Investigating the geographical, phenotypic and genetic diversity of Sickleweed populations by bioclimatic parameters, morphological traits and SCoT molecular markers

Abstract

Sickleweed (Falcaria vulgaris) is important due to its nutritional value and medicinal effects on the human body. The 15 different Sickleweed populations were collected based on an unbalanced nest design with 10 replications and nine morphological traits were measured on them. The diversity was investigated with 15 primers of SCoT marker. The genetic diversity was investigated by ANOVA, cluster analysis and Bayesian statistical model based on morphological traits, bioclimatic and SCoT. Grouping the study areas based on bioclimatic parameters by UPGMA method showed that these areas were divided into two groups and were similar in terms of climatic similarities and bioclimatic information. The results of analysis of variance showed that there was a significant difference between populations at the level of one percent for the studied traits. The cluster analysis based traits by the UPGMA method divided these populations into two groups. The phenotypic diversity of these populations was largely consistent with the geographical diversity. The primers used for SCoT marker produced 137 polymorphic bands on the populations, The UPGMA cluster analysis with molecular data placed the studied populations into three groups and four subgroups. Grouping based on the Bayesian method placed the populations into nine groups, although the populations were not differentiated and were a mixture of all nine groups. High genetic diversity for the studied Sickleweed populations have showed valuable insights into the evolution of this plant and provides basic data for designing appropriate management practices for breeding Sickleweed populations.

Introduction

Falcaria vulgaris is the name of a species of annual and rangeland plants from the Apiaceae family and is known by the names Longleaf and Sickleweed, and due to its nutritional value and medicinal effects on the human body, it is an important plant (Korman 2011; Piya 2013). The geographical distribution of Sickleweed is in America, Europe, Turkey, Iran, Caucasus, Central Asia and Northwest Africa (Afonin et al. 2008; Khazaei and Salehi 2006). Today, collecting information on cytogenetic and taxonomic relationships and characteristics of wild and domestic relatives of Falcaria vulgaris and other vulgaris species can have profound effects on breeding programs. Therefore, identification of desirable species that can be used in agriculture can solve some of the problems of this plant.

One of the first steps in the breeding programs is to examine the available germplasm in terms of different morphological and molecular traits, organize and manage the existing genetic resources, establish different gene banks, and finally select the best cultivars and genotypes to be used in the breeding programs and achieve the desired goals (Hohenlohe and Rajora 2021; Rajora 2019). In addition to reducing interspecific diversity in crop plants, plant breeding has reduced intraspecific diversity by creating breeding populations with high adaptability, selecting the best genotypes, creating uniform local cultivars, and promoting compatible varieties. The lack of diversity between and within species among agricultural plants can cause many problems. Also, the resistance of plants against pests, diseases and environmental stress depends a lot on genetic diversity (Caliskan 2012; Hohenlohe and Rajora 2021).

Different climate change scenarios have been used in various studies to investigate its effects on the distribution of different plants (Tekin et al. 2022; Varol et al. 2022a). For example, the study of Varol et al. (2022b) showed that the population loss might exceed beyond 25% at the altitudes below 1600 m for Carpinus betulus and beyond 30% at the altitudes below 1000 m for C. orientalis, that the appropriate distribution areas will expand at higher altitudes, and that this increase may be more than 100% at the altitudes of 1000–2000 m for C. orientalis. The results also showed that the species will not be able to migrate to the newly emerging appropriate distribution areas fast enough and, moreover, significant population losses may occur for this reason.

Genetic diversity is the basis of all selections. Therefore, in order to breed and produce high-yielding cultivars, access to genetic resources, knowledge of the genetic structure of genotypes and the way traits are inherited is necessary. Genetic resources provide desirable genes, which can be used to produce new and desirable cultivars if used correctly. According to the positive relationship between the amount of genetic diversity and the amount of evolutionary changes, with the increase of genetic diversity, it is easier to achieve the desired trait (Caliskan 2012; Hohenlohe and Rajora 2021). The evaluation of agricultural traits provides a simple technique for quantifying genetic changes, and subsequently increases the efficiency of investigating the studied genotypes under the desired conditions (Fufa et al. 2005).

Compared with molecular markers, morphological traits have limitations such as low polymorphism, low heritability and vulnerability to environmental changes. Despite this, the examination of genotypes in terms of morphological, physiological, morphological and biochemical traits is considered the first step for the evaluation and classification of germplasms (Smith and Smith 1992; Smith et al. 1991). The classic breeding methods are very time-consuming, one should look for methods that are somehow complementary to correctional works and increase their efficiency. In recent years, the development of DNA markers due to the acceleration and increase of their modification efficiency, certainty and frequency, has been emphasized a lot about their use (Nadeem et al. 2018). Due to the fact that classic breeding methods are very time-consuming, methods that complement breeding programs and increase their efficiency are considered. The use of DNA markers in recent years has been emphasized a lot due to the development, acceleration and increase of their breeding efficiency, certainty and frequency (Nadeem et al. 2018).

In general, the estimation of genetic differences and relatives in order to manage the germplasm and create strategies for its preservation and maintenance, genetic fingerprinting, preparation of genetic maps, location and isolation of genes, as well as identification of desirable parents for the production of hybrid varieties and in general, the design of breeding programs is very important, which can be done through morphological traits, biochemical markers, and molecular markers. Meanwhile, using a fast, cheap and relatively accurate method to determine and evaluate genetic diversity is one of the most important tasks of a breeder, which can be realized through molecular markers (Hohenlohe and Rajora 2021; Rajora 2019).

Start codon targeted or SCoT is a DNA marker system that is similar to RAPD and ISSR and operates based on conserved regions around the start codon (ATG) in plant genes. This technique has the advantages of RAPD and ISSR indicators, and it is also a simple and fast method, economical and with high reproducibility. This marker, like RAPD and ISSR, has a dominant nature and is used to study genetic diversity, genetic map, bulked segregant analysis and QTL mapping (Collard and Mackill 2009; Luo et al. 2010; Luo et al. 2011). The primer sequence of this marker is longer than that of RAPD, and this marker can be included among targeted molecular markers (Sawant et al. 1999). Molecular markers have been used in cases such as creating genetic and physical maps in living organisms and identifying quantitative and qualitative genes and their classification and the amount of genetic diversity between organisms (Kumar et al. 2009). In a study by Piya et al. (2014) on eight different populations and 12 individuals from each Sickleweed population collected from different regions of America, it was shown that these individuals were placed in three different groups based on SSR markers and showed good diversity. Investigating the genetic diversity of different masses and populations from different geographical regions with molecular markers as well as phenotypic traits in various medicinal plants has been investigated, which can be referred to the investigation of the diversity of medicinal plants orchid (Mafakheri et al. 2022), Safflower (Rahimi 2021), Plantago (Rahimi et al. 2018) and other plants (Heidari et al. 2016; Mutinda et al. 2022).

Many studies have not been done on Sickleweed plant in the world and Iran using agronomical traits and molecular markers, and the lack of study of genetic and morphological diversity in the plant species populations shows the risk of extinction of that plant. Therefore, an early study of its population diversity can prevent excessive reduction and extinction of those populations. Our main aim was not only to examine the levels and distribution of genetic variability of wild Sickleweed natural populations, but also to assist in breeding program and conservation practices for Sickleweed natural populations. Also, the secondary goal was to investigate the effect of the environment (provinces) and genotype (population within the province) on the studied traits. Therefore, the effect of the environment or the population genetics is determined on the phenotypic differences and a better understanding is obtained for breeding programs.

Materials and methods

In this experiment, 15 regions from 7 provinces of Iran with an unequal number of regions were selected and populations from these regions were collected and evaluated. The collection locations of plant samples and the climatic characteristics of the regions are shown in Table 1. The information related to the height of the location as well as the geographic longitude and latitude was recorded by Google Earth software, and the information related to the weather as well as various bioclimatic parameters (Fick and Hijmans 2017) were estimated from the WorldClim website (http://www.worldclim.com/version2) for these areas.

Table 1.

Characteristics of the collection areas of Sickleweed plant samples

Code	Province	City	Region	Longitude	Latitude	Height above sea level
G1	Kermanshah	Sonqor	Kivananat	47.27889	34.84972	1847
G2	Kermanshah	Sonqor	Bavaleh	47.69778	35.02722	2010
G3	Kermanshah	Sahneh	Sahneh	47.75833	34.44194	1465
G4	Hamedan	Asad abad	Chaharduli	48.065	34.93139	1890
G5	Kurdistan	Gorveh	Panjeh Ali	47.70834	35.18667	1912
G6	Kurdistan	Dehgolan	Bolbanabad	47.41195	35.15222	1851
G7	Kurdistan	Dehgolan	Amirabad	47.31084	35.10361	1974
G8	Kurdistan	Kamyaran	Gerger-e Sofla	47.27667	35.00334	1824
G9	Kurdistan	Kamyaran	Qaleh Gah	47.21196	34.95001	1611
G10	Kurdistan	Bijar	Nemat abad auliya	47.41333	35.68444	1922
G11	Kurdistan	Bijar	Seylatan	47.83054	36.03666	1617
G12	Qazvin	Qazvin	Ilan	50.64778	36.42528	1450
G13	Ardabil	Meshkinshahr	Shaban	47.44833	38.37556	1236
G14	Qom	Qom	Khalajastan	50.19028	34.67944	1952
G15	Gilan	Siahkal	Deylaman	49.90528	36.88889	1455

The 15 different native ecotypes of Sickleweed (Table 1) were collected from seven provinces of Iran (Ardebil, Kurdistan, Kermanshah, Gilan, Hamedan, Qazvin and Qom) and in each region 10 different samples from each ecotype were selected for evaluation and measurement of traits. These 10 samples were considered as replicates for each region, and the experiment was conducted based on the nested design with an unbalanced number of regions. In each area, samples were at least 10 m apart from others to avoid sample interference. In the vegetative stage in early and mid-spring of 2021, morphological and agronomic traits were measured and evaluated on ten samples of each Sickleweed ecotype. These traits included: leaf number, leaf length, leaf width, main petiole length, Lateral petiole length, plant height, 100-grain weight, fresh and dry weight. Also, the leaves of the plants were collected at the stage of sapling, and the fresh leaves were placed in foil after harvesting and placed in liquid nitrogen and transferred to the laboratory to be used in molecular tests.

The leaves of ten samples collected for each ecotype were mixed together and DNA extraction from this mixed sample was performed as a representative of that ecotype. DNA extraction was done using the Dellaporta method (Dellaporta et al. 1983) with a few changes. The quantity and quality of extracted DNA was determined using two methods: spectrophotometry and 2% agarose gel electrophoresis. Polymerase chain reaction for SCoT marker were used by Biometra machine and in a volume of 10 µl (2 µl template DNA (50 ng), 0.1 µl dNTP (10 mM), 0.48 µl MgCl2 (50 mM), 0.2 µl Taq polymerase enzyme (5u), 1 µl of PCR buffer (10×), 0.6 µl of primer (5 µM) and 5.8 µl of sterile deionized water. The thermal cycle includes 4 min of initial annealing at 94 °C, then 35 cycles of 30 s and annealing at 94 °C, 45 s of primer attachment at TM temperature (it was different depending on the primer, Table 3), 2 min of extension at 72 °C and 5 min of final expansion at 72 °C, then storage at 4 °C. Amplified DNA detection for SCoT primers was done by electrophoresis of PCR products with 2% agarose gel, and bands were photographed with a gel dock device (Bio-Rad).

Table 3.

Unbalanced nest variance analysis of physiological and biochemical traits in Sickleweed populations

S.O.V	Df	Mean square of traits
		100 seed weight	Dry weight	Fresh weight	Plant height	Lateral petiole length	Main petiole length	Leaf width	Leaf length	Leaf number
Province	6	477.202**	26.87**	250.55**	546.76**	3.71**	70.05**	17.80**	259.16**	37.36**
Populations (Province)	8	22.252**	19.76**	223.22**	309.08**	1.35**	59.79**	5.34**	48.72**	24.57**
Error	135	0.270	0.99	15.63	43.93	0.04	8.26	0.01	2.00	3.88
	CV of design (%)	2.96	14.54	14.91	13.79	6.55	20.59	3.65	5.46	13.65

**Significant at 1% probability level

Descriptive statistics of traits (mean, range and standard deviation, etc.) were performed on the average data of 15 ecotypes and the phenotypic coefficient variation was calculated based on the following equation.

$${\text{CV}}_{\text{p}}=\left(\sqrt{{\mathrm{\sigma }}_{\text{p}}^2}/\overline{\text{x}}\right)\times 100$$

The selected provinces for collecting samples were different in terms of climate and environmental conditions and were considered as environmental conditions or fixed effect. To investigate the difference between these provinces, the plan was considered as a complete nest with seven regions in each province. But due to the large distances between the provinces and the growth period of this plant, not all regions were investigated. Therefore, some regions were investigated in each province and therefore the plan was considered unbalanced. To determine how phenotypic variation is partitioned between different types of environments (fixed effect), among populations (random effect), and among individuals (error), an unbalanced nested design analysis of variance (ANOVA) was conducted for the measured traits and the analyses were implemented in PROC NESTED by using SAS software version 9.4 (SAS-Institute-Inc 2014).

Bands in the gel were scored based on zero and one (zero, absence of band and one, presence of band). The number of amplified bands, the number of polymorphic bands and polymorphic percentage were obtained for each primer, and the zero and one matrix obtained was used to group genotypes and estimate indices. Polymorphism Information Content (Botstein et al. 1980), Expected Heterozygosity (Liu 1998), Marker index, Effective Multiplex Ratio and Mean Heterozygosity (Powell et al. 1996) and marker detection power (Tessier et al. 1999) were calculated based on related formulas and the program written in Excel by the first author. The number of effective alleles, Shannon’s index (Shannon 2001) and Nei’s genetic diversity (Nei 1972) were determined using POPGEN software version 1.3.1 (Yeh 1999).

Grouping of ecotypes based on bioclimatic parameters, studied traits and also grouping of ecotypes based on DNA markers were done based on the cluster analysis to investigate the genetic structure and grouping of populations and to identify kinship relationships between them. The grouping of populations was done based on different methods of cluster analysis and different distance or similarity criteria. Then, the cluster method and criterion (distance or similarity) that had the highest Cophentic correlation coefficient was selected and the grouping was done based on it. Finally, the distribution of the populations was also done by principal component analysis and the biplot method. The cluster analysis method and principal component analysis were done with PAST software (Hammer et al. 2001). Determining the number of groups were done with factoextra (Kassambara and Mundt 2020) package and R software by elbow method.

Also, the grouping and identification of the population structure in Sickleweed populations was done using the Bayesian method with Structure software (Pritchard et al. 2000). To select the optimal level of K, first 10,000 replications were performed for each K, followed by 10,000 iterations of Marco Chain Monte Carlo (MCMC) based on the mixed model (K = 2 to K = 10) with three replications, and then the best K was identified by using HARVESTER STRUCTURE software (Earl and vonHoldt 2012) and based on the ∆K method (Evanno et al. 2005).

Results

To investigate the geographical diversity in this study, 19 bioclimatic parameters were measured and calculated for the collection areas of plant samples (Fig. 1), and then the diversity and similarity of these areas were analyzed by different cluster analysis methods and different distance criteria. The results of cluster analysis for these regions are shown in Fig. 2. The results showed that these regions are placed in two groups by Elbow method and they are similar in terms of weather similarities and bioclimatic information. In the first group were Seylatan and Qaleh Gah regions from Kurdistan province, Sahneh from Kermanshah, Deylaman from Gilan, Ilan from Qazvin and Shaban from Ardabil, and in the second group were Kiyunanat and Bavaleh regions from Kermanshah, Panjeh Ali, Bolbanabad, Amirabad, Gerger-e Sofla and Nematabad auliya from Kurdistan, Khalajastan from Qom and Chaharduli from Hamedan.

Fig. 1.

Regions and provinces of collecting different Sickleweed samples

Fig. 2.

The UPGMA cluster analysis of different areas of Sickleweed population collection based on bioclimatic parameters

The name of ecotypes is given in Table 1

Descriptive statistics including the minimum, maximum and range of changes of the studied traits are shown in Table 2. Examining the phenotypic coefficient variation (PCV) of the traits showed that leaf width, lateral petiole length followed by dry weight and leaf length had the highest PCV. Therefore, these traits can be used in breeding and effective selections can be made among the studied Sickleweed populations to improve and breeding these traits. Also, the lowest PVC was related to leaf number and plant height traits, and the improvement of this trait will be less successful than other traits through selection in the studied population. The lowest PCV was related to the leaf number trait (12.015) and the highest was related to the leaf width trait (38.45) and other traits were in between them. These observations marked a favorable variation among the traits for most of them and they can be used in the improvement of the studied populations of Sickleweed (Table 2). The reason for the great diversity of the leaf width trait can be the environmental conditions and also the different genetic background of the populations. Also, the amount of diversity of leaf length and secondary petiole length was high and therefore these traits can be of interest to breeders because the leaves of this plant are used as vegetables. The selection of populations based on these traits is forced to improve these traits, but other traits with low PVC have less chance for selection (Table 2).

Table 2.

Descriptive statistics of biochemical and physiological traits in Sickleweed populations

Traits	Statistical parameters
	Min	Max	Range	Avereage	Stand. dev	PCV
Leaf number	11.146	17.032	5.886	14.427	1.733	12.015
Leaf length	18.570	37.987	19.417	25.921	5.417	20.897
Leaf width	1.126	4.591	3.465	2.687	1.033	38.451
Main petiole length	8.740	17.489	8.749	13.953	2.533	18.157
Lateral petiole length	1.421	3.966	2.545	2.962	0.700	23.620
Plant height	39.658	60.006	20.348	48.048	6.410	13.342
Fresh weight	20.090	33.629	13.539	26.507	4.847	18.286
Dry weight	4.902	9.252	4.350	6.840	1.501	21.941
100 seed weight	7.693	8.905	1.211	8.222	0.360	4.374

The results of variance analysis of the unbalanced nest design for the studied morphological traits showed a significant difference at the level of 1% between the Sickleweed populations (Table 3). The significance of traits for the effects of the province and the population within the province showed that there is a significant difference between the provinces as well as between 15 different populations and showed the diversity among them (Table 3).

The results of variance components for morphological traits based on unbalanced nesting design are shown in Table 4. The results showed that the percentage of variance components for the studied traits among the studied populations for different traits was between 30 and 60%. The results showed considerable diversity between the populations and it can be used to select the superior population. Also, the variation within the population (variance components for the source of error) were observed for leaf number, main petiole length, plant height fresh and dry weight traits (above 30%). Therefore, it is possible to look for diversity among Sickleweed populations and select the best individual sample of each ecotype within the population.

Table 4.

Estimation of the variance components of the sources of variation in the unbalanced nest design of the studied physiological and biochemical traits

Traits	Sources of variation
	Province		Populations (province)		Error
	Variance components	%	Variance components	%	Variance components	%
Leaf number	0.711	10.68	2.069	31.09	3.876	58.23
Leaf length	23.382	67.33	9.342	26.90	2.004	5.77
Leaf width	0.692	56.08	0.533	43.14	0.010	0.78
Main petiole length	0.570	4.08	5.153	36.86	8.258	59.06
Lateral petiole length	0.262	46.63	0.262	46.58	0.038	6.79
Plant height	13.204	15.79	26.515	31.70	43.930	52.52
Fresh weight	1.519	4.01	20.759	54.76	15.629	41.23
Dry weight	0.395	12.13	1.878	57.63	0.985	30.24
100 seed weight	25.275	91.10	2.198	7.92	0.270	0.97

The results of different cluster analysis methods with different distance criteria based on agronomic traits showed that UPGMA method with Euclidean distance had the highest value of the Cophentic correlation coefficient (0.76), and therefore, cluster analysis was performed with this method. Also, the Elbow method result (Fig. 3) showed that the number of two groups is suitable for performing cluster analysis and these two main groups showed in Fig. 4. The first group including the populations of Ker-Kivananat, Ker-Bavaleh, Ker-Sahneh, Ham-Chaharduli, Kur-Panjeh Ali and Kur-Bolbanabad, and the second group including the population of Kur-Amirabad, Kur-Gerger-e Sofla, Kur-Qaleh Gah, Kur-Nemat abad auliya, Kur-Seylatan, Qaz-Ilan, Ard-Shaban, Qom-Khalajastan and Gilan-Deylaman. As mentioned, populations are placed in completely separate categories due to different genetic bases or other environmental factors, and it can be justified that morphological traits are able to determine this distinction. The reason for the difference between the populations of the groups can be due to the difference in the genetic structure or the effect of other environmental factors on the traits. The highest genetic distance was between Ker-Kivananat and Kur-Seylatan population (14.85) and the lowest genetic distance was between Kur-Seylatan and Qaz-Ilan population (0.74).

Fig. 3.

The number of cluster group by the Elbow method for agronomic trait cluster analysis

Fig. 4.

The UPGMA cluster analysis of different areas of Sickleweed population based on agronomic traits

The name of ecotypes is given in Table 1

In this study, the use of 15 SCoT primers resulted in a total of 164 bands, of which 137 were polymorphic bands, and the average number of polymorphic sites per primer for SCoT markers was 9.13 (Table 5). Among the primers used in the SCoT marker, primers SCoT4, SCoT16 and SCoT35 had the highest number of bands with 13 bands and primers SCoT1 with four bands and SCoT12 with 5 bands had the lowest number (Table 5). The percentage of polymorphism obtained in Ghaziaghi populations varied from 57.14% for SCoT1 to 100% for SCoT11 and SCoT16. The average percentage of polymorphism obtained in this research was 82.77% (Table 5). The polymorphic information content (PIC) is equivalent to genetic diversity and shows the power of distinguishing a marker by the number of polymorphic alleles and the relative frequency of these alleles in the population under study. The PIC was calculated separately for each of the studied primers and the corresponding results were presented in Table 5. The PIC in this research for the SCoT primers was between 0.38 and 0.43 and the average polymorphic information content was 0.39 (Table 5).

Table 5.

Calculated indices of molecular marker for SCoT primers

Primers	Indices*
	PB	TB	PP	PIC	EH	D	MI	EMR	Havp	NEA	SI	NGD
SCoT1	4	7	57.14	0.40	0.44	0.89	0.0099	1.33	0.0074	1.596	0.547	0.364
SCoT2	7	9	77.78	0.43	0.38	0.45	0.0189	5.20	0.0036	1.586	0.516	0.343
SCoT4	13	14	92.86	0.38	0.49	0.81	0.0145	5.73	0.0025	1.432	0.448	0.283
SCoT5	6	9	66.67	0.39	0.46	0.87	0.0114	2.20	0.0052	1.578	0.518	0.344
SCoT8	11	13	84.62	0.38	0.50	0.79	0.0153	5.07	0.0030	1.436	0.463	0.293
SCoT10	9	10	90.00	0.38	0.49	0.68	0.0185	5.07	0.0036	1.549	0.503	0.332
SCoT11	8	8	100.00	0.41	0.43	0.53	0.0197	5.47	0.0036	1.488	0.470	0.302
SCoT12	5	6	83.33	0.38	0.49	0.81	0.0145	2.20	0.0066	1.269	0.363	0.210
SCoT14	9	10	90.00	0.38	0.49	0.83	0.0134	3.73	0.0036	1.436	0.451	0.284
SCoT16	13	13	100.00	0.38	0.49	0.81	0.0143	5.67	0.0025	1.604	0.535	0.357
SCoT21	10	11	90.91	0.38	0.50	0.78	0.0157	4.73	0.0033	1.304	0.369	0.219
SCoT28	12	15	80.00	0.38	0.50	0.79	0.0151	5.47	0.0028	1.575	0.534	0.353
SCoT35	13	16	81.25	0.38	0.50	0.71	0.0178	7.00	0.0025	1.480	0.481	0.309
SCoT37	10	13	76.92	0.38	0.49	0.68	0.0186	5.67	0.0033	1.613	0.559	0.373
SCoT48	7	10	70.00	0.38	0.48	0.65	0.0190	4.13	0.0046	1.641	0.553	0.373
Average	9.13	10.93	82.77	0.39	0.48	0.74	0.0158	4.58	0.0039	1.506	0.487	0.316

*TB total amplified bands, PB number of polymorphic bands, PP Polymorphic percentage, PIC polymorphism information content, EH expected heterozygosity, D marker detection power, MI marker index, EMR effective multiplex ratio, Havp mean heterozygosity, NEA number of effective alleles, SI Shannon's index, NGD Nei's genetic diversity

The highest amount of PIC in primer SCoT2 was determined with a value of 0.43, which indicates the high efficiency of this primer in differentiating the populations used in this research. Also, the expected heterozygosity in this research for SCoT primers was between 0.38 and 0.50 and the average expected heterozygosity was 0.48 (Table 5). The highest amount of EH in primers SCoT8, SCoT21, SCoT28 and SCoT35 was determined with a value of 0.50, which indicates the high efficiency of these primers in differentiating the populations used in this research. In a study on other plants, the average PIC values for SRAP and SCoT markers were 0.35 and 0.30, respectively (Golkar and Mokhtari 2018). The results of this study were compared with the study of Golkar and Mokhtari (2018) and Talebi et al. (2018) on other plants and showed that the SCoT marker in this study have been able to study the genetic diversity and differentiation of populations.

In order to determine the effectiveness of markers in the occurrence of polymorphism, the marker index (MI) was calculated. The highest amount of MI was for the SCoT11 primer (0.0197) and the lowest for the SCoT1 primer (0.0099) (Table 5). Also, the average heterozygosity in SCoT marker was variable between “0.0025 and 0.0074’’. Primers SCoT1 and SCoT5 had the highest average heterozygosity (Havp) with 0.0074 and 0.0052 units, respectively, which showed the high efficiency of these primers in the occurrence of polymorphism (Table 5). The effective polymorphism ratio, which expresses the number of polymorphic gene positions in a germplasm, varied between 1.33 for SCoT1 and 7 for SCoT35. Also, the detection power (D) of the marker that can better distinguish between two people varied between 0.45 for SCoT2 and 0.89 for SCoT1 primers and it showed that primer SCoT1 had more detection power and was able to show better distinction between two people.

One of the most important indices for evaluating genetic diversity among cultivars and populations is Nei’s genetic diversity index. Nei’s index estimation showed that the amount of gene diversity varied from 0.210 to 0.373 among SCoT primers and its average in the studied population was equal to 0.316 (Table 5). The primers SCoT37, SCoT48 and SCoT1 showed the highest Nei's genetic diversity, respectively. Primer SCoT12 showed the least amount of Nei’s genetic diversity. Shannon’s coefficient indicates the degree of polymorphism among populations. In this research, the average Shannon coefficient for the SCoT marker was equal to 0.487, which indicates the average diversity in the investigated populations. SCoT37, SCoT48, SCoT1, SCoT16 and SCoT28 primers had the highest Shannon index value. This shows that the mentioned primers can better justify the genetic diversity within the population, and the SCoT12 primer has the lowest Shannon index (Table 5). Also, the number of effective alleles varied from 1.269 to 1.641 and its average in the studied population was 1.506 (Table 5).

Cluster analysis based on SCoT indicators using similarity coefficients and different methods of cluster analysis showed that UPGMA grouping based on the Dice coefficient with Cophentic coefficient 0.96 is the best grouping method and the degree of similarity between cultivars was ranged from 37.04 to 91.05%. Also, the result of elbow method showed three suitable groups and UPGMA grouping dendrogram showed in Fig. 5.

Fig. 5.

Dendrogram resulting from cluster analysis of Sickleweed populations with UPGMA and Dice coefficient based on SCoT markers

The highest similarity between Seylatan-Kurdistan and Bavaleh-Kermanshah populations was 0.9104, and the lowest similarity was between Dilaman-Gilan and Bolbanabad-Kurdistan populations with 0.3704 similarity. In the first group were the populations of Khalajastan-Qom and Dilaman-Gilan, in the second group were the populations of Ilan-Qazvin and Shaban-Ardebil, and the rest of the populations were in the third group, which were divided into four subgroups. The populations Amirabad and Qaleh Gah of Kurdistan were in the first subgroup, Sahneh-Kermanshah and Nemat abad auliya-Kurdistan populations were in the second subgroup, Chaharduli-Hamadan, Kivananat-Kermanshah and Bolbanabad-Kurdistan populations were in third subgroup, and finally, Panjeh Ali, Seylatan and Gerger-e Sofla of Kurdistan and Baule-Kermanshah populations were in the fourth subgroup.

In the present study, the population structure was done and two groups were selected for these populations using harvester structure website for best k (Fig. 6). The results of cluster analysis based on the Bayesian statistical model showed that there are two structural groups in the existing germplasm (Fig. 7), which are completely separated. Populations Khalajastan-Qom and Deylaman-Gilan were in one group and populations Kivananat-Kermanshah, Bavaleh- Kermanshah, Chaharduli-Hamedan, Panjeh Ali-Kurdistan, Gerger-e Sofla-Kurdistan and Seylatan-Kurdistan were in the other group, and the rest of the populations were a mixture of other groups and did not differentiate. According to Fig. 7, where the percentage of mixing can be seen on the left side, people who continue to the top of the chart with a certain color are pure, and people who have different colors with different percentages are mixed and hybrid. The results indicate that the mixing and genetic connection of people from different provinces is high and according to the results, these different geographical populations belong to the same population. In this study, different methods were used to investigate the structure and grouping of populations, and all the mentioned methods were able to show almost similar structure and grouping and showed that these populations have a lot of genetic commonalities.

Fig. 6.

Determining subgroups using Structure Harvester based on SCoT marker

Fig. 7.

Structure of Sickleweed populations using Bayesian clustering approach based on SCoT marker

The name of ecotypes is given in Table 1

Discussion

In population ecology, the range of geographical distribution of a species is a complex phenomenon that cannot be fully explained. In general, the success of an individual in providing biological needs such as nutritional, physiological and behavioral needs in natural habitats determines the possibility of the existence of that species in those habitats (Rockwood 2015). The amount and abundance of resources are affected by weather and topography, in other words, geographical diversity. According to the traditional theory of allopatric speciation, geographical barriers such as distances, lowlands and heights, etc. can cause the separation of populations, which also causes different trends for the diversity of populations separated from each other (Anacker and Strauss 2014; Givnish 2010). Perhaps the changes that separate populations have undergone in separate paths of evolution have led to a kind of predominance and superiority of reproduction and adaptation to different regions (Pontarotti 2016).

Based on the phenotypic characteristics, the high difference between the maximum and minimum values obtained in the studied populations, as well as the high values of the coefficient of variation, respectively, indicate the high genetic diversity between and within the populations in terms of measured traits. The results of many studies in different plants have shown that the agronomic and morphological traits have been able to identify the diversity in the populations well (Golkar and Mokhtari 2018; Piya et al. 2014; Sayed et al. 2022; Sharma et al. 2022; Talebi et al. 2018) and in this study the traits were also able to show the diversity between the populations. Placing samples collected from different geographical areas in the same clusters can be a sign of physical exchanges between different geographical areas or genetic similarity between samples (Anacker and Strauss 2014; Bhandari et al. 2017).

Due to the fact that the purpose of studying genetic populations is to compare genotypes in terms of the amount of existing differences, in this way, the range of data can be used for the initial comparison between the cultivars under study and to get a general view of the amount of existing differences (Govindaraj et al. 2015). The coefficient of variation parameter is one of the most important and valuable indicators for estimating diversity in populations, and because this criterion is not affected by the trait measurement unit or the range of its values, and in this sense, it is more important than other diversity criteria (Bedeian and Mossholder 2000).

Based on the results of variance component analysis and molecular variance analysis, the genetic diversity within the studied population was much less than the inter-population diversity. The reason can be due to random mutations and their preservation due to their ineffectiveness on the phenotypic traits considered by farmers or their low selection pressure (Fu 2015; Khoury et al. 2022).

Maximum heterosis can be caused by crossing cultivars or genotypes that are genetically distant from each other (Labroo et al. 2021). Therefore, the selection of parents of these crosses can be done by examining the genetic distance between genotypes based on phenotypic traits and selecting parents with a large genetic distance. For this purpose, multivariate analysis methods such as cluster analysis or biplot grouping through principal component analysis can be used (Mohammadi and Prasanna 2003). The genotypes are placed in distant groups as a result of grouping by multivariate analysis based on agricultural, biochemical and physiological traits, can be used as parents in crossbreeding programs in order to achieve more genetic diversity (Sharma et al. 2022).

The results of this study showed that the primers selected to connect to different target regions in the genome of the genotypes successfully amplified the genomic DNA of the sampled populations. The bioclimatic, morphological, and molecular data used in this study all differentiated the studied populations well. Based on bioclimatic traits, morphological traits and SCoT molecular markers, the studied population was grouped into two branches, three branches and two branches, respectively, and the grouping with these methods were highly consistent with each other. The results of many studies in different plants have shown the high capability and efficiency of SCoT markers to evaluate genetic diversity (Karagöz et al. 2022; Shaban et al. 2022; Sun et al. 2022; Yeken et al. 2022). In this study, SCoT fingerprinting showed high genetic diversity at the population level. Genetic diversity studies of plant species, using the combination of morphological evaluations with molecular markers, gives more reliable results of genetic diversity (Hong et al. 2021; Wahyuni et al. 2022). The findings of this study showed that genetic diversity within populations is less than this diversity at the level between populations; despite this, there is still high genetic variation within populations. This situation can be considered due to the different size of populations and the effects of genetic drift caused by extensive human interventions in the selection of genotypes (Teixeira and Huber 2021). The genetic structure of the populations of a species is a reflection of the interactions of its evolutionary background in the long term, mutation, genetic drift, crossing system, gene flow recombination, and artificial selection (Slarkin 1985; Slatkin 1994).

In a breeding program, more transgressive segregation will result in their progeny if the parents are genetically further apart (Sharma et al. 2022). The main and basic purpose of cluster analysis is to determine the degree of affinity or genetic distance of populations from each other, so that the researcher, instead of spending a lot of time and energy on a lot of random hybridizations in order to reach a desired genotype by chance, first classified the studied genotypes based on cluster analysis and then by selecting some samples of the best genotypes in distant clusters based on desirable and favorite traits, limited and better hybridization were performed (Labroo et al. 2021; Mohammadi and Prasanna 2003; Rockwood 2015).

Therefore, by performing crossbreeding between distant genotypes, which are selected from clusters with a large distance, the possibility of achieving desired results increases. In study of Piya et al. (2014) on eight different populations and 12 individuals from each Sickleweed population collected from different regions of America, the results showed that individuals were placed in three different groups based on SSR markers and had good diversity.

Genetic diversity in plant species is generally influenced by geographical distribution, population size and number, and breeding system (Hamrick and Godt 1996; Lambert et al. 2006). The knowledge of the amount of genetic diversity of a plant species is necessary for planning and providing conservation solutions for that species, regardless of its geographical distribution (Lambert et al. 2006). In other words, the wide geographic distribution of a species does not guarantee the preservation of its genetic diversity (Hamrick and Godt 1996).

As mentioned, the samples studied in this research were collected from different regions, and the results prove the existence of significant genetic differences in addition to conventional phenotypic differences. Despite the fact that the markers and primers used separated different populations, but due to the lack of a valid classification reported based on important physiological, morphological or agronomic traits, it was not possible to interpret and investigate the relationship between genetic and phenotypic differences. But on the other hand, the environmental conditions strongly influence the phenotypic traits, so that the change in the geographical location can also affect the fertility, yield and quality of the product. Therefore, it is possible to use this proven genetic diversity in addition to the unique climatic diversity and with regional tests of the phenotypic responses of indigenous populations in different climates, in addition to finding the best environment for each population were also optimistic to find specific traits such as Fertility and seed production.

The present study is the first report on the use of SCoT marker, agricultural traits and bioclimatic data in the study of diversity, distribution and genetic relationships on the existing ecotypes in Iran. Therefore, by extending the study on a larger population, by using more primers with new sequences or consisting of repetitive fragments selected based on the results of this study, or by combining the selected primers, a more saturated marker pattern can be achieved. Finally, we used them to identify repeated masses in the existing collections. Also, considering the importance of Sickleweed in industry, traditional and modern medicine, the importance of identifying and preserving diversity in breeding research and agricultural development, it is necessary to establish a national center and genetic bank in order to collect and preserve Sickleweed populations.

Conclusion

In conclusion, we successfully employed genetic diversity of selected Sickleweed populations using the 10 SCoT markers and we found a high genetic diversity. This high genetic variation is a result of cross-breeding, transfer of seeds through long distance and geographically connected spread. It is shown that the genetic diversity of the wild medicinal plant should be high to be adapted for the future climatic changes. Thus, genetic diversity will create a defence mechanism for the unpredicted future climatic changes. For this reason, a conservation and breeding program for wild Sickleweed should be developed with it which is of high genetic diversity despite the many risks it faces. The results of this study indicate the existence of high diversity in the study populations of Sickleweed. Due to the fact that the populations are from different regions and genetic bases and differ from each other in terms of traits, the existence of genetic diversity confirms that population differences are not only due to environmental effects, but are also controlled by genetic factors. Also, the selection based on molecular markers is a fast method in the breeding program and the genetic information obtained from the molecular markers play an important role in the breeding programs, therefore, in addition to traits, to select the superior genotype and population that have a high value in breeding programs, SCoT markers are better and easier than SSR markers due to their lack of requirement of prior information about the target sequences, and therefore can be effectively used to study the genetic diversity of Sickleweed populations. The grouping of Sickleweed populations with molecular data was largely consistent with the grouping with agricultural traits, as well as the Bayesian method, as well as geographic diversity. The populations of Dilaman-Gilan and Ilan-Qom had the highest values for leaf number traits, and also due to the high number of related traits that play a role in increasing production, they are recommended for cultivation in this region. It is also possible to find the superior genotype by examining these populations in different regions. Although the results of this research can be used for improvement as well as the breeding programs of Sickleweed, but in order for the results to be better and more acceptable, this experiment should be conducted in several years and in different environments so that the results are more stable and practical. Thus, these populations should be conserved insitu to maintain the wild Sickleweed high genetic diversity. In addition to insitu conservation, these populations should also be conserved exsitu to prevent from the risks they will face in the future.

Availability of data and materials

The data used to support the findings of this study are included within the article.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.