Pleistocene Climate Oscillations and Geographic Barriers Shaped the Phylogeographic Structure of Anaplecta omei (Blattodea, Blattoidea, Anaplectidae) in Southern China: Evidence from Mitochondrial Genomes

Pleistocene Climate Oscillations and Geographic Barriers Shaped the Phylogeographic Structure of Anaplecta omei (Blattodea, Blattoidea, Anaplectidae) in Southern China: Evidence from Mitochondrial Genomes | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 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Data may be preliminary. 12 September 2025 V1 Latest version Share on Pleistocene Climate Oscillations and Geographic Barriers Shaped the Phylogeographic Structure of Anaplecta omei (Blattodea, Blattoidea, Anaplectidae) in Southern China: Evidence from Mitochondrial Genomes Authors : Tunan Zhou , Jing Zhu , Chenhui Cao , Yanli Che , and Zong-qing Wang 0000-0001-9413-1105 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175768233.31960881/v1 Published Ecology and Evolution Version of record Peer review timeline 240 views 192 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Anaplecta omei is the only species of Anaplecta widely distributed in southern China. However, previous studies have failed to elucidate the factors underlying its distribution pattern. This study aims to clarify the genetic diversity, population structure, and evolutionary history of A. omei, and explores the events that have influenced its differentiation and distribution patterns. We analyzed 97 complete mitochondrial sequences from 24 geographic populations of A. omei and conducted phylogenetic reconstruction, genetic diversity assessment, and population structure analysis. Additionally, we performed a selection test and estimated divergence times. Demographic history was examined using neutrality tests, mismatched distribution analysis and Bayesian Skyline Plot (BSP) analysis. Our results reveal that geographic populations of A. omei exhibit high haplotype diversity, low nucleotide diversity and a clear phylogeographic structure forming four distinct lineages. The selection tests suggested that the isolation of A. omei was driven by physical barriers, rather than adaptive divergence. By integrating divergence time estimates with demographic history analyses, we demonstrated that tectonic uplift, glacial climate oscillations, and local environmental complexity have collectively shaped the speciation and distribution of A. omei. These findings provide novel insights into the process of speciation and distribution within Blattodea. Pleistocene Climate Oscillations and Geographic Barriers Shaped the Phylogeographic Structure of Anaplecta omei (Blattodea, Blattoidea, Anaplectidae) in Southern China: Evidence from Mitochondrial Genomes Tunan Zhou 1, 2 , Jing Zhu 1 , Chenhui Cao 1, 2 , Yanli Che 1, 2 and Zongqing Wang 1, 2* 1 College of Plant Protection, Southwest University, Beibei, Chongqing, China 2 Key Laboratory of Agricultural Biosafety and Green Production of Upper Yangtze River (Ministry of Education), Southwest University, Chongqing, China Correspondence Zongqing Wang, email: [email protected] . Abstract: Anaplecta omei is the only species of Anaplecta widely distributed in southern China. However, previous studies have failed to elucidate the factors underlying its distribution pattern. This study aims to clarify the genetic diversity, population structure, and evolutionary history of A. omei , and explores the events that have influenced its differentiation and distribution patterns. We analyzed 97 complete mitochondrial sequences from 24 geographic populations of A. omei and conducted phylogenetic reconstruction, genetic diversity assessment, and population structure analysis. Additionally, we performed a selection test and estimated divergence times. Demographic history was examined using neutrality tests, mismatched distribution analysis and Bayesian Skyline Plot (BSP) analysis. Our results reveal that geographic populations of A. omei exhibit high haplotype diversity, low nucleotide diversity and a clear phylogeographic structure forming four distinct lineages. The selection tests suggested that the isolation of A. omei was driven by physical barriers, rather than adaptive divergence. By integrating divergence time estimates with demographic history analyses, we demonstrated that tectonic uplift, glacial climate oscillations, and local environmental complexity have collectively shaped the speciation and distribution of A. omei . These findings provide novel insights into the process of speciation and distribution within Blattodea. Key words: demographic history, population genetic structure, divergence time, cockroach, biogeography 1 Introduction There has been considerable controversy over the phylogenetic relationship of the genus Anaplecta in recent years. Grandcolas (1996) suggested that this group should be elevated to a family under the superfamily Blaberiodea. However, Roth (2003) did not adopt Grandcolas’ view and still placed it within the family Blatellidae. Subsequently, it was restored to the taxonomic status of the family Anaplectidae within the superfamily Blattoidea (Djernæs et al., 2015; Wang et al., 2017). Therefore, this group has received extensive attention in the study of phylogenetic relationships. New species of this group have also been reported in recent years (Deng et al., 2020; Zhu et al., 2022; Deng et al., 2023). Anaplecta species are small in size and have limited flight capability (Djernæs, 2018). Nevertheless, this reduced flight ability has not restricted their dispersal, as the genus is now widely distributed across Asia, America, Africa, and Australia (Beccaloni, 2023). The factors underlying this broad distribution, however, remain unclear. Recent investigations have indicated that Anaplecta omei is the only species in this genus widely distributed in southern China (Deng et al., 2020; Zhu et al., 2022), in sharp contrast to other Chinese Anaplecta species which exhibit much narrower distribution ranges. Furthermore, Zhu et al. (2022) identified three cryptic species of A. omei from the Yunnan-Guizhou Plateau, suggesting that a geological event and climatic fluctuations may have promoted species divergence. Accordingly, investigating the processes and factors underlying the current distribution pattern of A. omei , as a representative species of the genus, can provide valuable insights into the biogeography and diversification of Anaplecta species. The diversification of Anaplecta species began in the Cretaceous (Deng et al., 2023). Following the Oligocene, Earth experienced frequent tectonic activity and climatic fluctuations that affected numerous species in southern China (Liu et al., 2018). The uplift of the Tibetan Plateau, in particular, had a significant impact on the geology and climate of southern China (Harrison et al., 1992; Li & Fang, 1999; Kou et al., 2006; Favre et al., 2015). However, previous studies were hampered by limited sample sizes of A. omei , undermining their representativeness. Consequently, these studies could neither infer divergence times among geographic populations nor determine whether population dynamics were influenced by tectonic and climatic changes. To address these gaps, we conducted extensive sampling of A. omei specimens across southern China and performed comprehensive phylogeographic analyses. In recent years, mitochondrial genomes have emerged as powerful tools for phylogeographic analysis, effectively revealing species’ genetic structures, population dynamics, and demographic history (Frandsen et al., 2020; Zelada-Mázmela et al., 2022; Palumbi et al., 2023). Mitochondrial genome analyses have elucidated the mechanisms underlying population divergence and expansion across diverse taxa (Du et al., 2019: Magicicada ; Kim et al., 2022: Bombyx mandarina ; De León et al., 2023: Brachyhypopomus occidentalis ; du Plessis et al., 2023: Lutra lutra ; and Oosting et al., 2023: Chrysophrys auratus ). In this study, we sequenced 97 mitochondrial genomes from 24 distinct geographic populations of A. omei across southern China, employing these as molecular markers to characterize genetic diversity, resolve lineage structure, and estimate divergence times among populations. We further examine how geological events have shaped the present-day distribution of A. omei. This phylogeographic investigation provides novel insights into the evolutionary processes and biogeographic determinants governing Blattodean diversification. 2 Materials and Methods 2.1 Sample collection and sequence assembly We sequenced 97 A. omei specimens collected from 24 localities in southern China (Table 1). All voucher specimens are preserved in 100% ethanol at −20 °C and deposited at the College of Plant Protection, Southwest University (SWU), Chongqing, China. Either the hind-leg coxa or thoracic muscle tissue was used for DNA extraction. Genomic DNA was isolated and subjected to whole-genome shotgun sequencing at Nanjing Personal Biotechnology Co., Ltd. (China). Individual libraries were constructed for each sample and sequenced in separate lanes of an Illumina NovaSeq platform using 150-bp paired-end chemistry, yielding with Trim Galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and de novo assembled in Geneious Prime v2021 (Biomatters Ltd., Auckland, New Zealand; https://www.geneious.com) with default settings, followed by reference-guided refinement. Transfer RNA genes were annotated with MITOS (http://mitos.bioinf.uni-leipzig.de/index.py) under the invertebrate genetic code (Bernt et al., 2013). Protein-coding and ribosomal RNA genes were identified by alignment with homologous mitochondrial genes available in NCBI. 2.2 Data set and phylogenetic analyses We analysed 110 mitochondrial genomes. The ingroup comprised 97 A. omei individuals collected in this study from 24 localities in southern China, whereas the outgroup contained 13 sequences of five congeneric species: A. longihamata , A. condensa , A. paraomei , A. strigata , and A. arcuata . Alignments were generated in MAFFT v7 (https://mafft.cbrc.jp/alignment/server/) (Katoh et al., 2019), using the G-INS-i algorithm for protein-coding genes (PCGs) and Q-INS-i for non-coding fragments (22 tRNAs, 12S rRNA and 16S rRNA). Afterward, manual adjustments were made in MEGA v.7.0 (Kumar et al., 2016). Alignments of PCGs were corrected by translation into amino acids; the remaining sequences were visually inspected and obvious misalignments in intergenic regions were removed. Two data matrices were then prepared: (i) PCGs (13 protein-coding genes) for selection-pressure analyses and (ii) MG (13 PCGs + 22 tRNAs + 2 rRNAs) for all other analyses. For phylogenetic reconstruction, codon-specific saturation was evaluated in DAMBE v7.2.136 (Xia et al., 2003; Xia & Lemey, 2009). The third codon positions were highly saturated ( Iss = 0.075) relative to the first and second positions ( Iss = 0.019) and were therefore excluded. Optimal substitution models for each partition were selected in PartitionFinder v2.1.1 (Lanfear et al., 2017) under the corrected Akaike information criterion (AICc): TRN+I+G for ND4, ND5, ND1 and ND4L; TVM+G for ATP8, ND6 and ND2; GTR+I for COI; GTR+I+G for COII, COIII and CYTB; GTR+G for ND3, ATP6, 12S and 16S; and HKY+I+G for the tRNA set. Maximum-likelihood (ML) was used in IQ-TREE v2.1.3 (Nguyen et al., 2015) and Bayesian inference (BI) was used in MrBayes v3.2.5 (Ronquist et al., 2012). The ML and BI trees were visualized in FigTree v1.4.2 (http://tree.bio.ed.ac.uk/software/figtree/), and a circular cladogram was generated using the Interactive Tree of Life (iTOL) website (Letunic & Bork, 2021). 2.3 Genetic diversity and genetic structure analyses Population genetic parameters including the number of haplotypes ( H ), the number of polymorphic sites ( S ), haplotype diversity ( Hd ), nucleotide diversity ( π ) and genetic differentiation coefficients (Gst and Nst) were calculated using DnaSP v6.12.03 (Rozas et al., 2017). Analysis of molecular variance (AMOVA) was completed in Arlequin v3.5 (Excoffier & Lischer, 2010). Fst was estimated in Arlequin v3.5 to quantify the differentiation between pairwise populations and pairwise genetic groups, with statistical significance tested by 1,000 non-parametric permutations at the 5% significance level. Gene flow ( N m ) was calculated according to Fst , N m = (1- Fst )/4 Fst (Wright, 1949). Bayesian population structure was analyzed with BAPS v6.0 (Corander et al., 2008), selecting the spatial clustering of individuals module, and the K value corresponding to the maximum log-likelihood value was determined as the optimal grouping. We used the Codeml program in PAML v4.9 (Yang, 2007) to test for selection across the PCG dataset, with branches defined according to BAPS grouping results. This approach allowed us to assess whether lineage divergence was accompanied by shifts in selective pressures. Substitution models for the PCG dataset were selected following the same criteria used in phylogenetic analyses (see Table S1 for model details). We implemented both the one-ratio model (M0), which assumes a uniform dN/dS ratio ( ω ) across all branches, and the two-ratio branch model, which allows different ω values for designated foreground and background branches (Yang et al., 2000). Haplotype networks were constructed using the median joining network algorithm in PopART v1.7 (Leigh & Bryant, 2015). 2.4 Divergence time estimation Divergence times of Anaplecta were estimated in BEAST v1.10.2 (Suchard et al., 2018) using the MG dataset, with the same substitution models as in the phylogenetic analyses. The molecular clock was calibrated with two secondary calibration points from Deng et al. (2023): the split between A. strigata and A. arcus , 89.9 Ma (95% credibility interval, 71.1–108.1 Ma), and between A. arcus and remaining Anaplecta species, 86.2Ma (95% credibility interval, 69.9–101.6 Ma). We specified a Birth-Death Process (Gernhard, 2008) and a strict molecular clock (Brown & Yang, 2011) in analysis. Two independent runs of 50 million Markov chain Monte Carlo (MCMC) generations were performed, sampling every 5,000 steps. Convergence was confirmed when ESS values exceeded 200 in Tracer v1.7.1 (Rambaut et al., 2018). The maximum clade credibility tree was summarized within TreeAnnotator v1.10.2. 2.5 Demographic history analysis Population demographic dynamics were reconstructed using neutrality tests, mismatch distribution analysis and Bayesian skyline plots (BSP). Neutrality tests and mismatch distributions were calculated in Arlequin v3.5 using the MG dataset. Tajima’s D and Fu’s Fs statistics tested for signatures of demographic equilibrium versus expansion, with significantly negative values indicating population growth (Tajima, 1989; Fu, 1997). Unimodal mismatch distributions suggest population expansion, whereas multimodal patterns indicate demographic equilibrium (Rogers & Harpending, 1992). The sum of squared deviations (SSD) and Harpending’s raggedness index ( r ) assessed goodness-of-fit between observed and expected distributions. The expansion time ( t ) was calculated as t=τ /2μk, where k=3,409bp (sequence length) and μ=0.0177 (the substitution rate of COI gene) (Rogers & Harpending, 1992). Finally, Bayesian Skyline Plot (BSP) analysis was implemented in BEAST v.2 (Bouckaert et al., 2014) using the MG dataset with a relaxed lognormal clock and a mean substitution rate of 3.342% per site per million years. The best-fitting partitioning schemes are detailed in Table S2. Analysis ran for 50 million MCMC generations, and then demographic trajectories were visualized in Tracer v1.7.1. 3 Results 3.1 Genetic diversity and population genetic structure We sequenced 97 complete mitochondrial genomes of A. omei with GenBank accession numbers @@@-@@@, each comprising 37 functional genes and one control region. Genome lengths ranged from 15,347 to 15,367 bp, with GC contents between 30.3% and 30.4%. The average nucleotide composition was 41.0% A, 19.1% C, 11.3% G, and 28.6% T. Across the 97 genomes, we detected 529 polymorphic sites, including 364 parsimony-informative sites and 165 singletons. These variants defined 79 haplotypes, yielding overall haplotype diversity ( Hd ) of 0.994 and nucleotide diversity ( π ) of 0.00371 (Table 1). This exhibits high haplotype diversity ( Hd >0.5) but low nucleotide diversity ( π <0.005). Among geographic populations, HNMS exhibited the highest nucleotide diversity ( π =0.00220) and a high genetic diversity ( Hd =0.833) (Table 1). BAPS clustering indicated that the log-marginal likelihood increased with K and stabilized at its maximum at K = 4. Accordingly, four groups were identified as the optimal population partition: Group 1, comprising populations mainly from Sichuan Province, Hunan Province, and Chongqing; Group 2, consisting largely of populations from southeastern China (Guangdong, Fujian, Jiangsu, Zhejiang, and Jiangxi Provinces); Group 3, containing only the Guangdong population (GDQLS) and Group 4, containing only the Hunan population (HNDPL) (Table 1, Figure S1). The genetic diversity of each group of A. omei populations based on the MG dataset is presented in Table 2. Branch-model tests in PAML showed that the single-ratio model (M0) estimated a genome-wide nonsynonymous/synonymous substitution rate ratio (ω) of 0.12368 for all A. omei populations, consistent with strong purifying selection. Likelihood-ratio tests comparing the two-ratio model with M0 were not significant, indicating no detectable lineage-specific differences in selective pressure (Table 3). AMOVA analysis (Table S3) showed significant differentiation among groups (77.02% of the variation) compared to the level among populations (12.68%) or within populations (10.30%). Moreover, the pairwise Fst values among groups were 0.6895-0.9495 (>0.25), and among populations were 0.0638-1. The Fst values between most geographic populations are greater than 0.25 (Table S4, Figure S2). The maximum gene flow ( N m ) among groups was 0.1126 (<1) with only 2% gene flow of population pairs less than 1 among populations (Table S2, Figure S2). All the above results indicate significant genetic differentiation in A. omei . 3.3 Phylogenetic constructions and networks For the MG dataset, our likelihood and Bayesian phylogenetic analyses yielded almost identical topologies with high support values (Figure 1, Figure S3). Four well-defined lineages were recovered, consistent with BAPS clustering (Group 1-4). The same geographic populations were clustered together to form one single branch, showed obvious phylogeographic structure. The haplotype network based on MG dataset (Figure 2) showed that the population structure was similar to that retrieved in the phylogenetic analyses. There were four Groups distinguished. No haplotypes were shared among geographic populations; haplotypes clustered strictly by Group. Group 3 (GDQLS population) and Group 4 (HNDPL population) exhibited the largest numbers of mutational steps, whereas Group 1 and Group 2 showed the fewest. The absence of shared haplotypes and the long mutational distances among Groups underscore pronounced genetic differentiation within A. omei . FIGURE 1 | Maximum likelihood (ML) phylogeny of A. omei inferred from the MG dataset Branch colors represent geographic lineages: red denotes Lineage 1 (Group 1 in BAPS clustering); green, Lineage 2 (Group 2); blue, Lineage 3 (Group 3) and purple, Lineage 4 (Group 4). Additional colors indicate out-group taxa. FIGURE 2 | Haplotype network of A. omei constructed from the MG dataset Each short bar represents a single mutational step, whereas small black dots denote unsampled (missing) haplotypes. Circle size is proportional to the number of sampled individuals sharing that haplotype . Each Group corresponds to that shown in Figure 1. 3.3 Divergence time estimates The time-calibrated phylogeny (Figure 3) dated the divergence between the clade A. longihamata + A. condensa and the clade A. omei + A. paraomei to 13.9 Ma (95% credibility interval, 10.1–18.5 Ma) in the Middle Miocene. Within the former clade, A. longihamata and A. condensa split at 6.3 Ma (95% CI, 3.9–8.8 Ma), whereas A. omei and its sister species A. paraomei diverged at 10.7 Ma (95% CI, 7.4–14.4 Ma), both during the Late Miocene. Among the four lineages, Lineage 3 separated first, at 1.6 Ma (95% CI: 10.3–2.4 Ma) in the Early Pleistocene. Lineage 4 diverged from the remaining two lineages at 0.83 Ma (95% CI, 0.53–1.18 Ma), and the final split between the Lineage 1 and Lineage 2 occurred at 0.62 Ma (95% CI, 0.40–0.88 Ma) (Figure 3). FIGURE 3 | Time-calibrated phylogeny of A. omei lineages inferred from the MG dataset Purple bars denote 95% highest posterior density intervals for node ages; numbers at nodes indicate mean divergence times in millions of years ago (Ma). Lineage colors correspond to those shown in Figure 1. 3.4 Population historic dynamics of A. omei Neutrality tests (Table 4) showed significantly negative Tajimaʼs D and Fuʼs Fs values for the total population of A. omei were all significantly negative, implying a recent demographic expansion. A low raggedness index ( r ) and the multimodal results of mismatch distribution (Figure 4) further supported this scenario. Within groups, both the Group 1 and Group 2 exhibited the same signature—significantly negative Tajimaʼs D and Fuʼs Fs , small r values, and multimodal mismatch curves—indicating recent growth. τ estimates placed the onset of expansion at ca 0.038 Ma for the Group 1 and 0.041 Ma for the Group 2 (≈38–41 ka). Bayesian skyline plots corroborated these results: the Group 1 showed a pronounced increase in effective population size whereas Group 2 expanded ~40–50 ka and has continued a gradual increase thereafter. Sample sizes for Group 3 and Group 4 were insufficient for reliable neutrality or mismatch analyses. FIGURE 4 | Mismatch distributions and Bayesian skyline plots (BSP) for A. omei Panels: (A) all populations combined, (B) Group 1, and (C) Group 2. Left-hand sub-panels: blue histograms show the observed pairwise-difference frequencies, and the red curve represents the distribution expected under a sudden-expansion model. Right-hand sub-panels: the blue line traces the mean effective population size through time, with the blue shaded band indicating the 95% highest posterior density interval. 4 Discussion 4.1 Genetic diversity and differentiation analysis All four groups of A. omei , as well as most sampling localities, displayed this signature—high Hd coupled with low π —suggesting a recent demographic expansion from a limited ancestral gene pool. Among adequately-sampled populations (> 5 individuals), HNMS (Mangshan) showed the highest π with similarly elevated Hd , consistent with an older, stable population fostered by the area’s mild climate and rich floristic diversity. Located at the junction of southern and central subtropical floras, Mangshan likely provided ecological conditions that fostered long-term accumulation of genetic variation in A. omei . Populations CQBB, FJTBY and FJTLS also harbored relatively high diversity, probably maintained by ongoing gene flow from neighboring regions (Slatkin, 1987). By contrast, the apparently high Hd and π values for HNJYS, HNDPL and JXLN are likely artifacts of small sample sizes. Finally, no genetic variation was detected in GZNM or GDSG, a pattern that could reflect extensive intra-population gene flow or the collection of individuals from a single egg case. 4.2 Population genetic structure BAPS clustering resolved four well-defined groups in A. omei , mirroring the structure recovered by both the phylogenetic tree and the haplotype network and pointing to pronounced lineage differentiation. AMOVA showed 77% of the total genetic variance resides among groups, confirming marked divergence. The observed pattern is largely shaped by regional topography: Group 1 is restricted to the Sichuan Basin and Guizhou Plateau, whereas Group 2 occupies the hilly southeast and the middle–lower Yangtze Plain. T hese areas are divided by the Wushan and Xuefeng Mountains. These mountain systems presumably function as dispersal barriers, curtailing gene flow in A. omei , as documented for other insects (Liu et al., 2018: Hyalessa maculaticollis ; Zhang et al., 2019: Lycorma delicatula ; Du et al., 2020: Dendrolimus punctatus ) and for a variety of animal taxa (Slatkin, 1993: gull and pocket gopher; Ye et al., 2013: Paa spinosa ). Consistency between branch-model tests—which detected no group-specific differences in selection—and the strong geographic signal suggests that isolation by physical barriers, rather than adaptive divergence, is the primary driver of the observed structure. High pairwise Fst values combined with low N m estimates further indicate the restricted gene flow among most populations —a restriction exacerbated by rugged terrain, habitat heterogeneity and the species’ aggregative behavior , low vagility, and weak dispersal ability. Collectively, geographic distance, complex landscape, and poor dispersal capacity interact to shape the genetic architecture of A. omei . 4.3 Phylogenetic relationships and divergence time The dated phylogeny resolved four strongly supported intraspecific lineages encompassing 24 populations, revealing a clear phylogeographic structure in A. omei . Regional uplift of the Tibetan Plateau triggered major shifts in regional temperature, atmospheric circulation, and precipitation—especially during the Late Miocene–Pliocene (ca. 3.5 Ma), when peripheral ranges such as the Hengduan Mountains were formed (Fang, 2005; Sun et al., 2011; Wang et al., 2014). Such geological upheavals commonly foster species diversification. Divergence‐time estimates indicate that the clade containing A. paraomei + A. omei split from A. longihamata + A. condensa at approximately 13.9 Ma, likely in response to continued plateau uplift. Subsequently, A. longihamata separated from A. condensa at ~6.3 Ma, and A. paraomei diverged from A. omei at ~10.7 Ma, coincident with further development of the Hengduan Mountains. Within A. omei itself, lineage differentiation began in the Early Pleistocene (~1.64 Ma), potentially associated with the Qingzang Orogeny, and continued during the Wangkun Glaciation (0.5–0.7 Ma), yielding the Lineage 1 and Lineage 2 split at ~0.62 Ma. Subsequent divergences within Lineage 1 occurred around 0.40 Ma, shortly after the Zhonglianggan Glaciation (0.46–0.52 Ma). The early divergence of the Mangshan population exemplifies this phase. Mountain barriers such as the Nanling Mountains have reinforced these splits, as illustrated by genetic discontinuity between Mangshan (north of Nanling Mountains) and Shaoguan (south of Nanling Mountains). Overall, tectonic uplift, glacial oscillations, and intricate local topography collectively shaped the speciation of A. omei and its congeners by impeding gene flow, promoting isolation, and driving genetic drift. 4.4 Population historic dynamics Although the Quaternary ice sheets were restricted to high-latitude regions of North America and Europe, the associated glacial stages brought colder and drier conditions to unglaciated China (Zhou et al., 2004; Liu et al., 2012). Such climatic deterioration typically forced species into lower-latitude or micro-refugia, restricting gene flow and preserving distinct ancestral haplotypes (Willis et al., 2004). In A. omei , the absence of shared or central haplotypes among groups, together with the star-like branches in the haplotype network, indicates that Group 1 and Group 2 persisted in separate refugia and expanded independently following the glacial retreats. Multiple lines of evidence support this scenario: neutrality tests, mismatch-distribution analyses and Bayesian skyline plots all point to post-glacial expansion. The skyline plot reveals a pronounced increase in effective population size for Group 1 at ~40–50 ka, coinciding with an intensified monsoon that delivered ample precipitation (Zhao et al., 2007) and likely facilitated dispersal. In contrast, Group 1 expanded mainly between ~20–30 ka before plateauing after ~20 ka, coincident with the onset of cooler, drier conditions during the Last Glacial Maximum (LGM) (~18 ka). Similar climate-driven demographic slowdowns have been documented in other taxa (Li et al., 2009; Tian et al., 2010; Qu et al., 2011; Zhang et al., 2015; Xue et al., 2019) and appear to have likewise limited demographic expansion in A. omei . 5 Conclusions This study presents the first comprehensive phylogeographic analysis of A. omei , combining extensive geographic sampling with complete mitochondrial genomes. Our results reveal pronounced genetic structuring dating to the Late Miocene–Pleistocene, shaped primarily by (i) tectonic uplift of the Tibetan Plateau, (ii) orographic barriers formed by the Hengduan Mountains, and (iii) Pleistocene climate oscillations. Quaternary glacial–interglacial cycles drove range contractions and subsequent demographic expansions, while post-LGM recolonization cemented the species’ present-day distribution. By clarifying how topography and climate act to promote divergence, our findings advance understanding of speciation processes in Blattodea and other montane insects. Future research incorporating denser sampling with nuclear and epigenomic data will further refine the reconstructions of A. omei ’s evolutionary history, refugial dynamics, and dispersal corridors. Author Contributions Tunan Zhou: conceptualization (equal), writing – original draft (lead), data curation (lead), visualization (lead), investigation (equal), methodology (equal), formal analysis (equal). Jing Zhu: conceptualization (equal), investigation (equal), methodology (equal), formal analysis (equal), visualization (supporting). Chenhui Cao: data curation (supporting), visualization (supporting). Yanli Che: supervision (equal), writing – review and editing (equal), methodology (equal), resources (equal), conceptualization (equal). Zongqing Wang: conceptualization (lead), data curation (equal), formal analysis (equal), investigation (lead), funding acquisition (lead), methodology (equal), project administration (lead), supervision (equal), writing – review and editing (equal), resources (equal). Acknowledgments We are grateful to Jinlin Liu, Rong Chen, Yong Li, Likang Niu, Wei Han, Xinxing Luo, Limin Qiao, Lang Peng and Deqiang Ai, who kindly sampled specimens of Anaplecta . We would like to thank John Richard Schrock for editing the manuscript. This study is supported by the National Natural Science Foundation of China (32170458) and a program of the Ministry of Science and Technology of the People’s Republic of China (2022FY202100). Competing Interests Statement The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study will openly available in GenBank under accessions numbers @@@ and @@@. 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ZooKeys , 1080:53-97. https://doi.org/10.3897/zookeys.1080.74286 TABLE 1 | Sampling information, genetic parameters and diversity of each group of A. omei based on MG dataset Group 1 CQBB Jinyun Mountain, Beibei, Chongqing 5 SWU-B-AN0029-33 5 1.000 41 0.00114 CQSMS Simian Mountain, Chongqing 3 SWU-B-AN0034-36 3 1.000 3 0.00014 CQTHY Taohuayuan, Youyang, Chongqing 5 SWU-B-AN0037-41 2 0.600 1 0.00004 GZFJS Fanjing Mountain, Guizhou 5 SWU-B-AN0067-71 4 0.900 13 0.00041 GZNM Nanming, Guizhou 5 SWU-B-AN0072-76 1 0 0 0 HNCB Chengbu, Hunan 5 SWU-B-AN0077-81 5 1.000 5 0.00014 HNJYS Jiuyi Mountain, Hunan 2 SWU-B-AN0084-85 2 1.000 19 0.00130 HNMS Mang Mountain, Hunan 4 SWU-B-AN0086-89 3 0.833 64 0.00220 HNWYJ Wuyun Boundary, Hunan 5 SWU-B-AN0090-94 5 1.000 25 0.00080 SCEM Emei Mountain, Sichuan 5 SWU-B-AN0117-121 5 1.000 14 0.00052 Group 2 FJTBY Tianbao Rock, Fujian 5 SWU-B-AN0042-46 5 1.000 30 0.00100 FJTLS Taimu Mountain, Fujian 5 SWU-B-AN0047-51 5 1.000 33 0.00100 FJWYS Wuyi Mountain, Fujian 6 SWU-B-AN0052-57 3 0.733 6 0.00018 GDSG Shaoguan, Guangdong 4 SWU-B-AN0058-61 1 0 0 0 JSBHS Baohua Temple, Jiangsu 5 SWU-B-AN0095-99 4 0.900 14 0.00041 JSNJ Nanjing, Jiangsu 5 SWU-B-AN0100-104 4 0.900 7 0.00027 JXLN Longnan, Jiangxi 2 SWU-B-AN0105-106 2 1.000 25 0.00127 JXNC Nanchang, Jiangxi 5 SWU-B-AN0112-116 5 1.000 23 0.00078 JXLS Lushan Mountain, Jiangxi 5 SWU-B-AN0107-111 4 0.9000 12 0.00036 ZJJS Jiangshan, Zhejiang 1 SWU-B-AN0122 1 - - - ZJQY Qingyuan, Zhejiang 2 SWU-B-AN0123-124 2 1.000 6 0.00041 ZJTMS Tianmu Mountain, Zhejiang 1 SWU-B-AN0125 1 - - - Group 3 GDQLS Qilin Mountain, Guangdong 5 SWU-B-AN0062-66 5 1.000 14 0.00044 Group 4 HNDPL Dupangling, Hunan 2 SWU-B-AN0082-83 2 1.000 19 0.00130 ALL 97 79 0.994 529 0.00371 TABLE 2 | Genetic diversity of each group of A. omei populations based on MG dataset Group 1 44 35 0.938 0.00133 Group 2 46 37 0.987 0.00111 Group 3 5 5 1.000 0.00044 Group 4 2 2 1.000 0.00130 ALL 97 79 0.994 0.00371 TABLE 3 | Group-specific estimates of ω and likelihood-ratio statistics for A. omei under alternative branch models One-ratio model Two-ratio model (B/F) Group1 0.12368 0.12515/0.06947 0.56850 Group2 0.12527/0.08609 0.63391 Group3 0.12962/0.10427 0.51430 Group4 0.12443/0.10829 0.85456 TABLE 4 | Group-specific estimates of ω and likelihood-ratio statistics for A. omei under alternative branch models One-ratio model Two-ratio model (B/F) Group1 0.12368 0.12515/0.06947 0.56850 Group2 0.12527/0.08609 0.63391 Group3 0.12962/0.10427 0.51430 Group4 0.12443/0.10829 0.85456 Note: *significant, *P<0.05, **P<0.01, ***P<0.001. 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Collection Ecology and Evolution Keywords evolutionary ecology invertebrate molecular genetics sequencing terrestrial Authors Affiliations Tunan Zhou Southwest University View all articles by this author Jing Zhu Southwest University View all articles by this author Chenhui Cao Southwest University View all articles by this author Yanli Che Southwest University View all articles by this author Zong-qing Wang 0000-0001-9413-1105 [email protected] Southwest University View all articles by this author Metrics & Citations Metrics Article Usage 240 views 192 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tunan Zhou, Jing Zhu, Chenhui Cao, et al. Pleistocene Climate Oscillations and Geographic Barriers Shaped the Phylogeographic Structure of Anaplecta omei (Blattodea, Blattoidea, Anaplectidae) in Southern China: Evidence from Mitochondrial Genomes. Authorea . 12 September 2025. 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