draft...draft 20 abstract 21 pink salmon, the most abundant pacific salmon, have an obligate...
TRANSCRIPT
Draft
SNP data describe contemporary population structure and
diversity in allochronic lineages of pink salmon (Oncorhynchus gorbuscha)
Journal: Canadian Journal of Fisheries and Aquatic Sciences
Manuscript ID cjfas-2017-0023.R1
Manuscript Type: Article
Date Submitted by the Author: 30-Jun-2017
Complete List of Authors: Tarpey, Carolyn; University of Washington, School of Aquatic and Fishery
Sciences Seeb, James; University of Washington, School of Aquatic and Fishery Sciences McKinney, Garrett; University of Washington, School of Aquatic and Fishery Sciences Templin, William; Alaska Department of Fish and Game, Bugaev, Alexander; Kamchatka Fishery and Oceanography Research Institute, Sato, Shunpei; Hokkaidoku Suisan Kenkyujo Seeb, Lisa; University of Washington, School of Aquatic and Fishery Sciences
Is the invited manuscript for
consideration in a Special Issue? :
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Keyword: GENETICS < General, MOLECULAR ECOLOGY < General, GEOGRAPHICAL DISTRIBUTION < General, Last Glacial Maximum, Glacial Refugia
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SNP data describe contemporary population structure and diversity in allochronic 1
lineages of pink salmon (Oncorhynchus gorbuscha) 2
3
Authors: Carolyn M. Tarpey¹a, James E. Seeb¹
b, Garrett J. McKinney
1c, William D. 4
Templin2d
, Alexander Bugaev3e
, Shunpei Sato4f
, and Lisa W. Seeb¹g
5
Affiliation: ¹University of Washington, School of Aquatic and Fishery Sciences, Box 6
355020 Seattle, Washington, 98195, USA 7
2Alaska Department of Fish and Game, 333 Raspberry Road, Anchorage, Alaska, 8
99518, USA 9
3 Kamchatka Fishery and Oceanography Research Institute (KamchatNIRO), 10
18, Naberezhnaya Str. Petropavlovsk-Kamchatsky 683602, Russia
11
4 Hokkaido National Fisheries Research Institute, Japan Fisheries Research and 12
Education Agency, 2-2 Nakanoshima, Toyohira-ku, Sapporo 062-0922, Hokkaido, 13
Japan 14
Email: [email protected];
Corresponding author: Lisa Seeb (206) 685-3723 18
19
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Abstract 20
Pink salmon, the most abundant Pacific salmon, have an obligate two-year life cycle 21
that leads to reproductively isolated even- and odd-year lineages. Using new and 22
existing data, we examined the genetic structure of both lineages across their 23
distributional range by genotyping 16 681 SNPs for 383 individuals originating from 24
seven pairs of even- and odd-year populations. Distinct differences in standing pools 25
of genetic variation were identified between the lineages; we observed higher levels 26
of heterozygosity, allelic richness, and significantly more private alleles in the odd-27
year lineage. However, the patterns of population structure were concordant 28
between lineages: the Asian and northern Alaska populations displayed little 29
differentiation but differed significantly from populations in southcentral Alaska and 30
the Pacific Northwest. Our population structure results, in context of known 31
paleoecological information, suggest that both lineages occupied a northern 32
Beringial refugium as well as a Cascadian refugium in North America during the 33
Last Glacial Maximum. These results highlight the influence of historical patterns of 34
habitat availability on contemporary population structure and support the hypothesis 35
of a pre-glacial origin of the lineages.36
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Introduction 37
An enduring goal in evolutionary biology is to describe the dynamics that result in population 38
structure, species distribution, and genetic diversity. Groups of organisms that use the same 39
habitat sequentially, separated in time (allochronic species; Alexander and Bigelow 1960), are an 40
intriguing resource to investigate the drivers of population genetic structure and speciation 41
(Taylor and Friesen 2017). These completely isolated groups use the same habitat in tandem, 42
allowing for the study of replicated wild systems and the role of temporal divergence in 43
speciation. Most known allochronic species are periodical insects such as the pine processionary 44
moth (Santos et al. 2007) and aphids (Abbot and Withgott 2004). Vertebrate allochrony as well 45
as yearly allochrony are rare (Taylor and Friesen 2017). 46
Pink salmon Oncorhynchus gorbuscha are the only Pacific salmon and one of the few vertebrates 47
that display allochrony. Pink salmon are anadromous and semelparous, homing to tributaries of 48
the North Pacific Ocean and Bering Sea ranging from Japan in Asia to Washington State in 49
North America (Ruggerone et al. 2010). Their obligate two-year life cycle leads to 50
reproductively isolated even- and odd-year lineages (Anas 1959, Turner and Bilton 1968). These 51
reproductively isolated lineages often spawn in the same locations offset by one year. However, 52
one lineage can be much more abundant than the other in a given location, and relative 53
abundance can shift from one lineage to the other through time (Krkošek et al. 2011, NPAFC 54
2016). Although dominance fluctuates, in recent years the odd-year lineage has been dominant 55
in Asia (Krkošek et al. 2011, Gordeeva 2014). In North America, the pattern is more static: the 56
even-year lineage is dominant in northern drainages while the odd-year lineage dominates in the 57
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southern drainages; some rivers at northern and southern extremes are inhabited by only one 58
lineage (Krkošek et al. 2011, Beacham et al. 2012). 59
There are significant phenotypic and life history differences between the lineages even though 60
the two lineages often occupy similar or identical spawning habitats: different gill raker counts 61
(Beacham et al. 1988), different temperature optima (Beacham and Murray 1988b, Beacham and 62
Murray 1988a), and differences in juvenile energy allocation and growth rates (Wechter et al. 63
2017). Beamish (2012) hypothesized that, in North America, the two lineages have adopted 64
different feeding strategies in the early marine period where odd-year pink salmon store fewer 65
lipids and have an extended growing period compared to even-year pink salmon. 66
The existence of distinct lineages, combined with differing abundance of each, has precipitated a 67
number of intriguing ecological observations (Ruggerone and Nielsen 2005). Pink salmon may 68
exert top-down control and are thought to drive major ecological shifts between years of high 69
and low abundances (Springer and van Vliet 2014). Seabird diet, body mass, and reproductive 70
success are reduced in odd-numbered years, when pink salmon abundance has been 71
exceptionally high, due to competition for identical prey (Toge et al. 2011, Springer and van 72
Vliet 2014). Similarly, the abundance of pink salmon affects other Pacific salmon species and 73
has been linked to a decline in productivity of sockeye salmon (O. nerka; Ruggerone et al. 2015). 74
The existence of distinct lineages has also been the focus of comparative studies of population 75
genetic structure. Early studies using allozymes, mtDNA, and microsatellites showed that the 76
strongest signal of divergence occurs between the two lineages: even- and odd-year lineages of a 77
single river system are more genetically diverged than populations of the same lineage from 78
adjacent rivers (Aspinwall 1974, Olsen and Seeb 1998, Churikov and Gharrett 2002, Beacham et 79
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al. 2012). More recently, genome scans combined with genetic mapping were used to identify 80
genomic regions of divergence and investigate parallel and divergent selection between the 81
lineages. Seeb et al. (2014) identified parallel signatures of selection in populations of two 82
lineages inhabiting the same environments using over 8 000 SNPs derived from restriction-site-83
associated DNA (RAD) sequencing. Limborg et al. (2014), using the same set of loci aligned to 84
a dense linkage map, identified not only genomic regions of parallel selection, but also regions of 85
divergent selection between lineages, suggesting that adaptation in the two lineages may have 86
arisen from different pools of standing genetic variation. 87
Previous studies of the genetic structure of pink salmon have been largely limited to a single 88
continent or single lineage (e.g., Shaklee and Varnavskaya 1994). We can better address two 89
lingering broad-scale questions by examining populations from both lineages across the entire 90
range of the species. How do the lineages compare in their distribution of diversity and genetic 91
structure across the range? Is there evidence of the same pattern of population structure repeated 92
within the lineages that could help illuminate the evolutionary history of the species? To answer 93
these questions, we built upon the genomic studies in North American populations by Seeb et al. 94
(2014) and Limborg et al. (2014) and examined four new paired replicates along the western 95
Pacific Ocean. This design provided consistent data for a comparison of paired populations from 96
throughout Asia and North America to explore the effects of demographic and evolutionary 97
factors influencing genetic population structure in each lineage. 98
Materials and Methods 99
Samples 100
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Paired populations (even- and odd-year lineage populations from the same river) were sampled 101
from four regions of Asia: Hokkaido Island and Amur, Magadan, and Kamchatka Provinces 102
(Figure 1). Populations were chosen based on the presence of both even- and odd-year lineages 103
at the site, the number of available samples, and the geographical spread of locations (Table 1). 104
Samples were collected as a part of North Pacific Anadromous Fish Commission cooperative 105
studies (http://www.npafc.org/new/index.html); fin clips were collected from spawning adults 106
and stored in 95% ethanol at room temperature. Thirty-two individuals from each population 107
were used for DNA sequencing. 108
Previously generated sequence data were obtained for paired populations from three regions of 109
North America: (Norton Sound, Prince William Sound, and Puget Sound (Norton Sound, Prince 110
William Sound, and Puget Sound; Seeb et al. 2014). Twenty-four samples had been sequenced 111
from each of the North American populations with the exception of the Norton Sound even-year 112
population which was limited to 20 samples (Table 1). 113
Detailed information on the 396 samples is included in Supplementary File S1. 114
RAD Sequence Data 115
We used the RAD protocol originally described by Miller et al. (2007), modified for genotyping-116
by-sequencing by Baird et al. (2008). DNA extraction and library preparation for sequencing of 117
the Asian samples followed that of the North American samples and were based on the protocol 118
in Everett et al. (2012). The complete protocol is included in Supplementary Information 119
(Protocol 1). We imported the raw DNA sequences for North American populations from Seeb 120
et al. (2014). 121
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Genotyping 122
The 100bp filtered raw sequences from the North American populations and the Asian 123
populations were jointly genotyped to provide continuity between studies. 124
Sequence data was analyzed using STACKS v. 1.31 (Catchen et al. 2013). Default settings were 125
used for all STACKS modules with the following exceptions: process_radtags (-c -r -q -t 94), 126
ustacks (-r --model_type bounded --bound_low 0 --bound_high 0.05), cstacks (-n 2). These 127
settings were used for consistency with analyses from Limborg et al. (2014). The STACKS 128
catalog of variation was created using four individuals from each population. The individuals 129
with the highest number of sequence reads were chosen to represent each population, as these 130
would likely also have the highest quality sequence. The catalog also included four female 131
parents of the crosses used to make a linkage map in collaborative studies; two females from 132
Washington (Limborg et al. 2014) and two from southeastern Alaska were included (unpublished 133
data). Locus names in the catalog were standardized for consistency with the earlier study by 134
Limborg et al. (2014). The genotypes of loci that were present in at least 80% of the individuals 135
were exported with the STACKS program populations. 136
The program GENEPOP v4.3 (Rousset 2008) was used to calculate basic diversity indices for 137
each locus by population. SNPs were retained in the final data set based on the following 138
criteria: 1) if there were multiple putative SNPs per locus, then we retained the single SNP with 139
the highest minor allele frequency (MAF); 2) SNPs that had a MAF > 0.05 in at least one 140
population, and 3) SNPs that conformed to Hardy-Weinberg equilibrium expectations (P> 0.05) 141
in at least seven populations (50% of tests). To filter paralogous loci, we calculated the 142
inbreeding coefficient (FIS) per locus (Robertson and Hill 1984) using GENEPOP v4.3 (Rousset 143
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2008), and retained those SNPs that had a global FIS > -0.5 (Hohenlohe et al. 2013). Finally, we 144
excluded individuals that genotyped at less than 80% of the final set of loci. 145
We used all retained SNPs for the analyses. We assumed that the non-neutral SNPs within the 146
dataset were a very small percentage of the total number of SNPs and that their signal would be 147
overwhelmed by the neutral SNPs. Analysis of these North American populations by Seeb et al. 148
(2014) found that 2% of the 8,036 loci they used were non-neutral; using different methods but 149
the same loci, Limborg et al. (2014) found a comparable 2.7% were non-neutral. Further, 150
Limborg et al. (2014) found that the neighbor-joining trees based on either neutral-only or all 151
SNPs were nearly identical. 152
Summary Statistics and Genetic Diversity Measures 153
Several diversity indices were calculated for each population using R, version 3.2.1 (R Core 154
Team 2015). We calculated the mean observed heterozygosity (Ho) within each population and 155
compared the values for the populations within the even-year lineage to those within the odd-156
year lineage using the nonparametric Mann-Whitney test (Mann and Whitney 1947) in R. We 157
also calculated the standardized observed heterozygosity per individual (HS_obs) within each 158
population with the R function genhet (Coulon 2010); the mean values of HS_obs for each 159
population were compared between lineages as above with the nonparametric Mann-Whitney test 160
in R. HS_obs is a conservative estimate of the observed heterozygosity of an individual averaged 161
over all the loci used, standardized by the mean observed heterozygosity of the genotyped loci. 162
HS_obs is standardized to account for individuals that are not genotyped at the same loci to ensure 163
that the heterozygosity is measured on the same scale (Coltman et al. 1999, Amos 2005). We 164
used the package hierfstat (Goudet and Jombart 2015) in R, to determine the rarefied allelic 165
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richness per locus by population as another measure of the genetic diversity in the populations 166
and calculated average values for each population and lineage; mean values for the populations 167
were compared between lineages as above using the nonparametric Mann-Whitney test in R. 168
Comparison of the distributions of private alleles between lineages was conducted in R using a 169
two sample Kolmogorov-Smirnov test. 170
Population Structure 171
Several methods were used to evaluate population structure within and between lineages. To get 172
a broad view of the relationship of the 14 populations, we constructed a Neighbor-Joining (N-J) 173
tree (Saitou and Nei 1987) using the DA distance (Nei 1983) with 10 000 bootstraps of the loci, 174
using the program POPTREE2 (Takezaki et al. 2010). We also estimated per locus FST, pairwise 175
FST between populations, and FST for groupings of populations based on Weir and Cockerham 176
(1984). Pairwise and per locus global FST estimates were obtained using GENEPOP. Overall 177
FST and FST of six different groups of populations along with their respective confidence 178
intervals were calculated with GENETIX v.4.05.2. The six groupings were as follows: 1) overall 179
FST of all 14 populations, 2) pairwise FST comparing populations of different lineages at the same 180
location, 3) all even-year lineage populations, and 4) all odd-year populations. We estimated FST 181
values separately for each lineage to characterize the magnitude of differentiation between the 182
populations within each lineage. Finally, we estimated the FST for groups of populations within 183
each lineage to compare the magnitude of the differentiation. The N-J tree informed the 184
grouping of the populations tested within each lineage, as a clear parallel population structure 185
was evident in the tree. The populations were grouped within each lineage as follows: 5) even-186
year lineage populations Prince William Sound and Puget Sound, called the Gulf of Alaska 187
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(GoA) populations, and Hokkaido, Amur, Magadan, Kamchatka and Norton Sound populations, 188
which we called the Asia/Bering Sea (Asia/Ber) populations and 6) odd-year lineage populations 189
grouped into the same Asia/Ber and GoA populations. Confidence intervals (95%) were 190
estimated using 1 000 bootstraps. 191
We used an individual-based analysis of principal components (PCA) that included all 14 192
populations to explore the relationships among populations. The eigenvalues were calculated in 193
PLINK v1.90 (Chang et al. 2015) which uses a variance-standardized relationship matrix; the 194
input was pairwise relatedness between individuals. We extracted the top 20 principal 195
components using the default settings with the following exceptions (flags: --allow-extra-chr --196
allow-no-sex). The top three principal components were visualized in R, using the package 197
ggplot2 (Wickham 2009). 198
Three analyses of molecular variance (AMOVA) were conducted in Arlequin v.3.5.1.2 199
(Excoffier and Lischer 2010), using 10 000 permutations, to test for hierarchical population 200
structure. The first AMOVA (A) evaluated the distribution of genetic diversity within and 201
between the two lineages. The second hierarchy was tested with two AMOVAs (B and C), each 202
evaluated the distribution of diversity within each lineage; populations within lineage were 203
grouped as follows: i) GoA: Puget Sound and Prince William Sound, and ii) Asia/Ber: Hokkaido, 204
Amur, Magadan, Kamchatka and Norton Sound. 205
Results 206
Bioinformatics and Genotyping 207
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Combining the new sequences for the Asian populations with the existing sequences for the 208
North American populations resulted in a total of 903.5 million retained reads for 396 209
individuals (Supplementary File S2). On average, each individual from Asia had two million 210
reads, and each individual from North America had 2.8 million reads. From these retained reads, 211
the Stacks pipeline identified 666 351 loci, of which 16 681 putative SNPs remained after 212
filtering (Supplementary File S2). Thirteen individuals, possibly of low sample quality, were 213
genotyped at fewer than 80% of the putative 16 681 SNPs and were dropped from the analysis. 214
These included five individuals from Magadan even-year population, four from Magadan odd-215
year population, two from Norton Sound even-year population, and one each from the 216
Kamchatka even- and odd-year populations. Final sample sizes for each population are listed in 217
Table 2. 218
Diversity within populations 219
We estimated diversity within each of the 14 populations and compared the results between the 220
lineages (Table 2). We found significant difference in the distributions of the average observed 221
heterozygosity (Ho) per population with 0.158 estimated in the even-year lineage and 0.167 in 222
the odd-year lineage (P-value = 0.0055, one-tailed). Additionally, for each comparison of the 223
paired populations, the population within the even-year lineage had lower average observed and 224
expected (He) heterozygosities (Table 2). The individual standardized observed heterozygosity 225
(Hs_obs) followed the same pattern seen in Ho and He (Table 2, Figure 2). In nearly every 226
location, the populations of the even-year lineage had lower mean values of Hs_obs than those in 227
the odd-year lineage. The overall mean of Hs_obs was 0.966 in the even-year and 1.024 in the 228
odd-year lineage, and the distributions in the two groups differed significantly (P-value = 0.0055, 229
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one-tailed). The mean allelic richness was also significantly lower for the even-year lineage 230
(1.60 for the even-year lineage and 1.65 for the odd-year lineage; P-value = 0.0189, one-tailed; 231
Table 2). We also tallied lineage-specific private alleles: some loci were variable in one lineage 232
but were fixed for a single allele in the other. The number of private alleles in the odd-year 233
lineage was greater than the number of private alleles in the even-year lineage (2 300 vs. 1 184). 234
In addition, odd-year private alleles had overall greater allele frequencies than even-year private 235
alleles (Figure 3). 236
Lower values for average Ho and He were seen in both lineages in the most southern populations 237
at the end of the ranges on both continents (Puget Sound and Hokkaido populations). Similarly, 238
the lowest allelic richness values were observed at the southern extent of the ranges. 239
Diversity among populations 240
The N-J tree shows a very distinct division between the lineages, with all branches having strong 241
bootstrap support. All but one node was supported by 100% of the bootstrap samplings (Figure 242
4). Within both lineages, the most divergent branch of the tree separated the GoA populations of 243
Prince William Sound and Puget Sound from the Asia/Ber group including all Asian populations 244
plus the Norton Sound populations from northwestern Alaska. The branch lengths for the GoA 245
group were longer in the odd-year lineage than the even-year lineage, reflecting the greater 246
differentiation seen in the pairwise FST for these populations. 247
The principal component analyses provide additional perspectives, but like the N-J tree, the 248
greatest amount of variation was explained by the division between the lineages (percent of 249
variation explained by principal component 1: 30.24%) (Figure 5A, B). The second greatest 250
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amount of variation in the data was explained by differentiation between the populations in the 251
odd-year lineage (principal component 2: 13.83%, Figure 5A, C). The differentiation appears to 252
be dominated by the differences between the GoA populations within each lineage, as the 253
Asia/Ber populations cluster more closely within lineages. Comparing the first principal 254
component to the third shows that the differentiation among populations in the even-year lineage 255
explains the third greatest amount of variation in the data (principal component 3: 8.73%, Figure 256
5B, C), and comparing the second and third principal components more clearly illustrates the 257
relationship of the populations within each lineage by removing the visual pattern of the 258
differences between the even and odd-year lineages that was shown in the first principal 259
component (Figure 5C). To compare the relationship of all 14 populations in three dimensions 260
simultaneously, the centroids of each population were plotted in a three dimensional PCA using 261
the R package scatterplot3d (Ligges 2017) (Figure S1). 262
Non-hierarchical diversity analyses were conducted to quantify the distribution of variation. We 263
estimated an FST value of 0.060 over all populations (Table 3A). Populations within the odd-264
year lineage had a significantly higher FST (0.033) than populations within the even-year lineage 265
(0.026) when estimates were conducted with all populations within a lineage (Table 3B). The 266
FST for the even-year Asia/Ber populations (0.011) and the GoA populations (0.030) differed 267
significantly, but the difference was smaller than that observed in the odd-year lineage: Asia/Ber 268
(0.008) GoA (0.052) (Table 3B). The estimate of FST for the odd-year Asia/Ber populations 269
(0.008) was smaller than that of the even-year lineage (0.011). The opposite pattern was seen in 270
the GoA populations, where the even-year lineage (0.030) had a smaller estimate of FST than the 271
odd-year lineage (0.052). Pairwise comparisons of FST for all 14 populations ranged from 0.001 272
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to 0.148 with the largest values generally resulting from comparisons between lineages (Figure 273
S2). 274
Hierarchical analyses were used to partition the genetic variation and estimate the distribution of 275
variation. The first AMOVA (Table 4A), estimated that a total of 5.72% of the variation was 276
explained by differences between the lineages with 2.87% attributed to differences among 277
populations within the lineages. Both estimates were significantly different from zero (P< 278
0.0001). 279
Separate AMOVAs were conducted for each lineage to evaluate the partitioning of genetic 280
variation between the GoA and Asia/Ber groups within lineage (Table 4B and C). In both 281
AMOVAs, differences among the groupings explained the largest amount of the variation, 282
followed by variation attributed to differences among populations within the groups. This was a 283
recurrent pattern observed in all the analyses, seen in both the diversity and the differentiation 284
statistics. 285
Discussion 286
The geographic overlap of allochronic lineages of pink salmon provides a rare opportunity to 287
examine the repeatability of evolutionary processes. We used a paired sampling design across 288
the entire range to provide a detailed comparison of the distribution of diversity in each lineage. 289
In general, population structure was similar between lineages, likely a reflection of similar 290
demographic histories. However, the extent and distribution of individual and population 291
diversity varied significantly, suggesting differences in the origin and age of the lineages. 292
Population structure within lineages 293
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The paired study design allowed us to compare patterns of population structure across the 294
species' range. Two predominant relationships were consistently supported by all analyses. The 295
greatest signal of divergence among the 14 populations was between the lineages, a result 296
consistent with earlier studies. The second predominant relationship was a strong parallel signal 297
of little differentiation between Asian and northern Alaska populations (Asia/Ber), but 298
significant differentiation between populations in northern Alaska and those from southcentral 299
Alaska and the Pacific Northwest of North America (GoA). This pattern was evident in both 300
lineages and is likely the effect of the isolation of the Asia/Ber populations (Hokkaido, Amur, 301
Magadan, Kamchatka and Norton Sound) from the GoA populations (Prince William Sound and 302
Puget Sound) in different glacial refugia. The historical reproductive isolation of the Asia/Ber 303
and GoA populations, adaptations to distinct refugial habitats, and differences incurred through 304
subsequent post-glacial colonization, would all have undoubtedly led to separate demographic 305
histories within the isolated groups. These drivers would explain the variation in pattern of 306
genetic differentiation seen in the two groups of populations. For example, in contrast to the 307
homogeneity within the lineages in Asia/Ber, the GoA populations show a greater genetic 308
differentiation within lineages. In North America, the greatest differentiation among populations 309
is in the odd-year lineage, a pattern that was also observed by Beacham et al. (2012) with 310
microsatellite loci. 311
For the Asian populations, earlier allozyme studies found significant population structure in the 312
even-year lineage but only weak population structure in the odd-year lineage (Noll et al. 2001, 313
Hawkins et al. 2002). Moreover, these studies found evidence for an isolation-by-distance 314
relationship among populations in both lineages with the strongest relationship observed in the 315
even-year lineage. We also found higher FST values among the even-year lineage than among 316
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the odd-year lineage, but the limited number of populations precluded an isolation-by-distance 317
analysis. 318
We observed an additional trend that the southern-most populations on both sides of the Pacific 319
Ocean exhibited the largest pairwise FST values between lineages at a single location. The 320
cumulative effect of random genetic drift on the populations at the edge of the range is a likely 321
explanation for the pattern of greater values of pairwise FST at the extremes of the range. 322
Diversity within lineages 323
There were consistent significant differences in population diversity between the lineages; these 324
differences may offer clues to differences in the pools of standing genetic variation and genetic 325
backgrounds. Limborg et al. (2014) found significantly higher observed heterozygosity in the 326
odd-year lineage for loci considered candidates for selection and hypothesized that adaptation 327
arose from different pools of standing genetic variation in the two lineages. In contrast, we 328
found significant differences in heterozygosity across the full range of loci, not just limited to a 329
subset of highly diverged loci. The ability to detect the higher heterozygosity across the full set 330
of loci is likely due to the inclusion of the Asian populations which had a strong signal of higher 331
observed heterozygosity in the odd-year lineage. Like Limborg et al. (2014), when we limited the 332
comparison of observed heterozygosity between the two lineages to three North American 333
populations (using all 16 681 SNPs), we did not find a significant difference between the 334
lineages (results not shown). 335
Our results can be compared to previous allozyme studies of Asian populations of pink salmon: 336
those studies surveyed a larger number of populations but with far fewer loci (<40). Hawkins et 337
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al. (2002) focused on odd-year pink salmon and contrasted results to the allozyme study of even-338
year pink salmon of Noll et al. (2001). Hawkins et al. (2002) found that the even-year lineage 339
had a significantly higher average heterozygosity (0.059) than the odd-year lineage (0.044). The 340
contradictory findings between the early allozyme studies and the significant findings of our 341
genomic (RAD) study are not easily reconciled. There are large differences in number and type 342
of loci (genome wide coverage vs. 40 enzyme coding loci most often important in oxidative 343
metabolism), but the allozyme studies had a larger number of populations with denser 344
geographic coverage. However, we believe our dataset provides very strong evidence that 345
heterozygosities are greater in the odd-year lineage. Surveys of additional Asian populations 346
with genomic methods could provide additional evidence. 347
A striking finding of our study was consistent and significant differences in observed individual 348
heterozygosities; individuals of the odd-year lineage have significantly greater observed 349
heterozygosity than individuals of the even-year lineage. Moreover, there are more private 350
alleles in the odd-year population than private alleles in the even-year populations. The allele 351
frequencies of odd-year private alleles are also significantly greater than the frequencies of the 352
even-year private alleles. These results likely reflect a combination of factors including a 353
potentially older age of the odd-year lineage or a past reduction in population size in the even-354
year lineage. 355
These trends are interesting in light of previous studies linking population diversity with adaptive 356
potential in pink salmon lineages. In a study of transplantation of both lineages from the 357
Kamchatka Peninsula to northern Europe, habitat outside their native range, the odd-year lineage 358
was able to readily establish self-sustaining populations, while the even-year lineage was not 359
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(Gordeeva and Salmenkova 2011). The pattern of routine failure of even-year pink salmon 360
transplants led Gordeeva and Salmenkova (2011) to propose that the lineages may differ in 361
adaptive plasticity and that the even-year lineage may have arisen from the odd-year lineage. 362
Further, the authors suggest that the even-year lineage may have lost a part of its genetic 363
variation with which adaptive potential is associated. Our results are consistent with these 364
hypotheses. 365
Phylogeographic relationship of the lineages 366
Northern Alaskan populations of many species share affinities with Asian populations due to the 367
transient geographical connection between the Asian and North American continents (Pielou 368
1991). The habitat along the coastal North Pacific Ocean has changed drastically with the 369
oscillating climate and resulting cycles of glaciation during the last million years (Brigham-370
Grette et al. 2003). The LGM was a period during the most recent glacial cycle with the highest 371
global ice volume (Ehlers and Gibbard 2007) and is thought to have reached its peak 372
approximately 26.5 thousand years ago (Clark et al. 2009). Most of eastern Asia and western 373
Alaska were ice-free, whereas extensive ice sheets covered the northern latitudes of North 374
America down to 40°N in some parts of the continent (Hewitt 2000, Brigham-Grette et al. 2003, 375
Gualtieri et al. 2005). These glaciers reduced the volume of water in the oceans, causing the sea 376
level to fall between 120 to 130m lower than present (Hopkins 1973, Rohling et al. 1998). The 377
lower coastline exposed the continental shelf connecting the Asian and North American 378
continents (Bering Land Bridge) and provided vast, ice-free habitat in Beringia (Hultén 1937). 379
The dynamic habitat of the North Pacific Rim has influenced the contemporary population 380
structure of many species of varying taxa (McPhail and Lindsey 1986a, Hoffman et al. 2006) 381
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including salmonids (Gharrett et al. 1988, Smith et al. 2001, Moran et al. 2013, Petrou et al. 382
2013). 383
The origin of the two pink salmon lineages has consistently been dated prior to the LGM. 384
Estimates of lineage divergence vary widely from approximately 26 thousand years ago and up 385
to one million years ago (Brykov et al. 1996, Churikov and Gharrett 2002), and the origin(s) of 386
the lineages and yearly allochrony has been debated in the literature. Various authors have 387
hypothesized that the even-year lineage was ancestral (e.g., Churikov and Gharrett 2002), the 388
odd-year lineage was ancestral (Gordeeva and Salmenkova 2011), or that the ancestral lineage 389
could have varied across the range with multiple independent origins (Churikov and Gharrett 390
2002, Hawkins et al. 2002). 391
The parallel population structure that we observe suggests that both lineages were established 392
around the Pacific Rim before vicariance due to Pleistocene glaciation, and the reduced diversity 393
at the extremes of the range on both continents support a northern origin for pink salmon. 394
Subsequently, evidence suggests that the contemporary Asian and Norton Sound populations of 395
both lineages are descended from pink salmon populations that persisted in Asia and the Bering 396
land bridge during the LGM. These refugial populations were likely not directly impacted by 397
glacial ice; instead, their habitat was primarily altered by lower coastlines and a drastic increase 398
in the amount of available habitat. 399
In addition to Beringia, there is evidence that Cascadia was a second major glacial refugium 400
(McPhail and Lindsey 1986b, Pielou 1991, Shafer et al. 2010) (Figure 1). With the advance of 401
the Cordilleran ice sheets in North America during the LGM, terrestrial species were forced 402
either northwest of the ice to the available habitat in Beringia or south of the ice, where much of 403
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the continent was ice-free (McPhail and Lindsey 1986b, Pielou 1991). Ocean-dependent species 404
in the northern latitudes relied on the Pacific Northwest for refuge south of the North American 405
ice sheets (Westlake and O'Corry-Crowe 2002, Hoffman et al. 2006) and as well as ice-free 406
regions of the Queen Charlotte Islands in British Columbia (Warner et al. 1982, Smith et al. 407
2001, Beacham et al. 2009). 408
Many hypothesize that allochrony and the divergence of even- and odd-year lineages was 409
accelerated through adaptation to the environment of distinct historical refugia (Aspinwall 1974, 410
Churikov and Gharrett 2002, Beacham et al. 2012). Their hypothesis is based on the 411
asymmetrical abundance distribution in North America where the even-year lineage is most 412
abundant in northern populations and the odd-year lineage is most abundant in southern 413
populations. We observed parallel signals across lineages in both continents when comparing 414
population structure in even- and odd-year populations across Asia and North America although 415
the odd-year populations were more divergent than the even-year populations in North America. 416
This suggests that the contemporary population structures of both lineages were shaped by 417
similar phylogeographic processes in the recent past and that both lineages shared two main 418
refugia during the LGM, Beringia in the north and Cascadia in the south. Other potential refugia 419
important for pink salmon include the Queen Charlotte Islands in British Columbia as well as the 420
southern Asian mainland and the islands of Japan (Beacham et al. 2009). 421
Within both lineages, the least amount of differentiation occurs between the Asia/Ber 422
populations, which may be evidence for a history of gene flow between those populations. There 423
is evidence that pink salmon have the highest straying rate of the Pacific salmon: early migration 424
to saltwater immediately after hatching suggest that pink salmon may have less opportunity for 425
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imprinting on their freshwater rearing environment, resulting in higher straying rates as returning 426
adults (Quinn 2005). Pink salmon are also rapid colonizers of newly emerging habitats (Milner 427
and Bailey 1989, Pess et al. 2012). There was an increase in the available habitat during the Last 428
Glacial Maximum (LGM) (Figure 1) as well as alternative river paths that joined the mouths of 429
Western Alaska rivers into a common drainage that flowed into the Bering Sea through the 430
Bering Land Bridge (Hopkins 1973, Knebel and Creager 1973, Lindsey and McPhail 1986). 431
This increase in habitat placed northwestern Alaska drainages into closer proximity to the 432
mouths of the rivers in Asia and may have resulted in connectivity and subsequent gene flow 433
among the Beringian populations. 434
The greatest amount of genetic differentiation within both lineages occurs between the GoA 435
populations. Populations within lineages may have been isolated within the Cascadia refugia by 436
glacial ice and experienced differing demographic processes of random genetic drift and post-437
glacial recolonization; or inherent differences in standing genetic variation could account for the 438
differing population structure within the lineages in North America. Additional populations 439
from North America would help to clarify the relationship among populations within each 440
lineage and evaluate whether the odd-year survived in more southern ice-free areas compared to 441
the even-year lineage. 442
Allochrony and pink salmon 443
The significant differences in observed heterozygosity, allelic richness among populations, and 444
the distribution of private alleles provide strong evidence for an overall reduction of genetic 445
diversity in the even-year compared to the odd-year lineage. These differences support the 446
hypothesis that the two allochronic lineages exhibit distinct genetic backgrounds and pools of 447
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standing variation. The differences appear consistent across the range with implications for 448
adaptive divergence and geographic distribution. 449
Looking into a future with a warming climate (Karl et al. 2009 , Nielsen et al. 2013), we 450
anticipate the response of the lineages to such pressures will often be correlated. Similar 451
responses to climate change have already been observed in both lineages with changes to 452
migration timing thought to better match new environmental conditions. Kovach et al. (2013) 453
observed earlier migration timing in both lineages, and evidence for the genetic basis of the 454
change in the odd-year lineage is accumulating (Kovach et al. 2012, Manhard et al. 2017). 455
However, the differing genomic backgrounds may also result in divergent adaptive responses and 456
differential range shifts of the lineages. Pink salmon are currently the most abundant Pacific 457
salmonid with dramatic increases in catches in the odd-year lineage particularly in the southern 458
portion of the range in North America (NPAFC 2012). Recent studies by Wechter et al. (2017) 459
on growth of juvenile pink salmon in the Bering Sea support the hypothesis of Beamish (2012) 460
that the odd-year lineage allocates more energy to growth rather than to energy storage; this life 461
history strategy may be particularly successful in warming conditions. Limborg et al. (2014) 462
identified genomic regions of differentiation between the lineages. Additional studies may help 463
identify the genomic regions important to lineage differentiation and response to climatic 464
variability. These genomic data should increase our understanding of whether current warming 465
trends in the southern portions of the range favor odd-year genotypes as well as better predict 466
how abundance and distribution of even- and odd-year populations might change in arctic and 467
subarctic regions of North America and Asia in response to warming conditions. 468
469
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Acknowledgements 470
Asian samples were provided through exchanges promoted by North Pacific Anadromous Fish 471
Commission; North American samples were provided by the Alaska Department of Fish and 472
Game and the Washington Department of Fish and Wildlife. We would like to thank members 473
of the Seeb Laboratory, especially Ryan Waples and Morten T. Limborg, for their advice and 474
contribution to the bioinformatics, and Carita Pascal for assistance in the laboratory. CT was 475
partially funded by Richard T. Whiteleather, Fisheries B.S. 1935 Endowed Scholarship from the 476
School of Aquatic and Fishery Sciences, University of Washington. Additional funding was 477
provided through Cooperative Agreement Number COOP-13-085 from the Alaska Department 478
of Fish and Game.479
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isolation through time: allochronic differentiation of a phenologically atypical population of the 662
pine processionary moth. Proceedings of the Royal Society B: Biological Sciences 274(1612): 663
935-941. 10.1098/rspb.2006.3767 664
Seeb, L.W., R.K. Waples, M.T. Limborg, K.I. Warheit, C.E. Pascal, and J.E. Seeb. 2014. Parallel 665
signatures of selection in temporally isolated lineages of pink salmon. Molecular Ecology 666
23(10): 2473-2485. 10.1111/mec.12769 667
Shafer, A.B., C.I. Cullingham, S.D. Cote, and D.W. Coltman. 2010. Of glaciers and refugia: a 668
decade of study sheds new light on the phylogeography of northwestern North America. Mol 669
Ecol 19(21): 4589-4621. 10.1111/j.1365-294X.2010.04828.x 670
Shaklee, J.B., and N.V. Varnavskaya. 1994. Electrophoretic characterization of odd-year pink 671
salmon (Oncorhynchus gorbuscha) populations from the Pacific Coast of Russian, and 672
comparison with selected North American populations. Canadian Journal of Fisheries and 673
Aquatic Sciences 51(Suppl 1): 158-171. 674
Smith, C.T., R.J. Nelson, C.C. Wood, and B.F. Koop. 2001. Glacial biogeography of North 675
American coho salmon (Oncorhynchus kisutch). Molecular Ecology 10(12): 2775-2785. 676
Springer, A.M., and G.B. van Vliet. 2014. Climate change, pink salmon, and the nexus between 677
bottom-up and top-down forcing in the subarctic Pacific Ocean and Bering Sea. Proceedings of 678
the National Academy of Sciences of the United States of America. 10.1073/pnas.1319089111 679
Takezaki, N., M. Nei, and K. Tamura. 2010. POPTREE2: Software for constructing population 680
trees from allele frequency data and computing other population statistics with Windows 681
interface. Mol Biol Evol 27(4): 747-752. 10.1093/molbev/msp312 682
Taylor, R.S., and V.L. Friesen. 2017. The role of allochrony in speciation. Molecular Ecology 683
26(13): 3330-3342. 10.1111/mec.14126 684
Toge, K., R. Yamashita, K. Kazama, M. Fukuwaka, O. Yamamura, and Y. Watanuki. 2011. The 685
relationship between pink salmon biomass and the body condition of short-tailed shearwaters in 686
the Bering Sea: can fish compete with seabirds? Proceedings of the Royal Society B: Biological 687
Sciences 278(1718): 2584-2590. 10.1098/rspb.201 688
Turner, C.E., and H.T. Bilton. 1968. Another pink salmon (Oncorhynchus gorbuscha) in its third 689
year. Journal of Fisheries Research Board of Canada 25(9): 1993-1996. 690
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Warner, B.G., R.W. Mathewes, and J.J. Clague. 1982. Ice-Free Conditions on the Queen 691
Charlotte Islands, British Columbia, at the Height of Late Wisconsin Glaciation. Science 692
218(4573): 675-677. 10.1126/science.218.4573.675 693
Wechter, M.E., B.R. Beckman, A.G. Andrews, A.H. Beaudreau, and M.V. McPhee. 2017. 694
Growth and condition of juvenile chum and pink salmon in the northeastern Bering Sea. Deep-695
Sea Research Part Ii-Topical Studies in Oceanography 135: 145-155. 696
10.1016/j.dsr2.2016.06.001 697
Weir, B.S., and C.C. Cockerham. 1984. Estimating F-statistics for the analysis of population 698
structure. Evolution 38(6): 1358-1370. 699
Westlake, R.L., and G.M. O'Corry-Crowe. 2002. Macrogeographic structure and patterns of 700
genetic diversity in harbor deals (Phocavitulina) from Alaska to Japan. Journal of Mammalogy 701
83(4): 1111-1126. 702
Wickham, H. 2009. ggplot2: elegant graphics for data analysis. URL : http://ggplot2.org/. 703
Springer-Verlag, New York. 704
705
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Table 1. Location and collection year for even- and odd-year populations of pink 706
salmon. 707
708
709
710
711
712
713
714
1From Seeb et al. (2014) 715
Location River Collection Year
Lat. Long. even odd
Hokkaido Island Kushiro River 2006 2007 42.980 144.378
Amur Province Amur River 2010 2011 52.938 139.727
Magadan Province Tauy River 2012 2009 59.714 148.929
Kamchatka Province Haylylulya River 2010 2009 57.769 162.485
Norton Sound1 Nome River 1994 1991 64.483 -165.301
Prince William Sound1 Koppen Creek 1996 1991 60.706 -145.898
Puget Sound1 Snohomish River 1996 2003 48.014 -122.181
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Table 2. Population genetic statistics for even- and odd-year populations of pink salmon, including final sample number, 716
allelic richness, mean observed heterozygosity (Ho), mean expected heterozygosity (He), and mean individual standardized 717
heterozygosity (HS_obs). 718
719
720
Location N
Allelic
Richness Mean Ho Mean He Mean HS_obs
even odd even odd even odd even odd even odd
Hokkaido 32 32 1.595 1.645 0.159 0.170 0.160 0.170 0.968 1.037
Amur 32 32 1.625 1.677 0.161 0.171 0.163 0.172 0.988 1.051
Magadan 27 28 1.623 1.677 0.161 0.171 0.161 0.171 0.986 1.048
Kamchatka 31 31 1.619 1.679 0.160 0.172 0.161 0.171 0.983 1.056
Norton Sound 18 24 1.617 1.678 0.153 0.170 0.160 0.172 0.937 1.045
Prince William
Sound 24 24 1.593 1.623 0.161 0.162 0.160 0.165 0.988 0.996
Puget Sound 24 24 1.558 1.555 0.149 0.154 0.154 0.156 0.910 0.939
Overall Mean 27 28 1.604 1.648 0.158 0.167 0.160 0.168 0.966 1.024
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Table 3. Estimates of FST for: A. all populations, B. by lineage: the even- and odd-721
year lineages and groups of populations (Asia/Bering Sea (Asia/Ber) and Gulf of 722
Alaska (GoA)) within each lineage, and C. pairwise by location. Confidence 723
intervals are based on 1 000 bootstraps. 724
725
Comparison No.
Populations FST 95% CI
Over all populations 14 0.060 (0.059 - 0.062)
By lineage
Odd-year lineage 7 0.033 (0.032 - 0.035)
Odd Asia/Ber 5 0.008 (0.007 - 0.009)
Odd GoA 2 0.052 (0.049 - 0.053)
Even-year lineage 7 0.026 (0.025 - 0.027)
Even Asia/Ber 5 0.011 (0.010 - 0.012)
Even GoA 2 0.030 (0.029 - 0.033)
Pairwise by location
Hokkaido 2 0.088 (0.084 - 0.089)
Amur 2 0.063 (0.062 - 0.066)
Magadan 2 0.060 (0.058 - 0.062)
Kamchatka 2 0.060 (0.059 - 0.063)
Norton Sound 2 0.062 (0.059 - 0.064)
Prince William Sound 2 0.094 (0.089 - 0.095)
Puget Sound 2 0.148 (0.143 - 0.145)
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Table 4. Results of AMOVA comparing the amount variation between lineages and 726
between groups of populations. 727
728
729
730
731
732
733
734
*significant at P < 0.05 735
**significant at P < 0.001 736
737
AMOVA Source of Variation Amount of
Variation (%) Fixation Indices
A. Between
lineages
Between lineages 5.72 FST
0.086**
Among populations within lineages 2.87 FSC
0.030**
Within populations 91.41 FCT
0.057**
B. Between
even lineage
groups
Between groups 2.82 FST 0.042**
Among populations within groups 1.40 FSC 0.014*
Within populations 95.78 FCT 0.028**
C. Between
odd lineage
groups
Between groups 4.13 FST
0.056**
Among populations within groups 1.46 FSC
0.015*
Within populations 94.40 FCT
0.041**
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Figure 1. Map of the present-day coastline (grey) overlaid with the exposed land 738
during lowered sea levels (120 to 130m lower than present, orange) and glacial cover 739
(blue) during the LGM, approximately 18-26 thousand years ago. Historical 740
coastline adapted from Grant et al. (2012), Brigham-Grette et al. (2003b) and Pielou 741
(1991). Glacial cover adapted from Shafer et al. (2010), Ehlers and Gibbard (2007), 742
and Pielou (1991). 743
Figure 2. Boxplot of individual standardized observed heterozygosity (Hs_obs) across 744
loci within each population. Hs_obs was measured as the proportion of heterozygotes 745
standardized by the mean observed heterozygosity of the genotyped loci. For each 746
population, the thick black lines show the median values, and the box depicts the 747
quartiles of the individual values. Whiskers show 1.5x the interquartile range or the 748
maximum value, with circles representing individuals with Hs_obs that fall outside of 749
that range. 750
Figure 3. Elevated allelic diversity in odd-year pink salmon. The histogram shows 751
allele frequencies for loci that are variable in a single lineage (private alleles). There 752
are more private alleles in the odd-year lineage (dark bars) than in the even-year 753
lineage (light bars). These private alleles also have greater frequency in the odd-year 754
lineage. 755
Figure 4. Unrooted Neighbor-Joining tree based on DA distance (Nei 1983) using 16 756
681 SNPs. All branches had 100% bootstrap support with 10 000 permutations, 757
except where noted. 758
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Figure 5. Individual based PCA exploring the relationship of all 14 populations: A) 759
principal components 1 vs 2, B) principal components 1 vs 3, C) principal 760
components 2 vs 3. The even-year lineage individuals are represented by circles and 761
the odd-year lineage individuals are represented by triangles, with each sample 762
location assigned a different color. The percent of variation explained by each 763
principal component is in parenthesis. 764
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765
766
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767
768
769
770
771
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772
773
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774
775
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776
777
778
779
780
781
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Supplemental Files 782
File S1. Detailed information for 396 population samples. 783
File S2. Sequence information for 16 681 loci. 784
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785
786
Figure S1. Three-dimensional visualization of population based PCA exploring the relationship 787
between all 14 populations. The centroid of the even-year lineage populations are represented by 788
circles and the centroid of the odd-year lineage populations are represented by triangles, with 789
each sample location assigned a different color. The percent of variation explained by each 790
principal component is in parenthesis. 791
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792
Figure S2. Comparisons of pairwise FST between all fourteen populations. Populations are 793
ordered geographically, from the southern Asian populations to the southern North American 794
populations. Orange highlights the pairwise comparisons with the greatest FST. 795
796
797
798
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Protocol 1. Detailed RAD sequencing protocol for Asian populations. 799
RAD sequencing 800
Asian samples were sequenced using a bulk pooling and re-sequencing method, whereby the 801
RAD libraries included many individually barcoded samples with the expectation that those that 802
had poor sequence coverage would be re-sequenced. This process should result in more even 803
coverage of retained sequence reads across individuals. Genomic DNA was digested using the 804
restriction site enzyme SbfI, and P1 adaptors were ligated. Each individual was barcoded using 805
6bp long adaptors that differed by at least 2 nucleotides. The DNA was pooled into three 806
libraries of no more than 86 individuals each and purified. The individuals were assigned to the 807
three libraries randomly to avoid any lane effect. 808
The pooled libraries were separated evenly into two subsets and sheared at the same time. The 809
ends of the DNA were repaired, and P2 adaptors were ligated to the sheared DNA. A final PCR 810
with conditions from Everett et al. (2012) amplified the P1 and P2 adaptor-ligated DNA 811
fragments, and the product was verified on a 1% agarose gel. Each library was assessed for 812
quality and concentration of genomic DNA using a Bioanalyzer DNA 1000 kit (Agilent 813
Technologies, Santa Clara, CA), and the final concentration of each library was determined using 814
the accompanying Bioanalyzer software. The prepared libraries were assessed for DNA quality 815
and sequenced on an Illumina HiSeq2000 sequencer (Illumina, San Diego, CA) at the University 816
of Oregon High-Throughput Sequencing facility. 817
Initial Filtering and Re-sequencing 818
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The raw sequence data for all fourteen populations were filtered, and polymorphic loci were 819
identified using the software package Stacks v.1.31 (Catchen et al. 2011). For each lane of raw 820
sequence, the program process_radtags was used to trim the terminal nucleotide from the 101 bp 821
raw reads, de-multiplex the individuals, and filter the low quality reads. The barcodes used as 822
sample identifiers were replaced with a unique sample name, and all reads from multiple lanes of 823
sequencing were combined for ease and coherence of downstream analysis. 824
After this initial filtering, samples with fewer than 1.5 million retained reads were re-sequenced 825
using the above protocol and their original barcodes. The resulting raw data from the re-826
sequenced samples was again analyzed using the Stacks program process_radtags, and all 827
sequences were combined for downstream analysis. 828
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