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Journal of Economic Entomology logoLink to Journal of Economic Entomology
. 2026 Feb 12;119(2):1389–1401. doi: 10.1093/jee/toag021

Population genetics and ecological niche modeling of Diglyphus albiscapus (Hymenoptera: Eulophidae) in China

Wei-Jie Wan 1, Zhen-An Jin 2, Su-Jie Du 3, Fu-Yu Ye 4, Jian-Yang Guo 5,✉, Wan-Xue Liu 6,✉
Editor: Adam C N Wong
PMCID: PMC13075816  PMID: 41677190

Abstract

The effects of global climate change on the biocontrol of agricultural pests remain unclear. Understanding the geographical distribution and genetic differentiation of parasitoid wasps in past and current environments can help predict how the future environment will affect these wasps. Diglyphus albiscapus (Hymenoptera: Eulophidae) is a dominant parasitoid wasp of agromyzid leaf miners. Our study analyzed the intraspecific diversity, population structure, and historical population dynamics of D. albiscapus based on cytochrome c oxidase subunit I and internal transcribed spacer II genes. The current and future potential geographical distributions (PGD) of D. albiscapus in China were predicted using an ensemble model. Although the genetic diversity of D. albiscapus in China was relatively high, its genetic variation was relatively low. The fixation indices (FST) and gene flow (Nm) for the D. albiscapus population were 0.09823 and 2.29505, respectively, indicating that interpopulation gene exchange was adequate. The effective population size of D. albiscapus expanded approximately twofold during the early stadial of the last glacial period (MIS 4), and 3 populations expanded substantially. Currently, this species occurs mainly in northeast, northwest, and southern China. The PGD of D. albiscapus would be expected to spread outward from its current potential distribution range under global climate change. The predicted results of population genetics and PGD showed that anthropogenic activities may have promoted the spread of D. albiscapus, enhancing gene exchange within the species and reducing genetic differentiation. This study provided a reference for the conservation and application of D. albiscapus in the field.

Keywords: genetic diversity, genetic structure, parasitoid wasps, population history, ecological niche

Introduction

Global climate change is expected to exacerbate the damage caused by agricultural pests, leading to their earlier emergence and expansion of their range toward higher altitudes (Tougeron and Tena 2019, Ramos Aguila et al. 2023). Global climate change may be beneficial for parasitoids (important biological control agents), because they may have a longer time window to find more hosts and follow their hosts toward higher altitudes (Thomson et al. 2010, Tougeron and Tena 2019, Spahn and Lill 2022). However, global climate change may also decrease intraspecific genetic diversity of parasitoids, as their geographical distribution ranges shift (Pfenninger et al. 2012). Specifically, the habitat fragmentation may isolate populations, restricting gene flow and increasing genetic drift (Castellanos-Labarcena et al. 2025). And environmental changes may impose strong directional selection on thermal tolerance genes, such as heat shock protein alleles that enhance survival under temperature fluctuations (Duffy et al. 2022). Besides, mismatches with shifting host distributions may create genetic bottlenecks (Castellanos-Labarcena et al. 2025). These processes collectively reduce the adaptive capacity of parasitoid communities facing climate change. The intraspecific genetic diversity of parasitoids is important to maintain their evolutionary potential and ability to respond to different habitats (Frankham 2010, Hu et al. 2021). Therefore, it is important to predict the potential geographical distributions (PGD) in the future and evaluate the genetic diversity of parasitoids, which help develop effective parasitoids conservation and use strategies (Simonato et al. 2019, Liang et al. 2023a, Huang et al. 2024).

Approximately 110 Agromyzidae species are major pests of cultivated crops worldwide, and most larvae remain internally on leaves, causing serious economic losses (Kang et al. 2009, Xuan et al. 2023). Diglyphus albiscapus (Hymenoptera: Eulophidae) feeds on 10 species in 4 genera of Agromyzidae, which are the dominant parasitoid wasps against agromyzid leafminers (Zhu et al. 2000, Hansson and Navone 2017). Several species of agromyzid leafminers have caused serious damage in China, among which are Phytomyza horticola, Liriomyza sativae, L. huidobrensis, and L. trifolii (Kang 1996, Liu et al. 2013, Tao et al. 2022). Furthermore, D. albiscapus is distributed across 28 provinces in China (Fig. 1), overlapping with the geographic ranges of these major agromyzid leafminers (Liang et al. 2023a, Du et al. 2025). This widespread distribution highlights the potential for large-scale biocontrol applications, thereby justifying the need to investigate its current and future geographical distribution patterns and ecological niche dynamics to optimize pest management strategies. Furthermore, the combination of genetic differentiation analysis and species distribution modeling (SDM) helps us better understand the evolutionary dynamics and dispersal processes of different geographical populations of species based on spatial genetic patterns and distribution dynamics (Ye et al. 2020, Liang et al. 2023a). The development of SDMs provides emerging tools for further understanding of the geographical distribution characteristics of species and their responses to climate change (Jin et al. 2022). To some extent, this helps clarify the spatial distribution range of enemy species, providing a basis for spatial matching in the development and use of parasitoids to control leaf miners (Zhao et al. 2023).

Fig. 1.

Fig. 1.

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Occurrence sites and geographical populations of Diglyphus albiscapus in China. Purple circles represent the locations of the distribution. Orange circles represent the populations in corresponding locations selected for population genetic analysis.

In this study, we used mitochondrial cytochrome c oxidase subunit I gene (COI) and nuclear gene internal transcribed spacer II (ITS2) to determine the population structure and demographic history of this species in China. We conducted a systematic biogeographical and spatial niche analysis of D. albiscapus in China based on a sampling dataset to determine its biogeographical patterns and simulate its responses to future climate warming under low and high-stress climate change scenarios. Furthermore, we clarified multiple scenarios of how genetic diversity and geographical distribution dynamics may interact under climate change, with the goal of informing effective conservation and biological control strategies for D. albiscapus.

Materials and Methods

Specimen Collection

We investigated and collected the leaves of vegetables, weeds, and other plants that had been harmed by agromyzid leaf miners during 2016 to 2020, based on a random sampling method. Leaf samples were randomly collected primarily from the following 3 types of habitats such as open-air farmland, human settlement area, and wasteland. The leaves were then placed in the insectary until agromyzid leaf miners and parasitoid wasps emerged. All agromyzid leafminers and parasitoid wasps were preserved in absolute ethanol and maintained at −20 °C at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

The specimens were examined under a stereomicroscope (SZX-16; Olympus). The morphological identification of D. albiscapus followed relevant references (Hansson and Navone 2017), the scape of D. albiscapus was completely pale; hind femur with basal two-thirds dark with metallic green; fore wing with speculum; “male gaster with a pale spot subbasally”. The morphological identification of agromyzid leaf miners followed the relevant references (Lonsdale 2011, Lonsdale 2017, Lonsdale and Eiseman 2019, Liang et al. 2023b). ArcGIS 10.8 was used to draw Fig. 1, according to the collection site information of D. albiscapus in China. From the nationwide sampling sites for D. albiscapus (purple sites), 10 populations (orange sites) were selected for genetic analysis. This selection was designed by choosing sites along a pronounced latitudinal gradient (22 to 46 °N) and verifying adequate parasitoid abundance. Each sampling location consisted of a 10 m × 10 m quadrat.

DNA Extraction and Amplification

A total of 251 female specimens from 10 populations (population means that a group of individuals of D. albiscapus living in the same area) (Fig. 1 and Table S1). Genomic DNA was extracted from the adult female specimens. The extraction method was as described in previous studies, with some modifications (De Barro and Driver 1997, Du et al. 2023). The DNA extraction was performed using a 200 µl microcentrifuge tube (Bioevopeak, Shandong, China) and a 200 µl pipette tip (Bioevopeak) sealed by heating to grind the metasoma into a homogenate. The homogenate was incubated at 65, 25, and 96 °C for 30, 2, and 10 min, respectively. The molecular markers cytochrome oxidase I of mitochondrion (COI) and ITS2 genes were used in this study. Primer information was listed in Table S2 (Campbell et al. 1993, Sha et al. 2006).

Amplification was performed as described previously (Hebert et al. 2003, Wan et al. 2023). Polymerase chain reaction (PCR) consisted of 0.4 µl Taq enzyme (2.5 Uµl−1), 0.4 µl deoxynucleotide triphosphate (2.5 mm), 2.5 µl 10× buffer (containing Mg2+), 0.4 µl forward primer, 0.4 µl reverse primer, 1 µl DNA template, and 19.9 µl double-distilled H2O. Next, the PCR cycling conditions consisted of an initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturation at 95 °C for 30 s, annealing for 45 s, extension at 72 °C for 60 s, and a single cycle of final extension at 72 °C for 5 min. The annealing temperatures for COI and ITS2 were 48 and 52 °C, respectively. Unpurified PCR products were sent to Sangon Biotech Co., Ltd (Beijing, China) for purification and bidirectional sequencing, and the primers were designed by Sangon Biotech Co., Ltd.

Species Distribution Models

SDMs based on species distribution sampling points and bioclimatic variables are powerful tools for describing species distribution (Venette et al. 2010, Liu et al. 2020). Individual SDMs have been widely used to predict the PGD of species (Jin et al. 2022, Li et al. 2024). However, owing to variations in species distribution patterns and modeling algorithms, there are differences in model accuracy (Thuiller 2004). In our study, the BIOMOD2 integrated modeling algorithm platform, developed using the R language, was used to predict the potential geographic distribution range of D. albiscapus. The platform integrates 10 mainstream individual SDMs using various modeling theories and methods to enhance the accuracy of current potential geographic distribution simulations and improve predictions of future potential geographic distribution for the target species (Thuiller et al. 2009, Thuiller et al. 2014). Furthermore, we employ the jackknife method in Maxent to assess the contribution of different predictor variables to species suitability, in order to support the interpretation of the effects of various predictor variables on the suitability distribution results within the ensemble models.

The coordinate information of the distribution sample points of D. albiscapus was derived from the results of sampling in China from 2016 to 2020 (Fig. 1). To avoid spatial autocorrelation of the sampling points, we used ENMtools to thin the occurrence points based on 2.5 arc-minute layer data, ensuring that only one distribution point was retained per raster (Warren et al. 2010). A total of 376 distribution points were included in the model analysis.

Climate data based on the 1970 to 2000 average was obtained from the WorldClim database (Fick and Hijmans 2017) with a resolution of 2.5 arc minutes at approximately 5 km resolution. We selected 6 known bioclimatic variables that are relevant to the life activities such as physiological traits and life history changes of insects to represent the environmental dimensions occupied by D. albiscapus, namely, the mean diurnal range (bio2), maximum temperature of the warmest month (bio5), minimum temperature of the coldest month (bio6), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), and precipitation seasonality (bio15) (De Meyer et al. 2010, Hill and Terblanche 2014, Hill et al. 2017). Considering that the potential distribution of P. horticola, that is, the dominant host species of D. albiscapus, may be limited by altitude and anthropogenic factors, we also incorporated altitude (Fick and Hijmans 2017) and anthropogenic activity factors (https://www.nasa.gov/) into the analysis (Liang et al. 2023a).

Future climate scenarios layers were selected for SSP1-2.6, that is, the sustainable development path, and SSP5-8.5, namely, the traditional fossil fuel path from the Beijing Climate Center Climate System Model (BCC-CSM2-MR) climate system model developed by the National Climate Center based on the CMIP6 of the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change. Compared to BCC-CSM1.1m, BCC-CSM2-MR demonstrates substantial improvements in tropospheric temperature and circulation at global and East Asian scales and climate variability across various time scales (Fick and Hijmans 2017, Wu et al. 2019). In our study, 2 future climate scenarios (SSP1-2.6 and SSP5-8.5) for the 2050s (average of 2041 to 2060) were included to predict the PGD range under future climatic conditions.

Data Analysis

Sequence Alignment

The 2 gene sequences of D. albiscapus were aligned using ClustalW in Molecular Evolutionary Genetics Analysis (MEGA) X ver. 10.1 (Kimura 1980, Kumar et al. 2018), respectively.

Haplotype and Genetic Diversity of Populations

Haplotype number, haplotype diversity (Hd), nucleotide diversity (Pi), and average (K) were estimated using DnaSP ver. 5 (Librado and Rozas 2009). Variable sites, base composition, and genetic divergence were estimated, and a phylogenetic tree was constructed using the Maximum Likelihood method in MEGA X ver. 10.1 (Kimura 1980, Kumar et al. 2018). A haplotype network was constructed using the PopART ver. 1.7 (Leigh and Bryant 2015). Analysis of molecular variance (AMOVA) was conducted with 10,000 permutations to estimate hierarchical genetic variation among and within populations using Arlequin ver. 3.5 (Excoffier and Lischer 2010).

Gene Flow, Principal Component Analysis, and Mantel Test

The fixation index (FST) and gene flow (Nm) neutrality tests of pairwise populations were performed using Arlequin ver. 3.5 (Excoffier and Lischer 2010). The range of fixation indices and gene flow can determine the level of genetic differentiation among pairwise populations. Fixation indices 0<FST≤0.05, 0.05<FST≤0.15, 0.15<FST≤0.25, and FST>0.25 were low, moderate, high, and very high genetic differentiation, respectively (Govindaraju 1989). Gene flow 0<Nm ≤ 1, 1<Nm ≤ 4, and Nm>4 were low, moderate, and high genetic differentiation (Boivin et al. 2004).

Subsequently, the Mantel test between FST/(1 − FST) and geographical distance (isolation by distance (IBD)) in GenAlEx ver. 6.5 (Peakall and Smouse 2012). And principal component analysis (PCoA) of populations was performed using GenAlEx ver. 6.5 (Peakall and Smouse 2012).

Population Genetic Structure and Demographic History Inference

Population genetic structures and lineages were estimated using the BAPS ver. 2.3 ver. 6.0 (Cheng et al. 2013). To ensure the consistency and convergence of the results, 10 runs for K = 1 to 10 were performed. Genetic structure results are presented as vertical stacking diagrams, with each represented by a thin vertical bar. Each vertical bar comprises one or more colors, with each color representing the proportion of the ancestral line of the individual.

The neutrality test of the pairwise populations was performed using Arlequin ver. 3.5 (Excoffier and Lischer 2010) to calculate Tajima’s D and Fu’s Fs values. A significantly negative result on the neutrality tests indicated that populations expanded or continued to grow in the past. Mismatch distributions were calculated for the observed and expected values using DnaSP ver. 5 (Librado and Rozas 2009). Generally, the mismatch distribution curve shows a single-peaked Poisson distribution, implying that the population has undergone expansion or sustained growth in the past. Demographic history was estimated using Beast ver. 2.6 (Bouckaert et al. 2014). An uncorrelated relaxed lognormal clock model was selected, and the substitution rate for COI was set to 1.77% per million years, as estimated by the relevant reference (Papadopoulou et al. 2010). Markov chain Monte Carlo was run for 200 million generations, and trees were sampled every 1,000 steps. The Bayesian skyline plots (BSPs) were analyzed using Tracer ver. 1.7 (Rambaut et al. 2018).

SDM Selected and Parameter Settings

Considering the advantages and disadvantages of 10 models, we decided to use a combination of 2 regression-based models, that is, generalized linear models and generalized additive models, and 3 machine learning models, namely, gradient boosting machines, random forests, and MaxEnt to construct the ensemble models (Aguilar-Fenollosa and Jacas 2014). These 5 techniques have proven useful in modeling complex relationships and have demonstrated strong transferability in novel environments (Elith et al. 2006, Heikkinen et al. 2012). Each model was run 20 times. The performance of each model was assessed using a repeated 4-fold cross-validation method, where 75% of the data were used to calibrate the model and the remaining 25% were used for evaluation (Jin et al. 2022). We set 5,000 pseudo-existence points for each existing dataset and generated 20 corresponding pseudo-absence datasets for D. albiscapus. The ensemble model results were evaluated using the area under the receiver operating characteristic curve (AUC) and true skill statistics (TSS) values (Jin et al. 2024). The TSS and AUC values larger than 0.8 and 0.9 indicated excellent performance, respectively (Coetzee et al. 2009). Anthropogenic variables were included in model training but excluded from future projections, the effective suitability function differs between current and future scenarios. Therefore, the future projections should be interpreted as potential climatic suitability rather than as a direct continuation of the present-day distribution model.

GIS Analysis

In the EM results, the raster layer presented the probability of the presence of D. albiscapus in each cell, which ranged from 0 to 1,000. According to the optimized thresholds, that is, the sum of the sensitivity and specificity TSS, the raster layer was divided into unsuitable (0 to 297) and suitable areas (>297) using ArcGIS 10.8. Furthermore, suitable areas were reclassified as low suitability habitats (297 to 400), moderately suitable habitats (400 to 600), and highly suitable habitats (>600).

Results

Genetic Divergence and Network of Haplotypes

The COI and ITS2 genes with lengths of 757 and 444 base pairs (bp), respectively, were successfully obtained from 251 D. albiscapus.

A total of 73 haplotypes were identified based on the COI gene. The network relationship between different haplotypes (Fig. 2) showed that Hap-1 was the center of the haplotype. All other haplotypes radiated around it in a star shape. Hap-1 may be a progenitor haplotype because it was widely distributed and had the highest frequency of distribution. Hap-1 was detected in 251 individuals from 10 populations, followed by Hap-10 in 7 populations.

Fig. 2.

Fig. 2.

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Haplotype network of Diglyphus albiscapus, based on COI gene sequences and 10 provinces. Notes: Haplotype circle size represents the number of individuals. Individuals from the same region were categorized into the same group in this analysis, with each group corresponding to a different color.

In the SD population, 25 haplotypes were detected in the samples (n = 64). In the HuN population, 17 haplotypes were detected in samples (n = 35), whereas 3 haplotypes were detected in samples from the HuB population (Hap_3, Hap_10, and Hap_24). Besides, a total of 251 individuals from 10 geographic populations were examined (Fig. 1).

The COI gene sequences were combined 689 bp conserved loci with 68 bp variable loci, which included 27 singleton polymorphic loci and 41 parsimonious informative loci. And 73 haplotypes were detected, including 12 shared and 61 exclusive haplotypes. The genetic divergence among 73 haplotypes was 0.13% to 1.20% (Fig. S1).

However, only 3 variable loci were detected from 444 bp of ITS2 gene sequences, which included 2 singleton polymorphic loci and 1 parsimonious informative locus. And 4 haplotypes were detected, including 1 shared and 3 exclusive haplotypes. Hap-2 was only detected in the FJ population, and Hap-3 and Hap-4 were only detected in the HuN population, based on the ITS2 gene.

Genetic Diversity of Populations

The results of genetic diversity parameters and neutrality tests of 251 individuals from 10 populations in China based on COI gene sequences were shown in Table 1. The haplotype diversity, nucleotide diversity, and average number of nucleotide differences in the 251 individuals were 0.913, 0.00309, and 2.337, respectively. Genetic diversity was high in the total population and in most geographical populations of D. albiscapus. The highest genetic diversity was observed in the JL population. The FJ population exhibited low genetic diversity.

Table 1.

Genetic diversity parameters and neutrality test results of 10 Diglyphus albiscapus geographical populations in China, based on COI gene sequences

Population N h Hd k Pi Fu’s Fs test Tajima’s D
BJ 28 7 0.833 1.884 0.00249 −0.753 −0.259
FJ 8 2 0.250 0.500 0.00066 0.762 −1.310
HL 17 8 0.728 2.529 0.00334 −1.849 −1.079
HeN 25 10 0.690 1.573 0.00208 −4.950** −2.21848**
HuB 26 8 0.840 2.132 0.00282 −1.358 −1.084
HuN 32 17 0.837 1.766 0.00233 −14.699** −2.3522**
JL 8 7 0.964 2.464 0.00326 −3.848** −0.973
LN 22 12 0.926 2.926 0.00387 −4.781** −1.7343
SD 61 25 0.928 2.518 0.00333 −18.639** −2.05621*
ZJ 24 11 0.924 1.909 0.00252 −5.428** −1.191
Total 251 73 0.913 2.337 0.00309 −4.01718** −2.42858**
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Abbreviations: N, number of individuals; h, Number of Haplotypes; Hd, Haplotype (gene) diversity; k, average number of nucleotide differences; Pi, nucleotide diversity.

*

p < 0.05;

**

p < 0.01.

The AMOVA result indicated that the molecular genetic variation within populations was 90.18%, and that among populations was 9.82% (Table 2). This indicates that the genetic differentiation of D. albiscapus resides primarily within populations. The AMOVA result of 251 individuals grouped by host insect species was shown in Table 3. The AMOVA result indicated that the molecular genetic variation within populations was 97.49%, and that among populations was 2.51% (Table 3). The AMOVA result of 251 individuals grouped by host plant species was shown in Table 4. The AMOVA result indicated that the molecular genetic variation within populations was 93.50%, and that among populations was 6.50% (Table 4).

Table 2.

Analysis of molecular variance (AMOVA) for Diglyphus albiscapus geographical populations in China, based on COI sequences

Source of variation df Sum ofsquares Variance of components Percentage variation F ST
Among populations 9 34.933 0.11624 9.82 0.09823
Within populations 241 257.174 1.06711 90.18
Total 292.108 1.18335
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Table 3.

Analysis of molecular variance (AMOVA) for Diglyphus albiscapus grouped by host insect species in China, based on COI sequences

Source of variation df Sum of squares Variance of components Percentage variation F ST
Among populations 3 8.014 0.02965 2.51 0.02513
Within populations 247 284.093 1.15018 97.49
Total 250 292.108 1.17983
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Table 4.

Analysis of molecular variance (AMOVA) for Diglyphus albiscapus grouped by host plant species in China, based on COI sequences

Source of variation df Sum of squares Variance of components Percentage variation F ST
Among populations 8 25.381 0.07658 6.50 0.06496
Within populations 242 266.726 1.10217 93.50
Total 250 292.108 1.17875
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Based on ITS2 gene, the haplotype diversity, nucleotide diversity, and average number of nucleotide differences in the 251 individuals were 0.055, 0.00012, and 0.055, respectively. The genetic diversity was only observed in the FJ and the HuN populations, but other populations were detected only one haplotype, without genetic diversity. The AMOVA results indicated that the molecular genetic variation within populations was 53.99%, and that among populations was 46.01%.

Population Genetic Structure and Mantel Test

The population genetic structure was observed among populations of from the BAPS simulations, with K = 2 best explaining the structure in the COI genes (Fig. 3A). Most individuals from the 10 populations were assigned predominantly to a single cluster. In addition, Cluster 1 (including BJ, FJ, ZJ population) mainly consisted of red clusters; Cluster 2 (including HL, HeN, HuB, HuN, JL, LN, SD population) mainly consisted of green clusters.

Fig. 3.

Fig. 3.

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Genetic structure analysis and Mantel test for isolation by distance (IBD) of Diglyphus albiscapus geographical populations, based on COI gene. A) Genetic structure of 10 geographical groups. Two ancestral clusters are shown; B) principal coordinate analysis (PCoA) shows clustering of geographical populations; C) scatter plots show the relationships between genetic distance and geographical distance.

The fixation indices (FST), and gene flow (Nm) for 251 individuals were 0.09823 and 2.29505, respectively. The FST and Nm values for the 10 populations were shown in Table 5. The total population was less genetically differentiated, and there was sufficient gene exchange (Table 5).

Table 5.

Pairwise genetic differentiation coefficient (FST, below the diagonal) and gene flow (Nm, above the diagonal) for groups

Group BJ FJ HL HeN HuB HuN JL LN SD ZJ
BJ 0.26 0.74 3.25 2.10 3.67 1.67 3.31 3.03 3.33
FJ 0.49 0.23 0.23 0.30 0.26 0.23 0.39 0.42 0.26
HL 0.25 0.52 1.13 1.94 1.33 16.66 1.86 1.77 1.00
HeN 0.07 0.52 0.18 7.87 inf 5.89 35.06 35.53 7.17
HuB 0.11 0.46 0.11 0.03 8.30 110.73 6.75 7.85 2.91
HuN 0.06 0.49 0.16 0.00 0.03 10.92 64.05 103.78 10.58
JL 0.13 0.53 0.01 0.04 0.00 0.02 12.04 12.84 2.82
LN 0.07 0.39 0.12 0.01 0.04 0.00 0.02 22.09 6.27
SD 0.08 0.38 0.12 0.01 0.03 0.00 0.02 0.01 4.81
ZJ 0.07 0.49 0.20 0.03 0.08 0.02 0.08 0.04 0.05
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Abbreviation: inf, infinity.

The first and second PCoA axes explained 63.84% and 18.24% of the total variance, respectively, based on Nei’s genetic distance (Fig. 3B). PCoA showed that the FJ population was far from all other populations on the first axis, indicating that it had a different population structure from the other populations. Meanwhile, the inter-population FST results (Table 5) showed that the difference in FST values between Fujian populations and other geographic populations was large (FST > 0.25) and Nm < 1. This indicated that there was insufficient gene exchange between the FJ populations and other populations, which was consistent with the results of the population and FST analyses.

The HL population was far from all other populations on the second axis, indicating that it had a different population structure from the other populations. Meanwhile, the inter-population FST results (Table 5) showed that the difference in FST values between the HL population and other 2 geographic populations, FJ, and BJ populations was large (FST > 0.25). This indicated that gene exchange between the HL population and the 2 geographic populations was less adequate, which was consistent with the results of the population and FST analyses.

The PCoA result of 251 individuals grouped by host insect species was shown in Fig. 4. The first and second PCoA axes explained 75.40% and 15.05% of the total variance, respectively (Fig. 4). Single-species populations Ph and Ls exhibit vertical segregation in the left-hand region, indicating genetic differences between the 2 populations. The mixed-species populations Lsb and Phb were far from the single-species populations. Mixed hosts may promote population differentiation or adaptive evolution.

Fig. 4.

Fig. 4.

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Principal coordinate analysis (PCoA) shows clustering of populations grouped by host insect species. Abbreviations: Ls, Liriomyza sativae; Lsb, L. sativae and L. bryoniae; Ph, Phytomyza horticola; Phb, P. horticola and L. bryoniae.

The PCoA result of 251 individuals grouped by host plant species was shown in Fig. 5. The first and second PCoA axes explained 48.72% and 12.56% of the total variance, respectively (Fig. 5). Host plants from different families presented significant distribution characteristics. Brg and Bro populations of the Brassicaceae family didn’t overlap in the lower left area, showing a differentiation within the same family; Vu, Ps, and Pv populations of the Fabaceae family were distributed in a vertical gradient, reflecting intergeneric differentiation within the same family; Ca, Lsa, and Cs populations of the Asteraceae family were distributed in a vertical gradient, reflecting intergeneric differentiation within the same family; Cm, Pv, Cs and Brg populations of the different family formed a cluster in the center, indicating a low level of genetic differentiation.

Fig. 5.

Fig. 5.

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Principal coordinate analysis (PCoA) shows clustering of populations grouped by host plant species. Abbreviations: Brg, Brassica rapa var. oleifera; Bro, Brassica rapa var. oleifera; Ca, Cirsium arvense var. integrifolium; Cm, Cucurbita moschata; Cs, Crepidiastrum sonchifolium; Lsa, Lactuca sativa var. asparagina; Ps, Pisum sativum; Pv, Phaseolus vulgaris; Vu, Vigna unguiculata.

The Mantel test for IBD in all populations showed no significant positive correlation between genetic distance and geographic distance among the 10 populations (y = 0.0004× + 0.0048, R2 = 0.0411, P = 0.16 > 0.05; Fig. 3).

Demographic History

Neutrality tests were conducted using Tajima’s D and Fu’s Fs (Table 1), based on the COI gene. The values of Tajima’s D and Fu’s Fs for 10 populations in China were significantly negative (Tajima’s D = −2.42858, P < 0.01; Fu’s Fs = − 4.01718, P < 0.01; Table 1). The values of Fu’s Fs were significantly negative in the HeN, HuN, JL, LN, SD, and ZJ populations (Table 1). Besides, based on the ITS2 gene, the value of Fu’s Fs for 10 populations in China was significantly negative (Fu’s Fs =− 5.443, P < 0.01).

The BSPs indicated that the effective population sizes of populations remained stable over a long period and then expanded twice at approximately 70 ka during the early glacial staircase of the last glacial period (MIS4), based on the concatenated genes (Fig. 3A). The mismatch distribution results of the population in China were unimodal based on concatenated genes (Fig. 6A). This suggests that the D. albiscapus populations have recently undergone demographic expansion (Fig. 6B).

Fig. 6.

Fig. 6.

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Bayesian skyline plots (BSPs). A) and mismatch distribution analyses; B) of Diglyphus albiscapus based on COI genes.

Potential Geographic Distribution of D. albiscapus in China under Current and Future Scenarios

Based on the TSS and AUC evaluation metrics, the ensemble model achieved average TSS and AUC values of 0.81 ± 0.029 and 0.97 ± 0.006, respectively (Table S3). This indicates that the ensemble model demonstrates strong reliability. The importance of human factors in shaping the PGD patterns of D. albiscapus was significantly higher than that of climatic factors (Fig. S3), and species suitability increased with increasing human factors (Fig. 7A, Figs S2A and S4A). Among the climate variables, the minimum temperature of the coldest month (Bio6) plays a crucial role in shaping the PGD patterns of D. albiscapus (Fig. S3), and the species’ suitability for the minimum temperature of the coldest month peaks at temperatures ranging from −10 to 0 °C based on ensemble model and Maxent models (Fig. 7A, Figs. S2B and S4B).

Fig. 7.

Fig. 7.

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Potential geographic distribution of Diglyphus albiscapus in China under current and future scenarios. A) Important values of different predictors for D. albiscapus potential distribution results. B) The current potential geographic distribution of D. albiscapus in China. C) The future potential geographic distribution of D. albiscapus in China under SSP1-2.6. D) The future potential geographic distribution of D. albiscapus in China under SSP5-8.5.

Under the current climate scenario, the PGD of D. albiscapus is primarily found in the North China Plain, Sichuan Basin, and hilly regions of southern China (Fig. 7B). High-suitability areas were concentrated in Beijing, Tianjin, Hebei, Shandong, Anhui, Jiangsu, Henan, Hubei, and Hunan (Fig. 7B). By the 2050s, the potential distribution range and high-suitability areas expanded further under both climate scenarios, including the North China Plain, Northeast China Plain, Sichuan Basin, and South China Hills (Figs 7C and D and 8). Under the SSP1-2.6 climate scenario, the species was expected to expand outward from its current potential distribution range, covering areas such as Sichuan, Hunan, Hubei, Jiangxi, Anhui, Inner Mongolia, Liaoning, Jilin, and Heilongjiang (Fig. 8A). In contrast, under the SSP5-8.5, while the species also showed an outward expansion trend from its current potential distribution range, the range of expansion in Northeast China will be larger than that in the SSP1-2.6 scenario. Additionally, suitable areas in the southern provinces, such as Zhejiang, Guizhou, Hunan, and Hubei, are expected to contract significantly (Fig. 8B).

Fig. 8.

Fig. 8.

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Change in potential geographic distribution of Diglyphus albiscapus in China from current to 2050s. A) The change of potential geographic distribution of D. albiscapus under SSP1-2.6. B) The change in future potential geographic distribution of D. albiscapus in China under SSP5-8.5.

Discussion

D. albiscapus is a widespread and dominant parasitoid in China. We aimed to predict the genetic differentiation among populations of this species across different regions of China by examining its phylogeographic characteristics and potential spatial distribution patterns. This can help us understand the population expansion traits of the species and the current and future spatial ecological niche. Furthermore, we sought to provide spatial support for the use of this species for controlling P. horticola, L. sativae, and L. trifolii in China.

The results of PCoA and genetic differentiation in 10 populations showed that 10 geographic populations, the FJ and HL populations, did not have sufficient genetic exchange with other populations, and their genetic structures differed. The low genetic diversity and different genetic structure were observed in the southern FJ population. The PGD predicted that southern provinces, including the FJ population, were expected to experience a significant contraction of suitable habitats in the future. Therefore, the FJ population may be represented as a relic population that became isolated following the initial historical expansion. As the core distribution shifted northwards or contracted, the FJ population was left behind in a diminishing southern habitat. This prolonged isolation and the associated reduction in population size would have led to the loss of genetic diversity through genetic drift and limited gene flow, explaining its current genetic distinctiveness.

In contrast, the genetic pattern observed in the northeastern HL population was consistent with that of a contemporary expansion front, as identified by the PGD model that predicted Northeast China as a critical zone for future climate-driven range expansion. The genetic differentiation of the population likely originated from a recent founder event, initiated by a limited number of colonists. Subsequently, its isolation had been reinforced by geographical barriers and the extensive tracts of arable land in northern regions, which significantly restricted gene flow with source populations. Over time, such prolonged isolation was expected to elevate the risk of genetic diversity loss within this peripheral population.

Meanwhile, the rest of the geographic populations had similar genetic structures, indicating sufficient genetic exchange among them. This was likely facilitated by the recently expanding vegetable cultivation area and growing agricultural product trade in China. These factors had promoted the spread of the main hosts, thereby contributing to the development of the observed population structure (Liu et al. 2013). This has allowed D. albiscapus to spread and counteract the genetic drift of D. albiscapus and increase the genetic diversity of the populations (Oliver 2006, Liang et al. 2023a). From the SDM results, we also found that anthropogenic activity played the most important role in determining the potential distribution of species, indicating that the species, to a certain extent depended on frequent agricultural product transportation in the process of population spreading.

Analyses grouped by host plant or host insect revealed that while host identity contributed to genetic structure, it was not the primary driver of population differentiation in D. albiscapus. On the one hand, AMOVA results demonstrated that majority of genetic variation resided within groups rather than among them, both when grouped by host insect (97.49% of variation within groups, FST = 0.025) and host plant (93.5% within groups, FST = 0.065). On the other hand, PCoA results revealed populations associated with different plant families showed distinct clustering, with genetic differentiation between single-host and mixed-host populations, respectively. These results suggested that the host plant or host insect acted as a secondary driver of genetic structure, while broader factors such as geographical isolation and historical processes likely played more dominant roles in shaping the population genetic pattern of D. albiscapus. This species enlarged significantly at about 70 ka (MIS4, early glacial staircase of last glacial period), and then stabilized. This indicated that it was highly adaptable to the cold, based on the 3 methods of neutrality test, mismatch distribution, and Bayesian skyline. Similarly, the results of the SDM indicated that BIO6 was considered a key predictor variable. Since this species is mainly distributed in North China, it may show high tolerance to low temperature during overwintering. In addition, the presence of the host may provide a suitable habitat or breeding site for its enemies, further affecting the ecological niche occupied by the enemies of the host (Zhao et al. 2023). The main host of the species has shown that the primary host of D. albiscapus had a survival rate of approximately 20% after being stored at 0 °C for 45 d, demonstrating significant cold resistance for D. albiscapus (Iwasaki et al. 2008). And the fitness prediction of P. horticola also showed that the Bio6 was the strongest bioclimatic variable and large areas of cold northern regions were highly suitable for its survival (Liang et al. 2023a). D. albiscapus is highly suitable for cultivation in northern China. In the future, particularly under the SSP5-8.5, its suitability and potential geographic distribution range in the northern regions will increase further. Previous research findings that the P. horticola, the primary host of D. albiscapus, had high suitability in North China and that climate warming will further enhance its suitability and potential distribution in North and Northeast China (Liang et al. 2023a). This indicated that D. albiscapus had a favorable temporal and spatial compatibility with P. horticola. Therefore, cold-adapted D. albiscapus may have a higher biocontrol potential in future management practices targeting P. horticola.

Given the distinct genetic patterns and the projected northward range shift, D. albiscapus adaptive responses were considered through 3 critical scenarios. Scenario A, which posited that low-diversity populations struggled to expand, was exemplified by the FJ population. The population showed low genetic diversity, significant differentiation (FST > 0.25), and limited gene flow (Nm < 1). Projected habitat contraction in southern regions implied that the population risked local decline or failed northward expansion. Scenario B, which posited that high-diversity populations maintain stability, was exemplified by core North China populations. These populations exhibited high haplotype diversity (Hd = 0.913), a star-like COI network centered on Hap-1, 90.18% within population variation, and significant negative neutrality tests (Tajima’s D = −2.42858, Fu’s Fs = −4.01718). Projected northward range expansion under climate scenarios implied these populations retained adaptive capacity, ensuring continued pest suppression service and highlighting the value of conserving genetic reservoirs in core distribution areas. Scenario C, which posited that specific high-diversity populations acted as genetic sources during range shifts, was exemplified by the JL population. The population displayed the highest genetic diversity (Hd = 0.964) and was grouped into a large, genetically cohesive cluster (Cluster 2). Projected northward expansion of suitable habitats into Northeast China (Liaoning, Jilin, Heilongjiang) implied that the 3 populations were poised to supply genetic variation to newly colonized areas.

The genetic analysis indicated that high-diversity populations from Jilin and Shandong should be prioritized as high-quality source populations for mass rearing, while the genetically distinct FJ population was recommended for local application to leverage its local adaptation. And then the ecological niche modeling predicted the expansion of D. albiscapus into Northeast China, suggesting proactive introduction in these emerging areas while implementing conservation measures in current core regions like the North China Plain. These findings provided direct guidance for resource utilization and regional management of this natural enemy and lay the foundation for future development of developmental models to precisely predict its pest control efficacy under different climate scenarios.

Supplementary Material

toag021_Supplementary_Data
toag021_supplementary_data.zip (1.7MB, zip)

Contributor Information

Wei-Jie Wan, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory for Prevention and Control of Invasive Alien Species of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Zhen-An Jin, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory for Prevention and Control of Invasive Alien Species of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Su-Jie Du, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory for Prevention and Control of Invasive Alien Species of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Fu-Yu Ye, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory for Prevention and Control of Invasive Alien Species of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Jian-Yang Guo, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory for Prevention and Control of Invasive Alien Species of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Wan-Xue Liu, State Key Laboratory for Biology of Plant Diseases and Insect Pests, Key Laboratory for Prevention and Control of Invasive Alien Species of Ministry of Agriculture and Rural Affairs, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China.

Author Contributions

Wei-Ji Wan (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Writing—original draft [lead], Writing—review & editing [lead]), Zhen-an Jin (Formal analysis [equal], Methodology [equal], Writing—original draft [equal]), Su-Jie Du (Formal analysis [equal], Methodology [equal], Writing—original draft [equal]), Fuyu Ye (Formal analysis [equal], Methodology [equal], Writing—original draft [equal]), Jian-Yang Guo (Conceptualization [equal], Funding acquisition [equal], Resources [equal], Writing—review & editing [equal]), and Wan-Xue Liu (Conceptualization [equal], Data curation [equal], Funding acquisition [equal], Methodology [equal], Project administration [equal], Resources [equal], Writing—review & editing [equal])

Supplementary Material

Supplementary material is available at Journal of Economic Entomology online.

Funding

This study was supported by the National Key R&D Program of China (Grant No. 2024YFC2607600) and the Key R&D Project of Tibet Autonomous Region (XZ202401ZY0001).

Conflicts of Interest

The authors have no conflicts of interest to declare.

Data Availability

All gene sequences in this study have been submitted to NCBI GenBank at https://www.ncbi.nlm.nih.gov. Accession numbers are listed in Table S1.

Ethical Statement

This study did not involve any human participants, animal subjects, or environmental impact that would require ethical approval. Therefore, no ethical approval was necessary for the conduct of this research.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

toag021_Supplementary_Data
toag021_supplementary_data.zip (1.7MB, zip)

Data Availability Statement

All gene sequences in this study have been submitted to NCBI GenBank at https://www.ncbi.nlm.nih.gov. Accession numbers are listed in Table S1.

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