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Article

Population Genetics, Demographic History, and Potential Distributions of the New Important Pests Monolepta signata (Coleoptera: Chrysomelidae) on Corn in China

by
Yang Liu
1,†,
Yacong Ge
1,†,
Liming Wang
1,
Jingao Dong
1,*,
Zhenying Wang
2,* and
Yuyu Wang
1,*
1
College of Plant Protection, Hebei Agricultural University, Baoding 071001, China
2
Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this paper.
Insects 2025, 16(3), 323; https://doi.org/10.3390/insects16030323
Submission received: 8 January 2025 / Revised: 4 March 2025 / Accepted: 10 March 2025 / Published: 19 March 2025
(This article belongs to the Special Issue Beetles in Integrative Entomology: Biology, Taxonomy, and Conservation)
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Simple Summary

The adults of Monolepta signata mainly feed on corn silks, anther and kernels at the filling stage, which result in great loss of yield and quality. Herein, we studied the population genetics and demographic history of M. signata based on mitochondrial genes (COI) and nuclear genes (ITS2, EF-1α). Based on the data of environmental factors and known sample points, the fitness of M. signata was also analyzed according to mathematical statistics and ecological niche theory. The distribution of the suitable habitat of M. signata in China and center-of-mass transfer under different climatic scenarios in the present and future time periods were predicted. The areas of potential serious damage in the future were also predicted. The population of M. signata has also experienced rapid expansion. The damage caused by M. signata in Xinjiang will be worse and more attention should be paid on.

Abstract

Monolepta signata are polyphagous pest widely distributed in China, and the damage as well as economic losses it caused were increasing in recent years. Knowledge of species diversity, population structure and habitat suitability could enhance the efforts of pest control. Here, we sampled the populations of M. signata in almost all of China’s major corn-producing regions. A total of 568 sequences were obtained from each gene. There were 48, 29, and 30 haplotypes of COI, ITS2 and EF-1α, respectively. The genetic distance between the HuangHuaiHai population and other populations was the largest. There were 61.90%, 71.43% and 61.90% of Nm values smaller than 1 in COI, ITS2 and EF-1α, respectively, which indicated that gene flow between most populations was weak. The degree of differentiation in most populations of M. signata was relatively high. The population of M. signata has also experienced rapid expansion. Population history dynamic analysis showed that the effective population size of M. signata remained relatively stable before 0.075 Ma. There was a slow contraction trend from 0.075 to 0.010 Ma. It has been rapidly and continuously expanding since 0.010 Ma. Among the investigated geographical populations, the “yellow-spot type” was only present in the populations of southern and southwestern regions, while the “two-spot type” and “four-spot type” were widely distributed in all other geographical populations. Predictions of the potential distribution areas of M. signata indicated that the northeast and north China regions will remain being the high suitability areas of M. signata in the future. Our results will not only facilitate studies on the phylogeography of M. signata but also benefit the effective monitoring and management of this agricultural pest.

1. Introduction

Monolepta signata (Coleoptera, Chrysomelidae) (Oliver 1808) are polyphagous pest widely distributed in China, whose hosts include ferns, dicotyledons and monocotyledons, with a total of 218 species (including subspecies and varieties) in 45 families of three classes [1]. Monolepta signata has a long damage period and strong adaptability to high temperatures [2]. Adults mainly feed on corn silks, anthers and kernels during the grain-filling stage, causing significant yield and quality losses. Feeding damage also facilitates fungal infections (e.g., Fusarium spp.), leading to corn ear rot [3,4,5,6,7,8]. M. signata damages cotton bracts and corollas, resulting in incomplete corolla and stamen exposure, which could affect cotton production and quality, particularly under heavy infestation [9]. M. signata can also infest vegetable leaves, meristems, and other plant parts [10]. M. signata was not only distributed in China, Russia (Siberia), Korea, Japan [11] but also in Bangladesh, Nepal and India [12,13,14,15,16,17]. The original distribution area of M. signata includes East Asia and Southeast Asia, specifically in regions such as Russia (Siberia), China, South Korea, Japan, the Philippines, Indonesia, Singapore, Malaysia, northern Vietnam, Myanmar, and eastern India [18,19]. Over time, the distribution range of M. signata has significantly expanded, spreading across nearly 30 provinces, autonomous regions, and municipalities in China, with particular severe damage reported in northern regions of China [20,21].
Due to changes in climate and cultivation practices, the reports of damage caused by M. signata have been increasing since 2000. In 2008, M. signata caused severe damage in Chencang District, Baoji City, Shaanxi Province, affecting 75.3% of the farmland, with the infestation rate reaching 100% in summer cornfields [22]. The corn silks were completely gnawed off, resulting in a yield loss of approximately 15% in the damaged fields in the most severely affected areas [22]. In 2010, cornfields in Qiqihar City, Heilongjiang Province, were severely damaged by M. signata, with the total affected area across the city reaching 303,000 hectares [23]. In 2014, large-scale damage caused by M. signata occurred in Xinxiang County, Henan Province, where 70% of the farmland was affected and the damaged plant rate reached 27% [24]. The occurrence regions of this pest have also expanded, resulting in an escalating economic loss of many crops such as soybeans, corn, peanuts, cotton, millet, and vegetables [25,26]. In recent years, the occurrence acreage of M. signata on corn has been increasing in China. M. signata has become an important pest on spring corn in north China and irrigated corn in northwest China as well as summer corn in the HuangHuaiHai region [27]. In particular, the northern part of China has suffered severe damage. Currently, most of the studies on M. signata are focused on the pattern of occurrence and effective control [3,4,28,29,30]. However, there are few studies on population genetics and demographic history of M. signata [31,32,33].
Phylogeography is an integrative field of science linking micro and macro evolutionary processes, contributing to the inference of vicariance, dispersal, speciation, and other population-level processes [18]. Phylogeography can provide strong support for addressing the relationships between current distribution patterns of species and climate change, geographic isolation, as well as clarify the timing of divergence of clades, origins and diffusion paths of species [21,34,35,36]. The maximum entropy model (MaxEnt) [37] is based on the maximum entropy theory, with species distribution data and climate variables as the foundation, and simulates the potential geographical distribution range of species through mathematical models [10]. The study of genetic diversity and population dynamics of pest populations not only clarifies scientific issues such as pest occurrence, gene exchange, genetic evolution and pesticide resistance, but also provides a scientific basis for optimizing ecological management and developing integrated pest management (IPM) [38,39].
Molecular markers, due to their advantages such as the independence from gene expression status, high reproducibility, and excellent stability, have been extensively utilized in multiple disciplines, including genetic structure analysis, assessments of population genetic diversity, species identification, phylogeography, and phylogenetic relationship investigations [40,41,42,43]. Among various molecular markers, mitochondrial DNA (mtDNA) is widely used in phylogenetic studies due to its structural simplicity, ease of amplification, and higher evolutionary rate, which enhances resolution in closely related species [44,45,46,47]. Nuclear gene sequences are widely used in phylogenetic reconstruction, gene loss analysis, population genetic structure, and biogeographic studies [48]. The Internal Transcribed Spacer (ITS) regions within nuclear rDNA are characterized by low selection pressure and rapid evolutionary rates, making them suitable for identifying closely related insect species, reconstructing phylogenetic relationships, and conducting population-level studies [49,50]. Notably, ITS2 has been widely applied in species typing [51,52] and phylogeographic investigations [53]. The eukaryotic elongation factor EF-1α plays a critical role in protein translation [54]. Numerous studies have employed EF-1α in conjunction with mitochondrial genes to explore insect genetic diversity and phylogeographic patterns [55,56]. The combination of mitochondrial and nuclear genes, which exhibit distinct inheritance patterns and evolutionary rates, offers a more reliable approach for investigating population genetic structure and inferring population dynamics [57].
Herein, we studied the population genetics and demographic history of M. signata based on mitochondrial genes (COI) and nuclear genes (ITS2, EF-1α). Based on the data of environmental factors and known sample points, the suitability areas of M. signata were also analyzed according to mathematical statistics and ecological niche modeling. The distribution of suitability areas of M. signata in China and geographic distribution shifts under different climatic scenarios in the present and future time periods were predicted. The areas of potential serious damage in the future were also predicted.

2. Materials and Methods

2.1. Sample Collection

The specimens used in this study were collected from Heilongjiang, Jilin, Liaoning, Inner Mongolia, Xinjiang, Hebei, Shaanxi, Yunnan, Sichuan, etc. (Table S1). A total of 568 specimens of M. signata from 38 localities were collected, and their coordinates were plotted using QGIS v.3.28 [58] (Figure 1). The administrative division map of China was downloaded from the National Geomatics Center of China (http://ngcc.cn/, accessed on 25 February 2025). Adult specimens of M. signata were sampled during 2020 and 2021 from almost all major corn planting areas in China, to comprehensively represent the current distribution of M. signata. The M. occifluvis specimens collected in 2018 were used as the outgroup for phylogenetic analyses (Table S1). All specimens were preserved in 95% ethanol and stored at −20 °C at Hebei Agricultural University, Baoding, China.

2.2. DNA Extraction and Sequencing

Genomic DNA was extracted from the thoracic muscle tissue using the Rapid Genomic DNA Kit (Biomed Biological Technology Co., Ltd., Beijing, China) following the instructions. The DNA concentration was measured using a nucleic acid protein analyzer (Thermo Scientific, Waltham, MA, USA) and preserved at −20 °C. The PCR primers used in this study were synthesized by Biomed (Beijing, China). COI and ITS2 were amplified using the 25 μL system: 2× Taq PCR Mix 12.5 μL, Forward primer 1 μL, Reverse primer 1 μL, template DNA 1 μL, and ddH2O 9.5 μL. The PCR conditions were: initial denaturation at 95 °C for 30 s; 40 cycles of denaturation at 95 °C for 10 s, annealing (COI: 48 °C, ITS2: 58 °C) for 50 s, and extension at 65 °C for 1 min; followed by a final extension at 65 °C for 10 min. The barcoding region of COI was amplified using the primers S-jerry (5′-CAACATYTATTYTGATTYTT-3′) and S-pat (5′-GCACTAWTCTGCCATATTAGA-3′) [59]. And ITS2 was amplified using the primers ITS-J-5.8S (5′-TGGRTCGATGGAGAACGCAGC-3′) and ITS2-N-610 (5′-TCTCACCTGCTCTGAGGTCGATAT-3′) [60]. The nested PCR procedure of EF-1α gene fragment was conducted with reference to two pairs of primers, i.e., EFS149 (5′-GARAARGARGCNCARGARATGGG-3′) and EFA1106 (5′-GTATATCCATTGGAAATTTGACCNGGRTGRTT-3′), EF1a-SN (5′-TGGGAAAAGGYYCCTTCAAATATGC-3′) and EF1a-AN (5′-CRTRACCACGACGYAATTCTTTGACAG-3′) [61]. PCR products were examined using 1% agarose gels with ethidium bromide following electrophoresis and sent to Biomed (Beijing, China) for sequencing in both directions.
Bidirectional sequencing reads were assembled in Cexpress [62]. COI and EF-1α sequences were validated by translating to amino acids in DNAMAN v.6.0 (Lynnon Biosoft, San Ramon, CA, USA) and then blasting with sequences on NCBI (https://www.ncbi.nlm.nih.gov/, accessed 6 May 2024) while the ITS2 sequences were verified by blasting with sequences on NCBI directly.

2.3. Genetic Diversity and Historical Dynamics Analysis

Single-nucleotide polymorphism (SNPs) and parsimony-information-sites were calculated using MEGA v7.0 [63]. The extent of haplotype diversity (Hd) and nucleotide diversity (π) of each population were calculated using DnaSP v5.0 [64]. The haplotype network was constructed using PopART v1.7 [65] based on the median-joining algorithm. The genetic differentiation indices and analysis of molecular variance (AMOVA) of different M. signata geographical populations were analyzed by Arlequin v3.5 [66]. The genetic distances within and among populations were calculated using MEGA v7.0 [63] based on the Kimura 2-parameter genetic distance model.
Mismatch distribution analysis was conducted in DnaSP v5.0 [64] to test whether the population of M. signata had undergone recent demographic expansion. Tajima’s D [67] and Fu’s Fs [68] neutrality tests were performed using Arlequin v.3.5 [66]. The Bayesian Skyline Plot (BSP) was plotted using BEAST v1.8 [69] based on COI gene under the GTR + G model. A Coleoptera-specific COI substitution rate of 1.77% per million years [70] was applied to calibrate the molecular clock in BEAST v1.8 [69]. The BSP was visualized in Tracer v1.4 [71] using the posterior tree distribution from BEAST v1.8 [69].

2.4. Phylogenetic Analysis

Phylogenetic trees were constructed based on COI, ITS2 and EF-1α genes of M. signata based on maximum likelihood (ML) and Bayesian inference (BI) methods using PhyloSuite v1.16 [72] with M. occifluvis as the outgroup. The best models of ML analyses were selected by ModelFinder [73], i.e., GTR + T + F for COI, HKY + G + F for ITS2 and K80 + I for EF-1α. ML trees were inferred using IQ-TREE v.1.6.10 [74] with 1000 ultrafast bootstraps. BI analyses were conducted using MrBayes v.3.2.2 [75] under the best substitution models selected by ModelFinder [73], i.e., TIM2 + I + F for COI, HKY + G4 + F for ITS2 and TNe + R2 for EF-1α. Bayesian Markov chain Monte Carlo (MCMC) simulations were run for 1 × 108 generations, sampling every 5000 generations and stopped when the average standard deviation of split frequencies < 0.01. The first 25% trees were discarded as burn-in. Finally, the resulting trees were visualized by Figtree v.1.4.1 [76].

2.5. Divergence Time Analysis

Molecular calibration of evolutionary rates for COI gene in Coleoptera (1.77% bases per million years) [70] was used to estimate the divergence time since lack of appropriate fossil records. The divergence times among M. signata haplotypes and its geographic populations were estimated using BEAST v1.8 [69] under the GTR + G model with strict molecular clock. Two independent MCMC runs were performed for 1 × 108 generations with sampling at every 5000 generations. The first 10% trees were removed as burn-in. Finally, the tree was visualized by Figtree v1.4.1 [74].

2.6. Potential Distribution of M. signata

There were 144 distribution points of M. signata in China used in this study, including field collection points and data points according to related literature and the Global Biodiversity Information Facility (GBIF) database.
To reduce the bias in the amount of occurrence data on the area, the localities were selected using ENMtool 1.3 [77], keeping only one average data for multiple distribution points located within the same raster (~1.5 km × ~1.5 km) (Table S3). There were 19 bioclimatic variables (BIO) in WorldClim (https://www.worldclim.org/data/v1.4/worldclim14.html, accessed on 15 May 2024) cited to model the distribution of M. signata. Pearson correlation analysis of the 19 variables was performed using R package v.3.4.2 [78], selecting variable factors with correlation coefficients |R| < 0.8. The spatial resolutions used for climate variable data were 2.5 arc-minutes. In order to investigate the changes in the distribution pattern and range of M. signata under different climate scenarios, the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) data were used in this study. Two climate scenarios (SSP126 and SSP585) for the present (1970–2000) and future (2041–2060 and 2061–2080) were selected.
The suitable geographic distribution of M. signata in China was analyzed using MaxEnt v 3.4.1 [79]. The maximum training sensitivity plus specificity logistic threshold (MTSS) was considered to be the most accurate method that predicted the presence or absence of species. The average of 10 replicates of the MTSS was used as a reference. The suitability zone of M. signata was classified into four classes according to the Natural Breaks in ArcToolbox of ArcGIS [80], with 0 ≤ p < 0.15 (non-suitability zone), 0.15 ≤ p < 0.3 (low suitable zone), 0.3 ≤ p < 0.5 (medium suitable zone), and 0.5 ≤ p ≤ 1 (high suitable zone) [81]. The spatial changes in overall suitable habitats were analyzed through the changes in centroid position at different periods of M. signata, taking their suitability areas as a whole. The direction and distance changes in fitness zone of M. signata were analyzed using the SDMtoolbox of ArcGIS [80].

3. Results

3.1. Genetic Structure

According to the geographical distribution, all samples were divided into seven geographical populations, namely DongBei population (DB), HuaBei population (HB), HuangHuaiHai population (HHH), ShanGanNing population (SGN), XiBei population (XB), NanFang population (NF) and XiNan population (XN).
Comparative analysis of mitochondrial (COI) and nuclear (ITS2, EF-1α) markers revealed distinct diversity patterns (Table 1). Geographic heterogeneity was evident in the genetic diversity patterns of the studied populations. The XB population exhibited the lowest diversity values (COI: π = 0.003; ITS2: π = 0.002), suggesting long-term isolation likely driven by the arid environmental conditions and geographic barriers in China’s northwestern regions. In contrast, the SGN population displayed a nuclear-specific reduction in diversity (EF-1α: π = 0.0008). The mitochondrial COI exhibited the highest haplotype diversity (Hd = 0.526) and nucleotide diversity (π = 0.006), consistent with its rapid evolutionary rate and maternal inheritance mode, whereas nuclear markers showed reduced variation (ITS2: Hd = 0.433, π = 0.004; EF-1α: Hd = 0.472, π = 0.002). Notably, haplotype sharing was limited across populations, with only 15% (7/48) of COI haplotypes distributed in multiple regions, compared to 24–33% shared haplotypes in nuclear loci (7/29 for ITS2; 10/30 for EF-1α), suggesting a stronger phylogeographic structure in the mitochondrial genome (Table S2).

3.2. Phylogenetic Analyses

The haplotype distributions of the three genes are summarized in Table S2, with COI, ITS2, and EF-1α comprising 48, 29, and 30 haplotypes, respectively. Phylogenetic reconstructions based on Bayesian inference (BI) and maximum likelihood (ML) methods yielded congruent topologies for each gene (Figure 2, Figure 3 and Figure 4). Median-joining networks further corroborated these phylogenetic relationships, revealing distinct clustering patterns among markers with COI resolved four major clades, whereas ITS2 and EF-1α grouped samples into three clades, likely reflecting differences in evolutionary rates and inheritance modes. Dominant haplotypes varied across markers. For COI, Hap_1 was the most frequent haplotype, predominantly distributed in northern populations (DB, HB, HHH, SGN, XB). For ITS2, Hap_2 was the most widespread, occurring across all seven geographic groups. For EF-1α, Hap_2 also dominated, presenting in six populations (DB, HB, HHH, SGN, XB, XN). Based on the phylogenetic tree topology and haplotype network structure, mitochondrial genes supported the division of M. signata from different regions of China into four distinct clades, whereas nuclear genes supported three major lineages.

3.3. Population Genetic Analysis

There were significant differences between the HHH population and the other six populations based on the pairwise differentiation coefficient (Table S6). The HHH population showed significant genetic differentiation compared to the DB, SGN, NF and XN populations (Fst > 0.25, p < 0.05) based on COI, to DB, SGN and XN populations (Fst > 0.25, p < 0.05) based on ITS2, and to DB, SGN and NF populations (Fst > 0.25, p < 0.05) based on EF-1α. There are 61.90%, 80.95% and 71.43% of Fst values exhibit high genetic differentiation (Fst > 0.15) in COI, ITS2 and EF-1α, respectively. Analysis of gene flow among different populations showed that there are 61.90%, 71.43% and 61.90% of Nm values smaller than 1 in COI, ITS2 and EF-1α, respectively, indicating that the gene flow of most populations were weak. Results from AMOVA analysis revealed that the main source of genetic variation existed within populations, whereas the remaining came from variation among populations for all these three genes (Table 2).

3.4. Divergence Time and Historical Demographic Reconstruction

The divergence time was estimated based on the phylogeny of the COI gene (Figure 5a). The initial divergence with extant M. signata in China was estimated to occur at 1.359 Ma (95% HPD = 0.294–3.114 Ma). M. signata further separated into Clade I at 1.359 Ma (95% HPD = 0.294–3.114 Ma), which comprised the HB and HHH populations, exhibiting both “two-spot type” and “four-spot type” patterns. Clade II, which also included “two-spot type” and “four-spot type” patterns, including DB, SGN, and HB populations, diverged from Clade III + Clade IV at 0.786 Ma (95% HPD = 0.167–1.828 Ma). Clade III, which included NF and XN populations with “yellow-spot type” patterns, diverged from Clade IV at 0.432 Ma (95% HPD = 0.088–1.004 Ma). Clade IV, including DB, HB, SGN, XB, and HHH populations, had both “two-spot type” and “four-spot type” patterns.
Tajima’s D and Fu’s Fs values of all populations for these three genes were significantly negative (p < 0.05), indicating recent population expansion occurred in M. signata (Table 3). The BSP results showed that the population of M. signata may have experienced three main demographic history periods (Figure 5b). The effective population size of M. signata remained relatively stable before approximately 0.075 Ma. There was a slow contraction trend between 0.075 and 0.010 Ma. The effective population size of M. signata has been experiencing rapid and sustained expansion from 0.010 Ma until now. Meanwhile, the mismatch distribution analysis based on data from geographic populations of M. signata did not display a single-peak curve, indicating that the population has not experienced recent expansion, which is conflicted with the results of the neutrality tests (Figure 6). We have also conducted mismatch distribution analysis using data from each genetically divergent clade instead of data from geographic populations, which is consistent with the results of geographic population (Figure S1).

3.5. Potential Distribution Prediction

A relatively high AUC value was obtained from the current potential distribution (AUC =  0.984), indicating good predictive model performance (Figure S2). Correlation analysis and model contribution screening of 19 climate variables were shown in Figure S2, with nine climate variables with high adaptability selected in this study. The contribution rate of environmental variables to the distribution of M. signata was shown in Table S7.
The climate variables that contributed more than 10% to the prediction of the MaxEnt model were bio18, bio4, and bio10, while the cumulative contribution of the three variables accounted for 66.8%. The replacement significance values that contributed more than 10% were bio4, bio1 and bio5, while the cumulative replacement significance values of the three climate variables accounted for 87.9%. The Jackknife test of the regularized training gains for climate variables in MaxEnt demonstrated that four environmental variable factors, i.e., bio1, bio10, bio18, and bio5, have relatively large gains when used alone (Figure S3). Five variables (bio1, bio4, bio5, bio10 and bio18) dominated the distribution of M. signata in China. The response curve of main environmental variables was shown in Figure S4. The optimal mean annual temperature of M. signata was 5 °C. The optimum value for the variance in temperature change was 1500. The optimal maximum temperature in the hottest month was 28 °C. The optimum average temperature during the warmest season was 22 °C. In the warmest season, the occurrence rate of M. signata increases with the increase in precipitation until the precipitation reaches 2500 mm.
The prediction of the suitability areas of M. signata in China under three different periods is shown in Figure 7. In the current period (1970–2000), Qinghai Province and Xizang Autonomous Region are areas where M. signata is distributed sporadically, while other regions show potential distribution areas. The current optimal habitat of M. signata was mainly concentrated in the Northeast Plain, North China Plain, Middle and Lower Yangtze River Plain, Sichuan Basin, Loess Plateau and Junggar Basin. The high-suitability area of M. signata in China accounted for 15.52%, while the medium-suitability area accounted for 20.42% and the low-suitability area accounted for 20.10%, respectively (Table 4).
The low- suitability areas of M. signata in China under the future climate scenarios SSP126 and SSP585 increased, while the medium-suitability and high-suitability areas decreased, compared to the results of the distribution projections under current climate scenarios (Table 4). The pattern of suitability areas was more variable in the south-central region. There was a gradual shift from high and medium to low-suitability areas especially in Shandong, Henan, Jiangsu, Anhui, Hubei, Hunan, Jiangxi and Fujian Province (Figure 7).
The center of mass of current suitability areas of M. signata was located in Gucheng County, Hengshui City, Hebei Province. From the current to the future (2041–2060 and 2061–2080) periods, the center of gravity distribution will still be in north China. The direction and distance of the center of gravity changed slightly under different climatic conditions (Figure 8). Under the SSP126 scenario, the centroid of the suitability areas shifted slightly to the northwest first and then to the northeast. Under the SSP585 scenario, its centroid tended to shift to the northeast first and then to the northwest. Further, the habitats of M. signata will all shift to the north in the future, indicating that the harm of M. signata will increase in the northern part of China in the future.

4. Discussion

4.1. Mitochondrial–Nuclear Discordance in Phylogenetic Resolution of M. signata

In the phylogenetic results, mitochondrial genes supported the division of M. signata from different regions of China into four distinct clades, whereas nuclear genes resolved three major lineages. The observed mitochondrial–nuclear discordance aligns with theoretical expectations, i.e., mitochondrial genes, due to their haploid and uniparental inheritance, have a smaller effective population size (effective population size ≈ 1/4 of nuclear genes), leading to faster lineage sorting and higher resolution of recent divergence events [82]. In contrast, nuclear genes, with their larger effective population size and biparental inheritance, are more likely to retain ancestral polymorphisms, obscuring finer phylogenetic signals [83].

4.2. Phenotypic Variation and Phylogeographic Patterns

The distribution of “two-spot type” and “four-spot type” across clades I, II, and IV, contrasted with the exclusive presence of the “yellow-spot type” in clade III, suggests a strong association between phenotypic traits and geographic regions (Figure 5a). This pattern likely results from continuous selection pressures along environmental gradients, leading to gradual phenotypic divergence and the formation of distinct ecotypes [84]. The geographic separation of the “four-spot type” and “yellow-spot type” populations, coupled with their genetic differentiation, may indicate ongoing speciation driven by local adaptation [85]. Future studies should focus on validating these patterns through offspring analysis and exploring the genetic basis of phenotypic variation.

4.3. Mitochondrial–Nuclear Discordance and Demographic History

The higher haplotype diversity (Hd > 0.5) and nucleotide diversity (π > 0.005) observed in COI, compared to the lower values in ITS2 and EF-1α (Hd < 0.5, π < 0.005), reflect the faster evolutionary rate and smaller effective population size of mitochondrial DNA [86]. The relatively low nucleotide diversity in nuclear genes, despite moderate haplotype diversity, may indicate historical bottlenecks followed by rapid population expansion [87,88]. During bottlenecks, mitochondrial diversity is more severely reduced due to its haploid nature, whereas nuclear genes retain higher haplotype diversity through recombination [89,90]. Post-bottleneck, mitochondrial diversity recovers more slowly, while nuclear genes rapidly accumulate new haplotypes through mutation and recombination [91]. The observed patterns are consistent with rapid population expansion, during which mitochondrial diversity increased due to its higher mutation rate, while nuclear diversity remained constrained by recombination [92].

4.4. Genetic Differentiation and Gene Flow Barriers

The genetic differentiation analysis based on COI, ITS2 and EF-1α showed significant differences between the HHH population and the other six populations, and significant genetic differentiation with some populations (Fst > 0.25, p < 0.5). The HHH population had a large genetic distance from other geographical populations. There was a negative correlation between gene flow and the genetic differentiation coefficient. The HHH population may have less gene exchange with other populations due to geographical barriers or other reasons, resulting in significant differences in the genetic differentiation coefficient between populations. Geographical isolation is considered an important factor affecting the genetic structure of species [93]. The HuangHuaiHai region is surrounded by mountains on three sides and faces the sea on one side. Coupled with the weak flight ability of M. signata [86], the obstruction of mountains such as the Qinling Mountains and the Taihang Mountains might be the main reasons for the large genetic distance and significant differentiation between the HHH population and other populations in China [94]. In contrast, gene flow between Chinese populations and those in Japan and North Korea may result from the dispersal of adult individuals by air currents. It is necessary to collect additional samples from countries and regions surrounding China to explore the formation causes of the current distribution pattern of M. signata.

4.5. Human Impacts and Host-Associated Differentiation

The lack of clear phylogeographic structure in China, as evidenced by the widespread distribution of haplotypes in the HB population, may reflect human-mediated dispersal through agricultural activities. Similar patterns have been observed in other pests, such as Sitobion avenae, where host crops significantly influence genetic structure [95]. Future studies should expand sampling to include more natural populations and different host crops to better understand the role of host-associated selection in shaping genetic diversity.

4.6. Environmental Constraints and Future Distribution

Temperature and precipitation were identified as the dominant factors limiting the distribution of M. signata, consistent with its known ecological requirements [96,97,98]. Under future climate scenarios (SSP126 and SSP585), northern regions (northeast China, north China, and Xinjiang) are projected to remain highly suitable, likely due to the expansion of corn and cotton cultivation, which are primary host crops [99,100,101,102]. In contrast, central and southern regions may experience reduced suitability, with high and medium suitable areas transitioning to low suitability. The northward shift of the centroid of M. signata’s distribution suggests increasing pest pressure in northern agricultural zones, necessitating targeted monitoring and control strategies.

5. Conclusions

This study provides a comprehensive analysis of the genetic diversity, population historical dynamics, and potential distribution of M. signata. Key findings reveal high genetic differentiation among populations, with larger genetic distances based on COI and ITS2 compared to EF-1α, indicating marker-specific evolutionary patterns driven by differences in effective population sizes and mutation rates. The primary source of genetic differentiation stems from significant intraspecific variation within populations, highlighting the role of local adaptation and geographic isolation in shaping population structure. Future distribution predictions suggest that northeast China, north China, and Xinjiang will remain highly suitable for M. signata, with increasing pest pressure in northern agricultural zones due to the expansion of corn and cotton cultivation. In central and southern China, shifts in suitability patterns are evident, with areas transitioning from low to medium suitability likely emerging as new hotspots for pest outbreaks, necessitating proactive monitoring and control measures.
Our findings have important implications for pest management. The genetic structure of M. signata provides a foundation for understanding its evolutionary history and adaptive potential, while the predicted distribution patterns offer critical insights for developing targeted monitoring and control strategies. Future research should focus on expanding sampling to include more natural populations and additional host crops, investigating the genetic basis of local adaptation and host-associated differentiation, and integrating genomic data to refine predictions of M. signata’s response to climate change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/insects16030323/s1, Figure S1: Mismatch distribution based on data from genetic divergent clades; Figure S2: The receiver operating characteristic (ROC) curve for the optimal model parameters (a) and correlation analysis of 19 bioclimatic variables (b); Figure S3: Jackknife test of the regularized training gain for environment variables in MaxEnt; Figure S4: Response curve of main environmental variables affecting the distribution of Monolepta signata; Table S1: Experimental specimen information of Monolepta signata; Table S2: Haplotype distribution and genetic diversity of Monolepta signata; Table S3: Effective distribution site coordinates of Monolepta signata; Table S4: Climate variables and descriptions; Table S5: Genetic distance of Monolepta signata based on COI gene, ITS2 gene, EF-1α genes; Table S6: The pairwise differentiation coefficient Fst (below the diagonal) and gene flow Nm (above the diagonal) of Monolepta signata based on COI gene, ITS2 gene, and EF-1α genes; Table S7: Contribution rate of environmental variables to the distribution of Monolepta signata.

Author Contributions

Conceptualization, Y.W., J.D. and Z.W.; methodology, Y.L., Y.G. and Y.W.; software, Y.L., Y.G. and L.W.; validation, Y.W., J.D.; formal analysis, Y.L., Y.G. and L.W.; investigation, L.W.; data curation, Y.G.; writing—original draft preparation, Y.L., Y.G. and Y.W.; writing—review and editing, Y.L., Y.W. and Z.W.; visualization, Y.L. and Y.W.; supervision, Y.W., J.D. and Z.W; project administration, Y.W.; funding acquisition, Y.W. and Z.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32170451), the Basic Research Project of Shijiazhuang for the University in Hebei Province (241791137A), the Science and Technology Planning Project of Baoding (2472P018), and the China Agriculture Research System of MOF and MARA (CARS-02).

Data Availability Statement

The data supporting the findings of this study are openly available from the National Center for Biotechnology Information at https://www.ncbi.nlm.nih.gov (accessed on 2 December 2024), accession numbers: COI: PQ591069, PQ609709 and PQ655538-PQ656103, ITS2: PQ672986-PQ673553, and EF-1α: PQ595069-PQ595636.

Acknowledgments

Thanks to Lansheng Tian from Mishan Agricultural Technology Extension Center; Xiumei Zhao and Xu Zheng of the Qiqihar Branch of Heilongjiang Academy of Agricultural Sciences; Shu Yang and Wu Zhang from the Heihe Branch of Heilongjiang Academy of Agricultural Sciences; Jing Zhang from the Tonghua Academy of Agricultural Sciences; Limei Bian of the Chifeng Agricultural and Animal Husbandry Research Institute; Wanxin Xiao and Shuping Zhang of the Corn Research Institute Liaoning Academy of Agricultural Sciences; Yueyang Liu, Jian Guo, Jianguo Zhou, Lina Cui, Songlin Lei, Lei Wang, Tingfeng Li, Xiaoxu Chen, Xinfang Liu, Xin Qi, Qingtao Kong, Weihua Xiao and Yi Fu as well as all those who helped in collecting specimens for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Gao, Y.; Xu, W.; Shi, S.S.; Cui, J.; Xu, B. A list of the host plants of the Monolepta hieroglyphica. Hubei Agric. Sci. 2017, 56, 865–869. [Google Scholar] [CrossRef]
  2. Ma, X.J.; Zhang, Z.H.; Wang, Z.; Zhang, Y.X.; Chen, J. Effects of brief exposure to high temperature on free amino acids, total sugar and trehalose of female adult Monolepta hieroglyphica (Motschulsky). Plant Prot. 2018, 44, 111–115. [Google Scholar] [CrossRef]
  3. Wang, L.R.; Liu, B.X.; Fu, H. Occurrence, damage and control measures of the leaf beetle of the long-tarsometatarsus in maize field. Shanxi Agric. Sci. 2006, 2, 123–131. [Google Scholar]
  4. Wang, L.R.; Liu, B.X.; Fu, H. Occurrence, damage and control of Monolepta hieroglyphica in maize field. China Agric. Technol. Promot. 2006, 22, 44. [Google Scholar]
  5. Zhao, Y.M.; Zhao, W.Q. Analysis the causes and control measures of corn northern leaf blight and Monolepta hieroglyphica (Motschulsky) insect pest. China Agric. Inf. 2013, 25, 112. [Google Scholar]
  6. Shi, J.; Wang, Z.Y.; He, K.L. Occurrence trend and cause analysis of summer corn diseases and insect pests in Huang-Huai-Hai area. Plant Prot. 2005, 5, 63–65. [Google Scholar]
  7. Zheng, X.M.; Xu, Z.; Guo, J.F.; Wang, L.D.; Luo, B.J.; Wang, L.X.; Li, Q.C.; Liu, Y.; Han, Y.H.; Wang, Z.Y. Influences of damaged silks by Monolepta hieroglyphica (Motschulsky) on corn yield. Plant Prot. 2021, 47, 109–114. [Google Scholar] [CrossRef]
  8. Liu, J.H. A research on laws of host selection migration and optimum period of chemical to Monolepta hieroglyphica (Motschulsky). Master’s Thesis, Jilin Agricultural University, Jilin, China, 2018. [Google Scholar]
  9. Wang, S.W. Occurrence and control of Monolepta hieroglyphica in cotton fields in Shihezi General Field of Xinjiang Corps. China Cotton 2014, 41, 32. [Google Scholar]
  10. Zhang, X.; Zhang, R.; Li, L.; Yang, Y.; Ding, Y.; Guan, H.; Wang, X.; Zhang, A.; Wen, H. Negligible transcriptome and metabolome alterations in RNAi insecticidal maize against Monolepta hieroglyphica. Plant Cell Rep. 2020, 39, 1539–1547. [Google Scholar] [CrossRef]
  11. Yu, P.Y.; Wang, S.Y.; Yang, X.K. Economic Insect Fauna of China. In Coleoptera: Chrysomeloidea (II); Science Press: Beijing, China, 1996; Volume 54, p. 166. [Google Scholar]
  12. Pal, P.K. Seasonal diversity with incidence and damage of insect pests in groundnut under red and laterite zone of West Bengal. Environ. Ecol. 2004, 22, 565–570. [Google Scholar]
  13. Neupane, F.P.; Sharma, M.D.; Neupane, K.R. Incidence of insect pests on chaoyote, Sechium edule (Swartz.) in Nepal. J. Inst. Agric. Anim. Sci. 2006, 27, 161–164. [Google Scholar] [CrossRef]
  14. Acharjee, P.; Mandal, S.K. Pest complex of some summer season flowers in West Bengal. Environ. Ecol. 2008, 26, 2385–2389. [Google Scholar]
  15. Boopathi, T. Influence of weather factors on the population dynamics of chewing pests of lowland paddy. Indian J. Entomol. 2012, 74, 329–335. [Google Scholar]
  16. Boopathi, T.; Singh, S.B.; Manju, T.; Dutta, S.K.; Saha, S.; Singh, A.R.; Ramakrishna, Y.; Chowdhury, S.; Lungmuana, D.V.; Ngachan, S.V. Effect of weather factors on the population of Chaetocnema spp., Monolepta signata Oliver and Aulacophora foveicollis (Lucas) on paddy under rainfed lowland conditions in Mizoram, India. J. Agrometeorol. 2017, 19, 145–148. [Google Scholar] [CrossRef]
  17. Hazarika, L.K.; Das, P.; Mohanasundaram, A.; Saikia, R.; Islam, A.N. A comprehensive study on pest complex of Flemingia spp. from Jorhat, Assam. J. Entomol. Zool. Stud. 2017, 5, 506–511. [Google Scholar]
  18. Jiang, Z.L.; Wang, D.M.; Wang, B.F.; Song, X.Y. Genome survey and mitochondrial genome analysis of Monolepta hieroglyphica. J. Environ. Insects 2019, 41, 1287–1296. [Google Scholar]
  19. Chen, G.; Yin, W.; Li, Q.; Hu, H. Research progress on Monolepta hieroglyphica (Motschulsky). China Plant Prot. 2016, 36, 19–26. [Google Scholar]
  20. Liang, R.X.; Wang, Z.Y.; He, K.L.; Cong, B.; Li, J. Genetic diversity of geographic populations of Monolepta hieroglyphica (Motschulsky) (Coleoptera: Chrysomelidae) from North China estimated by mitochondrial COII gene sequences. Acta Entomol. Sin. 2011, 54, 828–837. [Google Scholar] [CrossRef]
  21. Li, J.; Zhang, X.F.; Xu, L.L.; Shen, Y.Y.; Li, X.X.; Wang, Z.Y. Genetic structure and Wolbachia infection in geographical populations of Monolepta hieroglyphica (Coleoptera: Chrysomelidae) in South China. Acta Entomol. Sin. 2021, 64, 730–742. [Google Scholar] [CrossRef]
  22. Ma, H.M.; Liu, Z.Y.; Yang, Q.Y. Reasons for the Severe Occurrence of Monolepta hieroglyphica in Corn Fields in Chencang District, Baoji City and Control Countermeasures. J. Agric. 2009, 4, 50–51. [Google Scholar]
  23. Zhao, X.M.; Liu, Y.; Tan, K.F.; Chai, L.L. Occurrence Situation and Control Measures of Monolepta hieroglyphica in Maize Field. Heilongjiang Agric. Sci. 2011, 6, 51–52. [Google Scholar]
  24. Li, J.M.; Zhao, B.J.; Liu, Q.R. Occurrence Regularity and Control Method of Monolepta hieroglyphica (Motschulsky) in Corn Field in Xinxiang Country. J. Agric. Catastrophology 2015, 5, 3–4+14. [Google Scholar] [CrossRef]
  25. Chen, X.S.; Yang, F.J.; Qu, M.J.; Wang, S.L.; Li, X.; Gao, H.Y.; Li, C.Y.; Zhu, X.M.; Du, L. Investigation on harmful biology species and their occurrence and damage to peanut in Jilin. J. Peanut Sci. 2017, 46, 68–72. [Google Scholar] [CrossRef]
  26. Shi, S.S.; Wang, X.Q.; Tian, J.; Gao, Y.; Cui, J.; Zhu, S.Y. Occurrence regularity and economic threshold of Monolepta hieroglyphica adults in soybean fields. Chin. J. Oil Crop Sci. 2017, 39, 239–244. [Google Scholar]
  27. Wang, Z.Y.; Wang, X.M. Current status and management strategies for corn pests and diseases in China. Plant Prot. 2019, 45, 1–11. [Google Scholar] [CrossRef]
  28. Nie, Q. Study on Biological Characteristics and Control Strategy of Monolepta hieroglyphica (Motschulsky). Master’s Thesis, Heilongjiang Bayi Agricultural Reclamation University, Heilongjiang, China, 2009. [Google Scholar]
  29. Tian, J.; Cui, J.; Wu, L.; Xu, W.; Chen, B.C.; Shi, S.S. Screening of Pesticides for Controlling Monolepta hieroglyphica (Motschulsky). Agrochemicals 2014, 53, 767–770. [Google Scholar] [CrossRef]
  30. Wang, F.H.; Zhou, K.D.; Yu, Y.F.; Peng, L.Y.; Ye, Y.X.; Lin, C.Y.; Xu, C.; Shen, Z.C. RNAi-based transgenic maize to control double-spotted leaf beetle (Monolepta hieroglyphica). Pest Manag. Sci. 2025, 81, 1412–1421. [Google Scholar] [CrossRef]
  31. Ge, Y.C.; Shi, C.M.; Bai, M.; Cao, Z.Y.; Cao, L.P.; Wang, Z.Y.; Dong, J.G.; Wang, Y.Y. Molecular data confirm Monolepta hieroglyphica (Motschulsky, 1858) and M. quadriguttata (Motschulsky, 1860) being synonyms of M. signata (Oliver, 1808). Insect Syst. Evol. 2023, 54, 402–412. [Google Scholar] [CrossRef]
  32. Satsuki, T.; Naoki, S.; Ryutei, I.; Ryohei, N.; Yoshihisa, A.; Katsutoshi, W. Environmental DNA phylogeography: Successful reconstruction of phylogeographic patterns of multiple fish species from cups of water. Mol. Ecol. Resour. 2023, 23, 1050–1065. [Google Scholar] [CrossRef]
  33. Tang, X.T.; Lu, M.X.; Du, Y.Z. Molecular phylogeography and evolutionary history of the pink rice borer (Lepidoptera: Noctuidae): Implications for refugia identification and pest management. Syst. Entomol. 2022, 47, 371–383. [Google Scholar] [CrossRef]
  34. Wang, D.; Shi, Q.; Liu, D.G.; Yang, Y.J.; Shang, Z.M. Genetic Divergence of Two Sitobion avenae Biotypes on Barley and Wheat in China. Insects 2020, 11, 117. [Google Scholar] [CrossRef] [PubMed]
  35. Kaya, S.; Ciplak, B. Phylogeography and taxonomy of the Psorodonotus caucasicus (Orthoptera, Tettigoniidae) group: Independent double invasion of the Balkans from the Caucasus: Phylogeography and taxonomy of the Psorodonotus caucasicus group. Syst. Entomol. 2017, 42, 118–133. [Google Scholar] [CrossRef]
  36. Du, Z.Y.; Wu, Y.F.; Chen, Z.; Cao, L.M.; Tadashi, I.; Satoshi, K.; Teiji, S.; Song, F.; Li, T.; Cai, W.Z.; et al. Global phylogeography and invasion history of the spotted lanternfly revealed by mitochondrial phylogenomics. Evol. Appl. 2021, 14, 915–930. [Google Scholar] [CrossRef]
  37. Zhao, J.; Zou, X.; Yuan, F.; Luo, Y.; Shi, J. Predicting the current and future distribution of Monochamus carolinensis (Coleoptera: Cerambycidae) based on the maximum entropy model. Pest Manag. Sci. 2023, 79, 5393–5404. [Google Scholar] [CrossRef]
  38. Wang, T.; Zhu, Q.D.; Heller, K.G.; Zhou, Z.J.; Shi, F.M. Phylogenetic relationships and phylogeography of the genus Sinocyrtaspis Liu, 2000 (Orthoptera: Tettigoniidae: Meconematinae) reveal speciation processes related to climate change. Syst. Entomol. 2020, 45, 144–159. [Google Scholar] [CrossRef]
  39. Anderson, C.J.; Tay, W.T.; McGaughran, A.; Gordon, K.; Walsh, T.K. Population structure and gene flow in the global pest, Helicoverpa armigera. Mol. Ecol. 2016, 25, 5296–5311. [Google Scholar] [CrossRef]
  40. Hebert, P.D.N.; Penton, E.H.; Burns, J.M.; Janzen, D.H.; Hallwachs, W. Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc. Natl. Acad. Sci. USA 2004, 101, 14812–14817. [Google Scholar] [CrossRef] [PubMed]
  41. Lombaert, E.; Guillemaud, T.; Thomas, C.E.; Handley, L.J.L.; Estoup, A. Inferring the origin of populations introduced from a genetically structured native range by approximate Bayesian computation: Case study of the invasive ladybird Harmonia axyridis. Mol. Ecol. 2011, 20, 4654–4670. [Google Scholar] [CrossRef]
  42. Yang, X.M.; Sun, J.T.; Xue, X.F.; Li, J.B.; Hong, X.Y.; Crandall, K.A. Invasion genetics of the western flower thrips in China: Evidence for genetic bottleneck, hybridization and bridgehead effect. PLoS ONE 2012, 7, 165–166. [Google Scholar] [CrossRef]
  43. Tyagi, K.; Chakraborty, R.; Cameron, S.L.; Sweet, A.D.; Kumar, V. Rearrangement and evolution of mitochondrial genomes in Thysanoptera (Insecta). Sci. Rep. 2020, 10, 695. [Google Scholar] [CrossRef]
  44. Cao, L.Z.; Wu, K.M. Genetic diversity and demographic history of globe skimmers (Odonata: Libellulidae) in China based on microsatellite and mitochondrial DNA markers. Sci. Rep. 2019, 9. [Google Scholar] [CrossRef]
  45. Hendrich, L.; Pons, J.; Ribera, I.; Michael, B.; Carles, L.F. Mitochondrial cox1 sequence data reliably uncover patterns of insect diversity but suffer from high lineage-idiosyncratic error rates. PLoS ONE 2017, 5, e14448. [Google Scholar] [CrossRef]
  46. Dobelmann, J.; Alexander, A.; Baty, J.W.; Gemmell, N.J.; Lesterv, P.J. The association between mitochondrial genetic variation and reduced colony fitness in an invasive wasp. Mol. Ecol. 2019, 28, 3324–3338. [Google Scholar] [CrossRef] [PubMed]
  47. Saitoh, T.; Sugita, N.; Someya, S.; Iwami, Y.; Kobayashi, S.; Kamigaichi, H.; Higuchi, A.; Asai, S.; Yamamoto, Y.; Nishiumi, I. DNA barcoding reveals 24 distinct lineages as cryptic bird species candidates in and around the Japanese Archipelago. Mol. Ecol. Resour. 2015, 15, 177–186. [Google Scholar] [CrossRef]
  48. Zhang, M. Study on Genetic Diversity and Molecular Phylogenetic Relationships of Partial Species in Nymphalidae in China. Ph.D. Thesis, Shanxi University, Taiyuan, China, 2008. [Google Scholar]
  49. Gallego, D.; Galián, J. The internal transcribed spacers (ITS1 and ITS2) of the rDNA differentiates the bark beetle forest pests Tomicus destruens and T. piniperda. Insect Mol. Biol. 2010, 10, 415–420. [Google Scholar] [CrossRef] [PubMed]
  50. Wu, S.; Pan, J.Y.; Wang, X.Z.; Zhou, S.S.; Zhang, G.Q.; Tang, L.H. Species identification of Anopheles maculatus complex in malaria endemic area Motuo County, Tibet. Insect Mol. Biol. 2008, 26, 286–289. [Google Scholar]
  51. Mukha, D.; Wiegmann, B.M.; Schal, C. Evolution and phylogenetic information content of the ribosomal DNA repeat unit in the Blattodea (Insecta). Insect Biochem. Mol. Biol. 2002, 32, 951–960. [Google Scholar] [CrossRef] [PubMed]
  52. Zhou, S.S.; Tang, L.H.; Gu, Z.C.; Wang, Y. Sequence difference of ribosomal DNA second internal transcribed spacer in Anopheles minimus in different localities. Chin. J. Parasitol. Parasit. Dis. 2002, 20, 31–33. [Google Scholar]
  53. Shen, D.R.; Zhang, H.R.; Li, Z.R.; Dong, K.; He, S.Y. Genetic diversity and population expansion in different geographic populations of Frankliniella occidentalis in Yunnan, China. Chin. J. Appl. Entomol. 2011, 48, 504–512. [Google Scholar]
  54. Thornton, S.; Anand, N.; Purcell, D.; Lee, J. Not just for housekeeping: Protein initiation and elongation factors in cell growth and tumorigenesis. J. Mol. Med. 2003, 81, 536–548. [Google Scholar] [CrossRef]
  55. Amorim, I.R.; Emerson, B.C.; Borges, P.A.V.; Wayne, R.K. Phylogeography and molecular phylogeny of Macaronesian island Tarphius (Coleoptera: Zopheridae): Why are there so few species in the Azores? Phylogenetics and diversification of Azorean Tarphius. J. Biogeogr. 2012, 39, 1583–1595. [Google Scholar] [CrossRef]
  56. Letsch, H.; Balke, M.; Toussaint, E.F.A.; Riedel, A. Historical biogeography of the hyperdiverse hidden snout weevils (Coleoptera, Curculionidae, Cryptorhynchinae). Syst. Entomol. 2020, 45, 312–326. [Google Scholar] [CrossRef]
  57. Xiang, Y.; Dong, W.W.; Jiang, G.F.; Hong, F.; Zhang, Y.X.; Zhang, W.W. Analysis of the genetic differentiation among geographical populations of Luehdorfia chinensis chinensis (Lepidoptera: Papilionidae) based on mitochondrial gene and nuclear genes. Acta Entomol. Sin. 2020, 63, 1377–1384. [Google Scholar]
  58. QGIS Development Team. QGIS Geographic Information System, Version 3.28; Open Source Geospatial Foundation: Beaverton, OR, USA, 2023; Available online: https://www.qgis.org (accessed on 26 February 2025).
  59. Timmermans, M.; Dodsworth, S.; Culverwell, C.; Bocak, L.; Ahrens, D.; Littlewood, D.; Pons, J.; Vogler, A.P. Why barcode? High-throughput multiplex sequencing of mitochondrial genomes for molecular systematics. Nucleic Acids Res. 2010, 28, e197. [Google Scholar] [CrossRef]
  60. Swigoňová, Z.; Kjer, K.M. Phylogeny and host-plant association in the leaf beetle genus Trirhabda LeConte (Coleoptera: Chrysomelidae). Mol. Phylogenetics Evol. 2004, 32, 358–374. [Google Scholar] [CrossRef]
  61. Xue, H.J.; Li, W.Z.; Nie, R.E.; Yang, X.K. Recent speciation in three closely related sympatric specialists: Inferences using multi-locus sequence, post-mating isolation and endosymbiont data. PLoS ONE 2011, 6, e27834. [Google Scholar] [CrossRef]
  62. Lu, G.Q. Vector NTI, a balanced all-in-one sequence analysis suite. Brief. Bioinform. 2004, 5, 378–388. [Google Scholar] [CrossRef]
  63. Kumar, S.; Stecher, G.; Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 2016, 33, 1870–1874. [Google Scholar] [CrossRef] [PubMed]
  64. Librado, P.; Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 2009, 25, 1451–1452. [Google Scholar] [CrossRef]
  65. Leigh, J.W.; Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 2015, 6, 1110–1116. [Google Scholar] [CrossRef]
  66. Excoffier, L.; Lischer, H.E.L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 2010, 10, 564–567. [Google Scholar] [CrossRef] [PubMed]
  67. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 1989, 123, 585–595. [Google Scholar] [CrossRef]
  68. Fu, Y. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 1997, 147, 915–925. [Google Scholar] [CrossRef]
  69. Drummond, A.J.; Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 2007, 7, 214. [Google Scholar] [CrossRef]
  70. Papadopoulou, A.; Anastasiou, I.; Vogler, A.P. Revisiting the insect mitochondrial molecular clock: The mid-aegean trench calibration. Mol. Biol. Evol. 2010, 27, 1659–1672. [Google Scholar] [CrossRef] [PubMed]
  71. Rambaut, A.; Drummond, A.J.; Xie, D.; Baele, G.; Suchard, M.A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 2018, 67, 901–904. [Google Scholar] [CrossRef] [PubMed]
  72. Zhang, D.; Gao, F.L.; Ivan, J.; Zou, H.; Zhang, J.; Li, W.X.; Wang, G.T. PhyloSuite: An inteegrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies. Molecuar Ecol. Resour. 2020, 20, 348–355. [Google Scholar] [CrossRef]
  73. Kalyaanamoorthy, S.; Minh, B.Q.; Wong, T.K.F.; Haeseler, A.; Jermiin, L.S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 2017, 14, 587–589. [Google Scholar] [CrossRef]
  74. Nguyen, L.T.; Schmidt, H.A.; Von, H.A.; Minh, B.Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef]
  75. Ronquist, F.; Huelsenbeck, J.P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 2003, 19, 1572–1574. [Google Scholar] [CrossRef]
  76. Rambaut, A. FigTree; Institute of Evolutionary Biology, University of Edinburgh: Edinburgh, UK, 2009; Available online: http://tree.bio.ed.ac.uk/software/figtree (accessed on 22 February 2024).
  77. Warren, D.L.; Glor, R.E.; Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 2010, 33, 607–611. [Google Scholar] [CrossRef]
  78. Muscarella, R.; Galante, P.J.; Soley-Guardia, M.; Boria, R.A.; Kass, J.M.; Uriarte, M.; Anderson, R.P. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods Ecol. Evol. 2014, 5, 1198–1205. [Google Scholar] [CrossRef]
  79. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  80. Brown, J.L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 2014, 5, 694–700. [Google Scholar] [CrossRef]
  81. Jiménez, V.A.; Lobo, J.M. Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecologica 2007, 31, 361–369. [Google Scholar] [CrossRef]
  82. Avise, J.C. Phylogeography: The History and Formation of Species; Harvard University Press: Cambridge, MA, USA, 2000. [Google Scholar]
  83. Funk, D.J.; Omland, K.E. Species-level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annu. Rev. Ecol. Syst. 2003, 34, 397–423. [Google Scholar] [CrossRef]
  84. Araki, Y.; Sota, T. Population genetic structure underlying the geographic variation in beetle structural colour with multiple transition zones. Mol. Ecol. 2021, 30, 670–684. [Google Scholar] [CrossRef]
  85. Mayr, E. Population, Species, and Evolution: An Abridgment of Animal Species and Evolution; Belknap Press: Cambridge, MA, USA, 1970. [Google Scholar]
  86. Wei, S.J.; Chen, X.X. Progress in research on the comparative mitogenomics of insects. Chin. J. Appl. Entomol. 2011, 48, 1573–1585. [Google Scholar]
  87. Avise, J.C.; Neigel, J.E.; Arnold, J. Demographic influences on mitochondrial DNA lineage survivorship in animal populations. J. Mol. Evol. 1984, 20, 99–105. [Google Scholar] [CrossRef]
  88. Rogers, A.R.; Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 1992, 9, 552–569. [Google Scholar] [CrossRef]
  89. Avise, J.C.; Arnold, J.; Ball, R.M.; Bermingham, E.; Lamb, T.; Neigel, J.E.; Reeb, C.A.; Saunders, N.C. Intraspecific phylogeography: The mitochondrial DNA bridge between population genetics and systematics. Annu. Rev. Ecol. Syst. 1987, 18, 489–522. [Google Scholar] [CrossRef]
  90. Birky, C.W.; Fuerst, P.; Maruyama, T. Organelle gene diversity under migration, mutation, and drift: Equilibrium expectations, approach to equilibrium, effects of heteroplasmic cells, and comparison to nuclear genes. Genetics 1989, 121, 613–627. [Google Scholar] [CrossRef]
  91. Ballard, J.W.O.; Whitlock, M.C. The incomplete natural history of mitochondria. Mol. Ecol. 2004, 13, 729–744. [Google Scholar] [CrossRef]
  92. Brown, W.M.; George, M., Jr.; Wilson, A.C. Rapid evolution of animal mitochondrial DNA. Proc. Natl. Acad. Sci. USA 1979, 76, 1967–1971. [Google Scholar] [CrossRef] [PubMed]
  93. Lv, Z.Z.; Li, J.B.; Wang, Z.H.; Liu, G.J. Preliminary studies on two-spotted leaf beetle, Monolepta hieroglyphica on cotton. Plant Prot. 2007, 1, 97–99. [Google Scholar]
  94. Slatkin, M. Gene flow and selection in a cline. Genetics 1973, 81, 787–802. [Google Scholar] [CrossRef] [PubMed]
  95. Vialatte, A.; Dedryver, C.A.; Simon, J.C.; Galman, M.; Plantegenest, M. Limited genetic exchanges between populations of an insect pest living on uncultivated and related cultivated host plants. Proc. R. Soc. B Biol. Sci. 2005, 272, 1075–1082. [Google Scholar] [CrossRef]
  96. Li, G.W.; Chen, X.L. Investigation and study on the living habits and dynamics of the growth and decline of the long-tarsal beetle in Xinjiang cotton region. China Plant Prot. 2010, 30, 8–10. [Google Scholar]
  97. Zhang, Z.H.; Wang, Z.; Chen, J.; Han, X.Q. Effects of high temperature stress on the total protein and two protective enzymes of adults of the tarsal beetle. J. Environ. Entomol. 2018, 40, 440–445. [Google Scholar]
  98. Zhao, X.M.; Zheng, X.; Wang, L.X.; Yang, D.B.; Liu, Y.; Liu, Y.; Wang, L.D.; Li, Q.C.; Wang, Z.Y. Control effect of seed coating agents against Monolepta hieroglyphica (Motschulsky) in corn fields. Plant Prot. 2021, 48, 337–341+348. [Google Scholar] [CrossRef]
  99. Sandweiss, D.H.; Maasch, K.A.; Anderson, D.G. Climate and culture-Transitions in the mid-Holocene. Science 1999, 283, 499–500. [Google Scholar] [CrossRef]
  100. Hong, B. Genetic Structure, Suitability and Demographic History of Scythropus yasumatsui Populations in China. Ph.D. Thesis, Northwest A&F University, Yangling, China, 2019. [Google Scholar]
  101. Li, N.; Lin, H.; Wang, T.; Li, Y.; Liu, Y.; Chen, X.; Hu, X. Impact of climate change on cotton growth and yields in Xinjiang, China. Field Crops Res. 2020, 247, 107590. [Google Scholar] [CrossRef]
  102. Jiang, Z.; Wang, Q.; Ning, S.; Hu, X.; Yuan, S. Growth and yield response and water use efficiency of cotton under film-mulched drip irrigation to magnetized ionized water and bacillus subtilis in saline soil in Xinjiang. Agronomy 2023, 13, 1644. [Google Scholar] [CrossRef]
Insects 16 00323 g001
Figure 1. The geographic coordinates of 38 collection sites. Map approval number: GS(2024)0650.
Figure 1. The geographic coordinates of 38 collection sites. Map approval number: GS(2024)0650.
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Insects 16 00323 g002
Figure 2. Phylogenetic analysis and haplotype network of Monolepta signata based on the COI sequences. (a) Bayesian phylogenetic tree. (b) Maximum likelihood tree. (c) Haplotype network. Haplotypes are color-coded according to the population. Bright red represents the DB population. Green represents the HB population. Dark purple represents the SGN population. Yellow represents the XB population. Pink represents the NF population. Dark red represents the HHH population, and dark blue represents the XN population.
Figure 2. Phylogenetic analysis and haplotype network of Monolepta signata based on the COI sequences. (a) Bayesian phylogenetic tree. (b) Maximum likelihood tree. (c) Haplotype network. Haplotypes are color-coded according to the population. Bright red represents the DB population. Green represents the HB population. Dark purple represents the SGN population. Yellow represents the XB population. Pink represents the NF population. Dark red represents the HHH population, and dark blue represents the XN population.
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Insects 16 00323 g003
Figure 3. Phylogenetic analysis and haplotype network of Monolepta signata based on the ITS2 sequences. (a) Bayesian phylogenetic tree. (b) Maximum likelihood tree. (c) Haplotype network. Haplotypes are color-coded according to the population. Bright red represents the DB population. Green represents the HB population. Dark purple represents the HHH population. Yellow represents the SGN population. Pink represents the XB population. Dark red represents the NF population, and dark blue represents the XN population.
Figure 3. Phylogenetic analysis and haplotype network of Monolepta signata based on the ITS2 sequences. (a) Bayesian phylogenetic tree. (b) Maximum likelihood tree. (c) Haplotype network. Haplotypes are color-coded according to the population. Bright red represents the DB population. Green represents the HB population. Dark purple represents the HHH population. Yellow represents the SGN population. Pink represents the XB population. Dark red represents the NF population, and dark blue represents the XN population.
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Insects 16 00323 g004
Figure 4. Phylogenetic analysis and haplotype network of Monolepta signata based on the EF-1α sequences. (a) Bayesian phylogenetic tree. (b) Maximum likelihood tree. (c) Haplotype network. Haplotypes are color-coded according to the population. Bright red represents the DB population. Green represents the HB population. Dark purple represents the HHH population. Yellow represents the SGN population. Pink represents the XB population. Dark red represents the NF population, and dark blue represents the XN population.
Figure 4. Phylogenetic analysis and haplotype network of Monolepta signata based on the EF-1α sequences. (a) Bayesian phylogenetic tree. (b) Maximum likelihood tree. (c) Haplotype network. Haplotypes are color-coded according to the population. Bright red represents the DB population. Green represents the HB population. Dark purple represents the HHH population. Yellow represents the SGN population. Pink represents the XB population. Dark red represents the NF population, and dark blue represents the XN population.
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Insects 16 00323 g005
Figure 5. Divergence time estimation and Bayesian skyline analysis for Monolepta signata using COI gene. (a) Divergence time estimation. Node mean age estimates are provided with their respective 95% highest posterior density (HPD) intervals, denoted by purple bars. (b) Bayesian skyline analysis. The abscissa represents time. Unit: million years. The ordinate represents effective population size.
Figure 5. Divergence time estimation and Bayesian skyline analysis for Monolepta signata using COI gene. (a) Divergence time estimation. Node mean age estimates are provided with their respective 95% highest posterior density (HPD) intervals, denoted by purple bars. (b) Bayesian skyline analysis. The abscissa represents time. Unit: million years. The ordinate represents effective population size.
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Insects 16 00323 g006
Figure 6. Mismatch distribution based on data from geographic population for COI (a), ITS2 (b) and EF-1α (c) genes of Monolepta signata.
Figure 6. Mismatch distribution based on data from geographic population for COI (a), ITS2 (b) and EF-1α (c) genes of Monolepta signata.
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Insects 16 00323 g007
Figure 7. Modeled suitability areas of Monolepta signata under the current climate conditions and two future climate change scenarios (SSP126 and SSP585) in 2041–2060 and 2061–2080.
Figure 7. Modeled suitability areas of Monolepta signata under the current climate conditions and two future climate change scenarios (SSP126 and SSP585) in 2041–2060 and 2061–2080.
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Insects 16 00323 g008
Figure 8. Centroid transfer in the suitability areas of Monolepta signata under different climate scenarios.
Figure 8. Centroid transfer in the suitability areas of Monolepta signata under different climate scenarios.
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Table 1. Haplotype distribution and genetic diversity of Monolepta signata based on COI, ITS2 and EF-1α genes.
Table 1. Haplotype distribution and genetic diversity of Monolepta signata based on COI, ITS2 and EF-1α genes.
Gene NamePopulation CodeHaplotype Diversity (Hd)Nucleotide Diversity (π)Average Number of Nucleotide Differences (k)
COI
DB0.4670.0053.616
HB0.4200.0063.918
HHH0.5930.0139.358
SGN0.2650.0010.720
XB0.4190.0010.419
NF0.5430.0032.324
XN0.7120.0021.238
Total0.5260.0064.237
ITS2
DB0.1710.0000.192
HB0.5590.0072.870
HHH0.5900.0145.719
SGN0.5610.0031.080
XB0.0000.0000.000
NF0.4760.0020.952
XN0.5320.0020.677
Total0.4330.0041.591
EF-1α
DB0.3250.0010.409
HB0.5950.0031.563
HHH0.6430.0052.691
SGN0.1890.0000.197
XB0.4480.0010.552
NF0.6190.0031.371
XN0.8410.0052.788
Total0.4720.0021.026
Table 2. Analysis of molecular variance (AMOVA) of Monolepta signata based on COI, ITS2 and EF-1α genes.
Table 2. Analysis of molecular variance (AMOVA) of Monolepta signata based on COI, ITS2 and EF-1α genes.
GeneSource of
Variation		d.f.Sum of SquaresVariance
Components		Percentage of Variation
COIamong populations6298.4760.75918 Va30.75
within populations561958.9701.70939 Vb69.25
total5671257.4452.46857
ITS2among populations6127.0460.32550 Va36.01
within populations561324.5260.57848 Vb63.99
total567451.5720.90398
EF-1αamong populations652.8720.13262 Va23.97
within populations561236.0380.42075 Vb76.03
total567288.9100.55336
Table 3. Neutral test values of Monolepta signata based on COI, ITS2 and EF-1α genes.
Table 3. Neutral test values of Monolepta signata based on COI, ITS2 and EF-1α genes.
Population CodeCOIITS2EF-1α
Tajima’s D
Test		Fu’s Fs
Test		Tajima’s D
Test		Fu’s Fs
Test		Tajima’s D
Test		Fu’s Fs
Test
DB−0.511−1.887−2.245 *−26.425 *−1.756 *−9.543 *
HB−0.8110.992−0.5133.4030.224−0.741
HHH1.9148.0771.4976.5840.8782.111
SGN−2.249 *−3.556 *−0.198−1.882−1.574 *−4.741 *
XB0.7420.9090.0000.000−0.268−0.248
NF0.0632.9961.4432.5200.3680.376
XN−1.982 *−7.460 *−1.628 *−4.386 *−0.044−2.844
ALL−1.412 *−21.491 *−1.869 *−17.613 *−1.681 *−26.827 *
* p < 0.05.
Table 4. Potential distribution areas of Monolepta signata in different periods.
Table 4. Potential distribution areas of Monolepta signata in different periods.
PeriodScenarioArea (×104 km2)
Marginally Suitable RegionModerately Suitable RegionHighly Suitable Region
Present-192.80195.86148.59
2041–2060SSP126193.03 (+0.12%)183.54 (−6.29%)136.48 (−8.15%)
SSP585218.07 (+13.11%)165.48 (−15.51%)126.49 (−14.87%)
2061–2080SSP126210.60 (+9.24%)182.67 (−6.77%)128.60 (−13.45%)
SSP585225.66 (+17.04%)168.15 (−14.15%)126.98 (−14.54%)
Note: The data in the brackets are the changes in area compared with current time.
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MDPI and ACS Style

Liu, Y.; Ge, Y.; Wang, L.; Dong, J.; Wang, Z.; Wang, Y. Population Genetics, Demographic History, and Potential Distributions of the New Important Pests Monolepta signata (Coleoptera: Chrysomelidae) on Corn in China. Insects 2025, 16, 323. https://doi.org/10.3390/insects16030323

AMA Style

Liu Y, Ge Y, Wang L, Dong J, Wang Z, Wang Y. Population Genetics, Demographic History, and Potential Distributions of the New Important Pests Monolepta signata (Coleoptera: Chrysomelidae) on Corn in China. Insects. 2025; 16(3):323. https://doi.org/10.3390/insects16030323

Chicago/Turabian Style

Liu, Yang, Yacong Ge, Liming Wang, Jingao Dong, Zhenying Wang, and Yuyu Wang. 2025. "Population Genetics, Demographic History, and Potential Distributions of the New Important Pests Monolepta signata (Coleoptera: Chrysomelidae) on Corn in China" Insects 16, no. 3: 323. https://doi.org/10.3390/insects16030323

APA Style

Liu, Y., Ge, Y., Wang, L., Dong, J., Wang, Z., & Wang, Y. (2025). Population Genetics, Demographic History, and Potential Distributions of the New Important Pests Monolepta signata (Coleoptera: Chrysomelidae) on Corn in China. Insects, 16(3), 323. https://doi.org/10.3390/insects16030323

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