Journal of Ecology and Environment

pISSN 2287-8327 eISSN 2288-1220

Article

Home Article View

Research

Published online July 18, 2022
https://doi.org/10.5141/jee.22.039

Journal of Ecology and Environment (2022) 46:19

© The Ecological Society of Korea.

Assessing the potential invasiveness of transgenic plants in South Korea: a three-year case study on sunflowers

Sung Min Han , and Kyong-Hee Nam *

Division of Ecological Safety, National Institute of Ecology, Seocheon 33657, Korea

Received: May 23, 2022; Revised: June 29, 2022; Accepted: July 1, 2022

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/.

Background: The introduction of new living modified (LM) crops may pose a latent threat to the biodiversity of each country. Here, we used sunflowers (Helianthus annuus L.) as a study system to investigate the potential for invasiveness of LM crops under different environmental conditions when released into a natural ecosystem in South Korea. We examined the seed germination, survival, and flowering of sunflowers under competition with wild plants at different sowing dates (March–December) and plot sizes (1 m × 1 m and 2 m × 2 m).
Results: The germination rate showed a significant difference according to the sowing date. In addition, several sunflowers survived in plots with a high germination rate, which also led to a higher flowering rate. We found that the smaller the plot, the smaller the area available for inter-species competition, and the higher the number of surviving sunflower plants. The relative dominance and importance value of the species varied significantly between the sowing dates; in particular, sunflowers sown in March could compete with wild plants for longer than those sown on other sowing dates.
Conclusions: These observations indicate that the potential for invasiveness of sunflowers differs depending on the environmental conditions and seed density at the time of release.

Keywords: environmental conditions, interspecific competition, invasiveness, living modified crop, natural ecosystem, weediness

Despite the growing commercial availability of living modified (LM) organisms (LMOs) worldwide, the introduction of LM crops into the environment and agriculture is still debated (ISAAA 2019). LM crops may be considered invasive alien species if they do not have a typical distribution and do not occur anywhere in their natural environment until released (Shine et al. 2000). Thus, the escape of transgenes can have serious impacts on environmental safety, such as in the case of invasive alien species (Shine et al. 2000). The potential for invasiveness of LM crops begins with the transfer of transgenes to wild or weedy relatives and the resulting transgenic hybrids can persist and spread within wild populations through reproduction, leading to changes in existing ecosystems (Pilson and Prendeville 2004; Warwick et al. 2009). Therefore, it is critical to examine whether a new LM crop is likely to persist or become invasive in natural ecosystems and agricultural habitats (Dale et al. 2002).

Newly introduced genes that confer resistance to biotic and abiotic stresses (such as disease, herbivory, herbicides, and environmental stresses) can have significant effects on plant persistence and invasiveness (Snow and Palma 1997). These transgenes in the natural ecosystems could improve the competitiveness and fitness of their wild relatives (Stewart et al. 2003). In particular, genes conferring resistance to herbivores could decrease wild herbivore populations that feed on wild plants (Pilson et al. 2002). However, the analysis of these outcomes is complicated by the variability of events and the environmental conditions at the location where the release occurs. Before the mass release of LM crops into the environment, it is imperative to assess their environmental impact under the specific conditions of each ecosystem (Bošković and Žuža 2019). Thus, the prediction of adaptable LM crops under various environmental conditions is an important component of ecological risk assessment for LM crops, and determining their potential for weediness is a primary step of this process (Raybould 2010).

The proposed guidelines for the evaluation of LM crops in the European Union (EU) include the following requirements: a description of the species involved, the environment in which they occur, and their potential for interaction with other organisms within the ecosystem (EFSA 2006). Expert advice suggests that the methods for assessing the environmental impact of transgenic crops need to be adapted to the specific conditions of each agricultural system (Cantamutto and Poverene 2007; Ghosh and Jepson 2006). In addition, the guidance for the permission of LM crops in the United States Department of Agriculture - Animal and Plant Health Inspection Service (USDA-APHIS) biotechnology regulatory services includes the following requirements: monitoring for deleterious effects on plants, non-target organisms, or the environment (USDA-APHIS 2011).

Watkinson et al. (2000) modeled the impacts of introduced herbicide-resistant LM sugar beets on weed abundance and examined the consequences for seed-eating bird populations in farmlands in the United Kingdom. Raybould et al. (2012) evaluated the persistence and spread of feral insect-resistant LM maize in Mexico, suggesting that this species has low invasiveness and poses non-significant ecological risks. In Japan, researchers measured the persistence of herbicide-tolerant LM Brassica napus spilled along roadsides leading from ports over six years. The results showed that there was no increase in the populations of this species and no invasion of native vegetation in the Japanese environment (Katsuta et al. 2015).

Although LM crops are not cultivated in South Korea, more than 134,000,000 tons of LMOs have been imported for use as food and feed over the last 14 years (KBCH 2022). An increase in the amount of imported LMOs has led to their unintentional release into the environment, and transgenic volunteers have been found along roadsides around the import ports and near feed factories and livestock barns along the transport routes (Han et al. 2015; Kim et al. 2006; Park et al. 2010). In addition, an accident has led to the recent mass distribution and cultivation of herbicide-resistant LM canola (GT73) and pest-resistant cotton (MON531) in natural areas as well as in agroecosystems (KBCH 2017a; KBCH 2017b). To date, the annually conducted LMO monitoring of natural ecosystems has continuously confirmed reports of such unintentional releases (Lim et al. 2021).

South Korea has four distinct seasons; however, over the last decade, frequent high- and low-temperature events have shown a long-term warming trend (KMA 2020). These changes in environmental conditions are expected to increase the risk of the northward spread of invasive species and have effects on the survival and reproduction of invasive plants, such as the unintentionally released LM crops (Adhikari et al. 2019; Hong et al. 2021). However, there is little data regarding the effects of South Korean environmental conditions on the potential for invasiveness of LM crops. Therefore, in this study, we developed a study system to evaluate the invasiveness of LM crops using sunflower and determined their presence and spread when released into a natural ecosystem—through germination, survival, and competition with wild plants—according to various sowing dates (representing different climatic conditions).

Plant material and field experiment

To investigate the survival, germination, and flowering of sunflowers according to their sowing date, we used the standard cultivar Jaeraejongja (height, 1.6–1.8 m; large seeds) and the extreme dwarf type cultivar Jaeraejong3 (height, < 0.5 m; small seeds) (Nam and Han 2020). Field trials were conducted from 2020 to 2022 in a confined field at the National Institute of Ecology, Seocheon-gun, South Korea (36° 01’ 43.0” N, 126° 43’ 22.5” E; elevation: 20 m), in accordance with the “Guideline for the operation of the institute of LMO risk assessment under the jurisdiction of the Ministry of Environment” (Fig. 1). Before sowing, the fields were moldboard-plowed to a depth of < 15 cm and then flattened. The soil type was loam with a moisture content of 10.13 ± 0.94%, pH of 6.45 ± 0.19, organic matter content of 4.25 ± 1.01%, P2O5 level of 20.13 ± 9.48 mg kg−1, total nitrogen of 577.85 ± 87.6 mg kg−1, electrical conductivity of 0.31 ± 0.11 ds m−1, and cation exchange capacity of 15.57 ± 6.26 cmol+ kg−1. Soil characteristics were determined by following previously described soil and plant analysis methods (NIAST 2000).

Figure 1. Photographs of the experimental field during each season and sunflowers at different stages of growth. (A) August 2020; (B) February 2021; (C) June 2021; (D) September 2021; (E) seedling stage; (F) flowering stage; (G) seed-filling stage.

To examine the possibility of invasiveness according to the seed density, sunflower seeds were sown on square plots made of wooden grids in two sizes: 1 m × 1 m and 2 m × 2 m. Sixty plots were established in four fully randomized blocks, and the spacing between plots was 1 m. Weeds outside the experimental plot were controlled by mulching with black non-woven fabrics. The sowing dates were in the last weeks of June, August, October, and December of 2020, as well as March of 2021. The sunflower seeds were sown without overlap, with 100 seeds being sown in each plot. The seeds were covered with 0.5 cm of soil and allowed to germinate and grow without irrigation or weed control.

Environmental data

Climate data during the study period were obtained from the Gunsan Meteorological Station and included the average, maximum, and minimum temperatures, average surface temperature, precipitation, sum of sunshine hours, and average relative humidity (Fig. 2) (KMA 2022). In June 2020, the surface temperature was 26.2°C, which was 4°C warmer than the average temperature. The sum of sunshine hours and monthly precipitation were 194.0 hours and 196.6 mm, respectively. In August 2020, the surface temperature was 30.1°C, and monthly precipitation was 520.6 mm, indicating a high level of rainfall. The surface temperature in October 2020 was 18.2°C. The environment was dry, with 226.4 hours of sunshine and 4.1 mm of precipitation per month. December 2020 was cold and dry, with a surface temperature and monthly precipitation of 2.6°C and 11.8 mm, respectively. In March 2021, the surface temperature was 10.2°C. This was approximately 1.9°C warmer than the average temperature. The sum of sunshine hours and monthly precipitation were 201.8 hours and 137 mm, respectively.

Figure 2. Meteorological characteristics during the study periods. (A) Average, maximum, and minimum air temperatures and average surface temperatures; (B) Monthly precipitation, sum of sunshine hours, and average relative humidity. Data were obtained from the daily weather report of the Gunsan Meteorological Station (36° 00’ 19.1” N, 126° 45’ 40.9” E).

Germination and plant growth characteristics

The germination of sunflowers was investigated one to two times per week, starting from two weeks after sowing. Sunflower seedlings for which cotyledons had emerged above the soil surface were counted as having germinated. Sunflower plants that survived after germination were counted after being classified into the following stages: germination stage (V2), growth stage (V3 to R1), flowering stage (R2 to R6), and maturity stage (R7 to R9) (Schneiter and Miller 1981). The final germination percentage was calculated as the ratio of the number of final germinated sunflowers per 100 seeds sown. The flowering rate was calculated as the ratio of the final number of flowering sunflowers to the highest number of surviving individuals. Changes in the growth characteristics of the sunflowers were investigated by measuring the plant height, stem diameter, and head diameter of surviving sunflower plants from four weeks after sowing. These data were recorded for five individuals from each plot at two-week intervals. Plant height was measured at the highest point of the plant from the soil surface. Stem diameter was measured at the part of the stem closest to the soil surface. The head diameter indicated the width of the first flower head that bloomed at the time of flowering.

Analysis of changes in the vegetation

A vegetation survey was conducted for each plot at intervals of one to two times per month, starting from four weeks after sowing. Taxonomic identification of the plant species was based on field observations and the National Species database of Korea (NIBR 2020). The importance value (IV) was used as an index to characterize the status and dominance of sunflowers and wild plants in the community. Relative dominance (RD) was obtained by measuring the dominance class of sunflowers and wild plants in each plot. The dominance class was calculated by substituting the median in the Braun-Blanquet approach (Braun-Blanquet 1964). Relative frequency (RF) was obtained from plots where plant species were present. The IV, RF, and RD were calculated using the following formulae (Williamson and Brown 1986):

IV = (RD + RF) / 2

RF = (frequency of one species/sum of the frequency of all species) × 100

RD = (density of one species/sum of densities of all species) × 100.

Statistical analysis

All data were analyzed using the SAS Studio (version 3.8; SAS Institute Inc., Cary, NC, USA), Primer 6 (version 6.1.13) and Permanova+ (version 1.0.3; PRIMER-E Ltd., Plymouth, UK) software. The effects of sowing date on final germination percentage and flowering rate were evaluated using one-way analysis of variance (ANOVA). Statistically significant differences between the means were identified using Duncan’s multiple range test (p < 0.05). An ANOVA model (PROC GLM) was used to test the differences in final germination percentage and flowering rate between the two sunflower cultivars, five sowing dates, and two plot sizes. Non-metric multidimensional scaling (NMDS) and permutational multivariate analysis of variance (PERMANOVA) were used to compare changes in RD according to sowing date, cultivar, and plot size.

Comparison of surviving sunflowers by sowing date, cultivar, and plot size

The germination and growth of sunflowers were strongly affected by the sowing date (that is, dates with different environmental conditions), cultivar, and plot size (Fig. 3). Seeds sown in March, June, August, and October germinated into sunflower plants; however, the sunflower seeds sown in December did not germinate. Although there was a difference between cultivars, sunflower seeds sown in March, June, and August flowered, whereas those sown in October did not. On average, compared to the 2 m × 2 m plot of Jaeraejongja, 10.5 more sunflowers survived in March and 14.3 more survived in June in the 1 m × 1 m plot. Between cultivars in the 2 m × 2 m plots, 6.3 more plants survived in the Jaeraejong3 plot than in the Jaeraejongja plot in March, whereas 3.5 more survived in the Jaeraejongja plot than in the Jaeraejong3 plot in June. The survival rate of seeds sown in August was very low, and 3.3 more individuals of Jaeraejong3 survived than those of Jaeraejongja. Among the seeds sown in October, 16.8 sunflower plants survived in the 1 m × 1 m plot of Jaeraejongja, and 5.5 and 5 individuals survived in the 2 m × 2 m plots of Jaeraejongja and Jaeraejong3, respectively.

Figure 3. Number of surviving sunflowers for seeds sown at different dates in plots of two sizes. Stacked bar plots represent the mean number of surviving individuals from four replicates. The developmental stages of sunflower plants are color-coded (orange, germination; gray, growth; yellow, flowering; blue, maturity).

The developmental phases—including the germination, growth, flowering, and seed maturity stages of sunflower plants—varied with sowing date, cultivar, and plot size. The germination of Jaeraejongja seeds sown in a 2 m × 2 m plot in March was observed for up to 10 weeks after sowing. The seeds sown in August and October had a short germination period and germinated within two to four and six weeks after sowing, respectively. The number of days to flowering was highest for plants sown in March, which flowered at 12 weeks after sowing, and lowest for plants sown in August, which flowered at eight to ten weeks after sowing. Seed maturation of sunflowers started from 12 to 18 weeks after sowing in various experimental plots. Overall, the survival period of sunflower plants was longer if sown earlier. Sunflowers sown in March survived for up to 20 weeks after sowing, whereas all surviving sunflower plants sown in October withered within eight weeks after sowing. Changes in the phenotypic characteristics of surviving individuals (including plant height, stem diameter, and head diameter) are shown in Figure S1.

No germinated or surviving individuals were observed in any of the experimental plots the following year after winter.

Rate of germination and flowering

The final germination percentage of sunflowers differed significantly according to the sowing date for both cultivars and plot sizes (Table 1). The sowing date with the highest final germination percentage was in June for Jaeraejongja and March for Jaeraejong3. Seeds sown in December did not germinate in any experiment plots. The seeds sown in August had a very low germination rate, especially for the Jaeraejongja cultivar. The final germination percentages for the 1 m × 1 m plot of Jaeraejongja were 21.8%, 16.8%, and 15.3% in June, October, and March, respectively. These values were all higher than the respective germination percentages in the 2 m × 2 m plot of Jaeraejongja. In the 2 m × 2 m plots, the final germination percentage was higher for the Jaeraejong3 cultivar than for the Jaeraejongja cultivar for seeds sown in March and August. In contrast, among seeds sown in June and October, the germination percentage was higher for Jaeraejongja than for Jaeraejong3.

Table 1 . Final germination percentage and flowering rate of surviving sunflower plants.

ComponentSowing dateJaeraejongja (1 m × 1 m)Jaeraejongja (2 m × 2 m)Jaeraejong3 (2 m × 2 m)
Final germination percentage (%)p-value0.0050.0160.012
March15.3 ± 12.8a3.5 ± 4.0a,b11.0 ± 6.5a
June21.8 ± 7.3a8.0 ± 4.3a4.0 ± 1.6b
August0.3 ± 0.5b0.3 ± 0.5b3.5 ± 1.0b
October16.8 ± 11.3a5.5 ± 4.4a5.0 ± 4.7b
December0.0 ± 0.0b0.0 ± 0.0b0.0 ± 0.0b
Flowering rate (%)p-value0.0070.019< 0.001
March58.2 ± 39.7a83.3 ± 28.9a,b23.1 ± 19.1b
June61.9 ± 7.1a49.4 ± 9.2b0.0 ± 0.0c
August100.0 ± 0.0a100.0 ± 0.0a100.0 ± 0.0a
October0.0 ± 0.0b0.0 ± 0.0c0.0 ± 0.0c
December---

Two cultivars of sunflower seeds were sown at five different dates in two plot sizes.

Data are presented as the mean ± standard deviation (n = 4).

The p-values shown are from the one-way ANOVA. Different letters in columns indicate significant differences between sowing dates (Duncan’s test, p < 0.05).



The proportion of sunflowers that survived and reached the flowering stage after germination also varied significantly between the sowing dates (Table 1). Of the seeds that germinated, flowering occurred among those sown in March, June, and August. However, the seedlings of seeds sown in October did not flower. The flowering rate for sunflowers sown in March was 25.1% lower in the 1 m × 1 m plot of Jaeraejongja than in the 2 m × 2 m plot of Jaeraejongja. The flowering rate of seeds sown in June was 12.5% higher in the 1 m × 1 m Jaeraejongja plot than in the 2 m × 2 m Jaeraejongja plot. Although seeds sown in August had a low final germination percentage in all the experimental plots, all the surviving plants flowered. The flowering rate was considerably lower in Jaeraejong3 than in Jaeraejongja in the 2 m × 2 m plots. Jaeraejong3 seeds sown in March showed a flowering rate of 23.1%, and the surviving individuals sown in June did not flower at all.

A GLM analysis showed that sowing date and plot size had significant main effects on final germination percentage (both p < 0.001). Sowing date also had a significant main effect on flowering rate (p < 0.001) (Table 2).

Table 2 . Results of a general linear model for final germination percentage and flowering rate.

ComponentSourceSSDFMSFp-value
Final germination percentageSowing date0.13240.03310.590< 0.001
Cultivar0.00810.0082.5200.119
Plot size0.05410.05417.380< 0.001
Flowering rateSowing date2.86640.95519.860< 0.001
Cultivar0.09210.0921.9100.176
Plot size0.04610.0460.9600.334

SS: sum of squares; DF: degrees of freedom; MS: mean square; F: F-statistic.



Changes in vegetation and crop–weed competition

Table 3 lists the plant taxa found in the experimental plots between 2020 and 2022, after the sunflower seeds had been sown. The vegetation data before the start of the experiment were displayed in Table S1 (Nam et al. 2019). These included a total of 36 species (including sunflowers) belonging to three classes, 11 orders, and 14 families. The plants consisted mainly of species from the class Magnoliopsida, followed by Liliopsida and Equisetopsida. At the family level, several species belonged to Asteraceae (which includes sunflower) and Fabaceae.

Table 3 . List of plants that occurred during the experimental period.

ClassOrderFamilySpecies
MagnoliopsidaAsteralesAsteraceaeHelianthus annuus
Artemisia indica
Conyza canadensis
Crepidiastrum sonchifolium
Erechtites hieracifolia
Erigeron annuus
Lactuca indica
Senecio vulgaris
Sonchus oleraceus
Taraxacum officinale
CaryophyllalesChenopodiaceaeChenopodium album
MolluginaceaeMollugo stricta
PortulacaceaePortulaca oleracea
FabalesFabaceaeAeschynomene indica
Glycine soja
Kummerowia striata
Lespedeza bicolor
Lespedeza cuneata
Medicago sativa
Robinia pseudoacacia
Trifolium repens
Vicia sativa
LamialesLamiaceaeLamium amplexicaule
Perilla frutescens
MyrtalesOnagraceaeOenothera biennis
PlantaginalesPlantaginaceaePlantago asiatica
PolygonalesPolygonaceaePersicaria hydropiper
RanunculalesRanunculaceaeClematis apiifolia
ScrophularialesScrophulariaceaeMazus pumilus
LiliopsidaCyperalesCyperaceaeCyperus iria
PoaceaeEchinochloa crus-galli
Echinochloa esculenta
Eleusine indica
Festuca arundinacea
Setaria viridis
EquisetopsidaEquisetalesEquisetaceaeEquisetum arvense


An NMDS analysis was performed on the relative dominance of plant species in the current study. The data were collected two months after sowing (when the sunflowers had survived with high relative dominance) and five months after sowing (when no sunflowers were present) (Fig. 4). Two months after sowing, the relative dominance of plant species was classified according to the sowing date. The plots sown in June were divided by plot size, and those sown in August were grouped by cultivar (Fig. 4A). Five months after sowing, there was a noticeable partition between sowing dates, whereas plots sown in March were grouped by cultivar (Fig. 4B). A PERMANOVA was performed to analyze the relative dominance value of plant species. For data collected two months after sowing, the results indicated significant differences according to sowing date (p < 0.001) and plot size (p < 0.001). For data collected five months after sowing, the results showed that sowing date had a significant effect (p < 0.001) (Table 4).

Table 4 . Permutational multivariate analysis of variance for the relative dominance of plant species.

Months after sowingSourceSSDFMSFp-value
TwoSowing date79,317.0419,829.029.4050.001
Cultivar91.1191.10.1350.986
Plot size4,929.914,929.97.3110.001
FiveSowing date90,118.0422,530.022.3600.001
Cultivar391.81391.80.3890.848
Plot size1,701.811,701.81.6890.134

SS: sum of squares; DF: degrees of freedom; MS: mean square; F: F-statistic.



Figure 4. Ordination diagram from a non-metric multi-dimensional scaling analysis of the relative dominance of plant species. Symbols indicate sowing date (March: green; June: red; August: blue; October: light blue; December: purple), cultivar (Jaeraejongja: triangle; Jaeraejong3: inverted triangle), and plot size (2 m × 2 m: closed symbols; 1 m × 1 m: open symbols). (A) two months after sowing, and (B) five months after sowing.

The importance values of plant species that occurred in the experimental plots were measured (according to sowing date, cultivar, and plot size) until 12 months after sowing (Fig. 5). The importance value of sunflower was high in the plot sown in March but decreased in later months due to the emergence of plants such as Kummerowia striata, Glycine soja, and Lespedeza bicolor. Jaeraejongja sown on a 1 m × 1 m plot in March had a higher importance value (60.0%) than plots sown on other dates. For up to five months after sowing, the importance value of sunflower was higher than that of other plant species. In the plot sown in June, the importance value of sunflower was highest (25.5%) in the 1 m × 1 m plot of Jaeraejongja. In the plot sown in August, the importance value of sunflower was the highest (22.2%) in the 2 m × 2 m plot of Jaeraejongj3, and this pattern was maintained for three months. Almost no other plants appeared in the early stages in the plot sown in October. The importance value for sunflower was 46.2% in the 1 m × 1 m plot of Jaeraejongja, 50.4% in the 2 m × 2 m plot of Jaeraejongja, and 21.6% for the 2 m × 2 m plot of Jaeraejong3. Sunflowers were not recorded in the plot sown in December.

Figure 5. Changes in the importance value of plant species occurring in experimental plots. Two cultivars (Jaeraejongja and Jaeraejong3) of sunflower seeds were sown on five different dates (March, June, August, October, and December) in two plot sizes (1 m × 1 m and 2 m × 2 m).

We investigated the competition between sunflowers and other wild plants in the plot sown in March, which showed the highest importance value for sunflowers (Fig. 6). Competition between sunflower plants and wild plants was possible for up to five months after sowing in the 1 m × 1 m plot of Jaeraejongja, but was more difficult in the 2 m × 2 m plot of Jaeraejongja. In the 2 m × 2 m plot of Jaeraejong3, sunflower–wild plant competition was possible for up to 3 months after sowing. For the remaining sowing dates (June, August, October, and December), competition between sunflowers and wild plants either did not occur or occurred for a short duration (less than 1 month).

Figure 6. Critical periods of weed interference and the importance values of sunflower and wild plants. Two cultivars of sunflower seeds were sown in two plot sizes in March. Colored symbols indicate the importance value (red, sunflower; blue, sum of wild plants), and the orange box indicates the critical period for weed interference.

Sunflowers are known to be widely adaptable to various climates and habitats (Khalifa et al. 2000). Sunflower seeds germinate at 5 to 40°C, and 25°C is the optimum temperature for germination (Gay et al. 1991). Here, we found that sunflowers germinated in March, June, August, and October in South Korea, when the surface temperature was 10.2 to 30.1°C. The germination rate was higher in June, which had temperatures closest to the optimum temperature for sunflower germination. However, sunflower seeds sown in December did not germinate in any experimental plots because of the low temperature (2.6°C). Although the temperature in August was suitable for germination, most of the sunflowers did not germinate; this is thought to be due to the high levels of precipitation. Excess soil moisture has been reported to have a negative effect on the sowing–emergence period of sunflowers, and overwatering results in a sharp drop in germination rate when sunflower seeds have high vitality (Albuquerque and Carvalho 2003; Loose et al. 2017).

Only a small number of sunflower plants survived from seeds sown in August, perhaps due to seed predation by animals. We observed a marked increase in bird activity near the experimental field in August. Moreover, the Jaeraejong3 cultivars sown in August had a higher survival rate than the Jaeraejongja cultivars. This may have reduced the chances of animal feeding because of the small seed size of the Jaeraejong3 cultivars, as suggested by Celis-Diez et al. (2004).

The surviving sunflowers sown in March, June, and August had a high flowering rate, whereas the seeds sown in October did not flower. The surviving seedlings of sunflower seeds sown in October could not grow and bloom, as the significant decrease in temperature after germination made this impossible. Although the flowering rate of surviving sunflowers was high for several sowing dates (March, June, and August), sunflowers did not appear in the following year. Munir et al. (2007) and Ebrahimian et al. (2019) reported that a lack of nutrients and moisture can inhibit seed maturation or negatively affect seed production in sunflowers. Commercially available cultivars produce seeds with low dormancy and high germination rates in various environments (Gao and Ayele 2014; Simpson 1990). Our previous study demonstrated that buried sunflower seeds do not successfully establish dormancy during the summer season in South Korea (Nam and Han 2020). In the current study, we did not observe any newly–emerged sunflower plants in the following year (that is, after the rainy season in summer and low temperatures in winter). These findings indicate that the surviving individuals did not produce seeds, did not mature well even if they produced seeds, or did not survive even if the mature seeds fell to the ground.

Crops and weeds share and compete for the same resources, including sunlight, atmospheric gas, nutrient, and water (Kaur et al. 2018). As the weeds grow, they can become a serious threat to crop production because of their ability to survive in adverse conditions and extract more nutrients and water from the soils (Kaur et al. 2018). The growth habits, emergence timing, and density of these weeds affect the competition between crops and weeds (Sardana et al. 2017). Studies have reported that when grown in competition without weed control, sunflower yield is affected in the period 14–26 days after emergence (Elezovic et al. 2012). In the current study, sunflowers sown in March (when the temperature and humidity were suitable for germination and growth) survived longer and competed with wild plants longer than those sown on other sowing dates. Moreover, the competitiveness of sunflowers increased when they were sown in a smaller plot.

Sunflowers that survived during the current study were quickly eliminated through competition with the wild plants. Without weed management, the relative frequency and predominance of wild plants increased dramatically—either with the predominance of certain wild plants or the emergence of a variety of plants—thus reducing the importance of the sunflowers. Tonev et al. (2020) reported that 65% of the total weed species in sunflower fields in Bulgaria were late-spring weeds, which coincided with the sunflower growing season. In addition, da Silva Alcântara et al. (2018) reported that Panicum maximum (Poaceae) impacts the initial development of sunflowers and reduces yield. The results of the current study also indicate that different dominant species were affected by the sowing date. The plants that had the greatest influence on the importance value of sunflower included Chenopodium album, K. striata, G. soja, L. bicolor, Eleusine indica, Conyza canadensis, and species in the Poaceae family such as K. striata and G. soja. According to Raunkiaer’s (1934) classification of life forms, therophytes were the most abundant among plants with high coverage in our experimental field, and their influence rapidly increased during the period of sunflower growth.

Our experiment is a case study of sunflowers released into a natural environment without any irrigation and weed control. However, the survival rates were higher when these seeds were released into agricultural areas where crops are grown, and the plants also emerged the following year (data not shown). Although the growth of the surviving sunflowers in this study was lower than that of commonly cultivated sunflowers, some experimental plants were able to grow and progress to the seed maturity stage after flowering. Moreover, the germination rate was higher in the 1 m × 1 m plot with high seed density, and competition between sunflower plants and wild plants was possible for up to five months after sowing in the plots sown in March. Seed spillage often occurs along the roadside during transport, and these seeds may spread to agricultural fields (Meffin et al. 2015). Bailleul et al. (2012) reported that in their study area, an estimated two million seeds were spilled in eight days while being transported. Thus, even if the survival rate is low for sunflowers released into the natural environment, their potential for weediness is expected to be high if the amount released is high.

Our results indicate that when sunflowers (as an LM crop) are unintentionally released into the natural environment in South Korea, their stable germination and survival are determined by the time of release and seed density. We also showed that it is difficult for sunflowers to exist for multiple generations or appear at a time when they can survive dormancy in the South Korean climate. However, several studies have revealed that sunflowers can adapt to a wide range of climates, and concerns about the emergence of wild populations through hybridization with native sunflowers continue to be raised (Cantamutto and Poverene 2007; Casquero et al. 2013). Therefore, to prevent the invasion of sunflowers (as an LM crop) into natural ecosystems in South Korea, efforts should be made for continuous monitoring, especially between March and June when survival and reproduction are possible for this species. Based on these findings, we suggest that when evaluating the potential for invasiveness of LM plants, it is important to consider climatic and other abiotic and biotic factors.

Supplementary information accompanies this paper at https://doi.org/10.5141/jee.22.039.

Fig. S1. Changes in phenotypic characteristics of surviving sunflowers sown at different dates in two plot sizes. Table S1. List of plants that occurred during the vegetation of 2019.

jee-46-19-supple.pdf

SMH performed the experiment and wrote the draft manuscript. KHN designed the research and wrote the manuscript. All authors read and approved the final manuscript.

  1. Adhikari P, Jeon JY, Kim HW, Shin MS, Adhikari P, Seo C. Potential impact of climate change on plant invasion in the Republic of Korea. J Ecol Environ. 2019;43:36. https://doi.org/10.1186/s41610-019-0134-3.
    CrossRef
  2. Albuquerque MCD, de Carvalho NM. Effect of the type of environmental stress on the emergence of sunflower (Helianthus annus L.), soybean (Glycine max (L.) Merril) and maize (Zea mays L.) seeds with different levels of vigor. Seed Sci Technol. 2003;31(2):465-79. https://doi.org/10.15258/sst.2003.31.2.23.
    CrossRef
  3. Bailleul D, Ollier S, Huet S, Gardarin A, Lecomte J. Seed spillage from grain trailers on road verges during oilseed rape harvest: an experimental survey. PLoS One. 2012;7(3):e32752. https://doi.org/10.1371/journal.pone.0032752.
    Pubmed KoreaMed CrossRef
  4. Bošković J, Žuža M. Impact of genetically modified plants on the environment. J Agron Technol Eng Manag. 2019;2(4):294-311.
  5. Braun-Blanquet J. Pflanzensoziologie. 3rd ed. Wien: Springer; 1964.
    CrossRef
  6. Cantamutto M, Poverene M. Genetically modified sunflower release: opportunities and risks. Field Crops Res. 2007;101(2):133-44. https://doi.org/10.1016/j.fcr.2006.11.007.
    CrossRef
  7. Casquero M, Presotto A, Cantamutto M. Exoferality in sunflower (Helianthus annuus L.): a case study of intraspecific/interbiotype interference promoted by human activity. Field Crops Res. 2013;142:95-101. https://doi.org/10.1016/j.fcr.2012.11.022.
    CrossRef
  8. Celis-Diez JL, Bustamante RO, Vásquez RA. Assessing frequency-dependent seed size selection: a field experiment. Biol J Linn Soc. 2004;81(2):307-12. https://doi.org/10.1111/j.1095-8312.2003.00287.x.
    CrossRef
  9. da Silva Alcântara LM, Lisboa LAM, Storti SMM, da Silva Lima AE, de Olibveira Crispim NP, da Silva GG, et al. Initial development of sunflower (Helianthus annuus L.) under weed competition with different species of grasses. J Exp Agric Int. 2018;29(2):1-11. https://doi.org/10.9734/JEAI/2019/45613.
    CrossRef
  10. Dale PJ, Clarke B, Fontes EM. Potential for the environmental impact of transgenic crops. Nat Biotechnol. 2002;20(6):567-74. https://doi.org/10.1038/nbt0602-567. Erratum in: Nat Biotechnol. 2002;20(8): 843.
    Pubmed CrossRef
  11. Ebrahimian E, Seyyedi SM, Bybordi A, Damalas CA. Seed yield and oil quality of sunflower, safflower, and sesame under different levels of irrigation water availability. Agric Water Manag. 2019;218:149-57. https://doi.org/10.1016/j.agwat.2019.03.031.
    CrossRef
  12. Elezovic I, Datta A, Vrbnicanin S, Glamoclija D, Simic M, Malidza G, et al. Yield and yield components of imidazolinone-resistant sunflower (Helianthus annuus L.) are influenced by pre-emergence herbicide and time of post-emergence weed removal. Field Crops Res. 2012;128:137-46. https://doi.org/10.1016/j.fcr.2011.12.020.
    CrossRef
  13. Gao F, Ayele BT. Functional genomics of seed dormancy in wheat: advances and prospects. Front Plant Sci. 2014;5:458. https://doi.org/10.3389/fpls.2014.00458.
    Pubmed KoreaMed CrossRef
  14. Gay C, Corbineau F, Côme D. Effects of temperature and oxygen on seed germination and seedling growth in sunflower (Helianthus annuus L.). Environ Exp Bot. 1991;31(2):193-200. https://doi.org/10.1016/0098-8472(91)90070-5.
    CrossRef
  15. Ghosh K, Jepson PC. Genetically modified organisms in crop production and their effects on the environment: methodologies for monitoring and the way ahead. Rome: Food and Agriculture Organization of the United Nations; 2006.
    CrossRef
  16. Han SM, Oh T, Uddin MR, Shinogi Y, Lee B, Kim C, et al. Monitoring the occurrence of genetically modified maize in Korea: a 3-year observations. J Fac Agric Kyushu Univ. 2015;60(2):285-90. https://doi.org/10.5109/1526339.
    CrossRef
  17. Hong SH, Lee YH, Lee G, Lee DH, Adhikari P. Predicting impacts of climate change on northward range expansion of invasive weeds in South Korea. Plants (Basel). 2021;10(8):1604. https://doi.org/10.3390/plants10081604.
    Pubmed KoreaMed CrossRef
  18. ISAAA. Global status of commercialized biotech/GM crops in 2019: biotech crops drive socio-economic development and sustainable environment in the new frontier. ISAAA Brief No. 55. Ithaca: International Service for the Acquisition of Agri-Biotech Applications; 2019.
  19. Katsuta K, Matsuo K, Yoshimura Y, Ohsawa R. Long-term monitoring of feral genetically modified herbicide-tolerant Brassica napus populations around unloading Japanese ports. Breed Sci. 2015;65(3):265-75. https://doi.org/10.1270/jsbbs.65.265.
    Pubmed KoreaMed CrossRef
  20. Kaur S, Kaur R, Chauhan BS. Understanding crop-weed-fertilizer-water interactions and their implications for weed management in agricultural systems. Crop Prot. 2018;103:65-72. https://doi.org/10.1016/j.cropro.2017.09.011.
    CrossRef
  21. KBCH. Biosafety Vol. 18 No. 2. Daejeon: Korea Biosafety Clearing House; 2017a.
    CrossRef
  22. KBCH. Biosafety Vol. 18 No. 3. Daejeon: Korea Biosafety Clearing House; 2017b.
    CrossRef
  23. KBCH. Import and Export Status; 2022 [Accessed 20 Jan 2022]. https://www.biosafety.or.kr/portal/page/f_03.
  24. Khalifa FM, Schneiter AA, El Tayeb EI. Temperature-germination responses of sunflower (Helianthus annuus L.) genotypes. Helia. 2000;23(33):97-104. https://doi.org/10.1515/helia.2000.23.33.97.
    CrossRef
  25. Kim CG, Yi H, Park S, Yeon JE, Kim DY, Kim DI, et al. Monitoring the occurrence of genetically modified soybean and maize around cultivated fields and at a grain receiving port in Korea. J Plant Biol. 2006;49:218-23. https://doi.org/10.1007/BF03030536.
    CrossRef
  26. KMA. Korean climate change assessment report 2020: the physical science basis. Seoul: Korea Meteorological Administration; 2020.
    CrossRef
  27. KMA. Synoptic Weather Observation, Statistics by Condition. 2022. https://data.kma.go.kr. Accessed 20 Jan 2022.
  28. Lim HS, Kim IR, Lee S, Choi W, Yoon AM, Lee JR. Establishment and application of a monitoring strategy for living modified cotton in natural environments in South Korea. Appl Sci. 2021;11(21):10259. https://doi.org/10.3390/app112110259.
    CrossRef
  29. Loose LH, Heldwein AB, Lucas DDP, Hinnah FD, Bortoluzzi MP. Sunflower emergence and initial growth in soil with water excess. Eng Agric. 2017;37(4):644-55. https://doi.org/10.1590/1809-4430-Eng.Agric.v37n4p644-655/2017.
    CrossRef
  30. Meffin R, Duncan RP, Hulme PE. Landscape-level persistence and distribution of alien feral crops linked to seed transport. Agric Ecosyst Environ. 2015;203:119-26. https://doi.org/10.1016/j.agee.2015.01.024.
    CrossRef
  31. Munir MA, Malik MA, Yaseen M. Performance of sunflower in response to nitrogen management at different stages. Pak J Agric Sci. 2007;44(1):12-5.
  32. Nam KH, Han SM, Lee JR, Park JH, Choi W, Jung YJ, et al. Establishment of LMO risk assessment system and operation of the institute of LMO risk assessment under the jurisdiction of the Ministry of Environment. Seocheon: National Institute of Ecology; 2019.
  33. Nam KH, Han SM. Seed germination of sunflower as a case study for the risk assessment and management of transgenic plants used for environmental remediation in South Korea. Sustainability. 2020;12(23):10110. https://doi.org/10.3390/su122310110.
    CrossRef
  34. NIAST. Methods of soil and plant analysis. Suwon: National Institute of Agricultural Sciences and Technology; 2000.
    CrossRef
  35. NIBR. National species list of Korea. Incheon: National Institute of Biological Resources; 2020.
    CrossRef
  36. Park KW, Lee B, Kim C, Kim DY, Park J, Ko E, et al. Monitoring the occurrence of genetically modified maize at a grain receiving port and along transportation routes in the Republic of Korea. Food Control. 2010;21(4):456-61. https://doi.org/10.1016/j.foodcont.2009.07.006.
    CrossRef
  37. Pilson D, Prendeville HR. Ecological effects of transgenic crops and the escape of transgenes into wild populations. Annu Rev Ecol Evol Syst. 2004;35:149-74.
    CrossRef
  38. Pilson D, Snow A, Rieseberg L, Alexander H. Fitness and population effects of gene flow from transgenic sunflower to wild Helianthus annuus. Paper presented at: Scientific Methods Workshop: Ecological and Agronomic Consequences of Gene Flow from Transgenic Crops to Wild Relatives; 2002 Mar 5-6; Columbus, USA. Columbus: Ohio State University, 2002. p. 58-70.
  39. Raunkiaer C. The life forms of plants and statistical plant geography. Oxford: Clarendon Press; 1934.
  40. Raybould A, Higgins LS, Horak MJ, Layton RJ, Storer NP, De La Fuente JM, et al. Assessing the ecological risks from the persistence and spread of feral populations of insect-resistant transgenic maize. Transgenic Res. 2012;21(3):655-64. https://doi.org/10.1007/s11248-011-9560-4.
    Pubmed KoreaMed CrossRef
  41. Raybould A. The bucket and the searchlight: formulating and testing risk hypotheses about the weediness and invasiveness potential of transgenic crops. Environ Biosafety Res. 2010;9(3):123-33. https://doi.org/10.1051/ebr/2011101.
    Pubmed CrossRef
  42. Sardana V, Mahajan G, Jabran K, Chauhan BS. Role of competition in managing weeds: an introduction to the special issue. Crop Prot. 2017;95:1-7. https://doi.org/10.1016/j.cropro.2016.09.011.
    CrossRef
  43. Schneiter AA, Miller JF. Description of sunflower growth stages 1. Crop Sci. 1981;21(6):901-3. https://doi.org/10.2135/cropsci1981.0011183X002100060024x.
    CrossRef
  44. Simpson GM. Seed dormancy in grasses. Cambridge: Cambridge University Press; 1990.
    CrossRef
  45. Snow AA, Palma PM. Commercialization of transgenic plants: potential ecological risks. BioScience. 1997;47(2):86-96. https://doi.org/10.2307/1313019.
    CrossRef
  46. Stewart CN, Halfhill MD, Warwick SI. Transgene introgression from genetically modified crops to their wild relatives. Nat Rev Genet. 2003;4(10):806-17. https://doi.org/10.1038/nrg1179. Erratum in: Nat Rev Genet. 2004;5(4):310.
    Pubmed CrossRef
  47. Tonev T, Kalinova S, Yanev M, Mitkov A, Neshev N. Weed association dynamics in the sunflower fields. Sci Papers Ser A Agron. 2020;63(1):586-93.
  48. USDA-APHIS. USDA-APHIS biotechnology regulatory services user guide: notification. Riverdale: United States Department of Agriculture (USDA); 2011.
  49. Warwick SI, Beckie HJ, Hall LM. Gene flow, invasiveness, and ecological impact of genetically modified crops. Ann N Y Acad Sci. 2009;1168:72-99. https://doi.org/10.1111/j.1749-6632.2009.04576.x.
    Pubmed CrossRef
  50. Watkinson AR, Freckleton RP, Robinson RA, Sutherland WJ. Predictions of biodiversity response to genetically modified herbicide-tolerant crops. Science. 2000;289(5484):1554-7. https://doi.org/10.1126/science.289.5484.1554.
    Pubmed CrossRef
  51. Williamson MH, Brown KC. The analysis and modelling of British invasions. Phil Trans R Soc Lond B. 1986;314(1167):505-22. https://doi.org/10.1098/rstb.1986.0070.
    CrossRef

Share this article on :

Related articles in JEE

  • There is No Related Article.
Close ✕

Journal of Ecology and Environment

pISSN 2287-8327 eISSN 2288-1220