extracted DNA from each individual was digested using the restriction enzyme
Sbf1. Digested DNA was then barcoded, with a unique six base par sequence identifying each
individual. Barcoded DNA of 48 individuals was pooled in libraries. DNA was furthered
fragmented using sonication. DNA fragments of 300-600 bp were selected. Illumina sequencing
adaptors were added to the ends of size-selected fragments. RAD libraries were then amplified
using PCR and quantified using gel electrophoresis. 25 µl PCR reactions were run (12.5 µl
Phusion Master Mix, 10.5 µl purified water, 1 µl RAD primer mix, 1 µl RAD library). The
thermocycler profile for this reaction was: 98°C initial denaturation for 30 second, 18 PCR
cycles of (denaturation at 98°C for 10 sec, annealing at 65°C for 30 sec, extension at 72°C for 30
sec), followed by a final extension at 72°C for 5 min. Gel electrophoresis (1.0 g agarose, 100 ml
TBE) was run at 150v for 45 minutes to visualize results. RAD libraries were sent to an external
core laboratory for sequencing.
Bioinformatics
Returned sequences were identified to individual fish by extracting barcodes. Sequences were
compared to O. tshawytscha reference sequences in order to determine which locus was being
looked at. Sequences were trimmed to 75 bp and scanned for single nucleotide polymorphisms
(SNPs). 200 polymorphic loci were used in analysis. Individuals were genotyped at each locus. If
fewer than 10 sequences were available for the loci the genotype was unknown. Genotypes were
scored only if a minimum of total read count of 10 was met. Genotypes were called homozygous
if one allele appeared 90% of the time. Genotypes were called heterozygous if both alleles
appeared at least twice. Genotype information was used to perform population structure analyses.
Population Structure Analysis
Population structure analyses were done using R 3.0.3 (R Development Core Team, 2013). For
the analyses, each generation was treated as a population, yielding one wild population, three
segregated populations, and three integrated populations. T-tests were conducted to see if
populations were in Hardy-Weinberg Equilibrium over all loci. Hardy-Weinberg Equilibrium
was then calculated for all loci in all populations using a Bonferroni correction to adjust the pvalue for multiple hypothesis testing. Population homogeneity was tested using the R package
“heiferstat” version 4.1 implemented in R version 3.0.3 (Goudet J, 2005, R Developmental Core
Team, 2013). Genetic distance was determined using the R package “diveRsity” version 1.7.6
implemented in R version 3.0.3 (R Development Core Team, 2013). Genetic distance was
calculated as Weir and Cockerham’s FST between all pairs of populations. FST values and 95%
confidence intervals around those values were calculated for each pairwise comparison. FST was
determined to be significantly different from zero if the 95% confidence interval did not include
zero. Negative FST values were considered to be zero. Multidimensional scaling (MDS) plots
were created in R 3.0.3 (R Development Core Team, 2013). MDS plots were created using both
Weir and Cockerham’s FST as well as Nei’s genetic distance. Two distances were used to
determine if the relationship observed was supported across methods. Eigenvalues were
calculated for MDS plots to determine influence of each principle component axis on variation in
the data. Finally a plot of assignment proportions for population clusters was created using
STRUCTURE (Pritchard et al. 2000).
Results
Genotypes were called for a total of 350 specimens at 200 polymorphic loci. Each population
had 50 individuals (Table 1). In the founder population (F0) 8% of individuals had genotypes for
all loci, 6% had only one locus where a genotype could not be called, and 86% had more than
one locus where a genotype could not be called (Table 1). In the 2002 generation of the
integrated line population (I1) 6% of individuals had genotypes for all loci, 6% had only one
locus where a genotype could not be called, and 88% had more than one locus where a genotype
could not be called (Table 1). In the 2002 generation of the segregated line population (S1) 10%
of individuals had genotypes for all loci, 4% had only one locus where a genotype could not be
called, and 86% had more than one locus where a genotype could not be called (Table 1). In the
2006 generation of the integrated line population (I2) 2% of individuals had genotypes for all
loci, 2% had only one locus where a genotype could not be called, and 96% had more than one
locus where a genotype could not be called (Table 1). In the 2006 generation of the segregated
line population (S2) 4% of individuals had genotypes for all loci, 2% had only one locus where
a genotype could not be called, and 94% had more than one locus where a genotype could not be
called (Table 1). In the 2010 generation of the integrated line population (I3) 2% of individuals
had genotypes for all loci, 0% had only one locus where a genotype could not be called, and 98%
had more than one locus where a genotype could not be called (Table 1). In the 2010 generation
of the segregated line population (S3) 0% of individuals had genotypes for all loci, 4% had only
one locus where a genotype could not be called, and 96% had more than one locus where a
genotype could not be called (Table 1).
The founding population appears to be in Hardy-Weinberg Equilibrium when tested across all
loci (paired t-test, p>0.05). All the other populations appear to be out of Hardy-Weinberg
Equilibrium when tested across all loci (paired t-tests, p<<0.001 for all populations). When
individual loci were tested, all loci for all populations were in Hardy-Weinberg Equilibrium, all
calculated p-values were greater than the test p-value calculated using the Bonferonni Correction
(p>0.00003). Tests for homogeneity revealed no subpopulation structure (p>
Pairwise genetic distance, calculated as Weir and Cockerham’s FST showed 15 out of 28
population comparisons were significantly different from zero (Table 2). All populations had FST
values significantly different from zero (95% confidence interval did not include zero) when
compared to the founding population (F0) (Table 2). The 2006 and 2010 segregated line
populations (S2 and S3) each had FST values significantly different from zero (95% confidence
interval did not include zero) when compared to all other populations (Table 2). All the
integrated line populations (I1, I2, and I3) and the 2002 segregated line population (S1) did not
have significant FST values when compared to each other (Table 2).
A principle component analysis, based on Weir and Cockerham’s FST and yielding a
multidimensional scaling plot, revealed that the founding population (F0) is different from all the
other populations (Figure 2). The principle component axis one explained 99.4% of the variation
(Eigenvalue= 0.994). The principle component axis two explained 0.37% of the variation
(Eigenvalue= 0.0037). The integrated line populations (I1, I2, and I3) were clustered together
along principle component axis one (Figure 2). The 2002 integrated line population (I1) was the
closest to the founding population (F0) along the principle component axis 1 (Figure 1). The
2002 segregated line population (S1) was close to the integrated line cluster (Figure 2). The 2006
segregated line cluster (S2) was farther away from founding population along principle
component axis one (Figure 2). The 2010 segregated line cluster (S3) was the farthest away from
the founding population along principle component axis one (Figure 2). Principle component
analysis based on Nei’s genetic distance revealed very similar trends to that yielded by Weir and
Cockerham’s FST.
A self-assignment plot of the populations revealed two initial assignments within the founding
population, with one making up roughly one third of the assignments and the other the remaining
two thirds (Figure 3). This less probable assignment in the founding population rises in
frequency in the segregated line populations over time until in the 2010 segregated line
population (S3) it makes up roughly more than three quarters of the assignments (Figure 3). This
assignment also rises in probability in the integrated line populations but not as dramatically and
by the 2010 integrated line population (I3) it makes up a little less than half the assignments
(Figure 3).
Discussion
The aim of this study was to determine the genetic impact of two different hatchery management
methods in Chinook salmon (O. tshawytscha).Multiple analyses of 350 individuals genotyped for
200 loci polymorphic for SNPS revealed that both hatchery lines appear somewhat different
from the wild founding population and that the segregated line is more different from the
founders than the integrated line. Tests across all loci revealed that only the founding population
was in Hardy-Weinberg Equilibrium. However, attempts to determine specific loci out of HardyWeinberg equilibrium were not able to find any individual loci in this study to be out of HardyWeinberg equilibrium. Additionally, homogeneity tests show no evidence for subpopulation
structure. Pairwise FST analysis and multidimensional scaling plots showed that there is
differentiation between the populations to some extent.
These results lead to the conclusion that segregated hatchery management leads to populations
that are more different from the wild population than integrated hatchery populations. It also
shows that both strategies result in populations that are significantly different from the wild
population they came from. That being said, the actual FST values, while different from zero, are
not that large. This explains why the divergence between populations was detectable in some
analyses and not in others. If these hatchery lines were propagated in the same way for many
more generations, it is likely that divergence would continue occurring and would be detectable
across all analyses. Additionally, it is possible that looking at more loci would make differences
between populations more detectable.
These conclusions are novel because the study system used was novel. Other studies have
looked at the impacts of these management systems but only over one generation (Christie et al.
2011, Christie et al. 2013). This study gives insight not only into how different management
strategies can cause changes in hatchery populations but the relative rates at which those changes
occur over multiple generations. It appears that segregated lines change much more quickly than
integrated lines, probably due to the fact that they are undergoing continued domesticating
selection (Fraser 2008, Williams and Hoffman 2009, Christie et al.2011).
It should be noted that the generations discussed here, are treated as distinct populations based on
the idea of them being strict generations. However, four years is the average age at maturity for
O. tshawytscha and some individuals may return before or after this age. Four years is the
average so by using this time span it is probable that what is mostly being captured is one
generation. If it were possible to ensure strict generations in a study system like this, it would
improve the strength of the conclusions of this study.
This study uses a population of wild fish that the hatchery lines were drawn from as the baseline
for comparison. This is slightly problematic because if the study continues for more generations
it becomes difficult to say whether differences from the historic wild population are due to
hatchery management or partially due to the fact that the wild environment has changed since the
baseline was collected. For the purposes of this study this comparison is the best that can be done
however. Ideally, the hatchery populations would be compared to a contemporaneous wild
population from the same area that has had no hatchery influence. But with an integrated
hatchery system, after the first generation there is always some potential for hatchery influence.
Thus no contemporaneous, uninfluenced wild population exists in this system so comparison to
the original wild population is best.
Further research should be done in cross-generational studies such as this. Further work could
include linking phenotypic data to genomic data. The Cle Elum Supplementation and Research
Facility (CESRF) collected both phenotypic data as well as genetic sample for all of the
specimens studied here. By linking these two data sets it could be possible to determine genes
responsible for certain phenotypes, such as those associated with domestication. Further research
could also be done to determine if there are cross-generational effects on reproductive success in
the wild between integrated line hatchery fish and wild fish. By doing a cross-generational study
in the wild, it might also be possible to quantify the amount of hatchery influence in the wild
population. In addition to integrated line hatchery fish reproducing in the wild, it would also be
useful to do research on rates of escapement to the wild of segregated line hatchery fish. Since
this study concludes that these fish are the most different from the wild population, if a
proportion of them wander and manage to reproduce in the wild, it could cause a greater change
in the genetic make-up of the wild population than integrated line individuals reproducing.
Captive breeding programs, such as hatcheries, are often controversial as the impact that they
may have on the wild population is often unknown. In spite of that controversy hatcheries are
quite common in the Pacific Northwest and will probably persist for some time. Thus it stands to
reason that the best thing to do for hatcheries is determine a management strategy that has the
minimum impact possible on a population. This study showed that using an integrated hatchery
system cause less differentiation from the wild population than a segregated hatchery system.
However, if the segregated hatchery system were completely prevented from breeding with the
wild population in some way, it stands to reason that even though the segregated population
would be more different than the integrated population, it would have less impact on the wild
population. Further research should be done to determine if there are methods of making
completely segregated hatchery lines. The results of this study suggest that using an integrated
system seems to be the best way to minimize differentiation from the wild population, but some
amount of differentiation will still occur.
Acknowledgements
Thank you to Kerry Naish for all her guidance and assistance in doing this project. Thank you to
Charlie Waters for creating the RAD libraries, visualizing PCRs, preparing R code for use in
analysis, and preparing a useable genepop file for analysis. Thanks also to my classmates in
Conservation Genetics for their help, collaborations, and assistance.
Figure 1. Sampled generations of segregated and integrated hatchery lines produced by the Cle
Elum Supplementation and Research Facility from 1998 to 2010. Each generation was
considered as a population for population structure analyses based on 200 loci that were
polymorphic for SNPs. Labels used in subsequent analyses are found in the upper right corner of
the colored boxes, next to the year. 1998 wild founders are F0. Segregated line populations begin
with S. Integrated line populations begin with I. 2002, 2006, and 2010 are signified by a 1, 2, and
3, respectively, following the hatchery line designator.
Table 1. Genotype results for each populations.
Pop. Number of Percentage of
individuals individuals with no
genotyped missing genotypes
50
8%
F0
50
6%
I1
50
10%
S1
50
2%
I2
50
4%
S2
50
2%
I3
50
0%
S3
Percentage of
individuals with one
missing genotype
6%
6%
4%
2%
2%
0%
4%
Percentage of individuals
with more than one
missing genotypes
86%
88%
86%
96%
94%
98%
96%
Principle Component Axis 2
Table 2. Pairwise FST comparisons of all populations. Population names are abbreviated
according to Figure 1. Bolded values indicate significant deviation from zero, determined by a
95% confidence interval that did not contain zero. Negative FST values were considered to be
zero and thus not significantly different from zero.
Principle Component Axis 1
Figure 2. Multidimensional Scaling Plot based on pairwise FST distance. Principle component
axis 1 explains 99.4% of the variation. Principle component axis 2 explains 0.37% of the
variation. Population abbreviations given in Figure 1.
Probability of
Assignment
Populations
Figure 3. Self-assignment plot for populations created using STRUCTURE. Population labels
are those given in Figure 1.
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