References
Ai W, Liu Y, Mei M, et al (2022) A chromosome‐scale genome assembly of
the Mongolian oak (Quercus mongolica). Molecular Ecology Resources
22:2396–2410. doi: 10.1111/1755-0998.13616
Aizawa M, Maekawa K, Mochizuki H, Iizuka K (2021) Taxonomic revision of
quercus serrata subsp. mongolicoides. Acta Phytotaxonomica et
Geobotanica 72:113–123. doi: 10.18942/apg.202017
Alexander DH, Lange K (2011) Enhancements to the ADMIXTURE algorithm for
individual ancestry estimation. BMC Bioinformatics 12:246. doi:
10.1186/1471-2105-12-246
Anderson E (1953) Introgressive hybridization. Biological reviews of the
Cambridge Philosophical Society 28:280–307.
Anderson E, Stebbins GL (1954) Hybridization as an evolutionary
stimulus. Evolution 8:378–388.
Arnold BJ, Lahner B, DaCosta JM, et al (2016) Borrowed alleles and
convergence in serpentine adaptation. Proceedings of the National
Academy of Sciences of the United States of America 113:8320–8325. doi:
10.1073/pnas.1600405113
Asai T, Shinmura Y, Usui G (1986) Mortality factor of the winter buds of
Quercus dentata and Quercus mongolica var. grosseserrata in natural
coastal forests of northern Hokkaido. Journal of Japanese Forest Society
68:368–374. doi: 10.11519/jjfs1953.68.9_368
Barton NH, Hewitt GM (1985) Analysis of hybrid zones. Annual Review of
Ecology and Systematics 16:113–148. doi:
10.1146/annurev.es.16.110185.000553
Bickford CP (2016) Ecophysiology of leaf trichomes. Functional Plant
Biology 43:807–814. doi: 10.1071/FP16095
Bresadola L, Caseys C, Castiglione S, et al (2019) Admixture mapping in
interspecific Populus hybrids identifies classes of genomic
architectures for phytochemical, morphological and growth traits. New
Phytologist 223:2076–2089. doi: 10.1111/nph.15930
Browning BL, Tian X, Zhou Y, Browning SR (2021) Fast two-stage phasing
of large-scale sequence data. The American Journal of Human Genetics
108:1880–1890. doi: 10.1016/j.ajhg.2021.08.005
Browning BL, Zhou Y, Browning SR (2018) A one-penny imputed genome from
next-generation reference panels. American Journal of Human Genetics
103:338–348. doi: 10.1016/j.ajhg.2018.07.015
Buerkle CA, Lexer C (2008) Admixture as the basis for genetic mapping.
Trends in Ecology and Evolution 23:686–694. doi:
10.1016/j.tree.2008.07.008
Burke JM, Arnold ML (2001) Genetics and the fitness of hybrids. Annual
Review of Genetics 35:31–52.
Cavender-Bares J (2019) Diversification, adaptation, and community
assembly of the American oaks (Quercus), a model clade for integrating
ecology and evolution. New Phytologist 221:669–692. doi:
10.1111/nph.15450
Cavender-Bares J, Ramírez-Valiente JA (2017) Physiological evidence from
common garden experiments for local adaptation and adaptive plasticity
to climate in American live oaks (Quercus section Virentes):
Implications for conservation under global change. In: Gil-Pelegrín E,
Peguero-Pina J, Sancho-Knapik D (eds) Oaks Physiological Ecology.
Exploring the Functional Diversity of Genus Quercus L. Tree Physiology,
vol 7. Springer, Cham, pp 107–135
Chhatre VE, Evans LM, DiFazio SP, Keller SR (2018) Adaptive
introgression and maintenance of a trispecies hybrid complex in
range‐edge populations of Populus. Molecular Ecology 27:4820–4838. doi:
10.1111/mec.14820
Ciccarelli D, Bona C (2022) Exploring the functional strategies adopted
by coastal plants along an ecological gradient using morpho-functional
traits. Estuaries and Coasts 45:114–129. doi:
10.1007/s12237-021-00945-y
Danecek P, Auton A, Abecasis G, et al (2011) The variant call format and
VCFtools. Bioinformatics 27:2156–2158. doi:
10.1093/bioinformatics/btr330
Denk T, Grimm GW, Manos PS, et al (2017) An updated infrageneric
classification of the oaks: review of previous taxonomic schemes and
synthesis of evolutionary patterns. In: Oaks Physiological Ecology.
Exploring the Functional Diversity of Genus Quercus.
Dias-Alves T, Mairal J, Blum MGB (2018) Loter: A software package to
infer local ancestry for a wide range of species. Molecular Biology and
Evolution 35:2318–2326. doi: 10.1093/molbev/msy126
Endelman JB (2011) Ridge regression and other kernels for genomic
selection with R package rrBLUP. The Plant Genome 4:250–255. doi:
10.3835/plantgenome2011.08.0024
Engqvist MKM, Kuhn A, Wienstroer J, et al (2011) Plant
D-2-hydroxyglutarate dehydrogenase participates in the catabolism of
lysine especially during senescence. Journal of Biological Chemistry
286:11382–11390. doi: 10.1074/jbc.M110.194175
Fernández V, Sancho-Knapik D, Guzmán P, et al (2014) Wettability,
polarity, and water absorption of holm oak leaves: Effect of leaf side
and age. Plant Physiology 166:168–180. doi: 10.1104/pp.114.242040
Fitzpatrick BM (2012) Estimating ancestry and heterozygosity of hybrids
using molecular markers. BMC Evolutionary Biology 12:131. doi:
10.1186/1471-2148-12-131
Fu R, Zhu Y, Liu Y, et al (2022) Genome-wide analyses of introgression
between two sympatric Asian oak species. Nature Ecology & Evolution
6:924–935. doi: 10.1038/s41559-022-01754-7
Goulet BE, Roda F, Hopkins R (2017) Hybridization in plants: Old ideas,
new techniques. Plant Physiology 173:65–78. doi: 10.1104/pp.16.01340
Hamilton JA, Miller JM (2016) Adaptive introgression as a resource for
management and genetic conservation in a changing climate. Conservation
Biology 30:33–41. doi: 10.1111/cobi.12574
Hesp PA (1991) Ecological processes and plant adaptations on coastal
dunes. Journal of Arid Environments 21:165–191. doi:
10.1016/S0140-1963(18)30681-5
Hipp AL, Manos PS, Hahn M, et al (2020) Genomic landscape of the global
oak phylogeny. New Phytologist 226:1198–1212. doi: 10.1111/nph.16162
Ishida TA, Hattori K, Sato H, Kimura MT (2003) Differentiation and
hybridization between Quercus crispula and Q. dentata (Fagaceae):
Insights from morphological traits, amplified fragment length
polymorphism markers, and leafminer composition. American Journal of
Botany 90:769–776. doi: 10.3732/ajb.90.5.769
Khodwekar S, Gailing O (2017) Evidence for environment-dependent
introgression of adaptive genes between two red oak species with
different drought adaptations. American Journal of Botany
104:1088–1098. doi: 10.3732/ajb.1700060
Kim BY, Wei X, Fitz‐Gibbon S, et al (2018) RADseq data reveal ancient,
but not pervasive, introgression between Californian tree and scrub oak
species (Quercus sect. Quercus, Fagaceae). Molecular Ecology
27:4556–4571. doi: 10.1111/mec.14869
Kushiro T, Okamoto M, Nakabayashi K, et al (2004) The Arabidopsis
cytochrome P450 CYP707A encodes ABA 8′-hydroxylases: Key enzymes in ABA
catabolism. EMBO Journal 23:1647–1656. doi: 10.1038/sj.emboj.7600121
Leroy T, Louvet JM, Lalanne C, et al (2020) Adaptive introgression as a
driver of local adaptation to climate in European white oaks. New
Phytologist 226:1171–1182. doi: 10.1111/nph.16095
Lind-Riehl J, Gailing O (2016) Adaptive variation and introgression of a
CONSTANS-like gene in North American red oaks. Forests 8:3. doi:
10.3390/f8010003
Lind-Riehl JF, Sullivan AR, Gailing O (2014) Evidence for selection on a
CONSTANS-like gene between two red oak species. Annals of Botany
113:967–975. doi: 10.1093/aob/mcu019
Lindtke D, González-Martínez SC, Macaya-Sanz D, Lexer C (2013) Admixture
mapping of quantitative traits in Populus hybrid zones: Power and
limitations. Heredity 111:474–485. doi: 10.1038/hdy.2013.69
Lorenzo O, Nicolás C, Nicolás G, Rodríguez D (2002) Molecular cloning of
a functional protein phosphatase 2C (FsPP2C2) with unusual features and
synergistically up-regulated by ABA and calcium in dormant seeds of
Fagus sylvatica. Physiologia Plantarum 114:482–490. doi:
10.1034/j.1399-3054.2002.1140318.x
Ma Y, Wang J, Hu Q, et al (2019) Ancient introgression drives adaptation
to cooler and drier mountain habitats in a cypress species complex.
Communications Biology 2:213. doi: 10.1038/s42003-019-0445-z
Martin NH, Bouck AC, Arnold ML (2006) Detecting adaptive trait
introgression between Iris fulva and I. brevicaulis in highly selective
field conditions. Genetics 172:2481–2489. doi:
10.1534/genetics.105.053538
Martin SH, Davey JW, Jiggins CD (2015) Evaluating the use of ABBA-BABA
statistics to locate introgressed loci. Molecular Biology and Evolution
32:244–257. doi: 10.1093/molbev/msu269
Martin SH, Van Belleghem SM (2017) Exploring evolutionary relationships
across the genome using topology weighting. Genetics 206:429–438. doi:
10.1534/genetics.116.194720
Matsumoto A, Kawahara T, Kanazashi A, et al (2009) Differentiation of
three closely related Japanese oak species and detection of
interspecific hybrids using AFLP markers. Botany 87:145–153. doi:
10.1139/B08-121
Mckown AD, Guy RD, Klápště J, et al (2014a) Geographical and
environmental gradients shape phenotypic trait variation and genetic
structure in Populus trichocarpa. New Phytologist 201:1263–1276. doi:
10.1111/nph.12601
Mckown AD, Klápště J, Guy RD, et al (2014b) Genome-wide association
implicates numerous genes underlying ecological trait variation in
natural populations of Populus trichocarpa. New Phytologist
203:535–553. doi: 10.1111/nph.12815
Menon M, Bagley JC, Page GFM, et al (2021) Adaptive evolution in a
conifer hybrid zone is driven by a mosaic of recently introgressed and
background genetic variants. Communications Biology 4:1–14. doi:
10.1038/s42003-020-01632-7
Mokryakova M V., Pogorelko G V., Bruskin SA, et al (2014) The role of
peptidyl-prolyl cis/trans isomerase genes of Arabidopsis thaliana in
plant defense during the course of Xanthomonas campestris infection.
Russian Journal of Genetics 50:140–148. doi: 10.1134/S1022795414020100
Nagamitsu T, Shimizu H, Aizawa M, Nakanishi A (2019) An admixture of
Quercus dentata in the coastal ecotype of Q. mongolica var. crispula in
northern Hokkaido and genetic and environmental effects on their traits.
Journal of Plant Research 132:211–222. doi: 10.1007/s10265-018-01079-2
Nagamitsu T, Shuri K (2021) Seed transfer across geographic regions in
different climates leads to reduced tree growth and genetic admixture in
Quercus mongolica var. crispula. Forest Ecology and Management
482:118787. doi: 10.1016/j.foreco.2020.118787
Nagamitsu T, Uchiyama K, Izuno A, et al (2020) Environment‐dependent
introgression from Quercus dentata to a coastal ecotype of Quercus
mongolica var. crispula in northern Japan. New Phytologist
226:1018–1028. doi: 10.1111/nph.16131
Ohba H (2006) Fagaceae. In: Iwatsuki K, Boufford DE, Ohba H (eds) Flora
of Japan. Volume IIa. Kodansha, Tokyo, pp 42–60
Ortego J, Gugger PF, Riordan EC, Sork VL (2014) Influence of climatic
niche suitability and geographical overlap on hybridization patterns
among southern Californian oaks. Journal of Biogeography 41:1895–1908.
doi: 10.1111/jbi.12334
Peterson BK, Weber JN, Kay EH, et al (2012) Double digest RADseq: An
inexpensive method for de novo SNP discovery and genotyping in model and
non-model species. PLoS ONE 7:e37135. doi: 10.1371/journal.pone.0037135
Petit RJ, Bodénès C, Ducousso A, et al (2004) Hybridization as a
mechanism of invasion in oaks. New Phytologist 161:151–164. doi:
10.1046/j.1469-8137.2003.00944.x
Plomion C, Aury JM, Amselem J, et al (2018) Oak genome reveals facets of
long lifespan. Nature Plants 4:440–452. doi: 10.1038/s41477-018-0172-3
Puritz JB, Hollenbeck CM, Gold JR (2014) dDocent: a RADseq,
variant-calling pipeline designed for population genomics of non-model
organisms. PeerJ 2:e431. doi: 10.7717/peerj.431
R_Core_Team (2019) A language and environment for statistical
computing. R Foundation for Statistical Computing
Ramírez-Valiente JA, Sánchez-Gómez D, Aranda I, Valladares F (2010)
Phenotypic plasticity and local adaptation in leaf ecophysiological
traits of 13 contrasting cork oak populations under different water
availabilities. Tree Physiology 30:618–627. doi:
10.1093/treephys/tpq013
Rendón-Anaya M, Wilson J, Sveinsson S, et al (2021) Adaptive
introgression facilitates adaptation to high latitudes in European aspen
(Populus tremula L.). Molecular Biology and Evolution 38:5034–5050.
doi: 10.1093/molbev/msab229
Rieseberg LH, Buerkle CA (2002) Genetic mapping in hybrid zones.
American Naturalist 159:S37–S50. doi: 10.2307/3078920
Rieseberg LH, Carney SE (1998) Plant hybridization. New Phytologist
140:599–624. doi: 10.1046/j.1469-8137.1998.00315.x
Rieseberg LH, Kim S-C, Randell RA, et al (2007) Hybridization and the
colonization of novel habitats by annual sunflowers. Genetica
129:149–165. doi: 10.1007/s10709-006-9011-y
Riordan EC, Gugger PF, Ortego J, et al (2016) Association of genetic and
phenotypic variability with geography and climate in three southern
California oaks. American Journal of Botany 103:73–85. doi:
10.3732/ajb.1500135
Sancho-Knapik D, Escudero A, Mediavilla S, et al (2021) Deciduous and
evergreen oaks show contrasting adaptive responses in leaf mass per area
across environments. New Phytologist 230:521–534. doi:
10.1111/nph.17151
Shimizu H, Kikuchi K, Yamada K (1995) Local variation of bud number on
axillary buds of bud-scales of Quercus dentata in coastal forest along
Japan sea of Hokkaido. Trans Hokkaido Branch Jpn For Soc 43:140–142.
Solé-Medina A, Robledo-Arnuncio JJ, Ramírez-Valiente JA (2022)
Multi-trait genetic variation in resource-use strategies and phenotypic
plasticity correlates with local climate across the range of a
Mediterranean oak (Quercus faginea). New Phytologist 234:462–478. doi:
10.1111/nph.17968
Sork VL (2018) Genomic studies of local adaptation in natural plant
populations. Journal of Heredity 109:3–15. doi: 10.1093/jhered/esx091
Sork VL, Cokus SJ, Fitz-Gibbon ST, et al (2022) High-quality genome and
methylomes illustrate features underlying evolutionary success of oaks.
Nature Communications 13:1–15. doi: 10.1038/s41467-022-29584-y
Sork VL, Riordan E, Gugger PF, et al (2016) Phylogeny and introgression
of California scrub white oaks (Quercus section Quercus). International
Oaks 27:1–14.
Stacklies W, Redestig H, Scholz M, et al (2007) pcaMethods: A
bioconductor package providing PCA methods for incomplete data.
Bioinformatics 23:1164–1167. doi: 10.1093/bioinformatics/btm069
Stebbins GL, Matzke EB, Epling C (1947) Hybridization in a population of
Quercus marilandica and Quercus ilicifolia. Evolution 1:79. doi:
10.2307/2405406
Suarez-Gonzalez A, Hefer CA, Christe C, et al (2016) Genomic and
functional approaches reveal a case of adaptive introgression from
Populus balsamifera (balsam poplar) in P. trichocarpa (black
cottonwood). Molecular Ecology 25:2427–2442. doi: 10.1111/mec.13539
Suarez-Gonzalez A, Hefer CA, Lexer C, et al (2018a) Introgression from
Populus balsamifera underlies adaptively significant variation and range
boundaries in P. trichocarpa. New Phytologist 217:416–427. doi:
10.1111/nph.14779
Suarez-Gonzalez A, Hefer CA, Lexer C, et al (2018b) Scale and direction
of adaptive introgression between black cottonwood (Populus trichocarpa)
and balsam poplar (P. balsamifera). Molecular Ecology 27:1667–1680.
doi: 10.1111/mec.14561
Suarez-Gonzalez A, Lexer C, Cronk QCB (2018c) Adaptive introgression: A
plant perspective. Biology Letters 14:20170688. doi:
10.1098/rsbl.2017.0688
Ubukata M, Koono K, Iizuka K (1996) Morphological characteristics of
Quercus crispula x dentata hybrids. Trans Hokkaido Branch Jpn For Soc
44:113–116.
Van Damme M, Zeilmaker T, Elberse J, et al (2009) Downy mildew
resistance in arabidopsis by mutation of Homoserine Kinase. Plant Cell
21:2179–2189. doi: 10.1105/tpc.109.066811
Wang W, He X, Yan X, et al (2023) Chromosome‐scale genome assembly and
insights into the metabolome and gene regulation of leaf color
transition in an important oak species, Quercus dentata. New Phytologist
238:2016–2032. doi: 10.1111/nph.18814
Whitney KD, Randell RA, Rieseberg LH (2010) Adaptive introgression of
abiotic tolerance traits in the sunflower Helianthus annuus. New
Phytologist 187:230–239. doi: 10.1111/j.1469-8137.2010.03234.x
Yu J, Pressoir G, Briggs WH, et al (2006) A unified mixed-model method
for association mapping that accounts for multiple levels of
relatedness. Nature Genetics 38:203–208. doi: 10.1038/ng1702
Zhao K, Wright M, Kimball J, et al (2010) Genomic diversity and
introgression in O. sativa reveal the impact of domestication and
breeding on the rice genome. PLoS ONE 5:e10780. doi:
10.1371/journal.pone.0010780
Table and Figure Legend
Figure 1. Locations of provenances of Quercus mongolicavar. crispula (Qc , red), Q. ×angustilepidota (green), and Q. dentata (Qd , blue),
which were planted in the inland site (a) and the coastal site (b).
Provenances are categorized into southwestern (inverse triangles),
northwestern (triangles), northern (diamonds), eastern (squares)
districts of the northernmost area of Hokkaido and other areas of
Hokkaido (circles). Enlarged symbols indicate reference individuals ofQc and Qd for inferring local ancestry. Locations of the
inland site (red arrow head) and the coastal site (blue arrow head) are
shown.
Figure 2. Quercus dentata ancestry (S ) and
inter-ancestry heterozygosity (H ) of sampled trees in the inland
site (a) and the coastal site (b). Ancestral populations of Q.
mongolica var. crispula are S = 0 and H = 0, and
those of Q. dentata are S = 0 and H = 0. Solid
lines indicate backcrossing from F1 hybrids (S =
0.5 and H = 1) to ancestral populations, and a dotted curve
indicate random mating between the ancestral populations. Symbols are
shown in the same way as Figure 1.
Figure 3. Principal component analysis for phenotypes of eight
morphological traits of leaves and shoots. Proportion (%) of variances
for the first five principal components (PCs) and vectors (arrows) of
the traits contributing to the first and second PCs are shown (a).
Coordination of sampled trees in the inland site (b) and the coastal
site (c) on the first and second PCs are shown. Symbols are shown in the
same way as Figure 1.
Figure 4. Relationship between Quercus dentata ancestry
and the first principal component of phenotypic variation of sampled
trees in the inland site (a) and the coastal site (b). Solid and dotted
lines are fitted to observations in the focal site and the other site,
respectively, using the lowess method. Symbols are shown in the same way
as Figure 1.
Figure 5. Relationship between mean values of the first
principal component of phenotypic variation of sampled trees and mean
values of the stem basal area of recorded trees at plantation rows in
the inland site (a) and the coastal site (b). Solid and dotted lines are
fitted to observations in the focal site and the other site,
respectively, using the lowess method. Symbols are shown in the same way
as Figure 1.
Figure 6. Genome-wide patterns of local ancestry (a) and mean
number of Quercus dentata alleles in admixed individuals (b).
Along vertical axis (a), Q. dentata reference individuals
(white), admixed individuals sorted by Quercus dentata ancestry,
and Q. mongolica var. crispula reference individuals
(black) are arranged. White, black, and grey bars (a) indicate
homozygote of Q. dentata alleles, homozygote of Q.
mongolica var. crispula alleles, and heterozygote of both
alleles, respectively, in admixed individuals. Blue vertical lines
indicate borders of chromosomes, and red vertical lines indicate
positions of loci potentially associated with traits shown in Figure 7
and Table S2.
Figure 7. Manhattan plots of likelihood of genome-wide
association with eight morphological traits of leaves and shoots. Left
panels (a, c, e, g, i, k, m, o) are association of SNP genotypes
(association mapping), and right panels (b, d, f, h, j, l, n, p) are
association of local ancestry (admixture mapping). Taxon-specific
traits: relative lead width (a, b), lateral vein interval (c, d), tooth
angle (e, f), and stellate hair length (g, h); and habitat-specific
traits: stellate hair density (i, j), leaf mass per area (k, l), shoot
diameter (m, n), and scale bud number (o, p) are shown. Black and grey
circles indicate loci in different chromosomes, and green circles
indicate loci potentially associated with traits (FDR < 0.15).
Supporting Information
Table S1. Provenances of sampled trees in inland and coastal sites.
Figure S1. Photographs of inland site (a) and coastal site (b) in 2017.
Figure S2. Locations and sizes of trees planted in three blocks (a–c)
in inland site. Solid circles, circles with crosses, and open circles
indicate sampled reference, sampled non-reference, and non-sampled
living trees, respectively. Crosses, + and ×, indicate dead and thinned
trees, respectively. Triangles indicate non-experimental trees. Colors
indicate taxa: Quercus mongolica var. crispula (red),Q. × angustilepidota (green), and Q. dentata(blue).
Figure S3. Locations and sizes of trees planted in two blocks (a, b) in
coastal site. Solid circles, circles with crosses, and open circles
indicate sampled reference, sampled non-reference, and non-sampled
living trees, respectively. Crosses, + and ×, indicate dead and thinned
trees, respectively. Triangles indicate non-experimental trees. Colors
indicate taxa: Quercus mongolica var. crispula (red),Q. × angustilepidota (green), and Q. dentata(blue).
Figure S4. Measurements for morphological traits of leaves and shoots.
Figure S5. Bayesian clustering of SNP genotypes at ddRAD loci of sampled
trees in both inland and coastal sites. Log likelihood when the number
of clusters (K ) are 1, 2, and 3 (a). Genetic differentiation
(F ST) between clusters when K = 2 andK = 3 (b). Bar plots of ancestry proportions of trees sorted by
the ancestry proportion of clusters, when K = 2 (c) and K= 3 (d). Colors correspond to clusters (b, c, d). Colors of circles
indicate taxa: Quercus mongolica var. crispula (red),Q. × angustilepidota (green), and Q. dentata(blue), and enlarged symbols indicate reference samples (e).
Figure S6. Frequency distributions (scatter plots) and correlations
(histograms) for pairs of shoot hair (0: absent and 1: present) and
eight morphological traits: relative leaf width, lateral vein interval
(mm), tooth angle (˚), loge length (mm) and loge density
(mm–2) of stellate hair on lower leaf surface, lead
mass per area (mg mm–2), shoot diameter (mm), and
loge number of axillary buds at bud-scale scars. Numbers
indicate Kendall’s correlation coefficients, and red lines indicate
linear regression lines.
Figure S7. Patterson’s D statistics at neighboring 101 ddRAD loci
along chromosomes. Positive D values indicate introgression fromQuercus dentata to Q. mongolica var. crispula . Red
vertical lines indicate positions of trait-associated loci shown in
Figure 7 and Table S2.
Figure S8. Phenotypic values of four taxon-specific traits in inland
(left) and coastal (middle) sites and those values scaled in each site
and pooled together (right) in relation to Quercus dentataancestry. Solid and dotted lines are fitted to observations in the focal
site and the other site, respectively, using the lowess method. Symbols
are shown in the same way as Figure 1.
Figure S9. Phenotypic values of four habitat-specific traits in inland
(left) and coastal (middle) sites and those values scaled in each site
and pooled together (right) in relation to Quercus dentataancestry. Solid and dotted lines are fitted to observations in the focal
site and the other site, respectively, using the lowess method. Symbols
are shown in the same way as Figure 1.
Figure S10. Principal component analysis for SNP genotypes at ddRAD loci
of sampled trees in both inland and coastal sites. Proportion (%) of
variances for the first 10 principal components (PCs) are shown (a).
Coordination of sampled trees on the first and second PCs (b), the first
and third PCs (c), and the first and fourth PCs (d) are shown. Symbols
are shown in the same way as Figure 1.
Figure S11. Q-Q plots of expected and observed –log10p values for four taxon-specific traits. Red lines indicate
identical expected and observed –log10 p values.
Figure S12. Q-Q plots of expected and observed –log10p values for four habitat-specific traits. Red lines indicate
identical expected and observed –log10 p values.
Table S2. ddRAD loci of SNP genotypes (G) and local ancestry (A)
associated with traits (FDR < 0.15) and proteins nearest to or
between trait-associated loci obtained from PM1N v2.3 Quercus
robur annotation database.
Data S1. Data of phenotypes of morphological traits of leaves and shoots
and performance of trees in plantation rows in inland and coastal common
gardens.