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Environmental Science and Pollution Research (2024) 31:33960–33974
https://doi.org/10.1007/s11356-024-33410-x
RESEARCH ARTICLE
Environmental impacts onintraspecific variation inAmbrosia
artemisiifolia genome size inSlovakia, Central Europe
MichalHrabovský1 · SilviaKubalová1· KarolMičieta1· JanaŠčevková1
Received: 8 June 2023 / Accepted: 16 April 2024 / Published online: 2 May 2024
© The Author(s) 2024
Abstract
The quantity of DNA in angiosperms exhibits variation attributed to many external influences, such as environmental factors,
geographical features, or stress factors, which exert constant selection pressure on organisms. Since invasive species possess
adaptive capabilities to acclimate to novel environmental conditions, ragweed (Ambrosia artemisiifolia L.) was chosen as
a subject for investigating their influence on genome size variation. Slovakia has diverse climatic conditions, suitable for
testing the hypothesis that air temperature and precipitation, the main limiting factors of ragweed occurrence, would also
have an impact on its genome size. Our results using flow cytometry confirmed this hypothesis and also found a significant
association with geographical features such as latitude, altitude, and longitude. We can conclude that plants growing in
colder environments farther from oceanic influences exhibit smaller DNA amounts, while optimal growth conditions result
in a greater variability in genome size, reflecting the diminished effect of selection pressure.
Keywords Ragweed· Absolute DNA amount· Flow cytometry· Climatic factors· Geographical variables
Introduction
Genome size, a karyological characteristic denoting the
amount of DNA within a cell (Greilhuber etal. 2005),
exhibits significant variation in land plants and is known
to exert a notable influence on their evolutionary trajec-
tory (Gregory and Hebert 1999; Gregory 2005; Knight
etal. 2005; Lysak etal. 2009; Pellicer etal. 2018). Inter-
specific genome size variation is well known due to many
phylogenetic and taxonomic investigations (e.g., Kron etal.
2007; Kolář etal. 2009, 2013; Marhold etal. 2010; Španiel
etal. 2011; Dirkse etal. 2014; Abbasi-Karin etal. 2022).
In contrast, the intraspecific variation among populations
or individuals was revealed for a small number of species
(e.g., Festuca pallens, Šmarda and Bureš 2006; Šmarda
etal. 2008, 2010, Lythrum salicaria, Kubátová etal. 2008;
Phragmites australis, Meyerson etal. 2016; Pyšek etal.
2020, or Euphrasia arctica, Becher etal. 2021). The plant
genome size can be linked for various species or taxonomic
groups with life cycle and life form (Bennett 1972; Albach
and Greilhuber 2004; Hidalgo etal. 2015; Shao etal. 2021;
Carta etal. 2022), growth form (Ohri 2005; Dušková etal.
2010; Trávníček etal. 2019), climatic factors (Bennett
etal. 1982; Bennett 1987; Wakamiya etal. 1993; Caceres
etal. 1998; Carta and Peruzzi 2016), geographical features
(Levin and Funderburg 1979; Rayburn 1990; Bottini etal.
2000; Knight etal. 2005; Meyerson etal. 2016; Bureš etal.
2024), invasiveness (Bennett etal. 1998; Grotkopp etal.
2004; Kubešová etal. 2010; Pyšek etal. 2020), stress fac-
tors or pollution (Madlung and Comai 2004; Vidic etal.
2009; Meyerson etal. 2020), metabolic resources such as
phosphorus and nitrogen (Hessen etal. 2010; Guignard
etal. 2016), or phylogenetic age (Farah etal. 2018; Hoang
etal. 2020). Despite much evidence of adaptive selection,
genetic drift cannot be excluded from the genome size dis-
tribution (Blommaert 2020).
On a broad scale encompassing both geographical and
environmental factors, correlations between genome size
and these variables have been predominantly observed
in native perennial species (e.g., Wakamiya etal. 1993;
Bottini etal. 2000; Mahdavi and Karimzadeh 2010; Enke
etal. 2011; Yotoko etal. 2011; Carta and Peruzzi 2016;
Responsible Editor: Gangrong Shi
* Michal Hrabovský
michal.hrabovsky@uniba.sk
1 Department ofBotany, Faculty ofNatural Sciences,
Comenius University, Révová 39, 81102Bratislava,
Slovakia
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33961Environmental Science and Pollution Research (2024) 31:33960–33974
Meyerson etal. 2016; Sayadi etal. 2022). On the other
hand, annual plant species can exhibit more rapid evolu-
tionary adaptation (Franks etal. 2007; Osnato 2022) or
undergo faster natural selection (Larios etal. 2014) in
response to environmental changes than perennials. How-
ever, these evolutionary mechanisms may not be effective
in small populations subject to genetic drift (Andrews
2010), whereas gene flow in large populations can poten-
tially counteract selective pressures (Sork 2015). The alien
invasive species often have smaller genomes than their
non-invasive relatives (Bennett etal. 1998; Grotkopp etal.
2004; Kubešová etal. 2010; Pyšek etal. 2020), and in a
changing world, they can adapt better to changing climatic
conditions than their relatives with large genomes (Grime
1998; Knight and Ackerly 2002; Vidic etal. 2009). They
can evolve rapidly in response to selection pressures in
the new environment (Dlugosch and Parker 2008; Zenni
etal. 2017).
For the study of the adaptation processes on the level of
the genome size, the common ragweed (Ambrosia artemisi-
ifolia L.) belonging to the Asteraceae family is a suitable
model organism, due to its following characteristics. It has
a high spread potential in Europe (Lambdon etal. 2008),
where it was introduced from North America and natural-
ized more than 70–150years ago, depending on the region
(Dessaint etal. 2005; Hrabovský etal. 2016; Skálová etal.
2017; Pinke etal. 2019). Climate change, in conjuction
with anthropogenic influences, can facilitate the spread of
invasive thermophilic ragweed to colder regions (Cunze
etal. 2013; Rasmussen etal. 2017; Skálová etal. 2017;
Mang etal. 2018; Lemke etal. 2021; Liu etal. 2021). As an
annual plant, it produces seeds within one growing season,
4 to 6months after germination (Kazinczi etal. 2008). It
is known that the range of its occurrence is limited by air
temperature and precipitation (Gentili etal. 2019); how-
ever, the species has demonstrated the ability to adapt in
mountainous regions recently (Kochjarová etal. 2023). The
large invasive ragweed populations exhibit high levels of
genetic diversity attributable to complete outcrossing (Gen-
ton etal. 2005; Chun etal. 2010; Meyer etal. 2017), while
the related perennial Ambrosia psilostachya shows clonal-
ity and low genetic diversity (Karrer etal. 2023). Some
intraspecific variation in ragweed genome size is docu-
mented as its DNA amount varies from 2.08 to 2.27pg/2C
(Kubešová etal. 2010; Bai etal. 2012; Battlay etal. 2023).
However, this variability has not been linked to environ-
mental factors that are known to affect genome size selec-
tion (Knight and Ackerly 2002; Knight etal. 2005; Bureš
etal. 2024).
The European continent is characterized by diverse eco-
logical conditions and is therefore divided into a number of
different environmental zones (Metzger etal. 2005). The
territory of Slovakia extends into two zones, the lowland
Pannonian and mountain Carpathian, thus providing the
opportunity to investigate the effect of altitude, tempera-
ture, and precipitation gradients on the genome size vari-
ation. Ragweed was only found in the warm Pannonian
flora region until 2000 (Jehlík 1998). After 2000, moun-
tainous areas showed an increase in ragweed occurrence
(Hrabovský and Mičieta 2018; Kochjarová etal. 2023).
We assume, that the genome size of annual alien spe-
cies may exhibit variation within a limited geographical
region, where diverse environmental factors can act upon
naturalized populations over a specific period. This study
endeavors to address the following inquiries: (i) To what
extent does intraspecific variation exist in the DNA content
of naturalized alien species? (ii) Which environmental fac-
tors are associated with genome size variability? (iii) Is there
evidence to suggest that environmental factors contribute
to variability in genome size during or after naturalization,
possibly as a result of climate change?
Materials andmethods
Field sampling
Plant material was collected from 37 naturalized popula-
tions of invasive annual species Ambrosia artemisiifolia
from southern Slovakia in the northernmost regions of the
Pannonian Basin and adjacent Carpathian area, where the
populations have been expanding since the late twentieth
century (Jehlík 1998) (Fig.1). Populations of the rag-
weed were selected accidentally from the Vienna Basin,
Danubian Lowland, Western Carpathians, and the Eastern
Slovak Lowland. The abundance of common ragweed in
the examined area influenced the choice of populations,
as more populations were selected from the west of the
study area, where ragweed is widespread (Hrabovský etal.
2016). The populations can be classified as Carpathian
or Pannonian based on the phytogeographic division of
the area (Futák 1984). Anthropogenic habitats, includ-
ing roadsides, arable land, railways, and ruderal habitats,
were among the diverse habitats sampled. A distal leaf of
at least five individuals was collected from each popula-
tion. Planar-colline levels at the interface of the Pannon-
ian Basin and the Western Carpathians exhibit distinct
variations in elevations, temperatures, and precipitation.
The majority of the examined region exhibits an elevation
range of 110 to 330m a. s. l., with localized exceptions
where altitude may fall below 100m or exceed 800m.
According to WorldClim datasets (Fick and Hijmans
2017), the mean annual temperature in the region fluctu-
ates between 10 and 12.2°C, while the average annual
precipitation totals for the previous decade are reported
to range from 600 to 700mm.
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33962 Environmental Science and Pollution Research (2024) 31:33960–33974
Genome size estimation
To estimate the 2C value, the methodical recommenda-
tions of Sliwinska etal. (2022) were followed, except for
the direct analysis of fresh material. Fresh leaves from
flowering plants were dried in silica gel. Plant material
was collected during the 1-week period in August–Sep-
tember 2022. Leaf tissues were analyzed sequentially
with an established standard, and the ratios of their G0/1
peak positions were recorded. As the established stand-
ard for the samples, leaves of Bellis perennis were cho-
sen (2C = 3.159pg; Temsch etal. 2022). The nuclei
were isolated using a commercial reagent kit, Cystain PI
OxProtect (Sysmex, United Kingdom), from a sample and
co-chopped standard, and stained with propidium iodide
(PI). The analyses were performed using a Partec CyFlow
Ploidy Analyzer, equipped with a green laser (532nm).
At least 5000 nuclei were analyzed from each sample at
least three times on different days. Samples that exhib-
ited a coefficient of variation (CV) greater than 3% were
excluded from the analysis, and additional samples were
analyzed to ensure that data from at least five individuals
per population were obtained (TableS1).
This study was preceded by verification of intrapopulation
variability in three populations, where 25 2-week-old seed-
lings grown under the same conditions were analyzed from
fresh leaves using the same procedure as above (TableS2,
Fig.S1). We also estimated the genome size of the first rag-
weed individuals introduced to the study area (1949–1956)
and later occuring populations (1957–1984) from herbarium
specimens. According to Viruel etal. (2019), it is difficult
to obtain results from old specimens, but it is possible to
estimate a DNA amount even from 100-year-old speci-
mens (Michalová etal. 2024). A part of a young green leaf
(0.5 × 0.5cm) was macerated for 30min in a Cystain PI
OxProtect nuclei extract solution. Then it was co-chopped
with the standard and stained with PI. The quality of the her-
barium item determines the degree of success. The samples
with a CV greater than 7% were not included in this study.
The conformity of the results with prior estimations,
which were based on the standards calibrated from Oryza
sativa ssp. japonica cv. Nipponbare with an outdated value
of 2C = 0.910pg/2C, could be attained by recalculating the
published data based on the current value of Nipponbare
rice, which is 2C = 0.778pg/2C as stated by Temsch etal.
(2022).
Climatic variables
For each population at each location, the following envi-
ronmental factors were obtained from WorldClim (Fick and
Hijmans 2017) using GIS software (QGIS 3.22.3): mean
annual air temperature (Tmean), mean May–October air tem-
perature relating to the ragweed vegetation season (Tmean
May–October), mean December − March air temperature
relating to the dormancy period (Tmean December–March),
annual precipitation totals (P), seasonal May − October pre-
cipitation totals (P May–October), and December − March
precipitation totals (P December–March). These factors
were extracted for both the historical period of ragweed nat-
uralization in the study area (1970 − 2000) and the current
period (2020–2021), air temperature in °C, and precipitation
totals in mm. The air temperature and precipitation are the
most notable indicators of continentality in the study area
(Labudová etal. 2013; Vilček etal. 2016). The influence
of continentality on ragweed genome size was presented
through the Tmean and P May–October of the current period.
Data analysis
All data analyses were performed in the statistical soft-
ware R (version 4.2.2). The individuals were experimen-
tally divided into two groups: group 1 with a DNA amount
(2C) smaller than 2.1pg and group 2 with a DNA amount
larger than 2.1pg (Fig.2). Applying the ctree() function
of the “party” package (Hothorn etal. 2006), a non-par-
ametric decision conditional inference tree was created to
find the most important environmental factors affecting the
Fig. 1 The study area with selected populations for measurement.
First-occurrence records were measured from herbarium specimens.
After measurements, the populations with a median genome size
smaller than 2.1pg/2C were labelled as group 1, and the other pop-
ulations with a genome size larger than 2.1pg/2C were labelled as
group 2
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33963Environmental Science and Pollution Research (2024) 31:33960–33974
ragweed genome size groups distribution in the study area.
As environmental factors, geographical and meteorological
variables such as altitude, latitude, longitude, anthropogenic
habitats, and historical and current abovementioned climatic
variables were tested.
Additionally, a linear mixed-effects model with individu-
als, populations, and measurement dates handled as random
factors was conducted to test the impact of the abovemen-
tioned environmental factors on ragweed genome size dis-
tribution in the study area. The analysis was performed for
all genome size measurements, but also without outliers
(2C < 1.85pg and 2C > 2.4pg), using the “nlme” package
(Pinheiro and Bates 2000) with the lme() function for the
linear mixed-effects model. The effects of the previous fac-
tors on the DNA amount distribution in the studied area
were also assessed using the cca() function of the “vegan”
package (Oksanen 2012) for the constrained correspond-
ence analysis (CCA). The differences in genome size in
different anthropogenic habitats were assessed using the
Kruskal–Wallis test, which was applied following Levene’s
test for homogeneity using the Levene’s test() function of
“cor” package (Guo 2020). Dunn’s test with Bonferroni
correction was performed as a non-parametric post-hoc test
using the Dunn’s test() function of “FSA” package (Ogle
2017).
The distribution of both genome size groups in the
potential ragweed area (M. Hrabovský, unpublished data)
was modelled using species distribution modelling (SDM).
SDM is used to predict the distribution of species in an area
based on environmental predictors (Farashi and Alizadeh-
Noughani 2023). The ideal (presences) and unfavorable
environmental conditions (pseudo-absences) are essential to
determine for the modelling (Wang etal. 2023). For group
1 evaluation, environmental factors associated with group 2
were substituted for pseudo-absences, and vice versa. Mod-
els and predictions were calculated by the sdm() and ensem-
ble() functions of the “sdm” package (Naimi and Araújo
2016) using the support vector machines (SVM) algorithm.
The models were evaluated with cross-validation (tenfold),
and the obtained true skill statistic (TSS) and area under the
relative operating characteristic curve (AUC) values were
higher than 0.7.
Results
DNA amount ofragweed
The mean value of the absolute DNA amount (2C) esti-
mated from 185 individuals was 2.11pg. The range of the
DNA amount (2C) was spanning from 1.819 to 2.516pg.
The exact data for each population are depicted in Table1
and TableS1. The 16 populations belong mostly to group 1
with a smaller genome size, and the 21 populations belong
mostly to group 2 with a larger DNA amount, but some
populations are mixed (Fig.2). The analysis of herbarium
specimens suggests that group 1 with a smaller genome
was introduced to the territory of Slovakia first (Fig.S2).
DNA amount andenvironmental variables
After dividing the populations into groups, 33% of Pannon-
ian populations belong to group 1 and 67% to group 2. On
the contrary, 86% of Carpathian populations are classified
as group 1, whereas only 14% are in group 2. According
to the conditional inference tree (Fig.3), the main factor
affecting the distribution of both groups in the studied area
is latitude and mean annual air temperature in the historical
period (1970 − 2000). Group 1 occurs mainly in areas with
Fig. 2 Interpopulation variability of Ambrosia artemisiifolia genome
size in the study area. Density plots represent the variability in every
population from Table1. The populations with a smaller genome size
(2C < 2.1 pg) belong to group 1, and the populations with a larger
genome size (2C > 2.1 pg) belong to group 2. Some populations are
mixed (e.g., FE1, OB1)
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33964 Environmental Science and Pollution Research (2024) 31:33960–33974
a latitude greater than 48.447 N (p < 0.001) and where the
mean annual air temperature was below 9.567°C (p < 0.001)
during the time of ragweed introduction (Fig.S3). This
redistribution of groups was also captured by SDM (Fig.S4).
Results of the linear mixed-effects model (Table2)
indicated that there was a significant (p < 0.001) positive
association between the estimated DNA amounts of group
1 and the current period mean annual air temperature
(beta = 0.017, SE = 0.003, R2 marginal = 0.274), the mean
May–October air temperature (beta = 0.015, SE = 0.003,
R2 marginal = 0.265), and the mean December − March air
temperature (beta = 0.021, SE = 0.004, R2 marginal = 0.290),
and a significant negative association with the current period
annual precipitation totals (beta = − 0.0002, SE = 0.0004,
R2 marginal = 0.173), and the seasonal May − October
precipitation totals (beta = − 0.0003, SE = 0.0001, R2 mar-
ginal = 0.193). Using historical meteorological data instead
of the current period climate for model calculation results
Table 1 The absolute DNA
amount of naturalized ragweed
populations in the northern
Pannonian Basin
* Mean value of absolute DNA amount averaged per population; Pop, population code; Hab, habitat (AL
arable land, RH ruderal habitats, RS roadsides, RW railways); Group—prevalent genome size group
(1—2C < 2.1 pg, 2—2C > 2.1 pg); Lat, latitude; Long, longitude; Alt, altitude; CV, coefficient of varia-
tionof population
Pop Hab Group Lat Long Alt [m. a. s. l.] 2C* [pg] (min–max) CV [%]
VT1 RH 2 47.75079 18.31760 105.3 2.247 (2.192–2.289) 1.5
VK1 RW 1 48.55485 22.10377 107.3 2.07 (1.859–2.205) 5.7
PA1 RS 2 47.73917 18.31495 108 2.208 (2.154–2.25) 1.4
MH1 AL 2 47.85146 18.68839 111 2.242 (2.159–2.508) 5.9
VM1 AL 2 47.8478 17.78612 112.4 2.228 (2.197–2.291) 1.6
NZ1 RS 2 47.96135 18.18768 115.3 2.162 (2.107–2.203) 1.5
JA1 RS 2 48.12879 18.02957 117.2 2.201 (2.06–2.285) 3.6
OB1 AL 2 47.77706 18.65024 117.4 2.133 (2.016–2.218) 3.7
NV1 AL 1 48.63933 21.685 119.1 2.02 (1.977–2.059) 1.5
GA1 RH 2 48.18709 17.71218 119.3 2.256 (2.197–2.321) 1.8
FE1 AL 1 48.76618 22.08028 119.6 2.069 (1.952–2.243) 6.0
ST1 RW 1 47.8021 18.68476 120.8 1.998 (1.96–2.047) 1.7
GB1 AL 2 47.86129 18.49871 121.5 2.277 (2.249–2.294) 0.7
BA3 RH 2 48.10522 17.10971 133 2.362 (2.151–2.361) 3.8
NR1 RH 2 48.28626 18.09509 136.4 2.155 (2.113–2.228) 1.9
SH1 RS 2 48.08334 18.94056 139.8 2.196 (2.151–2.244) 1.9
VN1 RS 1 48.66862 22.25716 142.8 1.966 (1.819–2.166) 6.7
KP1 AL 2 48.29594 17.44926 151.8 2.202 (2.157–2.26) 1.6
CC1 RS 2 48.23428 18.04753 152 2.158 (2.141–2.176) 0.5
VR1 AL 2 48.23695 18.33368 152.7 2.258 (2.142–2.516) 5.9
KT1 RS 2 48.6582 17.04409 157 2.146 (2.117–2.164) 0.7
BA1 RH 2 48.26141 16.96355 162 2.143 (2.126–2.16) 0.5
PK1 RS 1 48.29365 17.29239 167.5 1.979 (1.931–2.036) 2.1
BO1 RS 1 48.45007 17.50928 170.1 2.017 (1.969–2.048) 1.3
TM1 RS 1 48.34993 18.3644 170.6 2.024 (1.941–2.116) 3.2
BA2 RS 1 48.17538 16.99063 182.4 2.097 (2.029–2.17) 2.2
LC1 RS 2 48.34716 19.65759 187.1 2.234 (2.168–2.332) 3.1
MA1 RS 2 48.44702 17.07451 189.2 2.255 (2.22–2.283) 1.0
KE1 RS 1 48.68977 21.28078 195.9 1.965 (1.881–2.012) 2.4
ZC1 RW 2 48.48316 18.72734 217.8 2.145 (2.115–2.187) 1.3
HR1 RS 1 48.46828 19.95121 239.4 1.959 (1.884–2.082) 3.4
ZH1 RH 1 48.57629 18.83322 240.5 1.971 (1.923–2.052) 2.2
TC1 RS 2 48.35703 18.52149 263.5 2.159 (2.102–2.21) 1.8
SY1 RS 1 48.83974 19.11476 494.6 2.003 (1.956–2.051) 1.9
LI1 RS 1 49.38852 20.63251 775.3 1.911 (1.779–1.95) 2.0
VY1 RS 1 49.15063 20.25393 982 1.88 (1.835–1.91) 1.8
CE1 RS 1 48.90538 19.73179 1206.8 1.877 (1.824–1.926) 2.5
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33965Environmental Science and Pollution Research (2024) 31:33960–33974
in stronger and statistically significant connections (higher
marginal R2 and t values) between the DNA amount and
the aforementioned environmental parameters. A significant
negative association between geographical variables (alti-
tude, latitude, and longitude) and the absolute DNA amount
was also found by the linear mixed-effects models for group
1 (Table2). The same regression trends (except longitude)
were maintained for group 2, but with a low R2 (Table2). In
addition, a linear-mixed effects model was computed for the
following scenarios: plants were not divided into two groups
(TableS3), outliers were excluded (TableS4), and popula-
tions (TableS5) or measurement dates (TableS6) were used
as random factors. No fundamental differences between the
results of previous models were observed; therefore, a spa-
tio-temporal bias resulting from different habitat selection
or measurement dates can be excludedasthe reason for the
observed trends. CCA analysis yields similar results as the
linear mixed-effects models. The first ordination axis CCA1
corresponds to the altitude and the seasonal May − October
precipitation totals, while the second ordination axis CCA2
correlates to longitude (Fig.4).
DNA amount andhabitats
Based on Kruskal–Wallis test, the genome size seems to
vary with selected types of anthropogenic biotopes such
as roadsides, arable land, railways, or ruderal habitats
(χ2 = 26.6, p < 0.001). A post-hoc test shows no differences
between arable land and ruderal habitats (adjusted p = 1) and
roadsides and railways (adjusted p = 1). However, these cou-
ples (Fig.5) differ significantly from one another (adjusted
p < 0.001).
Discussion
Intraspecific variation ofAmbrosia artemisiifolia
genome size
It is a controversial subject, whether there is any diversity in
genome size below the level of a species (Greilhuber 1998).
Despite the doubts, intraspecific variability of genome size
was confirmed in Asteraceae family (Suda etal. 2007; Slovák
etal. 2009; Dirkse etal. 2014) and in other flowering plants
(e.g., Poaceae, Šmarda and Bureš 2006; Šmarda etal. 2008;
Ranunculaceae, Cires etal. 2010; Amaryllidaceae, Ducho-
slav etal. 2013; Anacardiaceae, Aliyu 2014; Caprifoliaceae,
Frajman etal. 2015; Caryophyllaceae, Terlević etal. 2022).
Ragweed’s intraspecific variability has not yet been inves-
tigated. The known genome size values of the ragweed
were determined by examining specimens collected from
both its native North American area–2.08pg/2C (Bai etal.
2012) and non-native Europe–2.12pg/2C (Kubešová etal.
2010) or 2.18 to 2.27pg/2C (Battlay etal. 2023). We esti-
mated approximately the same genome size value averaged
from 185 ragweed individuals (2.11 ± 0.3pg/2C) as it was
Fig. 3 The conditional infer-
ence showing the main factors
(latitude and mean annual air
temperature in the historical
period) affecting the distribu-
tion of ragweed genome size in
the study area; yellow—group
1 (2C < 2.1pg), red—group 2
(2C > 2.1pg)
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33966 Environmental Science and Pollution Research (2024) 31:33960–33974
Table 2 Linear mixed-effects model coefficients and statistics for two different DNA amount groups of ragweed, and meteorological and geographical variables as fixed effects, and an indi-
vidual plant (i.e., results from repeated measurements of the same plant) as a random effects
* p < 0.05, **p < 0.01, ***p < 0.001
NS, non-significant; Tmean, mean air temperature; P, total precipitation; black square, historical period (1970–2000), white square, current period (2020–2021); SE, standard error, R2c, condi-
tional R2 for random and fixed effects, R2m, marginal R2 for fixed effects
Group 1 (2C < 2.1pg) Group 2 (2C > 2.1pg)
Variables (fixed effects) DNA amount
(intercept)
beta SE t
(p value) df = 76 R2c R2mDNA amount
(intercept)
beta SE t
(p value) df = 108 R2c R2m
Tmean ■1.808 0.0198 0.003 6.06*** 0.991 0.323 1.897 0.0305 0.0135 2.25* 0.990 0.044
□1.810 0.017 0.003 5.38*** 0.991 0.274 1.891 0.0279 0.012 2.3* 0.990 0.046
Tmean May–October ■1.715 0.017 0.0029 5.78*** 0.991 0.310 1.694 0.0305 0.0127 2.4* 0.990 0.050
□1.726 0.015 0.003 5.55*** 0.991 0.265 1.693 0.028 0.0113 2.5* 0.990 0.054
Tmean December–March ■1.977 0.024 0.004 6.46*** 0.991 0.350 2.172 0.0217 0.0126 1.72 NS 0.990 0.026
□1.965 0.021 0.004 5.62*** 0.991 0.290 2.161 0.0205 0.0112 1.82 NS 0.990 0.029
P■2.153 − 0.0003 0.00004 − 6.01*** 0.991 0.318 2.284 − 0.0001 0.0001 − 1.49 NS 0.990 0.029
□2.109 − 0.0002 0.00004 − 4.01*** 0.991 0.173 2.313 − 0.0001 0.0001 − 1.65 NS 0.990 0.024
P May–October ■2.141 − 0.0008 0.00006 − 6.46*** 0.991 0.350 2.254 − 0.0001 0.0001 − 1.2 NS 0.990 0.013
□2.111 − 0.0003 0.0001 − 4.23*** 0.991 0.193 2.258 − 0.0001 0.0001 − 1.1 NS 0.990 0.011
P December–March ■2.429 − 0.008 0.0002 − 3.73*** 0.991 0.154 2.323 − 0.0008 0.0004 − 2.02* 0.990 0.036
□2.034 − 0.0004 0.0002 − 1.91NS 0.991 0.046 2.363 − 0.001 0.0004 − 2.69** 0.990 0.062
Altitude 2.018 − 0.0001 0.00002 − 5.97*** 0.991 0.317 2.267 − 0.0004 0.0002 − 2.66** 0.990 0.059
Latitude 6.71 − 0.097 0.02 − 5.37*** 0.991 0.271 5.40 − 0.066 0.024 − 2.77** 0.990 0.066
Longitude 2.283 − 0.015 0.006 − 3.25*** 0.991 0.118 2.158 0.002 0.006 − 0.4 NS 0.990 0.001
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
33967Environmental Science and Pollution Research (2024) 31:33960–33974
Fig. 4 Results of the constrained correspondence analysis (CCA).
CCA1 axis corresponds to the altitude and the seasonal May − Octo-
ber precipitation totals, CCA2 axis to longitude; points repre-
sent 37 studied ragweed populations. The acute angle between the
arrows indicates a strong association between variables. The arrow
length represents the strength of the correlation, which can be posi-
tive or negative according to the arrow direction. The permutation
test (n = 999) confirmed the significance of the analysis (F = 1197,
p < 0.001)
Fig. 5 Differences in DNA amount at different biotope types. The
smaller genome size (2C < 2.1 pg) is more frequent along roadsides
and railways than in arable land and ruderal habitats
Fig. 6 Flower cytometry histogramwith the double peak showing the
differences in the ragweed genome size betweenthe selected sam-
ples of both genome size groups. The samples wereco-chopped for
the analysis. The ratio between peaks of group 1 (sample VN105) and
group 2 (sample KP103) is 0.81
in North America or Europe. A noticeable disparity in the
peak distance was observed between the sample with smaller
genome size (2C < 2.1pg) and the larger genome size sample
(2C > 2.1pg) (Fig.6). Intraspecific genome size variability
can be generally caused by a number of genomic mechanisms,
such as transposable elements, tandem repeats, or recombina-
tion rate, often driven by environmental changes (Tiley and
Burleigh 2015; Wang etal. 2021). Aneuploidy, as a possible
source of variation, has not been observed in both native pop-
ulations (Jones 1936; Bolkhovskikh etal. 1969; Bassett and
Crompton 1975) and naturalized individuals within the study
area (Májovský etal. 1974; Feráková and Javorčíková 1974).
It is less likely that all plants in populations with smaller or
larger genome sizes would be aneuploids. The observed vari-
ability can be clarified when the measured values are divided
into two groups. It is likely that these groups represent dif-
ferent races that originated in North America. There are two
genetic groups of ragweed in Europe (Gladieux etal. 2011),
but it is not certain whether they correlate with the groups we
have discovered. The first ragweed populations were brought
to Slovakia from Canada (Hrabovský etal. 2016). We now
estimated that their genome size was smaller. It might seem
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33968 Environmental Science and Pollution Research (2024) 31:33960–33974
that the sizes of larger genomes are more difficult to estimate
from herbarium specimens (Viruel etal. 2019) and there-
fore were not found. However, we also found sample with a
larger genome from 1971. Such plants could have occurred
earlier in the study area. The problem is the absence of a suf-
ficient amount of ragweed herbarium collections that would
help determine the exact period of introduction of the second
ragweed group with a larger genome size to Slovakia. Rag-
weed was brought to most European countries from the USA
(Makra etal. 2015). Therefore, only the group with a larger
genome size has been detected in Europe so far (Kubešová
etal. 2010; Battlay etal. 2023). Most likely, this group
migrated into Slovakia from Hungary and mixed with already
imported populations with smaller genome size.
The importance ofclimatic factors forragweed
genome size selection
Latitude is the most evident factor that correlates with the
genome size of many plant species. In the northern hemi-
sphere, the genome size increases from the equator to the
temperate zone and decreases again towards the pole (Yu
etal. 2018; Bureš etal. 2024). Monoploid genome size in
Ambrosia species increases from subtropical regions to the
temperate zone (Sliwinska etal. 2009; Zonneveld 2019),
but in temperate Ambrosia artemisiifolia, it is again smaller
(Kubešová etal. 2010; Pustahija etal. 2013; Zonneveld
2019). We observed two ragweed genome size groups that
correlate with latitude. This might be related to the shorter
life cycle of ragweed, which grows in northern latitudes
(Scalone etal. 2016). It can be assumed that the different
ecological preferences of the groups, which are dispersed
throughout the region, are the reason for the observed cor-
relations between ragweed genome size and other environ-
mental factors. However, our analyses showed that there
are associations between genome size and environmental
factors within each group. These associations have com-
parable regression trends but different R2 values. Plants
of group 1 growing in more northern latitudes seem to be
under higher selection pressure than plants of group 2. A
similar case was observed in perennial Phragmites austra-
lis, where different genome size groups are known, but their
monoploid genome size had opposing correlation trends
(Meyerson etal. 2024). Relationships between variability
in the genome size and various environmental factors due
to natural selection are also known in annual plants such as
Eragrostis (Hutang etal. 2023) or wild Zea mays (Rayburn
and Auger 1990; Bilinski etal. 2018). The negative cor-
relation between genome size, latitude, and seasonal pre-
cipitation and a positive association with the annual mean
temperature, as we observed in ragweed, are known in sun-
flowers (Qiu etal. 2019). This may be explained by their
genetic relatedness (Urbatsch etal. 2000).
The phenotypic variability of the ragweed is contingent
upon factors such as temperature, latitude, and longitude
(Dickerson and Sweet 1971; Leiblein-Wild and Tackenberg
2014). Our investigation has augmented the current under-
standing of this subject matter by highlighting the correla-
tion between genome size and the aforementioned factors.
In the studied area, the latitude, longitude, and elevation
are in a close relationship with temperature-precipitation
regime (Čimo etal. 2020). This regime exhibits a marked
shift from west to east, owing to the alternating maritime
transition zone and continentality (Vilček etal. 2016). The
influence of continentality on ragweed genome size distribu-
tion in studied area, manifested by increasing temperature
and decreasing precipitation, is reflected in our results. Thus,
indications of selection of group 1 with smaller genome can
be observed in areas with unfavorable climatic conditions
due to continentality or elevation, while in areas charac-
terized by optimal conditions, genetic drift could account
for the observed variability, and hence the selection is less
evident. Furthermore, the average genome size values are
significantly lower in regions with unfavorable climatic con-
ditions for ragweed survival. This is in line with the large
genome constraint hypothesis (Knight etal. 2005), according
to which extreme environmental conditions constrain species
with large genomes. Genome size reduction helps invasive
plants adapt better to a new environment (Lavergne etal.
2010). The reason for the selection of a smaller genome in
mountain regions could be attributed to an adaptive strategy
to cope with a shorter vegetation period, given that plants
with reduced genome sizes have comparatively shorter life
cycle (Bennett 1972; Leitch and Bennett 2007). Ragweed
populations that have evolved to a shorter life cycle as a
result of environmental conditions are known (Hodgins and
Rieseberg 2011; Scalone etal. 2016; Gentili etal. 2018).
Even outside mountainous areas, the temperature and pre-
cipitation can limit the life cycle of the ragweed (Dicker-
son 1968; Bassett and Crompton 1975). Although ragweed
thrives in warm environments, its geographic range is
restricted by low winter temperatures and limited precipita-
tion during the growing season (Shrestha etal. 1999; Gentili
etal. 2019). The positive correlation between the ragweed
DNA amount with winter and summer temperatures indi-
cates the influences of warm summer months as well as a
cold winter period for the adaptive DNA amount variation.
The precipitation regime, on the other hand, appears to be
an effective selective factor for genome size only during the
growing season (May–October).
Climatic change andadaptation reflected
ingenome size
The results of our study do not provide definitive evidence
as to whether the emergence of genome size variability
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33969Environmental Science and Pollution Research (2024) 31:33960–33974
occurred during the naturalization period (1970 − 2000) or
whether it is an ongoing selective process that extends to the
present. The observed differences in meteorological vari-
ables (i.e., an increase in mean temperature of 0.78–1.40°C
and an increase in precipitation of 42.6–80.9mm) between
historical and current periods at the study sites did not have
a significant impact on the statistical results. This is because
the correlation between historical and current period mean
temperature and precipitation is strong (ρ = 0.99, p < 0.001).
This fails to provide definitive evidence as to whether the
genome size variation is more strongly associated with his-
torical environmental factors or the current climate. How-
ever, climate change often produces new selection pressure
(Hoffmann and Sgrò 2011), and the evolutionary response
to such change can be rapid (Whitney and Gabler 2008).
Stressful conditions andragweed genome size
Anthropogenic biotopes such as roadsides, arable land, rail-
ways, or ruderal habitats are often exposed to mixtures of
various factors (e.g., traffic emissions, alkalinization of soils,
pesticides) with a stressful effect on the plant (Klumpp etal.
2006). We found that the genome size of ragweed growing
along the road and rail network is lower than that in arable
land and ruderal habitats. The observed difference between
ragweed genome size in different anthropogenic habitats
can be caused by the aforementioned relationships between
genome size and environmental factors, since ragweed tends
to grow primarily along roads and railways in the mountains,
which are associated with colder climates, while it grows in
every anthropogenic habitat in warmer and drier lowland
areas (Hrabovský and Mičieta 2018). However, along roads
and railways, unlike ruderal habitats and fields, there is fre-
quent mowing, and only individuals with a shorter life cycle
can produce seeds under disturbation pressure.
In conclusion, climate could be one of the factors in shap-
ing the distribution of two Ambrosia artemisiifolia genome
size groups in the study area. It is worth noting that due
to the localized nature of our research, the identified rela-
tionships may lack generalizability to other European or
non-European regions. In the future, it will be interesting
to monitor the genome size of more ragweed populations in
mountain regions where naturalization processes are cur-
rently underway.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s11356- 024- 33410-x.
Acknowledgements We are grateful to reviewers for their inspirational
ideas and recommendations.
Author contribution Michal Hrabovský: conceptualization, data
curation, formal analysis, software, writing—original draft, review,
and editing; Silvia Kubalová: writing—review and editing; Karol
Mičieta: conceptualization, validation; Jana Ščevková: supervision,
writing—review and editing. All authors read and approved the final
manuscript.
Funding Open access funding provided by The Ministry of Educa-
tion, Science, Research and Sport of the Slovak Republic in coop-
eration with Centre for Scientific and Technical Information of the
Slovak Republic. The study was supported by the Operation Program
of Integrated Infrastructure for the project, Advancing University
Capacity and Competence in Research, Development, and Inno-
vation, ITMS2014 + :313021X329, co-financed by the European
Regional Development Fund and by the Operation Program of Inte-
grated Infrastructure for the project, UpScale of Comenius University
Capacities and Competence in Research, Development, and Innovation,
ITMS2014 + : 313021BUZ3, co-financed by the European Regional
Development Fund. This study was supported by Grant Agency VEGA
(Bratislava), Grant No. 1/0180/22.
Data availability All data generated or analyzed during this study are
included in this published article.
Declarations
Ethics approval and consent to participate Not applicable.
Consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, 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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