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Spatial and Temporal Assessment of Brassica napus L. Maintaining Genetic Diversity and Gene Flow Potential: An Empirical Evaluation

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Vladimir Meglič and Barbara Pipan

Submitted: November 14th, 2017Reviewed: January 29th, 2018Published: October 24th, 2018

DOI: 10.5772/intechopen.74570

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Unpredicted persistence of all forms of B. napus present in the agro-ecosystem is the most common consequence of preservation and self-recruitment of seeds originating from soil seed bank. In nature, spontaneous intra- and inter-specific hybridization of B. napus is possible with sexually compatible species from the Brassicaceae family. The aim of this chapter is (a) to identify the distribution pattern and population dynamics of volunteers and feral populations along statistical regions in Slovenia; (b) to assess the global diversity of naturally appearing B. napus plants; (c) to evaluate the genetic differentiation between volunteers and feral populations; (d) to obtain the spatial and temporal distribution of spontaneous pollination potential and estimation of gene flow conservation; (e) to find the empirically assigned out-crossing rate of B. napus under a fragmented landscape structure, during 4-year monitoring; and (f) to observe that ecologically, evolutionary, and agronomically oriented studies could be conducted at the DNA level using short sequence repeat (SSR) markers. In total, we collected 261 samples of volunteer and feral populations. Our results showed that alleles from both volunteer and feral populations were distributed in three genetic clusters with relatively similar levels of diversity. Naturally occurring out-crossing rate is 13.71%. The global Mantel correlation coefficient of genetic and spatial relatedness between genotypes is 0.044.


  • Brassica napus L.
  • feral populations
  • volunteers
  • spontaneous pollination
  • out-crossing rate
  • temporal and spatial distribution
  • SSR markers
  • genetic diversity
  • population structure

1. Introduction

Pollination relations occur among all existing forms of Brassica napusL. from different habitats; crops (mainly oilseed rape varieties), volunteers (grown from seed losses in previous years inside cultivated areas), and feral populations (appearing outside cultivation areas, mainly along the transportation infrastructure) [1, 2]. In the case of coexistence of different cropping systems which includes genetically modified (GM) oilseed rape production, introduction of transgenes in B. napusor related species is possible [3, 4, 5, 6, 7]. In nature, spontaneous inter-specific hybridization of B. napusis possible with sexually compatible species (relatives that have high pollination affinity with B. napus) from the Brassicaceae family. Villaseñor and Spinosa-Garcia [8] reported 7.3% of alien flowering plants in Mexico including 45 species and 25 genera from Brassicaceae family compared with 5.1% of its alien floras of the world determined by Pysek [9]. The relatives of B. napusare cultivated as field crops, but can also appear as weeds or wild outside cultivated areas (e.g., field edges, shelterbelts, road verges, slag heaps, embankments) [4, 6, 10]. Unpredicted persistence of all existing forms of B. napusin the agro-ecosystem is the most common consequence of preservation and self-recruitment of seeds originating from soil seed bank [11, 12, 13, 14, 15]. Because of its physical characteristics, the seed is very mobile and therefore disposed to spillage. Uncontrolled seed loss represents the potential for the appearance of volunteer and feral populations of B. napusinside and outside production areas; B. napusseed remains viable in the soil for several years [16, 17]. The population dynamics of these plants is dependent on the soil seed bank potential and on the complex interactive characteristics of the genotype, soil, and agro-climatic factors [18, 19, 20, 21, 22, 23]. Pollen transfer is a primary source of gene flow and has direct influence on the level of genetic exchange within and among plants, depending on the landscape context within which it occurs [24, 25]. Non-native B. napusinvasions and migrations are possible by vehicles, which act as vectors of long-distance dispersal [26, 27]. The spread of biological propagules, both pollen and seeds, plays a pivotal role in a number of fundamental ecological and evolutionary processes [28]. Dispersal is a process of central importance for the ecological and evolutionary dynamics of populations and communities, because of its diverse consequences for gene flow and demography [29]. The presence of undefined pollination in both natural and agricultural systems presents the potential for spontaneous intra- and inter-specific hybridization, reflected in the genetic structure and biodiversity of B. napus.

B. napusoriginated through spontaneous inter-specific hybridization (followed by polyploidization) between turnip rape (B. rapaL.; genome AA, 2n = 20) and cabbage (B. oleraceaL.; genome CC, 2n = 18), resulting in an allotetraploid genome comprising the full chromosome complements of its two progenitors. Spontaneous hybridization between B. rapaand B. oleracea(from Europe and Asia) occurred due to contemporary cultivation of both species in a small geographic area in the Mediterranean region [30].

B. napusis a self-pollinated plant species with a variable out-crossing rate, influenced by genotype and environmental conditions. Due to the variable out-crossing rate, intra- and inter-specific gene flow may occur in nature [30, 31, 32]. Inside cultivation areas, the common rate of out-crossing is from 20 to 30% [23]. The out-crossing rate between different varieties with full fertility is up to 0.1% on the field-to-field scale, while in varieties with incorporated male sterility (bait plants; they produce no pollen on their own and represent the worst case scenario on the out-crossing rate), it is higher than 1% [23, 33]. Out-crossing potential is most prominent on field margins and starts diminishing after 10 m; however, pollination at greater distances is not excluded. This is more frequent in cases where there are no other flowering plants in the surroundings of the donor plant/cultivated crop. The out-crossing rate is significantly influenced by proportions between donor and recipient plants [23].

Different marker systems including short sequence repeat (SSR) markers are used for genetic characterization of agro-economically important plant species [10, 34, 35, 36, 37]. To assess the molecular variation, genetic structure and gene flow potential among B. napusgenome on a spatial and temporal scale, proved to be best suitable applying several molecular marker systems (RAPD, AFLP, SINE, ISSR, and SSR) [1, 6, 38, 39, 40]. There are also newly developed DNA marker types (e.g., SNP, KASP-SNP) and NGS (Next Generation Sequencing) based applications (e.g., GWS, GBS, RAD) [41, 42, 43, 44] for genotyping and breeding purposes of B. napus.

Fragmented landscape and small-sized field structure reflect the heterogeneous growth conditions in several parts of Europe and world. The presence of ecological barriers like landscape structural elements (small woods, hedges, overgrown paths, and hills) and the influence of different agro-climatic conditions manage pollen and seed distribution [45]. Consequently, the persistence of B. napusplants originating from seed in soil seed banks enables gene flow potential on a spatial and temporal scale, reflecting in the crop quality, seed purity, and long-term biodiversity. Therefore, the aim of this study is to empirically estimate the out-crossing potential of B. napusgene transfer, under a fragmented landscape (10 statistical regions) in Slovenia and study the conservation of spontaneous gene flow into B. napusgenome on a temporal level (4-year period). Through analysis of genetic diversity and calculation of population genetics parameters, implemented by advanced bioinformatics procedures, this study represents the important agronomical, biological, and ecological baselines. The presented results are provided on a DNA level, which is the most reliable way to determine changes in the genetic composition of B. napusgenome on a spatial and temporal scale. Our goals were (a) to identify the distribution pattern and population dynamics of volunteers and feral populations along statistical regions in Slovenia; (b) to assess the global diversity of naturally appearing gene pool structure of B. napus; (c) to evaluate the genetic differentiation between volunteers and feral populations; (d) to obtain the spatial and temporal distribution of spontaneous pollination potential and estimation of gene flow conservation; (e) to find the empirically assigned out-crossing rate of B. napusunder a fragmented landscape structure during a 4-year period of monitoring; (f) to observe that due to genetic diversity and population genetics parameters, ecologically, evolutionary, and agronomically oriented studies could be conducted at the DNA level using highly informative SSR markers.


2. Materials and methods

2.1. Study area

For the purpose of the study, we have selected macro-locations on a regional level—regions along Slovenia with high crop production share of B. napus(as oilseed rape) [2]. Therefore, from all statistical regions (12) of Slovenia, 10 were included in our research (Osrednjeslovenska-OSR, Gorenjska-GOR, Jugovzhodna Slovenia-JVS, Notranjsko-kraška-NTK, Obalno-kraška-OBK, Podravska-POD, Pomurska-POM, Savinjska-SAV, Spodnjeposavska-SPS, and Zasavska-ZAS) (Figure 1). Inside those regions, we identified agrotopes (field edges, meadows, loess slopes, shelterbelts, field margins, field paths, etc.) and ruderal habitats (road verges, railway embankments, slag heaps, construction sites, rest areas by the roads, uncultivated areas, mounds, roundabouts, etc.) as main orientation points for field survey. Meanwhile, volunteer populations were sampled inside field margins as weedy plants in other cultivated crops.

Figure 1.

Sampling locations of feral and volunteer populations ofB. napusin 2007–2010 along Slovenian statistical regions.

2.2. Field survey

Field survey was conducted in a 4-year period from 2007 to 2010 every year during the flowering time of the biennial B. napus(third week of April and first week of May). We sampled five young leaves from each individual plant per population from each micro-location on an area of approx. 5m2 including a minimum of five plants of B. napus. Sampled leaves were frozen (−20°C) and stored for DNA analysis.

2.3. DNA extraction

The leaf apex of each sample from the five young plants was bulked for DNA extraction with BioSprint 15 DNA Plant Kit (Qiagen) on a KingFisher (Thermo) isolation robot following the optimized method according to manufacturer’s instructions.

2.4. Genotyping procedure

A total of 45 nuclear SSR markers originating from different Brassicaceae family species, with various nucleotide repeat motives (listed in Table 1) were used. Thirty-seven SSR markers (with Na, Ol, Ni, Ra) were developed by Lowe et al. [46]; two SSR markers (with BRMS) were published by Suwabe et al. [47]; two SSR markers (with MR) were by Uzanova and Ecke [48]; one SSR marker (named BN83B1) was developed by Szewc-McFadden et al. [49]; and two SSR markers (with RES) were published by Wang et al. [50]. PCR reactions were performed on a final volume of 11.5 μl, containing 30 ng of genomic DNA and the following reagents with initial concentrations of: 10 x PCR buffer (Biotools), 10 mM of each dNTPs, 50 mM MgCl2 (Biotools), 10 μM of each primer, 10 μM 5′ fluorescently labeled universal primer (6-FAM, NED, HEX), and 0. 5 U of Taq DNA polymerase (Biotools). The forward primer of each SSR was appended with 18 bp tail sequence 5’-TGTAAAACGACGGCCAGT-3′ (M13(−21) as described by Schuelke [51]. PCR analyses were performed on ATC 401 (Apollo Instrumentations) under the following “touch-down” conditions, dependent on each primer pair: 94°C for 4 min; 15 cycles at 94°C for 1 min; auto decrement temperature from 60 (62)°C at 0.7°C per cycle for 30 s; 72°C for 1 min, followed by 23 cycles at 94°C for 30 s; 53°C for 30 s; 72°C for 1 min; and final extension for 5 min at 72°C. Fragment analysis was performed on a 3130XL genetic analyzer (ABI); the allele lengths were determined by comparison to a size standard GeneScan-350 ROX (ABI) using GeneMapper 4.0 (ABI).

LocusRepeat motifRa[bp]nHeHoN0PIPICF
BN83B1(GA)11 (AAG)4135–232130.4140.2010.1390.3930.3960.011

Table 1.

Parameters of genetic diversity within volunteer and feral populations among loci*.

Range of allele lengths (Ra), number of alleles (n), expected heterozygosity (He), observed heterozygosity (Ho), estimated frequency of null alleles (No), probability of identity (PI), polymorphic information content (PIC), and fixation index (F).

2.5. Data analysis

Parameters of genetic diversity among loci including ranges of allele lengths (Ra), numbers of alleles (n), frequencies of null alleles (No), and probability of identity (PI) were calculated using Identity v.1.0 [52]. MsToolkit [53] was used to evaluate expected heterozygosities (He), observed heterozygosities (Ho), and polymorphic information content (PIC). Locus-specific fixation indices and deviations of volunteer and feral populations from the Hardy–Weinberg equilibrium (HWE) were calculated using the GenAlEx v.6.4. [54]. Detecting the loci under selection was performed using Arlequin v. software [55] with 20,000 simulations. FSTAT v. [56] was used to determine allelic richness (R) as a measure of the number of alleles independent of sample size after 2000 permutations. The calculations of population statistics parameters at the spatial and temporal level including numbers of different alleles (Na), numbers of private alleles (Np), numbers of effective alleles (Ne), number of locally common alleles, fixation indices (F), population-specific expected heterozygosities (He), Shannon’s information index (I), and pairwise Nei’s genetic correlations were obtained using GenAlEx v.6.4 [54]. The out-crossing rate (t) was calculated from the fixation index using the equation t = (1 – F) / (1 + F) described by He et al. [57]. Gene flow among volunteer and feral populations was estimated by calculating the effective number of migrants (m) using the private allele method of Slatkin [58], implemented by Genepop v.4.1 [59]; the corrected estimated value of Barton and Slatkin were reported [60]. Two common estimators of volunteer and feral population differentiation (Fst and Rst as standard parameters of genetic distance) are Fst, based on allele identity, and Rst, which incorporates the SSR-specific stepwise mutation model. Calculations of both estimations were performed using GenAlEx v.6.4 [54], where the estimation of RST was evaluated by AMOVA with 999 permutations. Pairwise genetic and geographic (log10 [lat, long]) uniformity between genotypes in the 4-year period, was established by 999 permutations with the Mantel test [61]. The mean within region pairwise values (r), according to geographic and genetic distance, was calculated by 999 permutations and 1000 bootstraps using GenAlEx v.6.4 [54]. To assess the genetic structure of volunteer and feral populations, a Bayesian method was used. This analysis was performed using the model-based software Structure v.2.3.3 [62] that infers the number of genetic groups K present in a sample by comparing the posterior probability for different numbers of putative populations specified by the user and assigning individuals, giving a percentage of membership (Q value), for these clusters. The admixture model with 100,000 MCMC (Markov chain Monte Carlo) repetitions and 10,000 burn-in periods were used. Eleven independent runs were performed without prior information on groups assuming correlated allele frequencies. Temporal changes of genetic structure among volunteer and feral populations were estimated in PCoA (principal coordinate analysis) via covariance matrix with data standardization using GenAlEx v.6.4. [54].


3. Results

3.1. The dataset

In the 4-year period, 261 samples were collected in total—66 samples of volunteer populations and 195 samples of feral populations within 10 statistical regions in Slovenia (Figure 1).

3.2. Evaluation of genetic diversity

Genotypic results for 45 analyzed loci are summarized in Table 1. All loci were 100% polymorphic in both volunteer and feral populations. The selected set of SSR markers is highly applicable for genetic differentiation analysis within B. napusgenome, suggesting high mean PIC value (0.679) and low total PI value (2.480 × 10−46) (Table 1). The most informative locus with the highest PIC value was Ni4-D09, which originated from B. nigragenome (Table 1). Global genetic diversity (mean He value, Table 1) between all naturally present volunteer and feral populations in Slovenia is 0.709. Positive and low mean N0 value (Table 1) suggests that there was negligible mutation activity within the included SSR regions in B. napusgenome, during the 4-year period.

According to the exact HWE test, both volunteer and feral populations do not meet HWE conditions (P < 0.05) for any of the 45 loci, which is confirmed by the mean positive value of F (0.005) (Table 1), indicating spontaneous random mating and inbreeding potential. These findings reflect the characteristics of natural populations during the 4-year monitoring of non-cultivated B. napuspopulations. Significant changes (P < 0.05) in genetic structure of all included genotypes at each locus were detected for loci Ra3-H10 and NA10-A08; it is assumed that the level of gene flow for those loci was influenced by microevolution and natural selection. The calculated values of different alleles (Na = 12.40), private alleles (Np = 1.13), and fixation index (F = 0.072) within volunteer populations were lower compared to feral populations, where Na was 15.67, Np reached 4.40, and F was 0.074. Naturally occurring out-crossing rate among feral populations during the 4-year period on the national level is 13.71%; the global out-crossing rate among volunteer populations is lower (13.47%). These comparisons indicate the favorable introduction and conservation of new alleles via spontaneous gene flow in nature in self-recruited generations of feral populations.

The MCMC structure of 45 SSRs showed moderate genetic structure. When Evanno’s [63] ad hoc estimator of the real number of clusters was used, it indicated modes at K = 3 (Figure 2). The average genetic distances between genotypes in the first cluster is 0.794 (Fst = 0.062), following 0.627 (Fst = 0.169) in the second cluster and 0.646 (Fst = 0.092) in the third genetic cluster.

Figure 2.

Genetic structure of volunteer and feral populations, according to three genetic clusters.

3.3. Regional-spatial assessment of gene flow in fragmented field landscapes

Genetic diversity and allelic structure of volunteer and feral populations along statistical regions are presented in Figure 3 and Table 2. According to the highest values of expected heterozygosity (He) and Shannon’s information index (I), the most genetically diverse genotypes are from JVS (He = 0.731; I = 1.779), SAV (He = 0.726; I = 1.729), OSR (He = 0.688; I = 1.627), and POM (He = 0.662; I = 1.482) regions (Figure 3). The highest number of private alleles, Np = 0.867, was detected among genotypes from OSR (Figure 3); the out-crossing rate inside this region reached 10.45%. The highest out-crossing rate was calculated within SAV (t = 18.75%) and JVS (t = 18.31%) regions. The differences between the highest Np and low tvalues in the OSR region indicate the favorable potential of gene flow conservation in feral and volunteer populations; this is in contrast with the JVS and SAV regions, where the level of spontaneous gene flow was high, but conservation into naturally occurred populations, was low.

Figure 3.

Genetic patterns according to spatial distribution of volunteer and feral populations.


Table 2.

Values of pairwise comparisons of feral and volunteer populations according to statistical regions, Nei’s genetic identity (under diagonal) and FSTvalues (above diagonal).

Upper (U) and lower (L) confidence limits bound the 95% confidence interval about the null hypothesis of “No difference” across the regions as determined by permutation. The lowest mean r value was calculated across POD region (63.3%), where r was outside U and L limits reflecting the highest genetic and geographic difference of included genotypes along this region.

The estimation of RST(using stepwise mutation model) using AMOVA showed 4% molecular variability among statistical regions. High genetic relatedness between genotypes from different regions was also confirmed with pairwise comparisons between genotypes from different geographical areas, based on Nei’s genetic identity and FSTvalues (Table 2). The highest pairwise genetic correlation was calculated between genotypes from the OSR and GOR regions (0.977), which corresponds to the lowest FSTvalues, based on allele frequencies between these two geographic areas (FST = 0.006) (Table 2). These two regions are geographically neighboring areas (Figure 1).

According to the results from Table 2, the included genotypes are relatively homogenously dispersed along all geographic areas and no grouping of genetically similar genotypes within statistical regions was observed. This finding was confirmed by a global Mantel test, which compares the genetic and geographic distance matrix of all 261 genotypes. The Mantel correlation coefficient of genetic and spatial relatedness between genotypes was low, but positive (rxy = 0.044, P = 0.01), due to minor spatial linkage on the basis of genetic structure. The summary of the mean within region pairwise values, based on genetic and geographic distance, is presented in Figure 4.

Figure 4.

Mean within region pairwise values (r), according to geographic and genetic distance.

3.4. Temporal distribution of landscape gene flow and conservation of genetic variation

Temporal distribution of genetic variation, according to 100% polymorphic loci during the 4-year monitoring is presented in Table 3. Increasing values of Np, m, and molecular variance for every successive year, signify the gene flow potential, distribution, and conservation of new alleles into B. napusgenome in a relatively short period. However, for allelic richness, the highest contribution was determined in 2010 (see Table 3).

Parameter of population diversity and geneticsEcological interpretation2007200820092010
NeAllelic diversity4.013.803.644.17
NpEstimation of spontaneous gene flow conservation into naturally appearing populations0.580.930.981.64
FEstimated level of spontaneous gene flow0.
t (%)Actual gene flow potential5.742.8113.2712.52
Molecular variance (%)Conservation of naturally occurring spontaneous gene flow1.641.782.776.1
mLevel of gene flow2.163.364.415.47
RBasic genetic diversity parameter; allelic richness3.411.673.235.64

Table 3.

Ecologically important parameters of population genetics for genetic diversity distribution in 4-year sampling period.

According to PCoA results, there is a decreasing pattern of genetic linkages between all genotypes from 2007 to 2010 (Figure 5). This genetic differentiation reflects the spontaneous gene flow through the 4-year period in the surveyed agro-ecosystem.

Figure 5.

PCoA temporal distribution of genotypes.


4. Discussion

According to the 4-year field monitoring, volunteer/feral populations appeared within statistical regions, where B. napushave been widely cultivated as oilseed rape (OSR, 56; GOR, 45; POD, 36; JVS, 32; POM and SAV, 29). The actual regional cultivation of B. napusin 2009 was reported by Pipan et al. [2], where the highest proportion of oilseed rape production was inscribed along POM and POD regions. There was no volunteer or feral population found inside Goriška and Koroška region. Distribution of volunteer and feral populations (Figure 1) represents the highly-developed B. napuspersistence under the Slovenian fragmented landscape structure, according to soil seed bank potential as a consequence of seed movements. The regional pattern of B. napuspresence indicates that volunteer or feral populations most commonly originate from seed losses. Zhu et al. [17] report that seed losses during harvest could be limited to 0.7–1.1% of total seed production under Chinese farming systems. Consequently, uncultivated forms of B. napuscolonize mostly pioneer habitats, such as waste sites, cultivated grounds, rubble tips, arable fields, riverbanks, road sides, and tracks [6, 64].

In this study, spatial and temporal determination of genetic changes on 45 loci inside the B. napusgenome was proven to be useful and informative—there was low probability of identity value (PI = 2.480 × 10−46) and high polymorphic content value (PIC = 0.679) (see Table 1) among single species. These values also reflect the equal distribution of alleles among volunteer and feral genotypes. SSR markers are suitable to identify varieties of B. napus(e.g., [6, 39, 65]). A high level of genetic differentiation within the same species was obtained in our study. The composed structure of some SSR repeat motives, which originated from Brassica sp. (BN83B1, PIC = 0.396; BRMS-050, PIC = 0.345), could have a negative effect on the information content (Table 1). We would like to emphasize the highly distinctive loci RES1 (PI = 0.782, Table 1) developed from the sexually compatible relative of B. napus, Raphanussativus[50]. This study confirmed the finding reported by Elling et al. [38], Hasan et al. [39], Suwabe et al. [47], and Bond et al. [66] that SSR markers originating from related Brassicaspecies are highly applicable in investigations of B. napusgene pool.

Variable out-crossing rate, being a biological characteristic of B. napus, is 5–47% [30]. Likewise, empirically determined out-crossing rate in Slovenia was 13.6% and represents the spontaneous gene flow potential of B. napusunder a fragmented landscape structure during a 4-year period. Moreover, the ability for introgression and conservation of spontaneous gene flow into B. napusgenome through (self-recruited) generations in nature is possible. According to the increasing pattern of Np and m values in each following year during the 4-year period (Table 3), proves that genetic changes within volunteer/feral populations are reflected temporally. This finding is confirmed by PCoA distribution, where genetic relatedness between genotypes decreased (Figure 5) and the proportion of molecular variance during the 4-year period increased (Table 3). Additionally, genetic diversity within feral populations was higher, compared to volunteers due to uncontrolled pollination and introduction of new genes into feral populations. Pascher et al. [6] reported that feral populations shared less than 50% of the SSR alleles among 8 loci, compared to commercial varieties, which were cultivated in the previous year along the same region. Our results showed that alleles from both volunteer and feral populations were distributed in three genetic clusters (Figure 2) with relatively similar level of diversity. Considering this, we assume that high proportion of spatially and temporary distributed agro-biodiversity of B. napusgene pool was observed (global He = 0.709, F = 0.005; Table 1). Temporal determination among volunteers and feral populations was described by R, a measure of independent quantitative comparison of genetic diversity between all years. Overall, the most genetically diverse genotypes were determined in 2010, additionally confirmed with the highest Ne value (Table 3), indicating the ability and introduction of new alleles through spontaneous pollination of B. napusin nature.

Our study suggests that there is no specific distribution of genetically similar genotypes present within the same statistical region. Conversely, the proportion of shared molecular variability of volunteers/feral populations between regions is high (96%). These large-scale genetic similarities could be caused by common ancestry from commercial varieties of B. napus(oilseed rape), which were cultivated in the observed statistical regions. Pasher et al. [6] observed that genetic similarities among feral populations could be caused by selection favoring or eliminating certain alleles of loci linked to the markers, or by pollination and hybridization with sexually compatible relatives. However, Mantel correlation coefficient between genetic and geographic distance matrix assigned a low level of spatially and genetically related distribution among genotypes. The highest spatially distributed genetic diversity was observed in the JVS and SAV regions (He >0.700; Figure 3); the highest numbers of locally common alleles (< 50%) with a frequency > 5% (Figure 3) were detected along the JVS and OSR regions. Most likely, the highest potential for gene flow conservation into natural B. napuspopulations (highest Np values) was determined within the OSR region (Figure 3) due to favorable agro-climatic and geographic conditions. The most genetically heterogeneous genotypes, according to their spatial position, were formed along the POD region (Figure 3).


5. Conclusions

Distribution of volunteer and feral populations represents the highly developed B. napuspersistence under the Slovenian fragmented landscape structure, according to soil seed bank potential as a consequence of seed movements. The regional pattern of B. napuspresence indicates that volunteer/feral populations most commonly originate from seed losses. In this study, spatial and temporal determination of genetic changes on 45 loci within B. napusgenome was proven to be useful and informative. Empirically determined out-crossing rate in Slovenia was 13.6% and represents the spontaneous gene flow potential of B. napus, under a fragmented landscape structure during a 4-year period. This calculation reflects that the actual large-scale situation is an important basis for ecological, agronomical, and ecological evaluation of spontaneous pollination potential of B. napusin this agro-ecosystem. Moreover, the ability of introgression and conservation of spontaneous gene flow into the B. napusgenome through (self-recruited) generations in nature is possible. Our study suggests that there is no specific distribution of genetically similar genotypes present within the same statistical region.

Our empirically obtained results show the existing potential of large-scale spontaneous pollination and gene flow conservation into the B. napusgene pool in a short time period under a fragmented landscape structure. Genetic diversity of naturally present B. napusplants and spatially and temporally determined conservation of genetic variation, is proven to be successfully assessed using SSR markers, due to biologically, agronomically, evolutionary, and ecologically important parameters.



The authors acknowledge the financial support from the Slovenian Research Agency (research core funding No. (Agrobiodiversity P4-0072 and Young researcher grant: B. Pipan, contract number 1000-07-310099)). We are also grateful to MatejKnapič for spatial visualization of sampling locations.


  1. 1.Pascher K, Narendja F, Rau D. Feral oilseed rape-investigations on its potential hybridisation. Final Report in Commission of the Federal Ministry of Health and Women. Vienna, Austria: Federal Ministry of Health and Women; 2006
  2. 2.Pipan B, Šuštar-Vozlič J, Meglič V. Cultivation, varietal structure and possibilities for cross-pollination ofBrassica napusL. in Slovenia. ActaAgiculturea Slovenica. 2011;97:247-258
  3. 3.Devaux C, Klein EK, Lavigne C, Sausse C, Messean A. Environmental and landscape effects on cross-pollination rates observed at long distance among French oilseed rapeBrassica napuscommercial fields. Journal of Applied Ecology. 2008;45:803-812
  4. 4.Eastham K, Sweet J. Genetically modified organisms (GMOs): The significance of gene flow through pollen transfer. Environmental Issue Report. 2002;28:15-26
  5. 5.Garnier A, Lecomte J. Using a spatial and stage-structured invasion model to assess the spread of feral populations of transgenic oilseed rape. Ecological Modelling. 2006;194:141-149
  6. 6.Pascher K, Macalka S, Rau D, Gollman G, Reiner H, Glössl J, Grabherr G. Molecular differentiation of commercial varieties and feral populations of oilseed rape (Brassica napusL.). BMC Evolutinary Biology. 2010;10:63. DOI: 10.1186/1471-2148-10-63
  7. 7.Liu Y, Wei W, Ma K, Li J, Liang Y, Darmency H. Consequences of gene flow between oilseed rape (Brassica napus) and its relatives. Plant Science. 2013;211:42-51
  8. 8.Villaseñor JL, Espinosa-Garcia FJ. The alien flowering plants of Mexico. Diversity and Distributions. 2004;10:113-123
  9. 9.Pysek P. Is there a taxonomic pattern to plant invasions? Oikos. 1998;82:282-294
  10. 10.Pipan B, Šuštar-Vozlič J, Meglič V. Genetic differentiation among sexually compatible relatives ofBrassica napusL. Genetika. 2013;45:309-327
  11. 11.Colbach N, Granger S, Mézière D. Using a sensitivity analysis of a weed dynamics model to develop sustainable cropping systems. II. Long-term effect of past crops and management techniques on weed infestation. The Journal of Agricultural Science. 2013;151:247-267
  12. 12.Debeljak M, Squire G, Demšar D, Young MW, Džeroski S. Relations between the oilseed rape volunteer seed bank, and soil factors, weed functional groups and geographical location in the UK. Ecological Modelling. 2008;212:138-146
  13. 13.Neubert MG, Caswell H. Demography and dispersal: Calculation and sensitivity analysis of invasion speed for structured populations. Ecology. 2000;81:1613-1628
  14. 14.Pipan B, ŠuštarVozlič J, Meglič V. Preservation ofBrassica napusL. seed in soil seed bank. Acta Agriculturae Slovenica. 2013;101:277-285
  15. 15.Stump WL, Westra P. The seedbank dynamics of feral rye (Secalecereale). Weed Technology. 2000;14:7-14
  16. 16.Pessel FD, Lecomte J, Emeriau V, Krouti M, Messean A, Gouyon PH. Persistence of oilseed rape (Brassica napusL.) outside of cultivated fields. Theoretical Applied Genetics. 2001;102:841-846
  17. 17.Zhu YM, Li YD, Colbach N, Ma KP, Wei W, Mi XC. Seed losses at harvest and seed persistence of oilseed rape (Brassica napus) in different cultural conditions in Chinese farming systems. Weed Research. 2012;52:317-326
  18. 18.Grappin P, Bouinot D, Sotta B, Miginiac E, Jullien M. Control of seed dormancy inNicotiana plumbaginifolia: Post-imbibition abscisic acid synthesis imposes dormancy maintenance. Planta. 2000;210:279-285
  19. 19.Gruber S, Bühler A, Möhring J, Claupein W. Sleepers in the soil-vertical distribution by tillage and long-term survival of oilseed rape seeds compared with plastic pellets. European Journal of Agronomy. 2010;33:81-88
  20. 20.López-Granados F, Lutman PJW. Effect of environmental conditions on the dormancy and germination of volunteer oilseed rape seed (Brassica napus). Weed Science. 1998;46:419-423
  21. 21.Momoh EJJ, Zhou WJ, Kristiansson B. Variation in the development of secondary seed dormancy in oilseed rape genotypes under conditions of stress. Weed Research. 2002;42:446-455
  22. 22.Probert RJ. The role of temperature in germination ecophysiology. In: Fenner M, editor. Seeds: The Ecology of Regeneration in Plant Communities. 2nd ed. Wallingford: CAB International; 2000. pp. 285-325
  23. 23.Squire GR, Begg GS, Askew M. The Potential for Oilseed Rape Feral (volunteer) Weeds to Cause Impurities in Later Oilseed Rape Crops. London, UK: Department of Environment, Food and Rural Affairs; 2003
  24. 24.Rader R, Edwards W, Wetscott DA, Cunningham SA, Howlett BG. Pollen transport differs among bees and flies in a human-modified landscape. Diversity and Distributions. 2011;17:519-529
  25. 25.Ottewell KM, Donnellana SC, Lowe AJ, Paton DC. Predicting reproductive success of insect-versus birdpollinated scattered trees in agricultural landscapes. Biological Conservation. 2009;142:888-898
  26. 26.Von der Lippe M, Kowarik I. Do cities export biodiversity? Traffic as dispersal vector across urban-rural gradients. Diversity and Distributions. 2008;14:18-25
  27. 27.Taylor K, Brummer T, Taper ML, Wing A, Rew LJ. Human-mediated long distance dispersal: An empirical evaluation of seed dispersal by vehicles. Diversity and Distributions. 2012;18:942-951
  28. 28.Savage D, Barbetti MJ, MacLeod WJ, Salam MU, Renton M. Timing of propagule release significantly alters the deposition area of resulting aerial dispersal. Diversity and Distributions. 2010;16:288-299
  29. 29.Saastamoinen M, Bocedi G, Cote J, et al. Genetics of dispersal. Biological Reviews. 2017;93:574-599. DOI: 10.1111/brv.12356
  30. 30.Friedt W, Snowdon R. Oilseed rape. Oil crops. In: Vollman J, Rajcan I, editors. Handbook of Plant Breeding 4. Giessen: Springer Science+Business Media; 2009. pp. 91-126
  31. 31.Snowdon R, Lühs W, Friedt W. Genome mapping and molecular breeding in plants. In: Kole C, editor. Oilseeds. 2nd ed. Berlin: Springer-Verlag; 2007. pp. 55-114
  32. 32.Treu R.,Emberlin J. Pollen Dispersal of the Crops Maize (Zea mays), Oilseed Rape (Brassica napus), Potatoes (Solanum tuberosum), Sugar Beet (Beta vulgaris) and Wheat (Triticum aestivum). 2000. Bristol, UK: Soil Association, University College
  33. 33.Ramsay G, Thompson C, Squire G. Quantifying Landscape-Scale Gene Flow in Oilseed Rape. London, UK: Department for Environment, Food and Rural Affairs; 2003
  34. 34.Rusjan D, Pelengić R, Pipan B, Or E, Javornik B, Štajner N. Israeli germplasm: Phenotyping and genotyping of native grapevines (VitisviniferaL.). Vitis. 2015;54:87-89
  35. 35.Pipan B, Žnidarčič D, Meglič V. Evaluation of genetic diversity of sweet potato [Ipomoea batatas(L.) lam.] on different ploidy levels applying two capillary platforms. Journal of Horticultural Science and Biotechnology. 2016;92:192-198
  36. 36.Maras M, Pipan B, Šuštar-Vozlič J, Todorović V, Đurić G, Vasić M, Kratovalieva S, Ibusoska A, Agić R, Matotan Z, Čupić T, Meglič V. Examination of genetic diversity of common bean from the western Balkans. Journal of the American Society for Horticultural Science. 2015;140:208-316
  37. 37.Žnidarčič D, Vučajnik F, Ilin ŽM, Pipan B, Meglič V, Sinkovič L. The Influence of Different Substrates on the Growth, Yield and Quality of Slovenian Sweetpotato Cultivars under Greenhouse Conditions. Rijeka: InTech; 2018. forthcoming
  38. 38.Elling B, Neuffer B, Bleeker W. Sources of genetic diversity in feral oilseed rape (Brassica napus) populations. Basic and Applied Ecology. 2009;10:544-553
  39. 39.Hasan M, Seyis F, Badani AG, Pons-Kühnemann J, Friedt W, Lühs W, Snowdon RJ. Analysis of genetic diversity in theBrassica napusL. gene pool using SSR markers. Genetic Resources and Crop Evolution. 2006;53:793-802
  40. 40.Hasan M, Friedt W, Pons-Kühnemann J, Freitag NM, Link K, Snowdon RJ. Association of gene-linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napusssp.napus). Theoretical Applied Genetics. 2008;116:1035-1049
  41. 41.Clarke WE et al. A high-density SNP genotyping array forBrassica napusand its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome. Theoretical Applied Genetics. 2016;129:1887-1899
  42. 42.Schmutzer T et al. Species-wide genome sequence and nucleotide polymorphisms from the model allopolyploid plantBrassica napus. Scientific Data. 2015;2:150072
  43. 43.Lees CJ, Li G, Duncan RW. Characterization ofBrassica napusL. genotypes utilizing sequence-related amplified polymorphism and genotyping by sequencing in association with cluster analysis. Molecular Breeding. 2016;36:155
  44. 44.Bus A, Hecht J, Huettel B, Reinhardt R, Stich B. High-throughput polymorphism detection and genotyping in Brassica napus using next-generation RAD sequencing. BMC Genomics. 2012;13:281
  45. 45.Pipan B. Genetska raznolikost navadne ogrščice (Brassica napusL.) in njenih spolno kompatibilnih sorodnikov v sloevnskem pridelovalnem prostoru [Doctoral thesis]. Ljubljana: Univerza v Ljubljani; 2013
  46. 46.Lowe J, Moule C, Trick M, Edwards KJ. Efficient large-scale development of microsatellites for marker and mapping applications inBrassicacrop species. Theoretical Applied Genetics. 2004;108:1103-1112
  47. 47.Suwabe K, Iketani H, Nunome T, Kage T. Isolation and characterization of microsatellites in B. rapa. Theoretical Applied Genetics. 2002;104:1092-1098
  48. 48.Uzanova MI, Ecke W. Abundance, polymorphism and genetic mapping of microsatellites in oilseed rape (B. napusL.). Plant Breeding. 1999;118:323-236
  49. 49.Szewc-mcfadden K, Kresovich S, Bliek SM, Mitchell SE, Mcferson JR. Identification of polymorphic, conserved simple sequence repeats (SSRs) in cultivated Brassica species. Theoretical Applied Genetics. 1996;93:534-538
  50. 50.Wang N, Hu J, Ohsawa R, Ohta M, Fujimura T. Identification and characterization of microsatellite markers derived from expressed sequence tags (ESTs) of radish (Raphanus satviusL.). Molecular Ecology Notes. 2007;7:503-506
  51. 51.Schuelke M. An economic method for the fluorescent labelling of PCR fragments. Nature Biotechnology. 2000;18:233-234
  52. 52.Wagner HW, Sefc KM. Identity 1.0. Vienna, Austria: Centre for Applied Genetics, University of Agricultural Sciences; 1999
  53. 53.Park S. Microsatellite Toolkit. Dublin, Ireland: Department of Genetics, Trinity College; 2001
  54. 54.Peakall R, Smouse PE. GenAlEx 6: Genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes. 2006;6:288-295
  55. 55.Excoffier L, Lischer H. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources. 2010;10:564-567
  56. 56.Goudet J. FSTAT: A Program to Estimate and Test Gene Diversities and Fixation Indices. Version Lausanne, Switzerland: Institute of Ecology and Evolution, University of Lausanne; 2002
  57. 57.He S, Wang Y, Volis S, Li D, Yi T. Genetic divrsity and population structure: Implications for conservation of wild soybean (Glycine sojaSieb. EtZucc) based on nuclear and chloroplast microsatellite variation. International Journal of Molecular Sciences. 2012;13:12608-12628
  58. 58.Slatkin M. Gene flow in natural populations. Annual Review of Ecology and Systematics. 1985;16:393-430
  59. 59.Rousset F. Genepop 4.1.0: A complete reimplementation of the Genepop software for Windows and Linux. Molecular Ecology Resources. 2008;8:103-106
  60. 60.Barton NH, Slatkin M. A quasi-equilibrium theory of the distribution of rare alleles in a subdivided population. Heredity. 1986;56:409-416
  61. 61.Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Research. 1967;27:209-220
  62. 62.Pritchard JK, Wen X, Falush D. Documentation for STRUCTURE Software: Version 2.3. USA: Department of Human Genetics, University of Chicago & Department of Statistics, University of Oxford; 2009
  63. 63.Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Molecular Ecology. 2005;14:2611-2620
  64. 64.Pivard S, Adamczyk K, Lecomte J, Lavigne C, Bouvier A, Deville A, Gouyon PH, Huet S. Where do the feral oilseed rape populations come from? A large-scale study of their possible origin in a farmland area. Journal of Applied Ecology. 2008;45:476-485
  65. 65.Devaux C, Lavigne C, Falentin-Guyomarc’h H, Vautrin S, Lecomte J, Klein EK. High diversity of oilseed rape pollen clouds over an agro-ecosystem indicated long distance dispersal. Molecular Ecology. 2005;14:2269-2280
  66. 66.Bond JM, Mogg RJ, Squire GR, Johnstone C. Microsatellite amplification inBrassica napuscultivars: Cultivar variability and relationship to a long-term feral population. Euphytica. 2004;139:173-178

Written By

Vladimir Meglič and Barbara Pipan

Submitted: November 14th, 2017Reviewed: January 29th, 2018Published: October 24th, 2018