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Population genetic structure of the malaria vector Anopheles minimus in Thailand based on mitochondrial DNA markers

Parasites & Vectors volume 14, Article number: 496 (2021) Cite this article

Abstract

Background

The malaria vector Anopheles minimus has been influenced by external stresses affecting the survival rate and vectorial capacity of the population. Since An. minimus habitats have continuously undergone ecological changes, this study aimed to determine the population genetic structure and the potential gene flow among the An. minimus populations in Thailand.

Methods

Anopheles minimus was collected from five malaria transmission areas in Thailand using Centers for Disease Control and Prevention (CDC) light traps. Seventy-nine females from those populations were used as representative samples. The partial mitochondrial cytochrome c oxidase subunit I (COI), cytochrome c oxidase subunit II (COII) and cytochrome b (Cytb) gene sequences were amplified and analyzed to identify species and determine the current population genetic structure. For the past population, we determined the population genetic structure from the 60 deposited COII sequences in GenBank of An. minimus collected from Thailand 20 years ago.

Results

The current populations of An. minimus were genetically divided into two lineages, A and B. Lineage A has high haplotype diversity under gene flow similar to the population in the past. Neutrality tests suggested population expansion of An. minimus, with the detection of abundant rare mutations in all populations, which tend to arise from negative selection.

Conclusions

This study revealed that the population genetic structure of An. minimus lineage A was similar between the past and present populations, indicating high adaptability of the species. There was substantial gene flow between the eastern and western An. minimus populations without detection of significant gene flow barriers.

Graphical abstract

Background

Anopheles minimus is one of the principal malaria vectors in Southeast Asia, including Thailand [1]. The minimus group complex contains three species, An. minimus lineage A, An. harrisoni and An. yaeyamaensis [2]. In Thailand, species complex A is the dominant type, and is distributed throughout the country [2]. This species is the most commonly found in areas of high malaria transmission [1,2,3,4]. Anopheles minimus has an anthropophilic preference and is an important vector of indoor malaria transmission [3, 5]. External environmental stress impacts the vector survival rate, behavior, ecology, vectorial capacity, and host–pathogen interactions. External stress, consisting of climate change, changes in land use, host migration, and insecticide use, plays a vital role in selective pressure on mosquito populations [6]. These environmental factors can induce changes in surface temperature and ecosystem balance, contributing to vector development, evolution, reproductive isolation among populations, and increases in disease transmission [6, 7]. Such climate change affecting the natural environment drives the evolution of both vector and host [8].

The local adaptation of An. minimus in Vietnam appears to have been affected by genetic differentiation between populations, forced by ecotypic selection based on intraspecific behavioral differences and ecology in specific habitats [9]. The partial analysis of the COI gene sequence has been used to assess historical and current gene flow among An. albimanus populations in the Caribbean and the Pacific regions of Colombia, which showed high genetic differentiation influenced by specific ecological conditions, human migration, and activity [10]. Chen et al. investigated gene flow among populations of An. minimus in Southeast Asia using an analysis of partial mitochondrial cytochrome c oxidase subunit II (COII) [11]. Reconstruction of the phylogenetic relationships of An. minimus revealed that two different lineages, A and B, coexist in malaria transmission areas. These two lineages may split and expand to facilitate adaptation under different eco-climatic conditions. A previous study has shown that lineage A spread throughout Thailand, while lineage B was restricted to certain areas [11].

The mitochondrial protein genes are appropriate molecular markers to determine the population genetic structures of species. Mitochondrial DNA (mtDNA) sequence data have been used to investigate the genetic variations and phylogenetic relationships of different species and to accurately assess gene flow and differences among populations [12,13,14,15,16,17]. Several mtDNA sequences are sensitive to genetic drift and are particularly useful for analyzing the genetic diversity and genetic structure of populations. Variations in mtDNA can reflect the demographic history of invading mosquitoes [18]. Further, the mtDNA markers have been widely used in the studies of the population genetic structure of mosquitoes, including An. minimus [11], An. sinensis [19], An. baimaii [20], An. dirus [21,22,23], An. lesteri [24], An. darling [25], An. stephensi [26], Ae. aegypti [27, 28], and Ae. albopictus [29, 30].

Populations experience different selective pressures due to changing environmental factors and human behavior. The mitochondrial protein-coding genes were used to investigate whether there has been any differentiation of the genetic structure of An. minimus in Thailand in the past 20 years. Therefore, the aims of this focused population genetic study were (i) to determine the current population genetic structure and the potential gene flow by investigating genetic differentiation among the populations of An. minimus and (ii) to compare the population genetic structure of between the current and past An. minimus population of 20 years ago. The understanding of the population genetic structure and the potential gene flow among the populations gained from this study can be used as molecular tools for monitoring mosquito populations and developing guidelines for malaria vector control strategies in Thailand. Such information can also be used to measure and monitor gene flow or spread of vector populations after applying a control measure.

Methods

Mosquito collection and morphological identification

A total of 79 adult female An. minimus were collected from five different populations in malaria transmission areas along the western and eastern borders of Thailand, including Tak, Surat Thani, Yala, Ubon Ratchathani, and Chanthaburi-Trat provinces. The collection sites were selected according to the malaria operational plan report FY2018 [31] (Fig. 1), which was based on the identification of malaria transmission areas and malaria vector distribution areas. Adult mosquitoes were collected between 2016 and 2019 using the Centers for Disease Control and Prevention miniature light traps (CDC-LT). Twenty CDC-LTs were randomly placed around each village. The traps were placed about 50 m from each other in 10 selected houses for four consecutive nights. The mosquito specimens were stored individually in 1.5-ml microcentrifuge tubes at −20 °C. They were then morphologically identified based on the taxonomic keys of mosquitoes in Thailand [32]. All specimens identified as An. minimus sensu lato were selected and further used for molecular identification to confirm the species identification.

Fig. 1
figure 1

Map of the mosquito collection sites in the malaria transmission area from six provinces throughout Thailand: Tak, n = 15; Surat Thani, n = 20; Yala, n = 14; Ubon Ratchathani, n = 6; and Chanthaburi-Trat, n = 24. Anopheles minimus lineage A and B distributions overlap in each collection site (lineage A, blue color; lineage B, green color)

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Molecular identification

Genomic DNA of An. minimus was extracted from the abdomens of the mosquitoes using the Genomic Mini Kits (Geneaid Biotech Ltd., Taipei, Taiwan) following the manufacturer's instructions, except for the change of the lysis period to overnight at 60 °C. The partial mitochondrial genes COI, COII, and Cytb were used as genetic markers for molecular identification. The primers were designed based on the An. minimus complete mitochondrial genome (GenBank: KT895423) and gene-specific sequences with the most informative regions of each gene (Additional file 1: Table S1). The primers for polymerase chain reaction (PCR) were analyzed for suitability using OligoCalc, an online oligonucleotide properties calculator [33].

The PCR amplifications were performed in a final volume of 20 µl containing genomic DNA, 1× OnePCR™ PCR reaction mixture (GeneDireX, Inc., Taiwan), and 10 pmol of forward and reverse primers for each genetic marker (Additional file 2: Table S2). PCR consisted of initial denaturation at 95 °C for 3 min, followed by 35 cycles of denaturation at 95 °C for 45 s, annealing at 52 °C (for primers of COII and Cytb) and 54 °C (for primers of COI) for 40 s and extension at 72 °C for 50 s, and a final extension at 72 °C for 8 min. The PCR products were subsequently purified using Gel/PCR DNA fragments extraction kits (Geneaid Biotech Ltd., Taipei, Taiwan). The purified PCR products were sequenced with the same primers as conventional PCR by Sanger sequencing using a 3730xl analyzer (Thermo Scientific, USA), serviced by Bio Basic, Inc., Singapore.

Phylogenetic analyses

A phylogenetic tree for An. minimus from the five different populations was constructed with An. harrisoni (An. minimus complex C) using the maximum likelihood (ML) method implemented in the MEGA 7 program [34]. The ML was performed using a best-fit nucleotide substitution model with 1000 bootstrap replications for tree topology support. The nucleotide sequences of An. harrisoni were the results of previous studies retrieved from the GenBank database with accession numbers HQ877375–HQ877377 (COI), AF421307, AF421309, AY486111, AF417707, KT899887, KF687432 (COII), and EU071695–EU071697 (Cytb) [35,36,37,38,39,40]. Anopheles dirus was used as an outgroup with accession numbers AF417707 (COI), KT899887 (COII), and KF687432 (Cytb) [39,40,41]. The GenBank accession numbers MT651216–MT651294 (COI), MT663561–MT663639 (COII), and MT663640–MT663718 (Cytb) were also included as representative sequences of lineage A and lineage B (Additional file 3: Table S3, Additional file 4: Table S4, Additional file 5: Table S5).

Population genetic structure analyses

To determine the genetic diversity within the An. minimus population, haplotype diversity (Hd) and nucleotide diversity (Ï€) were calculated using the DnaSP program, version 6 [42]. The haplotype relationships were estimated using a median-joining (MJ) network under pairwise nucleotide difference between haplotypes in the PopART 1.7 program [43].

Pairwise F-statistics (FST) were computed based on the variance in allele frequencies to detect genetic differentiation among the populations using the ARLEQUIN 3.5.1.2 program [44]. The level of gene flow among the populations was estimated by measuring the numbers of migrants in a population per generation (Nm) using the FST variances. Analysis of molecular variance (AMOVA) was conducted among the different geographical populations using ARLEQUIN to calculate the proportion of genetic variation within and between populations.

Neutrality and demographic history

The frequency distribution of pairwise nucleotides was different between the observed and the expected distribution (mismatch distribution) under the expansion model of population demography implemented in ARLEQUIN [44] to examine historical demographic expansions. The sum of squared deviation (SSD) between the observed and expected mismatch distribution was performed as a test statistic to reflect a significant SSD (P < 0.05) value of historical demographic population expansion or contraction. Historical demographic expansions were also determined by neutrality tests conducted using two approaches, Tajima’s D [45] and Fu's Fs [46] tests, related to natural selection.

Changes in genetic structure of An. minimus population in Thailand over the past 20 years

To compare the population genetic structure of An. minimus between the present and 20 years ago, 60 sequences of An. minimus populations in Thailand were investigated using population genetic structure analysis, tests of neutrality, and demographic history events using ARLEQUIN, using the COII gene from the GenBank database accession numbers FN433526–FN433595 (Additional file 6: Table S6) [11].

Secondary data, including mean surface temperature data sets from 2000 to 2020 [47] and historical data about land-use changes and the forestry sector in Thailand from 2001 to 2018 [48], were analyzed to determine the fluctuation of environmental factors over the past two decades, which may have influenced changes in population structure.

Results

Mosquito collection and identification

A total of 79 An. minimus s.l. were collected from five populations from Tak (n = 15), Surat Thani (n = 20), Yala (n = 14), Chanthaburi-Trat (n = 24), and Ubon Ratchathani (n = 6) (Fig. 1). All specimens were morphologically identified as An. minimus s.l. before subjecting to molecular identification.

Phylogenetic relationships of An. minimus

We produced an alignment of 1330 bp of concatenated mitochondrial protein-coding sequences from 79 specimens. All mosquitoes were identified as An. minimus complex A, which belongs to the Minimus complex. The ML phylogenetic relationships indicated the presence of two genetically distinct lineages, A and B, in the current An. minimus population. Lineage A was the predominant population, with 58 individuals, while the other 21 individuals were of lineage B (Fig. 2). The ML phylogenetic relationship of each gene is shown in (Additional file 7: Figure S1). The average pairwise sequence divergence between An. minimus lineages A and B revealed 2.4–2.7% difference (Fig. 3). Although the populations of both lineages were distributed in the same area, it was found that the genetic distance within the population of lineage A (0.8%) was higher than that of lineage B (0.3%). An. minimus lineage A was genetically closer to An. harrisoni (1.4–2%) than An. minimus lineage B (Fig. 3).

Fig. 2
figure 2

The maximum likelihood (ML) tree of two different lineages of An. minimus. The An. harrisoni is in the red circle based on analysis of the concatenated sequences of the mtDNA COI, COII, and Cytb genes with An. dirus used as outgroup. The labels in the tree include haplotype codes, and color depicts the different populations

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Fig. 3
figure 3

Median-joining haplotype network generated using PopART 1.7 for the An. minimus population corresponding to their geographical distribution separated into five populations in Thailand. a Haplotype network of the COI gene. b Haplotype network of COII. c Haplotype network of Cytb. Color represents different populations: Tak = yellow; Surat Thani = orange; Yala = blue; Ubon Ratchathani = purple; and Chanthaburi-Trat = green. Each haplotype is represented by a circle in which the circle size is proportional to the haplotype frequency. Mutations between haplotypes are indicated by lines representing mutations from the common haplotype. The red color represents the An. harrisoni population

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Genetic diversity

The sampled population had a high level of genetic diversity, with the numbers of haplotypes within the population ranging from five (Ubon Ratchathani) to 14 (Chanthaburi-Trat) haplotypes of lineage A and four to five haplotypes of lineage B (Additional file 8: Table S7). The overall haplotype diversity (Hd) level of lineages A and B was high, 0.97822 and 0.96190, respectively, with low nucleotide diversity (π) of 0.00639 and at least 0.00295, respectively. The haplotype diversity of each population ranged from Hd = 0.8 to 1 (Additional file 8: Table S7). The nucleotide diversity of lineage B (π = 0.002 to 0.003) was lower than that of lineage A (π = 0.003 to 0.008) (Additional file 8: Table S7).

The relationships among the An. minimus haplotypes were drawn using MJ, visualizing the haplotype frequencies and mutational steps among the haplotypes. Each haplotype is represented by a circle in which the circle size is proportional to the haplotype frequency. Mutations between haplotypes are indicated by lines representing mutations from the common haplotype. The haplotype network structure has a star-like phylogeny, with the most relevant single haplotypes surrounding the common haplotype. Lineage A was found in all locations, whereas lineage B did not occur in Ubon Ratchathani province (Fig. 3).

Population genetic structure

Genetic differentiation among An. minimus populations was estimated using FST pairwise comparison (Additional file 9: Table S8). The Ubon Ratchathani population was the most divergent population of lineage A, with differentiation among the other populations ranging from 0.06657 to 0.58954 as present data on the pairwise FST values (below diagonal). The level of genetic differentiation of the Chanthaburi-Trat population was also divergent from other populations of lineage B (above diagonal) (Additional file 9: Table S8). The overall genetic differentiation based on the FST value was significantly low among the populations, ranging from 0.09–0.18 (P < 0.05). The high level of migration (Nm) in the range of 1 to 2.5 was estimated from FST variances, indicating considerable gene flow among the populations (Table 1).

Table 1 Neutrality test, sum of squared deviation (SSD), and analysis of molecular variance (AMOVA) of the concatenated mitochondrial genetic markers in the An. minimus populations
Full size table

Based on the geographical distribution of the An. minimus populations, AMOVA was conducted on different groupings (Table 1). The genetic differentiation indices (FST) of both lineages A and B were determined for the populations in different sides of the country (the western and eastern parts) and by geographic regions (north, south, and east). These populations were significantly different, with a low value of FST. A high level of migration (Nm > 1) of An. minimus between populations was also measured by the FST indices (Table 1). A high level of genetic variation of more than 81% was detected within the population, whereas a low level of genetic variation (12–16%) was observed between the populations (Table 1).

We hypothesized that the populations observed in this study had a genetic structure that arose by gene flow in the main population of An. minimus lineage A. Nonsignificant genetic variation within and among the populations was detected in lineage B. This evidence indicated that these populations of An. minimus lineage B had no differentiation between individuals collected from geographical isolation.

Selection and demographic history

As depicted in Fig. 4, historical demographic expansions of populations produce a characteristic smooth unimodal or bell-shaped pattern of population expansion. The hypothesis of sudden expansion indicated by mismatch distribution analysis and the SSD was not significant for all populations (Table 2). A nonsignificant SSD P-value (P < 0.05) indicated that the goodness of fit of the observed and expected mismatch distribution models is likely to be the same, leading to the acceptance of the population expansion model. Tajima’s D tests were not statistically significant for negative D values (Table 2), whereas Fu’s Fs tests were significantly negative for FS values (P < 0.05).

Fig. 4
figure 4

Mismatch distribution of the An. minimus population according to two distinct lineages. a Mismatch distribution of lineage A. b Mismatch distribution of lineage B. Bar represents the observed distribution of pairwise differences, whereas the line shows the expected distribution under the sudden expansion model

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Table 2 Analysis of molecular variance (AMOVA), fixation indices (FST), and neutrality test comparison between the historical and recent populations of An. minimus in Thailand
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Comparison of genetic structure of An. minimus populations in Thailand over the past 20 years

According to the analysis of the COII gene, the An. minimus population genetically separated into lineages A and B in the course of the past 20 years. The comparison of the genetic distances between the populations of lineages A and B had similar patterns; the distance was 2.2% in the past population and 2.4% in the current population. Analysis of the genetic diversity of the entire population in previous studies compared to the current population indicated that the level of haplotype diversity remained the same, with high haplotype diversity in the range Hd = 0.6–1 in every population. However, the nucleotide diversity was low (π = 0 to 0.01).

The comparison of population genetic structure between the present and 20 years ago showed that the number of migrations, representing gene flow among the population, has increased over the past 20 years. Population expansion under negative selection was detected by the strong negative D and Fs neutrality test values, and the lack of significance between the observed and expected distribution of mismatch analysis (Table 2).

Discussion

In this study, we describe the genetic structure of An. minimus populations in Thailand, which is essential for future management of malaria vectors [18, 49]. The population genetic structure was analyzed based on mtDNA gene sequences, which provided information about the genetic diversity, genetic differentiation, gene flow, and selection within and between populations. This study identified a large genetic distance indicating significant divergence between the two lineages of An. minimus.

The An. minimus population has been undergoing high recombination across its geographic distribution, reducing genetic variation and increasing the number of closely related haplotypes. Haplotype sharing existed between the populations, even those that are separated by a large geographic distance. Moderate FST values were obtained from pairwise comparisons among the populations within the different geographical distribution zones, with much more genetic variation within the population than between the populations. This observation suggested that gene flow occurred among recent An. minimus populations without geographic barriers throughout Thailand. Hence, demographic population expansion under negative selection was observed. There was an excess of rare mutations in all populations, reducing genetic variation within populations due to gene flow.

However, the comparison of population genetic structures between the past and the current populations of An. minimus in Thailand revealed a persistent genetic structure with a similar pattern of moderate genetic differentiation, population expansion, and negative selection. It may indicate that the An. minimus populations had free genetic exchange among the populations, as evaluated from the reduced genetic variance and genetic differentiation among populations.

The mtDNA is a suitable genetic marker and has been extensively used in evolutionary studies. The Anopheles mosquito mtDNA genes have been used to evaluate the genetic structure of mosquito populations [18,19,20, 40, 50]. MtDNA offers many advantages. First, the uniparental mode of maternal inheritance is specific in sexually isolated demes or lineages. Second, the MtDNA has a 5–10 times higher evolutionary rate than nuclear DNA, and is therefore widely used to determine gene frequencies and the effects of natural selection on intraspecific genetic variation. Finally, it lacks normal recombination, therefore reflecting only a single genealogical history of each genome [51, 52]. Using the mtDNA sequence data as a genetic marker allowed the measurement of genetic differentiation within and between the populations, producing evidence of gene flow and population expansion without limitation by geographic distance.

To reduce bias in marker selection, a combination of several mtDNA gene sequences was used to describe the overall population structure. Assessment of the population genetic structure using single-gene and multi-gene concatenated markers found that these populations present the same population structure, as follows. (1) The recent An. minimus population has high gene flow between the populations. (2) The population tends to undergo demographic expansion with no constraints from a geographic distance or geographical barriers. (3) These populations experienced exposure to negative selective pressure. The negative selection maintained their genetic structure by removing deleterious mutations [53]. Hence, the multi-gene concatenated markers were also used to increase the assessment accuracy of genetic relationships and population structure [54, 55].

Mitochondrial protein-coding genes have been used to evaluate the selective pressure acting on mosquitoes’ mitogenomes [40]. Several studies on malaria vector mosquitoes, including An. subpictus, An. peditaneatus, and An. vagus from five different localities of Sri Lanka [18], An. sinensis in China [19], An. baimaii in northeast India [20], An. dirus in Southeast Asia and China [21,22,23], and An. minimus across China, Thailand, and Vietnam [11], have been conducted to analyze the genetic diversity and population genetic structure, using the mtDNA genes as the genetic marker.

The coexistence of An. minimus lineages A and B in the current population was observed in the same habitat of the active transmission area. However, it is not clear whether they differ in vectorial capacity for malaria transmission. The dominant lineage in the An. minimus populations was lineage A. Therefore, the abilities for genetic exchange between An. minimus lineages A and B were considered, with the noticeable genetic differences between the two lineages and the potential for gene flow among the populations evaluated separately. This study identified the distribution of both lineages in western and eastern Thailand. Earlier studies found lineage B only in western Thailand and suggested expansion through India [11, 56].

A similar, low to moderate genetic differentiation level was observed in both lineages A and B, indicating possible gene flow between the populations in both lineages. No differences were observed in the pairwise comparison of the separated groups in the western and eastern regions, despite separation by land-use and urbanization changes. The low levels of genetic differentiation between the western and eastern populations suggest the lack of significant geographical barriers limiting gene flow. Alternatively, these populations might have exchanged genetic materials before being separated by urban areas as barriers, while gene flow between these populations could have been assisted by human activities. In previous studies, gene flow among malaria vectors was observed due to the absence of geographic distance and geographical barriers [18, 57,58,59,60]. It has been suggested that geographical barriers are the main mechanism of maintenance of gene flow between the populations [18].

Demographic inference tests (Tajima’s D and Fu’s Fs) [61] revealed that the most recent population of An. minimus experienced a population expansion. Population expansion was also suggested by the relatively high haplotype diversity and low nucleotide diversity observed, which indicated that the recent populations diverged from each other by rapid demographic expansion [62,63,64]. All statistical tests of neutrality reported negative values, indicating an excess of low-frequency mutations due to the evolutionary forces operating on the populations. The demographic history of malaria vector populations in Southeast Asia also indicated that An. dirus and An. aconitus populations in Southeast Asia experienced population expansion under significant negative selection [22, 65].

The An. minimus populations of 20 years ago had high genetic diversity, and we found evidence of population expansion. The mosquito populations in Southeast Asia are also affected by human activities such as deforestation and vector control, shaping the distribution and genetic variation within species [11]. Hence, this comparative study of population genetic structure between the present and 20 years previous populations found results inconsistent with those of the previous report [11]. External selective pressure appears to have impacted the population genetic structure. We found that the population genetic structure of An. minimus in Thailand was similar to the structure detected 20 years ago, despite the changes in environmental factors over time [47, 48]. For this reason, evidence of population expansion and gene flow among the populations might be necessary to explain the adaptative behavior for sustaining the vector capacity and transmitting malaria. Some of the mutations associated with the vector abilities, transmission, and insecticide resistance could be transferred to other populations, affecting the distribution of malaria [66].

Mosquito populations can rapidly adapt in response to changes in environmental conditions, such as climate change and human activities, which might influence the mosquito's survival rate, population density, and ecological distribution [67]. Therefore, environmental factors play an essential role in the evolutionary process, resulting in changes in factors such as the mosquito's interactions with the environment, genetic diversity within species at the population level, and gene flow (Additional file 10: Figure S2) [68,69,70]. Our results indicate that the presence of gene flow between An. minimus populations in Thailand might be impacted by environmental factors, enhancing gene flow among the populations studied, consistent with previous studies [68,69,70,71,72]. The average annual surface temperature in Thailand has increased by approximately 1 °C over the past 20 years (2000 to 2019) (Additional file 11: Figure S3) [47, 73]. The temperature change has also caused a decrease in the frequency of tropical cyclones entering Thailand, resulting in significant changes in rainfall patterns [74]. The increasing surface temperature could have enhanced the reproductive rates of mosquitos [75,76,77]. These factors also restricted the species or occurrence of sympatric populations, forcing the vector to migrate to better habitats.

Human activities and land-use change could have driven intraspecific divergence [78]. According to the land-use change data based on tree cover loss in Thailand from 2001 to 2018, natural forest areas were replaced by plantation or commercial agricultural areas (Additional file 11: Figure S3) [48]. These land-use changes, such as new plantation areas or rubber plantations, provide an ideal habitat for Anopheles spp., potentially leading to increases in the vector density and the re-emergence of An. minimus [79]. Similarly, the genetic differentiation and gene flow between An. funestus populations have been shaped by various factors, not only geographical distance, but also the consequence of different breeding sites, mosquito migration, environmental changes, and human activities [80]. Finally, insecticide use can impact genetic diversity due to population migration, leading to genetic exchange between populations [81,82,83].

The analysis of genetic differentiation between the populations in lineage B is still incomplete. There is little information on vector biology between the different genetic lineages of An. minimus. This study showed that lineage B had a population structure similar to lineage A. This study has limitations in collecting representative populations in eastern Thailand. Further studies are required to increase the sample sizes, and seasonal sampling design by generations may provide accurate results about the specific rate of population growth and dynamics. This study investigated the demographic history of population expansion, but cannot specify the direction and rate of population growth, a topic worthy of attention in future studies.

Conclusions

This study identified the coexistence of two lineages of An. minimus in both eastern and western Thailand. Gene flow was apparent among the geographically distant An. minimus populations, with no evidence of impact by external selective pressure, environmental changes, and geographical barriers. In addition, the population genetic structure of An. minimus populations was persistent in the past 20 years. Such information may be useful for developing and implementing local malaria vector control strategies and monitoring population spread in the face of control interventions.

Availability of data and materials

The data sets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Representative sequences were submitted to the GenBank database with accession numbers MT651216–MT651294 (COI), MT663561–MT663639 (COII), and MT663640–MT663718 (Cytb).

Abbreviations

COI :

Cytochrome c oxidase subunit I

COII :

Cytochrome c oxidase subunit II

Cytb :

Cytochrome b

NJ:

Neighbor-joining

F ST :

Genetic differentiation

Nm:

Number of migrations

CDC-LT:

Centers for Disease Control and Prevention miniature light traps

PCR:

Polymerase chain reaction

ML:

Maximum likelihood

Hd:

Haplotype diversity

Ï€ :

Nucleotide diversity

MJ:

Median-joining

AMOVA:

Analysis of molecular variance

SSD :

Sum square deviation

D :

Tajima’s D test

Fs:

Fu’s Fs test

s:

Second

min:

Minute

References

  1. Tainchum K, Kongmee M, Manguin S, Bangs MJ, Chareonviriyaphap T. Anopheles species diversity and distribution of the malaria vectors of Thailand. Trends Parasitol. 2015;31(3):109–19.

    Article  PubMed  Google Scholar 

  2. Taai K, Harbach RE, Aupalee K, Srisuka W, Yasanga T, Otsuka Y, et al. An effective method for the identification and separation of Anopheles minimus, the primary malaria vector in Thailand, and its sister species Anopheles harrisoni, with a comparison of their mating behaviors. Parasit Vectors. 2017;10(1):97.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Tainchum K, Ritthison W, Chuaycharoensuk T, Bangs MJ, Manguin S, Chareonviriyaphap T. Diversity of Anopheles species and trophic behavior of putative malaria vectors in two malaria endemic areas of northwestern Thailand. J Vector Ecol. 2014;39(2):424–36.

    Article  PubMed  Google Scholar 

  4. Tisgratog R, Tananchai C, Juntarajumnong W, Tuntakom S, Bangs MJ, Corbel V, et al. Host feeding patterns and preference of Anopheles minimus (Diptera: Culicidae) in a malaria endemic area of western Thailand: baseline site description. Parasit Vectors. 2012;5(1):114.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Sriwichai P, Samung Y, Sumruayphol S, Kiattibutr K, Kumpitak C, Payakkapol A, et al. Natural human Plasmodium infections in major Anopheles mosquitoes in western Thailand. Parasit Vectors. 2016;9(1):17.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Gubler DJ, Reiter P, Ebi KL, Yap W, Nasci R, Patz JA. Climate variability and change in the United States: potential impacts on vector-and rodent-borne diseases. Environ Health Perspect. 2001;109(suppl 2):223–33.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Carneiro MA, Alves BD, Gehrke FD, Domingues JN, et al. Environmental factors can influence dengue reported cases. Rev Assoc Med Bras. 2017;63(11):957–61.

    Article  PubMed  Google Scholar 

  8. Srivastava H, Huong NT, Arunyawat U, Das A. Molecular population genetics of the NADPH cytochrome P450 reductase (CPR) gene in Anopheles minimus. Genetica. 2014;142(4):295–315.

    Article  PubMed  Google Scholar 

  9. Van Bortel W, Trung HD, Roelants P, Backeljau T, Coosemans M. Population genetic structure of the malaria vector Anopheles minimus A in Vietnam. J Hered. 2003;91(5):487.

    Article  Google Scholar 

  10. Gutiérrez LA, Naranjo NJ, Cienfuegos AV, Muskus CE, Luckhart S, Conn JE, et al. Population structure analyses and demographic history of the malaria vector Anopheles albimanus from the Caribbean and the Pacific regions of Colombia. Malar J. 2009;8(1):259.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Chen B, Pedro P, Harbach R, Somboon P, Walton C, Butlin R. Mitochondrial DNA variation in the malaria vector Anopheles minimus across China, Thailand and Vietnam: evolutionary hypothesis, population structure and population history. J Hered. 2011;106(2):241.

    Article  CAS  Google Scholar 

  12. Hammer MP, Adams M, Unmack PJ, Walker KF. A rethink on Retropinna: conservation implications of new taxa and significant genetic sub-structure in Australian smelts (Pisces: Retropinnidae). Mar Freshw Res. 2007;58(4):327–41.

    Article  CAS  Google Scholar 

  13. Taylor A, Sherwin W, Wayne R. Genetic variation of microsatellite loci in a bottlenecked species: the northern hairy-nosed wombat Lasiorhinus krefftii. Mol Ecol. 1994;3(4):277–90.

    Article  CAS  PubMed  Google Scholar 

  14. Thévenon S, Thuy L, Ly L, Maudet F, Bonnet A, Jarne P, et al. Microsatellite analysis of genetic diversity of the Vietnamese sika deer (Cervus nippon pseudaxis). J Hered. 2004;95(1):11–8.

    Article  PubMed  CAS  Google Scholar 

  15. Banks SC, Lindenmayer DB, Ward S, Taylor AC. The effects of habitat fragmentation via forestry plantation establishment on spatial genotypic structure in the small marsupial carnivore Antechinus agilis. Mol Ecol. 2005;14(6):1667–80.

    Article  CAS  PubMed  Google Scholar 

  16. Moritz C, Dowling T, Brown W. Evolution of animal mitochondrial DNA: relevance for population biology and systematics. Annu Rev Ecol Evol Syst. 1987;18(1):269–92.

    Article  Google Scholar 

  17. Yamauchi M, Miya M, Nishida M. Use of a PCR-based approach for sequencing whole mitochondrial genomes of insects: two examples (cockroach and dragonfly) based on the method developed for decapod crustaceans. Insect Mol Biol. 2004;13(4):435–42.

    Article  CAS  PubMed  Google Scholar 

  18. Weeraratne TC, Surendran SN, Walton C, Karunaratne SP. Genetic diversity and population structure of malaria vector mosquitoes Anopheles subpictus, Anopheles peditaeniatus, and Anopheles vagus in five districts of Sri Lanka. Malar J. 2018;17(1):271.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Feng X, Huang L, Lin L, Yang M, Ma Y. Genetic diversity and population structure of the primary malaria vector Anopheles sinensis (Diptera: Culicidae) in China inferred by cox1 gene. Parasit Vectors. 2017;10(1):75.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sarma DK, Prakash A, O’Loughlin SM, Bhattacharyya DR, Mohapatra PK, Bhattacharjee K, et al. Genetic population structure of the malaria vector Anopheles baimaii in north-east India using mitochondrial DNA. Malar J. 2012;11(1):76.

    Article  PubMed  PubMed Central  Google Scholar 

  21. O’loughlin S, Okabayashi T, Honda M, Kitazoe Y, Kishino H, Somboon P, et al. Complex population history of two Anopheles dirus mosquito species in Southeast Asia suggests the influence of Pleistocene climate change rather than human-mediated effects. J Evol Biol. 2008;21(6):1555–69.

    Article  PubMed  Google Scholar 

  22. Walton C, Handley JM, Tun-Lin W, Collins FH, Harbach RE, Baimai V, et al. Population structure and population history of Anopheles dirus mosquitoes in Southeast Asia. Mol Biol Evol. 2000;17(6):962–74.

    Article  CAS  PubMed  Google Scholar 

  23. Wang D, Ma Y, Zhou H. Genetic variation of Anopheles dirus A and D (Diptera: Culicidae) in China: inferred by mtDNA-CO I gene sequences. Zhongguo Ji Sheng Chong Xue Yu Ji Sheng Chong Bing Za Zhi. 2007;25(5):368–71, 75.

  24. Yang M, Ma Y. Molecular population genetic structure of Anopheles lesteri (Diptera: Culicidae) based on mtDNA-COI gene sequences. Acta Entomol Sin. 2009;52(9):1000–7.

    CAS  Google Scholar 

  25. Gutiérrez LA, Gómez GF, González JJ, Castro MI, Luckhart S, Conn JE, et al. Microgeographic genetic variation of the malaria vector Anopheles darlingi root (Diptera: Culicidae) from Cordoba and Antioquia. Colombia Am J Trop Med Hyg. 2010;83(1):38–47.

    Article  PubMed  CAS  Google Scholar 

  26. Ali N, Hume JC, Dadzie S, Donnelly M. Molecular genetic studies of Anopheles stephensi in Pakistan. Med Vet Entomol. 2007;21(3):265–9.

    Article  CAS  PubMed  Google Scholar 

  27. Lv R-C, Zhu C-Q, Wang C-H, Lv H, Zhang B, Li C-M, et al. Genetic diversity and population structure of Aedes aegypti after massive vector control for dengue fever prevention in Yunnan border areas. Sci Rep. 2020;10(1):1–13.

    Article  CAS  Google Scholar 

  28. Liu P, Lu L, Jiang J, Guo Y, Yang M, Liu Q. The expanding pattern of Aedes aegypti in southern Yunnan, China: insights from microsatellite and mitochondrial DNA markers. Parasit Vectors. 2019;12(1):1–13.

    Article  CAS  Google Scholar 

  29. Zawani M, Abu H, Sazaly A, Zary S, Darlina M. Population genetic structure of Aedes albopictus in Penang, Malaysia. Genet Mol Res. 2014;13(4):8184–96.

    Article  CAS  PubMed  Google Scholar 

  30. Goubert C, Minard G, Vieira C, Boulesteix M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity (Edinb). 2016;117(3):125–34.

    Article  CAS  Google Scholar 

  31. CDC, US DOS, USAID; 2018. https://reliefweb.int/sites/reliefweb.int/files/resources/fy-2018-thailand-regional-malaria-operational-plan.pdf. Accessed 23 Oct 2020.

  32. Panthusiri P. Illustrated keys to the mosquitoes of Thailand IV Anopheles. Southeast Asian J Trop Med Public Health. 2006;37:2.

    Google Scholar 

  33. Kibbe WA. OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res. 2007;35(suppl_2):W43–6.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33(7):1870–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Sharpe RG, Harbach RE, Butlin RK. Molecular variation and phylogeny of members of the Minimus group of Anopheles subgenus Cellia (Diptera: Culicidae). Syst Entomol. 2000;25(2):263–72.

    Article  Google Scholar 

  36. Chen B, Butlin RK, Harbach RE. Molecular phylogenetics of the Oriental members of the Myzomyia Series of Anopheles subgenus Cellia (Diptera: Culicidae) inferred from nuclear and mitochondrial DNA sequences. Syst Entomol. 2003;28(1):57–69.

    Article  Google Scholar 

  37. Garros C, Harbach RE, Manguin S. Morphological assessment and molecular phylogenetics of the Funestus and Minimus groups of Anopheles (Cellia). J Med Entomol. 2005;42(4):522–36. https://doi.org/10.1093/jmedent/42.4.522.

    Article  CAS  PubMed  Google Scholar 

  38. Harbach RE, Garros C, Manh ND, Manguin S. Formal taxonomy of species C of the Anopheles minimus sibling species complex (Diptera: Culicidae). Zootaxa. 2007;1654(1):41–54.

    Article  Google Scholar 

  39. Sallum M, Schultz T, Foster P, Aronstein K, Wirtz R, Wilkerson R. Phylogeny of Anophelinae (Diptera: Culicidae) based on nuclear ribosomal and mitochondrial DNA sequences. Syst Entomol. 2002;27(3):361–82.

    Article  Google Scholar 

  40. Hao YJ, Zou YL, Ding YR, Xu WY, Yan ZT, Li XD, et al. Complete mitochondrial genomes of Anopheles stephensi and An. dirus and comparative evolutionary mitochondriomics of 50 mosquitoes. Sci Rep. 2017;7(1):1–13.

    Article  CAS  Google Scholar 

  41. Wang G, Li C, Guo X, Xing D, Dong Y, Zhao T. Molecular phylogenetic analysis of the subgenera Anopheles and Cellia (Diptera: Culicidae) based on nuclear ribosomal sequences. Afr Entomol. 2014;22(3):660–9.

    Article  Google Scholar 

  42. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol Biol Evol. 2017;34(12):3299–302.

    Article  CAS  PubMed  Google Scholar 

  43. Leigh JW, Bryant D. popart: full-feature software for haplotype network construction. Methods Ecol Evol. 2015;6(9):1110–6.

    Article  Google Scholar 

  44. Excoffier L, Laval G, Schneider S. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform. 2005;1:47–50.

    Article  CAS  Google Scholar 

  45. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123(3):585–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Fu YX. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics. 1997;147(2):915–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wan Z, Hook S, Hulley G; 2015. https://doi.org/10.5067/MODIS/MOD11C3.006. Accessed 13 Aug 2020.

  48. The Sustainability Consortium WRI, and University of Maryland; 2019. www.globalforestwatch.org. Accessed 13 Aug 2020.

  49. Kaddumukasa MA, Wright J, Muleba M, Stevenson JC, Norris DE, Coetzee M. Genetic differentiation and population structure of Anopheles funestus from Uganda and the southern African countries of Malawi, Mozambique, Zambia and Zimbabwe. Parasit Vectors. 2020;13(1):1–13.

    Article  CAS  Google Scholar 

  50. Makhawi AM, Liu X-B, Yang S-R, Liu Q-Y. Genetic variations of ND5 gene of mtDNA in populations of Anopheles sinensis (Diptera: Culicidae) malaria vector in China. Parasit Vectors. 2013;6(1):290.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ballard JWO, Kreitman M. Is mitochondrial DNA a strictly neutral marker? Trends Ecol Evol. 1995;10(12):485–8.

    Article  CAS  PubMed  Google Scholar 

  52. Castro JA, Picornell A, Ramon M. Mitochondrial DNA: a tool for populational genetics studies. Int J Microbiol. 1998;1(4):327–32.

    CAS  Google Scholar 

  53. Loewe L. Negative selection. Nat Sci Educ. 2008;1(1):59.

    Google Scholar 

  54. Men Q, Xue G, Mu D, Hu Q, Huang M. Mitochondrial DNA markers reveal high genetic diversity and strong genetic differentiation in populations of Dendrolimus kikuchii Matsumura (Lepidoptera: Lasiocampidae). PLoS ONE. 2017;12(6):e0179706.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Gadagkar SR, Rosenberg MS, Kumar S. Inferring species phylogenies from multiple genes: concatenated sequence tree versus consensus gene tree. J Exp Zool B Mol Dev Evol. 2005;304(1):64–74.

    Article  PubMed  CAS  Google Scholar 

  56. Dixit J, Arunyawat U, Huong NT, Das A. Multilocus nuclear DNA markers reveal population structure and demography of Anopheles minimus. Mol Ecol. 2014;23(22):5599–618.

    Article  CAS  PubMed  Google Scholar 

  57. Rongnoparut P, Rodpradit P, Kongsawadworakul P, Sithiprasasna R, Linthicum KJ. Population genetic structure of Anopheles maculatus in Thailand. J Am Mosq Control Assoc. 2006;22(2):192–7.

    Article  Google Scholar 

  58. Nyanjom S, Chen H, Gebre-Michael T, Bekele E, Shililu J, Githure A, et al. Population genetic structure of Anopheles arabiensis mosquitoes in Ethiopia and Eritrea. J Hered. 2003;94(6):457–63.

    Article  CAS  PubMed  Google Scholar 

  59. Ma Y, Yang M, Fan Y, Wu J, Ma Y, Xu J. Population structure of the malaria vector Anopheles sinensis (Diptera: Culicidae) in China: two gene pools inferred by microsatellites. PLoS ONE. 2011;6(7):e22219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Ma Y, Qu F, Xu J, Zheng Z. Study on molecular genetic polymorphism of Anopheles sinensis populations in China. Kun chong xue bao Acta Entomol Sin. 2001;44(1):33–9.

    CAS  Google Scholar 

  61. Dadi H, Lee SH, Jung KS, Choi JW, Ko MS, Han YJ, et al. Effect of population reduction on mtDNA diversity and demographic history of Korean cattle populations. Asian-australas. J Anim Sci. 2012;25(9):1223.

    CAS  Google Scholar 

  62. Grant W, Bowen BW. Shallow population histories in deep evolutionary lineages of marine fishes: insights from sardines and anchovies and lessons for conservation. J Hered. 1998;89(5):415–26.

    Article  Google Scholar 

  63. Avise JC. Phylogeography: the history and formation of species. Harvard: Harvard University Press; 2000.

    Book  Google Scholar 

  64. Song J, Hou F, Zhang X, Yue B, Song Z. Mitochondrial genetic diversity and population structure of a vulnerable freshwater fish, rock carp (Procypris rabaudi) in upper Yangtze River drainage. Biochem Syst Ecol. 2014;55:1–9.

    Article  CAS  Google Scholar 

  65. Chen B, Harbach RE, Walton C, He Z, Zhong D, Yan G, et al. Population genetics of the malaria vector Anopheles aconitus in China and Southeast Asia. Infect Genet Evol. 2012;12(8):1958–67.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Research UCfA: Climate Change and Vector-Borne Disease; 2011. https://scied.ucar.edu/longcontent/climate-change-and-vector-borne-disease. Accessed 22 May 2020.

  67. Suwonkerd W, Ritthison W, Ngo CT, Tainchum K, Bangs MJ, Chareonviriyaphap T. Vector biology and malaria transmission in Southeast Asia. Anopheles mosquitoes-new insights into malaria vectors. Rijeka: Intech; 2013.

    Google Scholar 

  68. Huang W, Zhao X, Zhao X, Li Y, Lian J. Effects of environmental factors on genetic diversity of Caragana microphylla in Horqin Sandy Land, northeast China. Ecol Evol. 2016;6(22):8256–66.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Sork VL, Davis FW, Westfall R, Flint A, Ikegami M, Wang H, et al. Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change. Mol Ecol. 2010;19(17):3806–23.

    Article  PubMed  Google Scholar 

  70. Freeland J, Biss P, Conrad K, Silvertown J. Selection pressures have caused genome-wide population differentiation of Anthoxanthum odoratum despite the potential for high gene flow. J Evol Biol. 2010;23(4):776–82.

    Article  CAS  PubMed  Google Scholar 

  71. Liu Y, Dietrich CH, Wei C. Genetic divergence, population differentiation and phylogeography of the cicada Subpsaltria yangi based on molecular and acoustic data: an example of the early stage of speciation? BMC Evol Biol. 2019;19(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Yawson AE, Weetman D, Wilson MD, Donnelly MJ. Ecological zones rather than molecular forms predict genetic differentiation in the malaria vector Anopheles gambiae ss in Ghana. Genetics. 2007;175(2):751–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Thai Meteorological Department: Thailand annual weather summary 2019; 2020. https://www.tmd.go.th/climate/climate.php?FileID=5. Accessed 7 Jul 2020.

  74. Limsakul A, Kachenchart B, Singhruck P, Saramul S, Santisirisomboon J, Apipattanavis S. Updated basis knowledge of climate change summarized from the first part of Thailand’s second assessment report on climate change. Appl Environ Res. 2019;41(2):1–12.

    Article  Google Scholar 

  75. De Souza D, Kelly-Hope L, Lawson B, Wilson M, Boakye D. Environmental factors associated with the distribution of Anopheles gambiae ss in Ghana; an important vector of lymphatic filariasis and malaria. PLoS ONE. 2010;5(3):e9927.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Ng’Habi KR, Knols BG, Lee Y, Ferguson HM, Lanzaro GC. Population genetic structure of Anopheles arabiensis and Anopheles gambiae in a malaria endemic region of southern Tanzania. Malar J. 2011;10(1):1–10.

    Article  Google Scholar 

  77. Debboun M, Nava MR, Rueda L. Mosquitoes, Communities, and public health in Texas. Cambridge: Academic Press; 2019.

    Google Scholar 

  78. Manguin S. Anopheles mosquitoes: new insights into malaria vectors. BoD–Books on Demand; 2013.

  79. Petney T, Sithithaworn P, Satrawaha R, Grundy-Warr C, Andrews R, Wang Y-C, et al. Potential malaria reemergence, northeastern Thailand. Emerg Infect Dis. 2009;15(8):1330.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Samb B, Dia I, Konate L, Ayala D, Fontenille D, Cohuet A. Population genetic structure of the malaria vector Anopheles funestus, in a recently re-colonized area of the Senegal River basin and human-induced environmental changes. Parasit Vectors. 2012;5(1):1–9.

    Article  Google Scholar 

  81. Aguirre-Obando OA, Bona ACD, Duque LJE, Navarro-Silva MA. Insecticide resistance and genetic variability in natural populations of Aedes (Stegomyia) aegypti (Diptera: Culicidae) from Colombia. Zoologia (Curitiba). 2015;32(1):14–22.

    Article  Google Scholar 

  82. Herrera F, Urdaneta L, Rivero J, Zoghbi N, Ruiz J, Carrasquel G, et al. Population genetic structure of the dengue mosquito Aedes aegypti in Venezuela. Mem Inst Oswaldo Cruz. 2006;101(6):625–33.

    Article  CAS  PubMed  Google Scholar 

  83. Pasteur N, Raymond M. Insecticide resistance genes in mosquitoes: their mutations, migration, and selection in field populations. J Hered. 1996;87(6):444–9.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We are thankful to the Department of Medical Entomology and the Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Thailand, for laboratory facilitation and technical support.

Funding

This study was supported by grants U19AI089672 and D43TW006571 from the National Institutes of Health, USA, and Development and Promotion of Science and Technology Talents Project (Royal Government of Thailand scholarship), Thailand. The partial financial assistance provided by Specific League Funds 2021 from Faculty of Tropical Medicine, Mahidol University, Thailand. The Anopheles mosquito collection was financially supported by the Armed Forces Health Surveillance Branch, Global Emerging Infections Surveillance and Response System (AFHSB-GEIS), Silver Spring, Maryland, USA (ProMIS ID: P0108_19_AF_06.01, Funding Year: 2019).

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Authors and Affiliations

  1. Department of Medical Entomology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

    Kamonchanok Bunmee & Patchara Sriwichai

  2. Department of Helminthology, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

    Urusa Thaenkham

  3. Department of Molecular Tropical Medicine and Genetics, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

    Naowarat Saralamba

  4. Department of Entomology, Armed Forces Research Institute of Medical Sciences (AFRIMS), Bangkok, Thailand

    Alongkot Ponlawat

  5. Program in Public Health, University of California at Irvine, Irvine, CA, 92697, USA

    Daibin Zhong

  6. Division of Infectious Diseases, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA

    Liwang Cui

  7. Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

    Jetsumon Sattabongkot

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  1. Kamonchanok Bunmee
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Contributions

KB, UT, NS, AP, and PS contributed to overall study design. KB, AP, and PS identified study location, mosquito collection, confirmation of vector species, and examination for malaria parasite infection rates. KB, PS, and UT analyzed the data. KB and UT drafted the manuscript, and DZ, LC, JS, AP, NS, and PS revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Patchara Sriwichai.

Ethics declarations

Ethics approval and consent to participate

The ethical protocol was approved by the Faculty of Tropical Medicine-Animal Care and Use Committee (FTM-ACUC 007/2020 E), Faculty of Tropical Medicine, Mahidol University, Thailand.

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Not applicable.

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The authors declare that they have no conflict of interest.

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Supplementary Information

Additional file 1: Table S1.

GenBank accession numbers as used for primer design.

Additional file 2: Table S2.

Primer list of mitochondrial genetic markers used in this study.

Additional file 3: Table S3.

The GenBank database accession numbers of cytochrome c oxidase subunit I (COI) gene.

Additional file 4: Table S4.

The GenBank database accession numbers of cytochrome c oxidase subunit II (COII) gene.

Additional file 5: Table S5.

The GenBank database accession numbers of cytochrome b (Cytb) gene.

Additional file 6: Table S6.

The GenBank database accession numbers of cytochrome c oxidase subunit II (COII) gene as the 20 years An. minimus population sequences.

Additional file 7: Figure S1.

The maximum likelihood (ML) tree of two different genetical lineages of Anopheles minimus, the An. harrisoni as in the red circle and the An. dirus used as outgroup. Bootstrap values less than 50% were excluded in the phylogenetic tree. a The ML tree of the COI gene. b The ML tree of the COII gene. c The ML tree of the Cytb gene. The labels in the tree include haplotype codes, and color indicates the different populations.

Additional file 8: Table S7.

Number of individuals, number of haplotypes, haplotype diversity (Hd), nucleotide diversity (Ï€).

Additional file 9: Table S8.

FST comparisons for all the five populations.

Additional file 10: Figure S2.

Fluctuations in environmental factors enhance gene flow, including temperature change, land-use change, urbanization, deforestation, plantation, and insecticide use.

Additional file 11: Figure S3.

Fluctuation in environmental factors from the past to the present. a Fluctuation of environmental factors (land-use change, mean surface temperature by day and night). b The average of mean daytime temperature from 2000 to 2019. c Average mean nighttime temperature from 2000 to 2019.

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Bunmee, K., Thaenkham, U., Saralamba, N. et al. Population genetic structure of the malaria vector Anopheles minimus in Thailand based on mitochondrial DNA markers. Parasites Vectors 14, 496 (2021). https://doi.org/10.1186/s13071-021-04998-7

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Keywords

Parasites & Vectors

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