Annals of Forest Science (2021) 78: 37
https://doi.org/10.1007/s13595-021-01055-2
RESEARCH PAPER
Low effective population size and high spatial genetic structure
of black poplar populations from the Oder valley in Poland
Błażej Wójkiewicz 1
&
Andrzewj Lewandowski 1 & Weronika B. Żukowska 1 & Monika Litkowiec 2 & Witold Wachowiak 1,3
Received: 26 September 2020 / Accepted: 5 March 2021 / Published online: 7 April 2021
# The Author(s) 2021
Abstract
Context Black poplar (Populus nigra L.) is a keystone species of European riparian ecosystems that has been negatively
impacted by riverside urbanization for centuries. Consequently, it has become an endangered tree species in many European
countries. The establishment of a suitable rescue plan of the remaining black poplar forest stands requires a preliminary
knowledge about the distribution of genetic variation among species populations. However, for some parts of the P. nigra
distribution in Europe, the genetic resources and demographic history remain poorly recognized.
Aims Here, we present the first study on identifying and characterizing the genetic resources of black poplar from the Oder valley
in Poland. This study (1) assessed the genetic variability and effective population size of populations and (2) examined whether
gene flow is limited by distance or there is a single migrant pool along the studied river system.
Methods A total of 582 poplar trees derived from nine black poplar populations were investigated with nuclear microsatellite markers.
Results (1) The allelic richness and heterozygosity level were high and comparable between populations. (2) The genetic
structure of the studied poplar stands was not homogenous. (3) The signatures of past bottlenecks were detected.
Conclusion Our study (1) provides evidence for genetic substructuring of natural black poplar populations from the studied river
catchment, which is not a frequent phenomenon reported for this species in Europe, and (2) indicates which poplar stands may
serve as new genetic conservation units (GCUs) of this species in Europe.
Key message The genetic resources of black poplar in the Oder River valley are still substantial compared to those reported for
rivers in Western Europe. On the other hand, clear signals of isolation by distance and genetic erosion reflected in small effective
population sizes and high spatial genetic structure of the analyzed populations were detected. Based on these findings, we
recommend the in situ and ex situ conservation strategies for conserving and restoring the genetic resources of black poplar
populations in this strongly transformed by human river valley ecosystem.
Keywords Conservation genetics . Gene flow . Forest genetic resources . Genetic structure . Populus nigra
Handling Editor: Bruno Fady
Contributions of the co-authors Conceptualization: BW and AL;
methodology: BW and WW; software: BW; investigation: BW and AL;
resources: BW and AL; data curation: BW, WBŻ, and ML;
writing—original draft: BW; writing—review and editing: BW, AL,
WW, WBŻ, and ML; supervision: WW; project administration: BW;
funding acquisition: BW.
* Błażej Wójkiewicz
bwojkiew@man.poznan.pl
Witold Wachowiak
witwac@amu.edu.pl
Andrzewj Lewandowski
alew@man.poznan.pl
1
Institute of Dendrology, Polish Academy of Sciences,
Kórnik, Poland
Weronika B. Żukowska
wzukowska@man.poznan.pl
2
Kostrzyca Forest Gene Bank, Miłków, Poland
3
Institute of Environmental Biology, Faculty of Biology, Adam
Mickiewicz University, Poznań, Poland
Monika Litkowiec
monika.litkowiec@gmail.com
37 Page 2 of 24
1 Introduction
Black poplar (Populus nigra L.) is a characteristic alluvial
forest tree species of great ecological and economic importance in European riparian ecosystems. As a pioneer tree species, it dominates at the first stage of succession in riverine
forests and contributes to riverbank restoration and flood control. Natural poplar forests, known as Salici-Populetum
(willow-poplar forest) or Populetum albae (poplar forest),
are centers of biodiversity and host ecosystems of threatened
and common animals, including insects. They also serve as
natural corridors that connect larger forest units (Šiler et al.
2014). Economically, black poplar has been used in breeding
programs aimed at the development of new highly resistant
and fast-growing poplar varieties for years. Moreover, due to
its high plasticity, the species plays an important role in
phytoremediation projects in postindustrial areas (Koskela
et al. 2004; lsebrands et al. 2014).
Due to habitat loss caused by the development of river
engineering and intensive land use management of riverside
areas over the last few decades, black poplar has become one
of the most endangered tree species in Europe, and it was
listed in the Strasbourg resolution of 1990 (Arbez and
Lefèvre 1997). More recently, the status of black poplar has
been specified by the International Union for Conservation of
Nature (IUCN) as data deficient, but the species is listed as
endangered in many European countries (Holub and
Procházka 2000; Dénes 2001; Cheffings and Farrell 2005;
Tylkowski 2010). To restore floodplain forests and to conserve the remaining genetic resources of this species, different
conservation strategies have been implemented across
European countries (Arens et al. 1998; Hughes et al. 2005).
In Poland, according to the European Union Habitats
Directive 92/43/EEC, the populations of black poplar
throughout its wide distribution are protected as units of the
Natura 2000 network (91E0). Nevertheless, although this conservation program prevents further devastation of wellpreserved and ecologically valuable fragments of river ecosystems, a progressive reduction in black poplar population
size is still being observed in Polish river landscapes. Based
on ecological data and our own field observations, the most
dramatic situation occurs along the most extensively transformed Warta and Oder river valleys and in the upper and
lower sections of the Vistula River valley (Boratyński et al.
2001; Tylkowski 2010).
The genetic erosion of black poplar in Poland results from
two major factors that can be classified as physical and biological drivers of extinction. The physical factor is associated
with severe disturbance of the regeneration niche of the remaining P. nigra populations and results from the lack of
suitable habitats for natural regeneration at most sites in which
the species survives. For example, when the “Wielka Kępa”
nature reserve near Bydgoszcz was created in 1953, much of it
Annals of Forest Science (2021) 78: 37
was covered by willow-poplar forest, with numerous black
poplars. However, since the damming of the Vistula River in
Włocławek in 1970 and the resultant flooding regulation, this
riparian forest stand has been transformed over time into an
elm-ash forest (Ficario-Ulmetum campestris), in which
P. nigra does not regenerate (Boratyński et al. 2001).
The biological factor of extinction is directly related to the
artificial introduction of fast-growing poplar cultivars
consisting of a narrow range of Euramerican hybrids
(Populus × canadensis Moench. (syn. P. x euramericana
(Dode) Guinier)) and varieties of P. nigra, such as the male
Lombardy poplar (P. nigra cv. ‘Italica’ Duroi), between the
1950s and 1970s. Native black poplar trees may hybridize
with these taxa (Vanden Broeck et al. 2004; Smulders et al.
2008a; Ziegenhagen et al. 2008; Wójkiewicz et al. 2019).
Therefore, there is a real threat that the fast-growing poplar
variants, which occur in Polish river landscapes mostly as
single trees or groups of individuals spread along the
river valleys, endanger native P. nigra populations not
only by competing with them for habitats but also by
reducing their gene pool integrity via backcrossing and
foreign gene introgression.
Taking all these factors into account, the development of a
conservation strategy for black poplar is needed to rescue the
remaining genetic resources of this species in Poland. Based
on the guidelines for genetic conservation of black poplar in
Europe proposed in the Forest Genetic Resources Program
(IUFRO), the establishment of a suitable rescue plan for this
species requires a preliminary evaluation of the background
genetic variation in candidate populations to select unique
local or regional gene pools that should be protected and
which could be classified as the new genetic conservation
units (GCUs) of this species at the pan-European level
(Vanden Broeck et al. 2004; Koskela et al. 2013). However,
little is known about the distribution of genetic variation
among the natural P. nigra populations in Poland.
Therefore, here, we present a case study on identifying and
characterizing the genetic resources of black poplar from the
Oder valley in Poland. Despite considerable anthropogenic
transformations of this river landscape, it has retained the natural qualities that are crucial for the preservation of the characteristics of riverside vegetation cover. Populus nigra is still
very common (frequency >70%) along the Oder valley, in
contrast to, e.g., the Warta valley, where this species occurs
with a frequency of only 20% relative to other forest tree
species (Danielewicz 2008).
For the purpose of this study, the genetic structure and
diversity level of nine natural black poplar populations from
the Oder valley were investigated with nuclear microsatellite
markers. Using different approaches, we investigated the population demographic history and gene flow patterns along the
studied fragments of the valley. Specifically, we (1) identified
the hybrid variants occurring within the black poplar
Annals of Forest Science (2021) 78: 37
populations based on the reference samples included in the
study, (2) determined the number of multilocus genotypes
(MLGs) and clustered them into clonal multilocus lineages
(MLLs) to estimate genotypic richness, (3) assessed the effective population size and fine-scale spatial genetic structure
(SGS) of the analyzed populations, (4) examined whether
there are any signatures of a recent reduction in population
size, and (5) established whether gene flow is limited by distance or there is a single migrant pool along the river system.
Finally, based on the obtained results and available ecological
data, we provide the first guidelines and suggestions on how
to conserve and restore the genetic diversity of black poplar in
the modern river landscapes of Poland.
2 Material and methods
2.1 The Oder valley
The Oder River is the second largest river in Poland. Its total
length is 854.4 km, 742.0 km of which lies in Poland. The
riverhead is located in the Odra Mountains (East Sudetes) in
the Czech Republic, 634 m above sea level. The river drains a
basin of 119,074 km2 and ends in the Roztoka Odrzańska near
Police. The riverbed of the Oder is highly regulated along
almost the entire length of the valley. According to historical
data, the first embankments were already built along the Oder
at the end of the 13th century (Hudak et al. 2018). Currently,
the flood protection system consists of levees, polder areas,
and, since the end of 2019, the Racibórz Dolny water reservoir. One of the most spectacular examples of the performed
transformations of the Oder valley is the shortening of the
natural river course by approximately 160 km, which constitutes 1/6 of the total river length (Migoń 2006).
Ecologically, despite far-reaching anthropogenic changes
in habitats as well as forest and thicket communities, the tree
and shrub flora in some parts of the Oder valley still retain its
basic riparian character. For this reason, this river valley
constitutes one of the major ecological corridors in the
European Ecological Network (EECONET) (Liro et al.
1994). Since 2004, approximately 2300 km2 of the valley has been included in the Natura 2000 network, and
this area is already legally protected.
With regard to poplar species, a characteristic chorological
feature of this group of riparian forest-forming trees in the
Oder valley is the much rarer occurrence of Populus alba
(frequency 23.5%) than of P. nigra (73.8%) (Danielewicz
2008). This discrepancy in frequency between species may
suggest that the white poplar is much more sensitive than
the black poplar to changes in riverside environments. On
the other hand, there is also some probability that because of
the difficulties associated with distinguishing pure black poplars from hybrids on the basis of morphological characters, the
Page 3 of 24 37
true frequency of P. nigra in the Oder valley is overestimated.
This hypothesis is supported by the fact that artificially
introduced P. × canadensis hybrid trees are very common along the entire Oder valley, with a frequency of
75% (Danielewicz 2008).
2.2 Study sites and plant material
Nine natural black poplar populations located in the Oder
valley were analyzed (Fig. 1). Sample sizes ranged from 27
to 99 trees per population (mean of 64.7 individuals), resulting
in a total of 582 specimens studied (Table 1). In each study
site, we focused on the adult trees that were in good condition.
Additionally, the reference individuals of P. × canadensis and
four representatives of Populus deltoides were included to
eliminate potential hybrids from the analyzed set of black
poplar trees in case they had not been recognized based
on phenotype. The included reference individuals grow
in the Kórnik Arboretum of the Institute of Dendrology,
Polish Academy of Sciences. The Euramerican hybrids
comprised variants marked as Populus ‘Serotina’ ♂, P.
‘Robusta’ ♂, P. ‘Marilandica’ ♀, and P. ‘Grandis’ ♀.
These cultivars represent the most popular male and
female poplar variants that have been planted in
Poland in the last 100 years as plantations for wood
production or singles trees for landscape purposes.
Currently, there are no large-area plantations of poplar
cultivars in Poland. However, the remnant poplar hybrids occur in the Polish river valleys as single trees
or groups of individuals spread along the river valleys
with similar frequency as native poplar species
(Danielewicz 2008).
DNA was extracted from young poplar leaves by using the
standard CTAB protocol (Dumolin et al. 1995). Qualitative
and quantitative assessments of the DNA isolates were conducted by absorbance measurement using an Eppendorf
BioPhotometer (Hamburg, Germany).
2.3 Genotype scoring
All samples were genotyped with the 12 nuclear microsatellite loci described by Van der Schoot et al. (2000)
and Smulders et al. (2001). Specifically, WPMS01,
WPMS04, WPMS06, WPMS07, WPMS08, WPMS09,
WPMS10, WPMS11, WPMS12, WPMS16, WPMS18,
and WPMS20 were used in the study. Marker amplification was performed according to the methodology described by Wójkiewicz et al. (2019). The products of each
polymerase chain reaction (PCR) were analyzed using an
ABI 3130 capillary sequencer (Thermo Fisher Scientific,
Waltham, Massachusetts, USA) with GeneScan LIZ500
internal size standard. The genotypes were scored using
GENEMAPPER vs. 4.0 (Wójkiewicz 2020).
37 Page 4 of 24
Annals of Forest Science (2021) 78: 37
Fig. 1 Geographical location and genetic structure of the studied black
poplar populations from the Oder valley. (a) Map showing the location of
the sampled populations (pointed by red points). (b) The distribution of
ΔK over K = 1-15. Proportion of the membership coefficient for each
black poplar population studied and for each individual of P. nigra tree to
the inferred clusters K = 2 (C,C’) and K = 4 (D,D’) from the
STRUCTURE analysis
2.4 Data analysis
Wójkiewicz et al. 2019). In order to identify the hybrids, the
STRUCTURE 2.3.4. software was used (Pritchard et al.
2000). For the 590 sampled trees, which comprised the four
reference individuals of P. deltoides, four P. × canadensis
representatives (marked as P. ‘Serotina’, P. ‘Robusta’, P.
‘Marilandica’, and P. ‘Grandis’), and 582 investigated trees
derived from the nine populations, we set K = 2 (the number of
species). Twenty independent runs were performed with a
burn-in length of 250,000 and 100,000,000 iterations, with
2.4.1 Identification of hybrids and clones
The recognition of hybrid trees was performed based on the
microsatellite markers used in study, among which WPMS01,
WPMS12, and WPMS18 were previously described as diagnostic and useful for the identification of P. × canadensis
cultivars (Smulders et al. 2008a; Jelić et al. 2015;
Annals of Forest Science (2021) 78: 37
Table 1 The table presents the
results of hybrids identification
and clonality analysis performed
with the use of STRUCTURE and
package RClone: N, number of
individuals analyzed; NH,
number of detected hybrids;
MLG, number of distinct
multilocus genotypes; MLL,
number of identified clonal
lineages; RMLL, genotypic
richness calculated based on the
number of defined MLL
Page 5 of 24 37
Nr.
Pop.
Location
N
NH
MLG
MLL
RMLL
1.
KK
Area near Kędzierzyn Koźle
55
1
49
48
0.87
2.
3.
4.
5.
B
WR
BD
CI
Area near Brzeg
Area near Wrocław
Area near Brzeg Dolny
Area near Ciechanów
55
38
90
99
0
0
0
0
42
32
78
56
42
28
77
52
0.76
0.73
0.85
0.52
6.
BO
Area near Bytom Odrzański
56
9
38
34
0.72
7.
8.
9.
Mean
CG
K
G
Area near Cigacice
Area near Kostrzyn nad Odrą
Area near Gozdowice
98
27
64
64.67
8
3
13
3.78
78
19
31
47.00
74
19
29
44.78
0.82
0.78
0.56
0.73
admixture model and correlated allele frequencies, without any prior information. Average admixture coefficients were estimated using the LargeKGreedy algorithm
as implemented in the program CLUMPP version 1.1
(Jakobsson and Rosenberg 2007). To assign the detected
hybrids to the reference poplar cultivars included in the
study, the genotypes of all hybrid individuals were
matched using the GenAlEx 6.5 software (Peakall and
Smouse 2006). Finally, all identified poplar hybrids
were excluded from the data set to analyze clonality.
To determine the genotypic resolution power of the 12
microsatellites used in the study, a test of the reliability of loci
was performed (Alberto et al. 2005). The number of distinct
multilocus genotypes (MLGs) was assessed, and then the
clones were identified as sets of individuals that presented
the same MLG using the package RClone (Bailleul et al.
2016). Furthermore, as the number of MLGs can be
overestimated due to the occurrence of slightly different
MLGs resulting from somatic mutations or scoring errors,
discrimination of clonal lineages and assembly of similar
MLGs into corresponding MLLs were performed.
Discrimination analysis was performed by calculating
Rozenfeld’s genetic distance (difference in length
between alleles; Rozenfeld et al. 2007) for each pair of
unique MLGs in the sample that were initially characterized molecularly and comparing these MLGs to each pair
of unique MLGs identified as sexually produced by simulations (Arnaud-Hanod et al. 2007). From this distribution, a threshold was determined, under which genetic
distances were considered to be due to somatic mutations
or scoring errors, and distinct MLGs belonging to the
same MLL were identified. With the aim of assessing
the relative importance of asexual reproduction, genotypic
richness R was calculated for each population based on
the number of detected MLLs (Dorken and Eckert 2001).
Finally, for the purpose of population genetic analyses,
only one ramet of each genet was left in the data set, as
clones do not result from sexual reproduction.
2.4.2 Genetic variation and differentiation
To estimate the frequency of null alleles and detect the loci
that deviate from the Hardy–Weinberg equilibrium, we used
the exact test based on the Markov Chain Monte Carlo
(MCMC) algorithm with Bonferroni correction implemented
in GENEPOP v. 4.6 (Rousset 2008). The basic information
about the markers used in the study are presented in Table 4 in
Appendix. To test for linkage disequilibrium (LD) between
the 12 pairs of loci at the individual population level and
across the populations, the Fisher’s exact test was used in
Arlequin 3.22 (Excoffier et al. 2005). Basic genetic diversity
parameters (i.e., A—mean number of alleles, Ae—mean number of effective alleles, PA—number of private alleles, Ho—
observed heterozygosity, and He—unbiased expected heterozygosity) were calculated in GenAlEx 6.5. FSTAT 2.9.4
(Goudet 2001) was used to estimate the inbreeding coefficient
(Fis) and allelic richness (AR) for the minimum sample size of
19 individuals. A Bayesian approach implemented in the
INEST 2.0 software (Chybicki and Burczyk 2009) was applied to estimate the inbreeding coefficient, including ‘null
alleles’ correction (Fisnull), according to the individual inbreeding model (IIM). The estimation was run with 500,000
MCMC cycles, with every 200th cycle updated and a burn-in
of 50,000. The deviance information criterion (DIC) was used
to compare the full model (‘nfb’, when Fis>0) with the
random mating model (‘nb’, when Fis = 0) to assess the
determinants of homozygosity level. The significance of
heterozygote deficiency in the sampled populations was
assessed by the U test (Guo and Thompson 1992) in
GENEPOP, and p values were obtained with the
Markov chain algorithm using default settings.
NeEstimator software ver. 2.01 (Do et al. 2014) was used
to estimate the effective population size (Ne^) of each studied
population with the LD approach (Waples and Do 2008). To
test the sensitivity of the method to the presence of rare alleles,
the results obtained with three different allele frequency cutoff thresholds (i.e., Pcrit = 0.01, 0.02, and 0.05) were
37 Page 6 of 24
compared. The 95% confidence intervals (CINe^) were
derived using the ‘parametric’ option with χ2 approximation (Waples 2005).
Finally, we assessed interpopulation differentiation by hierarchical analysis of molecular variance (AMOVA) using
both FST and RST values computed for all pairs of populations in Arlequin 3.11. To evaluate the influence of stepwise
mutations on the differentiation level of populations, RST and
permuted RST (pRST—which corresponds to FST) values
were compared using the test proposed by Hardy et al.
(2003) and implemented in the SPAGeDi ver. 1.4c software
(Hardy and Vekemans 2002). Moreover, overall and pairwise
FST values were also calculated with a correction for the
presence of null alleles (excluding null alleles (ENA),
FSTNA) with the use of FreeNA software (Chapuis and
Estoup 2007). The bootstrapped 95% confidence intervals
(CIs) of FSTNA were calculated using 2000 replicates over
the loci. The statistical significance (at the level of p = 0.01)
of the estimated pairwise FST values was tested by 10,100
random permutations using the Arlequin software.
2.4.3 Demographic history of the populations
We used two different approaches to elucidate the demographic history of the studied black poplar populations and
to test whether past environmental transformations of river
landscapes coupled with potential population size variation
left detectable signatures of genetic bottlenecks. For each population, we calculated the M ratio (MR, Garza and Williamson
2001) and ran the Wilcoxon test for heterozygote excess
(Cornuet and Luikart 1996) using the INEST ver. 2.2 software. The analysis was performed under the two-phase mutation (TPM) model with two parameters: pg = 0.22 (the proportion of multistep mutations) and δg = 0.31 (the mean size
of the multistep mutations). The significance of a potential
bottleneck was tested using Wilcoxon’s signed-rank test based
on 1,000,000 permutations.
Annals of Forest Science (2021) 78: 37
distances and genetic distances based on kinship coefficients
within each population were obtained for simple sequence
repeat (SSR) loci using SPAGeDi. Within each population,
the relationship between matrices was assessed using the
Mantel test implemented in GenAlEx 6. The significance of
the Mantel test was evaluated based on 1,000 permutations.
To investigate migration patterns among the analyzed populations, Bayesian assignment testing (Rannala and Mountain
1997) was performed using the Geneclass2 software (Piry
et al. 2004). Moreover, the Mantel (1967) test was applied to
evaluate whether the distribution of genetic variation was geographically structured and to verify the hypothesis of isolation
by distance (IBD) between populations. For this purpose, the
GenAlEx software was used, with 1,000 random permutations
of the relationship between genetic differentiation, quantified
as FST/(1- FST), and corresponding geographical distance
matrices between populations.
The substructuring of the black poplar gene pool was evaluated by a nonspatial Bayesian clustering model implemented
in STRUCTURE 2.3.4. Twenty independent runs were performed for each K from 1 to 15 (the user-defined number of
clusters), with a burn-in length of 250,000 and 100,000,000
iterations. The probability distributions of the data (LnP(D))
and the ΔK values (Evanno et al. 2005) were visualized using
the STRUCTURE HARVESTER Web application (Earl and
VonHoldt 2012). Average admixture coefficients were estimated for each value of K using the LargeKGreedy algorithm
as implemented in the program CLUMPP version 1.1.
STRUCTURE plots were generated using the
STRUCTURE PLOT v.2.0 Web application
(Ramasamy et al. 2014). Furthermore, AMOVA analysis
was conducted between the groups of populations defined by STRUCTURE, and significance was tested
using 10,000 random permutations in Arlequin 3.11.
3 Results
2.4.4 Genetic structure and gene flow patterns
3.1 Hybrid identification
To describe the spatial autocorrelation within the populations,
the average kinship coefficient over the pairs of studied individuals was computed using the SPAGeDi software. Distance
intervals were adjusted by SPAGeDi to obtain approximately
the same number of pairs of individuals within each of eight
distance classes. The statistical significance of the autocorrelations was tested by 10,000 random permutations, with a
95% CI. Average kinship coefficients between pairs of individuals for each distance interval were plotted against distance
classes in a diagram. Significant autocorrelation is shown as
an outlier in the observed data from the 95% CIs. Moreover,
SGS was also quantified using the Sp statistics (Vekemans
and Hardy 2004). Matrices of pairwise spatial physical
Out of the 582 poplar individuals analyzed, 34 were classified
as hybrids with a probability higher than 90% (Fig. 2 in
Appendix). In total, 1, 9, 8, 3, and 13 of the hybrids were
found in populations KK, BO, CG, K, and G, respectively
(Table 1). Among these individuals and based on the reference
P. × canadensis samples, we identified 15 trees with a genotype identical to that of P. ‘Marilandica’ ♀ (i.e., 1 individual in
population KK, 9 individuals in population BO, and 5 individuals in population CG), 3 trees with the P. ‘Robusta’ ♂
genotype (population CG), and 3 trees with the P. ‘Serotina’
♂ genotype (population K). The 13 hybrid individuals found
in population G had one unique genotype that could not be
assigned to any reference samples. However, taking into
Annals of Forest Science (2021) 78: 37
account the spatial distribution of these hybrid clones within
the population G, and the fact that we sampled old trees during
the plant material collection, there is a low probability that
those trees are the advanced generation of hybrids (F2 or advance BC) that result from the spontaneous hybridization between black poplar and variants of P. × canadensis trees in
nature. The locations of the detected hybrid individuals in the
studied populations are marked on the maps (Figs. 3, 8, 9, 10,
11 in Appendix).
3.2 Clonality analysis
The analytical power of the marker set used in this study was
high. Based on a box plot describing the genotypic resolution
of the microsatellites applied, we conclude that a set of nine
loci would be sufficient to accurately determine the number of
MLGs in the studied sample (Fig. 12 in Appendix). From the
548 sampling units genotyped, a total of 423 distinct MLGs
were detected, and among them, 53 were shared by several
individuals (more than two trees). All repeated genotypes
were unique to individual populations (i.e., there were no
same genotypes detected in different stands). The most frequent genotype, represented by 16 ramets, was found in population G. The maximum distances between the ramets of one
genet noted in the studied sample set were 150 m in population G and approximately 80 m in population CI. Based on the
pairwise genetic distances between the MLGs identified in
each poplar population compared with the MLGs sexually
produced according to simulations and taking into account a
threshold of 2 different alleles, 403 MLLs were detected. With
regard to this result, the estimated genotypic richness of the
investigated populations ranged from R = 52% in population
CI to R = 87% in population KK, with a mean value of R =
73% (Table 1). The clone positions in each study site are
indicated in Figs. 3–11 in Appendix.
3.3 Genetic diversity and effective population size
Genetic diversity estimators calculated per black poplar population estimated based on 12 loci are presented in Table 2.
The average number of alleles in a population ranged from A =
6.67 in populations K and G to A = 12.58 in population BD,
with a mean of A = 9.99. The number of effective alleles was
much smaller, with a mean value of Ae = 5.38. The highest
allelic richness estimated for the minimum sample size of 19
individuals (AR19) was observed in population CI (9.34),
whereas the lowest was observed in population G (6.20).
Private alleles were detected in all studied stands. However,
the frequency of all of the private alleles was low. Observed
heterozygosity (Ho) was high and comparable between the
analyzed populations, with a mean of Ho = 0.71, while expected heterozygosity (He) was only slightly higher, with an
average of He = 0.77. Significant positive Fis values,
Page 7 of 24 37
suggesting an excess of homozygotes, were observed in seven
populations (with mean Fis = 0.07). However, based on the
correction performed for the presence of null alleles (Fisnull)
and according to the DIC, in all analyzed populations, the
detected inbreeding signals were delusive (i.e., null alleles
were most likely responsible for the increased homozygosity
level). The effective population size (Ne^) calculated with the
lowest allele frequency criterion of 0.02 was comparable
among populations KK, B, WR, BD, BO, and CG.
The largest effective population size was detected for
population CI (133.5), whereas the lowest was detected
for two populations from the north, i.e., population K
(9.3) and population G (5.2) (Table 2).
3.4 Genetic differentiation and demographic history
analysis
Statistically significant differentiation between populations
was revealed by AMOVA. The results were similar for FST
and RST, with values of 4.43% and 5.27%, respectively. The
remaining 95.57% (FST) and 94.73% (RST) of the total molecular variance occurred within populations (Table 3). The
similarity of the genetic differentiation estimators used in the
investigation was also confirmed by a permutation test. The
obtained results revealed no statistically significant difference
between RST and permuted pRST values (RST = 0.054;
pRST = 0.046; CIpRST95% = 0.031-0.068; pH1: RST >
pRST = 0.231), which implied that the rate of migrant exchange by populations was higher than the mutation rate.
The global FST values with and without ENA correction were
also similar (FST = 4.43% and FSTNA = 4.42%, respectively).
Nevertheless, a wider range of differentiation was found with
regard to pairwise comparisons of populations. The highest
value of FST with ENA correction was noted between the
BD and K populations (FSTNA = 0.079), while the lowest
was found between the neighboring populations B and WR
(FSTNA = 0.025). With regard to pairwise distances evaluated
based on the RST method, populations B and G were the most
different (RST = 0.140), whereas populations KK and B were
the most similar (RST = 0.002) (Table 5 in Appendix).
The Wilcoxon test for heterozygosity, performed under the
TPM model, did not indicate recent bottlenecks in any populations analyzed. On the other hand, the MRs were significantly below the mean MR under mutation-drift equilibrium
(Mreq) for all black poplar forest stands examined (Table 2).
3.5 Genetic structure and gene flow patterns
The spatial autocorrelation analysis revealed significant SGS
in seven of the nine populations analyzed (Fig. 13 in
Appendix). Based on the results for the Sp statistic, the strongest SGS was identified in population K (Sp = 0.049), whereas the weakest was identified in population CI (Sp = 0.003).
37 Page 8 of 24
Table 2
Annals of Forest Science (2021) 78: 37
Measures of genetic diversity per population of black poplar based on 12 polymorphic nSSR loci. Population acronyms as in Fig. 1A
Pop.
NMLL
A
Ae
AR19
PA
Ho
He
Fis
Fisnull
CIFisnull
Ne^
CINe^
MR
Mreq
KK
B
48
42
11.17
10.25
5.16
5.64
8.86
8.57
3
5
0.67
0.72
0.76
0.79
0.09*
0.09*
0.012nb
0.007nb
0.000 - 0.036
0.000 - 0.022
52.7
37.6
43.8 - 65.2
31.3 - 46.2
0.62*
0.52*
0.81
0.80
WR
28
9.33
5.60
8.63
2
0.72
0.79
0.09*
0.008nb
0.000 - 0.028
25.4
20.5 - 32.3
0.57*
0.78
BD
CI
77
52
12.58
11.67
6.25
6.13
9.29
9.34
5
3
0.70
0.75
0.80
0.80
0.12*
0.06*
0.013nb
0.011nb
0.000 - 0.037
0.000 - 0.035
30.5
133.5
27.5 - 33.9
95.4 - 211.6
0.59*
0.60*
0.82
0.81
BO
34
9.92
5.44
8.71
2
0.71
0.78
0.09*
0.008nb
0.000 - 0.028
34.4
28.0 - 43.4
0.49*
0.79
CG
K
74
19
11.67
6.67
5.89
4.21
8.81
6.67
5
3
0.78
0.66
0.81
0.71
0.01
0.07*
0.009nb
0.024nb
0.000 - 0.023
0.000 - 0.074
56.3
9.3
49.4 - 65.1
7.2 - 12.1
0.64*
0.45*
0.83
0.78
0.000 - 0.023
5.2
3.4 - 6.4
G
29
6.67
4.07
6.20
2
0.70
0.71
0.01
0.006nb
Mean
44.77
9.99
5.38
8.34
3.33
0.71
0.77
0.07
0.01
42.76
0.47*
0.81
0.55
0.80
NMLL, number of analyzed multilocus lineages (MLL); A, mean number of alleles; Ae, mean number of effective alleles; AR19, allelic richness for a
minimum sample size of 19 individuals; PA, number of private alleles; Ho, observed heterozygosity; He, unbiased expected heterozygosity; Fis,
multilocus inbreeding coefficient (*p < 0.001); FisNull, multilocus inbreeding coefficient with a correction for the presence of the null alleles (nb—
random mating model (Fis = 0) was more probable than the full model; nfb—full model (Fis > 0) was more probable than random mating model),
CIFisNull and CINe^, Bayesian 95% confidence intervals; Ne^, effective population size (Pcrit.= 0.02); Wilcoxon’s Test, signed-rank test under the T.P.M.
model; MR, M-ratio (*—MR significantly < Mreq at p < 0.01); Mreq, mean MRs under mutation-drift equilibrium
Coancestry coefficients were positive and significant at a distance of approximately 150 m in all analyzed populations. For
three to seven distance classes, in most cases, the estimated
coancestry coefficients were within the 95% CI, becoming
negative and significant, for the last distance class in the populations BO, CG, K, and G (p < 0.05). Significant coancestry
coefficients for first distance classes indicate the natural origin
of the studied black poplar populations as artificial stands of
forest trees were usually established with the use of mixed
seed material derived from different populations. In this case,
the kinship coefficient between even close neighbors is low
and does not increase with distance.
The population assignment test implemented in
Geneclass2, performed based on Rannala’s & Mountain’s
method, showed that 324/403 (80%) black poplar trees were
assigned to their sampled populations (Table 6 in Appendix).
Misassignment frequency ranged from 14% in population BD
to 41% in population BO. The obtained results showed that
gene flow between the poplar populations is bidirectional
Table 3 AMOVA partitioning of
the genetic variation (A) among
studied populations and among
established genetic groups of
populations classified to the inferred clusters K = 2 (B) and K = 4
(C) based on clustering analysis in
STRUCTURE
along the study area and the most effective between neighboring populations. On the other hand, some restrictions on upstream migration from the northernmost populations G and K
were observed. No immigrants derived from these populations
were identified in the higher-situated poplar stands based on
test results (Table 6 in Appendix).
The Mantel test yielded a significant correlation (R2 = 0.63;
p = 0.01) between the level of genetic differentiation and
geographical distance estimated for each pair of the studied
populations (Fig. 14 in Appendix). This result is consistent
with an IBD model among the black poplar populations and
is in agreement with the detected migration patterns, which
suggest that the most effective gene flow occurs between the
most geographically proximal poplar stands (Table 6 in
Appendix).
The Bayesian assignment of samples by STRUCTURE
revealed that the overall gene pool of the analyzed black poplar populations is represented by two (K = 2) or four (K = 4)
genetic clusters (Fig. 1). According to this result, and based on
Source of variation
FST (%)
p value
RST (%)
p value
(A)
4.43
95.57
0.73
4.00
95.27
1.15
3.48
95.37
<0.000
<0.000
0.010
<0.000
<0.000
0.001
<0.000
<0.000
5.27
94.73
2.21
3.95
93.84
4.38
1.67
93.95
<0.000
<0.000
0.063
<0.000
0.010
0.006
<0.000
0.010
(B)
(C)
Among populations
Within individuals
Among groups
Among populations within groups
Within individuals
Among groups
Among populations within groups
Within individuals
Annals of Forest Science (2021) 78: 37
the proportion of the membership coefficient estimated by
STRUCTURE (Table 7 in Appendix), we classified the analyzed populations to the different groups (two or four) and
performed the AMOVA analysis to verify which clustering
model better explains the identified patterns of genetic structure. In reference to the two inferred genetic clusters (K = 2),
the populations KK, B, WR, and BD were classified to the
first group, whereas the populations CI, BO, CG, K, and G
were included in the second genetic cluster. With regard to
four inferred clusters (K = 4), we assigned populations KK, B,
and WR to the first group. The population BD was classified
to the second group. The third group comprised the populations CI, BO, and CG. The populations K and G were included in the last fourth group. The AMOVA analysis results
showed higher values of parameters of genetic differentiation
for the tested variants of groups of populations in the case of
population assignment performed based on the four genetic
clusters detected, with FST = 1.15% (p = 0.001) and RST =
4.38% (p = 0.006). Moreover, in the case of four genetic
clusters, the level of genetic differentiation among groups,
estimated based on RST, was higher than the level of differentiation among the populations established within the groups
(with RST = 4.38% (p = 0.006) and RST = 1.67% (p < 0.001),
respectively). In the case of partition to the two groups (K = 2),
the obtained results showed the opposite tendency with RST =
2.21% among groups (P = 0.063) and RST = 3.95% among
populations within groups (P < 0.001) (Table 3).
4 Discussion
4.1 Genetic resources and population history of black
poplar from the Oder River area
During the last two centuries, the area of riparian forests in
Poland has been reduced by approximately 95% (Tylkowski
2010), and many of these natural forest communities have
been changed by alien species. However, our results indicate
that the analyzed poplar forest stands have maintained their
genetic identity. Only 34 P. × canadensis individuals were
detected among which 21 were assigned to the reference cultivars of P. × canadensis included in the study. This observation is in agreement with previous findings by different ecologist groups that indicated the high natural qualities of plant
formations occasionally occurring in the small fragments of
the Oder valley, from which the material for this study was
mainly collected (Danielewicz 2008; Wojtkowiak et al. 2013).
On the other hand, a relatively large proportion of clones,
which ranged from 13% to 48% with an average of 27%,
was noted in the studied populations. These results are comparable to those of earlier studies by Smulders et al. (2002)
and Barsoum et al. (2005), who reported 42% and 22% replicated genotypes, respectively, among trees sampled along the
Page 9 of 24 37
Rhine and Garonne rivers. Concerning clonal structure, replicate genotypes were mostly found in our study as nearest
neighbors and formed relatively small (2-4 trees growing in
close proximity to one another) or moderately sized (8-16
ramets distributed in a circle with a maximum diameter of
150 m) clonal units. This finding is in line with the results of
Legionnet et al. (1996) and Barsoum et al. (2005), who found
mostly small clonal units within the studied populations, and
the reports presented by Arens et al. (1998) and Smulders et al.
(2002), who found substantially more replicates per P. nigra
clone (i.e., up to 24 trees per clone). However, in contrast to
Barsoum et al. (2005), we did not find replicate genotypes in
separate stands. The difference in the frequency of clones
between the studied populations most likely results from the
different flooding histories of the studied stands and age structures of the analyzed poplar populations. Our results indicate
an important contribution of asexual regeneration strategies to
the maintenance of P. nigra populations along the Oder valley
and suggest that the actual genetic resources of this species are
approximately 30% lower than expected on the basis of the
number of black poplar trees observed along the valley.
The level of genetic variation in the P. nigra populations determined in this work is consistent with previous estimates based on microsatellite markers. The number of alleles and level of observed heterozygosity
found in our investigation (mean A = 9.99/Ho = 0.71)
were comparable to those reported by Pospíšková and
Šálková (2006), with A = 11.00/Ho = 0.80; Smulders
et al. (2008b), with A = -(no data)/Ho = 0.74;
Rathmacher et al. (2010), with A = 11.57/Ho = 0.70;
Jelić et al. (2015), with A = 8.96/Ho = 0.70;
Lewandowski and Litkowiec (2016), with A = 14.7/Ho
= 0.73; Čortan and Tubić (2017), with A = 8.5/Ho =
0.76; and Wójkiewicz et al. (2019), with A = 12.17/Ho
= 0.70. The Fis values estimated with correction for the
presence of null alleles in all populations were positive
but close to zero, which suggests random gene exchange among distantly related poplar individuals. The
assignment analysis of individuals indicates bidirectional
gene flow along the Oder valley. A similar mode of
gene transmission was described by Imbert and
Lefèvre (2003), who studied dispersal and gene flow
among P. nigra populations from the Drome River area
in France. Asymmetry in gene transfer was noted only
in the lower reaches of the Oder River, where migration
in the downstream direction was predominant.
Nevertheless, this mating pattern may result from the
largest gap between sampled stands, which occurred between the population from the middle section of the
Oder valley (population CG) and the first population
from the upper section of the river (population K),
which reached approximately 150 km. Furthermore, it
may result from the smaller sample size of populations
37 Page 10 of 24
from the north than of populations from the middle
section of the valley. The assignment analysis also revealed that the most effective gene exchange occurred
between neighboring poplar stands. This observation is
consistent with expectations of a one-dimensional
stepping-stone gene flow model which assumes that an
individual can migrate at most one step in either direction between populations (Slatkin 1993). This is also in
line with the results presented by Rathmacher et al.
(2010), who reported short-distance gene flow between
poplar populations near the Eder River. According to
the authors’ conclusions, the majority of gene transfer
(i.e., 70%) among the studied poplar populations from
this river valley took place over a distance of less than
1 km. Limited gene transmission promotes the formation of small-scale SGS, which we detected in all black
poplar populations analyzed.
The Oder valley populations were not genetically homogenous. The clustering analysis showed that two (K
= 2) or four (K = 4) genetic clusters can be distinguished. Nevertheless, the estimated differentiation level
was low (FST = 4.43%; RST = 5.27%) and comparable
to levels presented in previous studies on black poplar
populations originating from one river catchment
(Imbert and Lefèvre 2003; Pospíšková and Bartáková
2004; Jiang et al. 2015; Čortan et al. 2016). Based on
these findings (high spatial genetic structure and low
overall genetic differentiation level), we hypothesize that
the interplay of different demographic forces during the
last several generations has driven the subdivision process of the once homogenous genetic structure of the
analyzed populations. This indication is supported by
the results of the Mantel test which showed that the
detected patterns of genetic differentiation among the
studied populations fit the IBD model well. The correlation between genetic and geographic distances was
significant (R2 = 0.63; p = 0.01). Comparable results
were also reported by, e.g., Imbert and Lefèvre (2003)
who studied gene flow among black poplar populations
from the Drôme river (France). This finding supports
our conclusions about the gene exchange model (onedimensional stepping-stone model) and indicates that the
detected disturbances in gene flow among the studied
populations seem to be more significant than those
shown in previous studies for this species. Moreover,
it seems that the observed gene pool substructuring of
the studied poplar populations and detected geographical
patterns of genetic variation along the Oder River have
their background also in the demographic history of
these forest stands. Specific tests for bottleneck effects
using microsatellites yielded interesting results, with the
MR test suggesting a bottleneck in all populations, although the heterozygote excess test, performed under
Annals of Forest Science (2021) 78: 37
the TPM model, showed no evidence of a bottleneck
in any of the populations analyzed. Detection of a bottleneck using MRs but not heterozygote excess is more
likely when the bottleneck is older and more severe.
Strong support for this hypothesis is provided by the
effective population size results for the studied poplar
stands. In two cases, we found that the Ne^ was less
than 10, and in only three populations, it was greater
than 50. In contrast to these findings, the mean effective
population size of two old black poplar populations
from the Vistula valley in Poland estimated based on
microsatellite markers was Ne^ = 263.75 (Wójkiewicz
et al. 2019). Taking all these results into account, we
speculate that the genetic structure of black poplar was
homogenous along the Oder catchment in the distant
past. The currently observed genetic subdivision of the
gene pool of populations of this species is most likely a
consequence of genetic drift, intensified due to a strong
reduction in population size in the past, restriction in
gene flow in consequence of transformation and fragmentation of natural riparian habitats, and diminishment
of the success of sexual reproduction for vegetative
propagation due to a lack of suitable places for seedling
establishment.
4.2 Implications for the conservation management
strategy of black poplar
The effective maintenance of existing genetic resources
is crucial for forest species survival and adaptation in
changing environments. For this purpose, population genetic studies provide key insights to assist in the development of suitable management and conservation strategies. The present study is by far the most extensive
report describing the distribution of genetic variability
and underlying processes among natural black poplar
populations in the Oder River region. Our results revealed significant differentiation among the black poplar
populations studied. However, the level of differentiation was low, suggesting that the black poplar populations from the Oder valley should be treated as a single
panmictic unit, which in the past was characterized by a
homogenous genetic structure. Nowadays, based on the
STRUCTURE and AMOVA analysis results, it seems
that four (K = 4) genetic clusters most likely represent
the overall genetic structure of the studied populations.
Therefore, we recommend establishing one clone archive in which 200 representatives of the four genetic
clusters detected are grown together in one location.
More precisely, we suggest collecting at least 50 cuttings (with a 1:1 ratio of female:male trees) from each
of the genetically distinct groups of populations, which
include the following stands: Group 1—populations KK,
Annals of Forest Science (2021) 78: 37
B, and WR; Group 2—populations BD; Group 3—
populations CI, BO, and CG; and Group 4—
populations K and G. This sampling strategy, based on
the assumptions presented by Brown and Hardner
(2000), should ensure to capture at least one copy of
alleles with a frequency of 0.05 from each defined
group with a certainty of 95%. Moreover, in the future,
this stable clone archive population with effective population size Ne^ > 50 (so that it can maintain a balance
between genetic drift and mutation (Franklin 1980)) can
be classified as the new seed source population. To
avoid close relatedness among the selected P. nigra individuals as well as duplicate genotypes in the archive,
the distance between the sampled trees in each site
should be at least 200 m. Moreover, as hybrid trees
occur among natural black poplar trees along the Oder
valley as single or grouped individuals, genetic assessment of the trees selected and planted in the clone archive should be performed in the final stage. As shown
in our study, there are molecular tools suitable for the
precise identification of hybrid individuals. Locally, the
black poplar gene pools may be protected by artificial
plantings of black poplars along the Oder River with
the use of seeds originating from the studied poplar
stands. Only in the case of populations from the north,
i.e., populations K and G, which had the smallest effective population sizes as well as quite a large proportion
of clones, should additional paternity analyses with the
aim of establishing the number of male trees that contribute to the pollination of selected maternal trees be
performed to assess the genetic value of the obtained
offspring population. During the selection of maternal
black poplar trees from which the seeds will be collected, we suggest focusing on the locations of the studied
trees reported in the Electronic Supplementary Material.
This should help avoid sampling the clones and hybrid
trees detected in the studied areas. Moreover, as P.
‘Marilandica’ is one of the most common female cultivars along the Oder valley, seeds from black poplar
trees should be collected directly from the selected maternal trees and not from the ground. We also recommend focusing on trees that are in good condition and
that grow in close neighborhoods of groups of pure
male black poplar trees. Targeting close neighborhoods
of male black poplar trees may help avoid sampling
seeds of female black poplar trees that could be potentially pollinated by males of hybrid poplar cultivars
such as P. ‘Robusta’ and P. ‘Serotina’, which were
detected in some of the studied populations.
Finally, based on the obtained results and
assumptions presented by Koskela et al. (2013)
concerning pan-European minimum requirements for dynamic conservation units of forest tree genetic diversity
Page 11 of 24 37
(GCUs), we recommend two candidate populations to
establish new GCUs of this species in the studied area
of black poplar distribution in Europe. The black poplar
stands which meet the genetic requirements are populations from the area near Brzeg (pop. B) and area near
Ciechanów (pop. CI) (Figs. 4 and 7 in Appendix).
These populations are still genetically stable, at least
in the short-term predictions, and were classified to different genetic clusters taking both K = 2 and K = 4
structure assumptions. However, before the final decision will be made, in both cases, the sex ratio should
be estimated to establish the number of female trees in
candidate populations which should not be lower than
50 in the case of dioecious tree species. Currently, although the genetic resources of black poplar seem to be
large along the Oder and Vistula river valleys, only two
GCUs of this species from forty registered across
Europe exist in the territory of Poland. Moreover, both
are located in close neighborhood along the Vistula river valley that suggests their homogenous genetic structure which, however, remains unstudied.
5 Conclusion
Clear evidence of negative genetic changes in the studied
black poplar populations from the Oder valley reflected in
the high spatial genetic structure and low effective population
sizes was presented in this study. The obtained findings suggest a small-scale isolation by distance of black poplar populations which results most likely from the alteration of natural
dynamics of the riparian ecosystems rather than from the presence of physical barriers for gene flow. The disturbance in
gene flow among the natural black poplar populations
noted in this study is not a frequent phenomenon observed for this species along the river systems of
Europe as it is known as a very good disperser through
both seeds and pollen. Nevertheless, the genetic resources of black poplar in the Oder River valley are
still substantial compared to those reported for rivers
in Western Europe. However, due to the lack of places
for natural regeneration of this species along the modern
river landscapes in Poland as well as taking into account assumptions for the development plans of inland
waterways in Poland for 2016-2020 with a 2030 perspective, the genetic erosion of black poplar populations
will likely progress, and the number of genetically
unique specimens will be lower in the future.
Considering the ongoing environmental changes and
degradation of places where black poplar naturally occurs, this seems to be the last chance to introduce conservation strategies in order to save the natural genetic
diversity of this endangered riparian forest species.
37 Page 12 of 24
Annals of Forest Science (2021) 78: 37
Appendix
Table 4 Characteristics of loci
used in the study; Hl, (genome)
chromosome localization (based
on Gaudet et al. 2008); N, number
of alleles; Md (%), percentage of
missing data; An (%), percentage
of null alleles
Marker
Hl
N
Md (%)
An (%)
WPMS01
WPMS04
WPMS06
WPMS07
WPMS08
WPMS09
WPMS10
WPMS11
WPMS12
WPMS16
XIII
VI
XVI
VI
XVII
VI
III
II
VI
VII
28
37
20
22
16
15
18
23
12
6
0
0
7.0
0
6.2
0
3.7
5.2
0
0
0.01
0
0.11
0
0.12
0.01
0.12
0.14
0.02
0.18
WPMS18
I
9
0
0.01
WPMS20
XIII
7
0
0.18
Table 5 Matrices of differentiation index (A) FST with ENA correction. (B) FST without ENA correction. (C) pairwise RST. Population acronyms as
in Fig. 1
(A)
B
WR
BD
CI
BO
CG
K
G
(B)
B
WR
BD
CI
BO
CG
K
G
(C)
B
WR
BD
CI
BO
CG
K
G
KK
B
WR
BD
CI
BO
CG
K
0.03942*
0.03706*
0.05428*
0.04908*
0.04511*
0.05237*
0.07178*
0.06688*
0.02487*
0.03705*
0.03372*
0.04126*
0.05602*
0.06751*
0.06598*
0.02831*
0.02638*
0.02858*
0.03708*
0.07396*
0.06401*
0.03601*
0.03318*
0.04339*
0.07851*
0.05885*
0.02872*
0.02752*
0.05477*
0.05026*
0.03277*
0.06023*
0.04756*
0.05849*
0.05238*
0.042208*
0.035668*
0.033862*
0.050387*
0.045361*
0.039990*
0.051652*
0.069310*
0.062033*
0.022606*
0.034842*
0.032645*
0.037084*
0.055264*
0.065606*
0.064601*
0.026311*
0.023345*
0.024503*
0.036714*
0.069453*
0.063805*
0.034208*
0.029998*
0.042892*
0.073395*
0.057046*
0.025711*
0.027817*
0.051463*
0.049124*
0.032069*
0.057032*
0.046823*
0.055602*
0.051497*
0.039514*
0.00212
0.01272
0.09022*
0.07870*
0.07916*
0.08245*
0.05595*
0.11962*
0.01082
0.09823*
0.08823*
0.10329*
0.10723*
0.08046*
0.14020*
0.04220*
0.03197*
0.04211*
0.05703*
0.02361
0.06542*
0.02570*
0.01993*
0.02972*
0.03072*
0.05544*
0.00756
0.01845*
0.00695
0.04043*
0.01253
0.01654
0.01975
0.02267
0.05028*
0.01879*
Annals of Forest Science (2021) 78: 37
Page 13 of 24 37
Table 6 Results of the population assignment test using the Rannala
and Mountain’s (1997) method and L_home/L_max likelihood computation option as implemented in Geneclass ver. 2 (Piry et al. 2004). The
table indicates the percentage of black poplar trees which were sampled in
a given region and were assigned to the sampled population or to any
other forest stand studied.
Assigned to
Sampled in
KK
B
WR
BD
CI
BO
CG
K
G
KK (48)
B (42)
WR (28)
BD (77)
CI (52)
BO (34)
CG (74)
K (19)
G (29)
82%
7%
7%
3%
0
0
0
0
0
2%
81%
1%
1%
0
0
0
0
0
8%
2%
68%
1%
2%
3%
1%
0
0
4%
2%
10%
81%
4%
12%
1%
5%
4%
4%
7%
7%
8%
83%
18%
9%
12%
4%
0
2%
4%
1%
4%
59%
3%
5%
0%
0
0
4%
4%
8%
9%
86%
5%
4%
0
0
0
0
0
0
0
73%
6%
0
0
0
0
0
0
0
0
82%
Table 7 Average proportion of
membership of individual poplar
tree grouped in nine predefined
populations, and assigned to one
of the two (K = 2 (A)) or four (K =
4(B)) inferred clusters (QI;
computed using STRUCTURE)
pop.
(A)
KK
B
WR
BD
CI
BD
CG
K
G
(B)
pop.
KK
B
WR
BD
CG
BD
CI
K
G
K1
K2
0.613
0.743
0.731
0.747
0.276
0.367
0.120
0.105
0.113
0.387
0.257
0.269
0.253
0.724
0.633
0.880
0.895
0.887
K1
0.7785
0.7242
0.413
0.0978
0.1498
0.1009
0.0575
0.116
0.0783
K2
0.034
0.093
0.3113
0.6084
0.095
0.2262
0.0532
0.024
0.0208
Fig. 2 Hybrid identification across the studied populations with the use of
STRUCTURE software. All samples are represented by the vertical bars
partitioned into segments of length proportional to the likelihood of
clustering to one of two genetic clusters. The eight reference individuals
K3
0.1097
0.0682
0.2084
0.125
0.591
0.5413
0.7756
0.0748
0.0257
K4
0.0778
0.1147
0.0673
0.1688
0.1642
0.1315
0.1137
0.7852
0.8752
included to the analysis are pointed as the population R. Based on the
proportion of the membership coefficient of each individual to the
inferred clusters (K = 2), among the analyzed black poplar trees 34
hybrids were detected (QI > 90%)
37 Page 14 of 24
Annals of Forest Science (2021) 78: 37
Fig. 3 The distribution map of black poplar trees at the studied area
located near Kędzierzyn Koźle city in Poland. The red points indicate
the location of black poplar trees. The green and yellow squares
indicate the location of black poplar trees which share the same
genotype. The blue cross indicates the location of the hybrid individual
recognized as the cultivar Populus ‘Marilandica’. The location data of
each studied tree are available online: https://mygeodata.cloud/map/#
9678297-Black-poplar-population-KędzierzynKozle
Fig. 4 The distribution map of black poplar trees at the studied area
located near Brzeg city in Poland. The red points indicate the location
of black poplar trees. Different color squares indicate the location of black
poplar trees which share the same genotype. The location data of each
studied tree are available online: https://mygeodata.cloud/map/#
9679027-Black-poplar-population-Brzeg
Annals of Forest Science (2021) 78: 37
Page 15 of 24 37
Fig. 5 The distribution map of black poplar trees at the studied area
located in Wrocław city in Poland. The red points indicate the location
of black poplar trees. Different color squares indicate the location of black
poplar trees which share the same genotype. The location data of each
studied tree are available online: https://mygeodata.cloud/map/#
9679625-Black-poplar-population-Wrocław
Fig. 6 The distribution map of black poplar trees at the studied area
located near Brzeg Dolny city in Poland. The red points indicate the
location of black poplar trees. Different color squares indicate the
location of black poplar trees which share the same genotype. The
location data of each studied tree are available online: https://
mygeodata.cloud/map/#9680702-Black-poplar-population-BrzegDolny
37 Page 16 of 24
Fig. 7 The distribution map of black poplar trees at the studied area located
near Ciechanów city in Poland. The red points indicate the location of black
poplar trees. Different color squares indicate the location of black poplar trees
Annals of Forest Science (2021) 78: 37
which share the same genotype. The location data of each studied tree are
available online: https://mygeodata.cloud/map/#2742885-Black-poplarpopulation-Ciechan%C3%B3w
Annals of Forest Science (2021) 78: 37
Fig. 8 The distribution map of black poplar trees at the studied area
located near Bytom Odrzański city in Poland. The red points indicate
the location of black poplar trees. Different color squares indicate the
location of black poplar trees which share the same genotype. The blue
Page 17 of 24 37
cross indicates the locations of the hybrid individuals recognized as the
cultivar Populus ‘Marilandica’. The location data of each studied tree are
available online: https://mygeodata.cloud/map/#9722590-Black-poplarpopulation-BytomOdrzanski
37 Page 18 of 24
Annals of Forest Science (2021) 78: 37
Fig. 9 The distribution map of black poplar trees at the studied area located
near Cigacice city in Poland. The red points indicate the location of black poplar
trees. Different color squares indicate the location of black poplar trees which
share the same genotype. The blue crosses indicate the location of the hybrid
individuals recognized as the cultivar Populus ‘Marilandica’, whereas the
yellow crosses indicate the location of the hybrid individuals recognized as
the cultivar P. ‘Robusta’. The location data of each studied tree are available
online: https://mygeodata.cloud/map/#9723423-Black-poplar-populationCigacice
Fig. 10 The distribution map of black poplar trees at the studied area located
near Kostrzyn nad Odrą city in Poland. The red points indicate the location of
black poplar trees. Different color squares indicate the location of black poplar
trees which share the same genotype. The blue crosses indicate the locations of
the hybrid individuals recognized as the cultivar Populus ‘Serotina’. The
location data of each studied tree are available online: https://mygeodata.
cloud/map/#9743889-Black-poplar-population-Kostrzyn-nad-Odr%C4%85
Annals of Forest Science (2021) 78: 37
Fig. 11 The distribution map of black poplar trees at the studied area
located near Gozdowice city in Poland. The red points indicate the
location of black poplar trees. Different color squares indicate the
location of black poplar trees which share the same genotype. The blue
Page 19 of 24 37
crosses indicate the locations of the hybrid individuals. The location data
of each studied tree are available online: https://mygeodata.cloud/map/#
9759575-Black-poplar-population-Gazdowice
37 Page 20 of 24
Annals of Forest Science (2021) 78: 37
Fig. 12 Genotype accumulation
curve describing the genotypic
resolution of microsatellites in a
data set of studied black poplar
trees containing 548 sampling
units genotyped using 12
microsatellites. The edges of the
boxes show the minimum and
maximum number of genotypes
and the central line shows the
average number of genotypes
identified in the sample using X
microsatellites
nb_loci
min
max
mean_MLG
SE
1
18
157
73.486
1.44623906
2
132
382
289.571
2.09732872
3
299
411
388.592
0.60332706
4
390
417
406.465
0.14881842
5
403
419
410.885
0.11472112
6
406
420
413.716
0.10547260
7
408
421
415.331
0.10280081
8
410
422
417.151
0.09162201
9
412
423
418.649
0.08107016
10
415
423
420.259
0.06787139
11
417
423
421.629
0.04945508
12
423
423
423.000
0.00000000
Annals of Forest Science (2021) 78: 37
Fig. 13 Correlograms of the
Spatial Genetic Structure (SGS)
analysis. Loiselle’s kinship coefficient (Fij) is plotted for discrete
distance classes with respective
95% confidence intervals (dotted
lines).
Page 21 of 24 37
37 Page 22 of 24
0.08
FST/(1-FST)
Fig. 14 The regression analysis
between the genetic distance
(FST/1-FST) and the geographic
distance (km) performed with the
use of the Mantel test
Annals of Forest Science (2021) 78: 37
0.06
y = 0.0001x + 0.0281
R² = 0.6252; P = 0.01
0.04
0.02
0.00
0
100
200
300
400
500
Geographical distance (km)
Acknowledgments We thank Maria Ratajczak from the Institute of
Dendrology, Polish Academy of Sciences for help in DNA isolation.
Funding This work was financed by the Polish National Science Centre
(Grant No. 2016/21/N/NZ9/01515).
Data availability The datasets analyzed during the current study are available online https://doi.org/10.18150/EIGSVA
Declaration
Conflict of interest The authors declare no conflict of interest.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included
in the article's Creative Commons licence, unless indicated otherwise in a
credit line to the material. If material is not included in the article's
Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/.
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