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Species distribution modeling, spatial climate segregation and niche widthTo explore the selective regimes of the four species on external environmental factors, we first constructed species distribution modeling (SDM). We obtained a dataset including 22 environmental factors represented by climate, relief, and vegetation variables from 620 localities for DS, 1028 localities for OS, 581 localities for MM and 332 localities for PR, covering most of the species’ distribution ranges (Supplementary Fig. 1). The distribution areas of the four species overlapped widely. The contributions of environmental factors to SDMs showed similarities among the four species. The summer NDVI made important contributions for DS (41.0), OS (44.8), MM (32.5) and PR (8.1), and sand cover contributed significantly to PR (72.7) and DS (16.0) (Fig. 1c). Then, we assessed which set of environmental variables was most closely associated with species distribution via principal component analysis. The bioclimatic space occupied by the four species revealed a large overlap (Fig. 1d), which was consistent with SDM (Supplementary Fig. 1). The distribution of OS was more closely associated with higher-precipitation areas, whereas MM seemed to prefer areas with higher temperatures. Finally, we evaluated the macrohabitat niche breadth of each species. The breadths of environmental space occupation were similar for DS (0.527), MM (0.571), and PR (0.548) and slightly higher for OS (0.622), which suggests that niche selection among the four species is partially overlapping. In total, the four species are mostly similar in the selection of external environmental factors.High-quality genomic landscapes of the four desert rodentsTo investigate the genetic mechanism for desert adaptation of the four sympatric desert rodents, we generated four high-quality de novo genomes (Supplementary Fig. 2). The DS was sequenced using a combined strategy and generated 377.67 Gb of data from Illumina reads, 261.01 Gb from PacBio long reads, 299.51 Gb from 10X Genomics reads, and 389.13 Gb from Hi-C reads (Supplementary Table 1). The final genome size was 2.81 Gb with contig N50 of 31.41 Mb and ~472X mean coverage (Table 1, Supplementary Fig. 3, and Supplementary Tables 2, 3). The contigs for DS were further assembled into pseudochromosomes with lengths on the order of full chromosomes and a scaffold N50 size of 147.24 Mb (Fig. 2a, b, Table 1, and Supplementary Fig. 4). The OS, MM and PR were sequenced using the same hybrid strategy and generated 162.58 Gb, 172.22 Gb, and 214.34 Gb Illumina reads and 183.09 Gb, 161.34 Gb, and 186.45 Gb Oxford Nanopore Technologies long reads, respectively (Supplementary Table 1). The final assembly of OS, MM and PR was 2.83 Gb, 2.43 Gb, and 2.16 Gb with contig N50 of 25.87 Mb, 24.08 Mb, and 42.68 Mb, respectively (Table 1, Supplementary Fig. 4, and Supplementary Tables 2, 3).Table 1 Genome assembly statistics of the four desert rodents.Full size tableFig. 2: High-quality assembly of Dipus sagitta genome and genomic elements of the four sequenced desert rodents.a Hi-C heat map of Dipus sagitta genome assembly. b CIRCOS plot showing the distribution of GC content, transposable elements (TE), and coding sequences (CDS) in the D. sagitta genome. c Orthologous coding sequences composition inferred for thirteen rodents’ genomes. Mcar Mus caroli, Mmus Mus musculus, Mpah Mus Pahari, Mmer Meriones meridianus, Mung Meriones unguiculatus, Cgri Cricetulus griseus, Prob Phodopus roborovskii, Sgal Spalax galili, Osib Orientallactaga sibirica, Dsag Dipus sagitta, Jjac Jaculus jaculus, Hgla Heterocephalus glaber, Cpor Cavia porcellus. d Proportion of transposable elements (TEs). The barplots show the proportions of different types of TEs in corresponding species on the phylogenetic tree.Full size imageAnalyses of the four draft genomes showed that 92.9–95.9% of mammalian BUSCOs were complete, and the GC content was 41.38–42.16% (Table 1 and Supplementary Table 3). Whole-genome annotation was performed via three complementary methods: ab initio prediction, homology-based prediction and RNA-seq based prediction. A total of 23,482, 22,859, 22,533, and 22,314 protein-coding genes were annotated for DS, OS, MM, and PR, respectively (Fig. 2c, Supplementary Fig. 5, Supplementary Table 4). Approximately 98.8–99.1% of genes were functionally annotated for the four species (Supplementary Table 4). Transposable elements (TEs) accounted for 31.38–53.02% of genome assemblies, which predominantly consisted of long-terminal repeats (LTRs), long interspersed nuclear elements (LINEs) and other unknown TEs (Fig. 2d). DS and OS displayed significant LTR expansion of 47.39% and 50.88% in four sequenced genomes, while MM showed an unexpectedly high LINE expansion of 28.99% and sharp LTR contraction to 9.38% (Supplementary Table 5).Phylogenetic relationship and evolutionary historyUsing 5,102 single-copy orthologous groups, we constructed a high-confidence phylogenetic tree using the maximum-likelihood algorithm, including time calibrations based on fossil records and previous studies (Figs. 1b, 2c)22. The phylogenetic tree strongly supported nodes uniting the subfamilies Murinae and Gerbillinae, which together represented the family Muridae (Supplementary Fig. 6). This group was sister to a clade containing cricetids. Spalacidae was recovered as the earliest divergent lineage from Muridae and Cricetidae in the superfamily Muroidea. The split of the most recent common ancestor of Dipodoidea and Muroidea dated to ~56.5 Mya (Fig. 1b, Supplementary Fig. 7). In the Miocene epoch (23 Mya–5.3 Mya), accelerated global geotectonic movement aggravated global climate drying and cooling23. Geological disruptions that modified landscapes and offered new habitats favored the early adaptive radiation of extant desert rodents. The ancestors of four sequenced species emerged separately during this period (Supplementary Note 1). 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MagtibayMaynilad Water Services, Inc., Quezon City, PhilippinesKris Catangcatang & Ruby SibuloNational Veterinary Research Institute, Pulawy, PolandDariusz WasylUniversidade Catσlica Portuguesa, CBQF – Centro de Biotecnologia e Quνmica Fina – Laboratσrio Associado, Escola Superior de Biotecnologia, Porto, PortugalCelia Manaia & Jaqueline RochaAguas do Tejo Atlantico, Lisboa, PortugalJose Martins & Pedro ÁlvaroGwangju Institute of Science and Technology, Gwangju, Republic of KoreaDoris Di Yoong Wen, Hanseob Shin & Hor-Gil HurKorea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaSukhwan YoonInstitute of Public Health of the Republic of North Macedonia, Skopje, Republic of North MacedoniaGolubinka Bosevska & Mihail KochubovskiState Medical and Pharmaceutical University, Chișinău, Republic of MoldovaRadu CojocaruNational Agency for Public Health, Chișinău, Republic of MoldovaOlga BurduniucKing Abdullah University of Science and Technology, Thuwal, Saudi ArabiaPei-Ying HongUniversity of Edinburgh, Edinburgh, Scotland, UKMeghan Rose PerryInstitut Pasteur de Dakar, Dakar, SenegalAmy GassamaInstitute of Veterinary Medicine of Serbia, Belgrade, SerbiaVladimir RadosavljevicNanyang Technological University, Singapore, SingaporeMoon Y. F. Tay, Rogelio Zuniga-Montanez & Stefan WuertzPublic Health Authority of the Slovak Republic, Bratislava, SlovakiaDagmar Gavačová, Katarína Pastuchová & Peter TruskaNational Laboratory of Health, Environment and Food, Ljubljana, SloveniaMarija TrkovIndependent consultant, Johannesburg, South AfricaKaren KeddyDaspoort Waste Water Treatment Works, Pretoria, South AfricaKerneels EsterhuyseKorea Advanced Institute of Science and Technology, Daejeon, South KoreaMin Joon SongSchool of Veterinary Sciences, Lugo, SpainMarcos Quintela-BalujaLabaqua, Santiago de Compostela, SpainMariano Gomez LopezIRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autonoma de Barcelona, Bellaterra, SpainMarta Cerdà-CuéllarUniversity of Kelaniya, Ragama, Sri LankaR. R. D. P. Perera, N. K. B. K. R. G. W. Bandara & H. I. PremasiriMedical Research Institute, Colombo, Sri LankaSujatha PathirageCaribbean Public Health Agency, Catries, Saint LuciaKareem CharlemagneThe Sahlgrenska Academy at the University of Gothenburg, Gothenburg, SwedenCarolin RutgerssonSwedish University of Agricultural Sciences, Uppsala, SwedenLeif Norrgren & Stefan ÖrnFederal Food Safety and Veterinary Office (FSVO), Bern, SwitzerlandRenate BossAra Region Bern AG, Herrenschwanden, SwitzerlandTanja Van der HeijdenCenters for Disease Control, Taipei, TaiwanYu-Ping HongKilimanjaro Clinical Research Institute, Moshi, TanzaniaHappiness Houka KumburuSokoine University of Agriculture, Morogoro, TanzaniaRobinson Hammerthon MdegelaFaculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, ThailandKaknokrat ChonsinFaculty of Public Health, Mahidol University, Bangkok, ThailandOrasa SuthienkulFaculty of Medicine Siriraj Hospital, Bangkok, ThailandVisanu ThamlikitkulNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsAna Maria de Roda HusmanNational Institute of Hygiene, Lomι, TogoBawimodom BidjadaAgence de Mιdecine Prιventive, Dapaong, TogoBerthe-Marie Njanpop-LafourcadeDivision of Integrated Surveillance of Health Emergencies and Response, Lomι, TogoSomtinda Christelle Nikiema-PessinabaPublic Health Institution of Turkey, Ankara, TurkeyBelkis LeventHatay Mustafa Kemal University, Hatay, TurkeyCemil KurekciMakerere University, Kampala, UgandaFrancis Ejobi & John Bosco KaluleAbu Dhabi Public Health Center, Abu Dhai, United Arab EmiratesJens ThomsenDubai municipality, WWTP Al Aweer, Dubai, UAEOuidiane ObaidiRashid Hospital, Dubai, UAELaila Mohamed JassimNorthumbrian Water, Northumbria House, Abbey Road, Pity Me, Durham, UKAndrew MooreUniversity of Exeter Medical School, Cornwall, UKAnne Leonard, Lihong Zhang & William H. GazeNewcastle University, Newcastle upon Tyne, UKDavid W. Graham & Joshua T. BunceBrightwater Treatment Plant, Woodinville, WA, USABrett LeforDepartment of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USADrew Capone & Joe BrownUniversity of North Carolina, Chapel Hill, USAEmanuele Sozzi & Mark D. SobseyUniversity of Washington, Seattle, WA, USAJohn Scott Meschke, Nicola Koren Beck, Pardi Sukapanpatharam & Phuong TruongBaylor University, Waco, USAMichael DavisColumbia Boulevard WWTP, Portland, USARonald LilienthalEastern Illinois University, Charleston, USASanghoon KangThe Ohio State University, Columbus Ohio, USAThomas E. WittumLaboratorio Tecnolσgico del Uruguay, Montevideo, UruguayNatalia Rigamonti & Patricia BaklayanInstitute of Public Health in Ho Chi Minh City, Ho Chi Minh, VietnamChinh Dang Van, Doan Minh Nguyen Tran & Nguyen Do PhucUniversity of Zambia, Lusaka, ZambiaGeoffrey Kwenda More

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Strain assessmentWe did not detect any C-strain individual following analysis of 138 fully assembled mitochondrial DNA genomes (mitogenomes) from Australian samples. Our results, particularly that from Northern Territory, are not dissimilar to the finding of Piggott et al.56 who detected only two (i.e., 4.2%) C-strain mtCOI haplotype individuals from a much larger (i.e., n = 48) Northern Territory sample size. Proportions of C-strain to R-strain also varied significantly across the different SEA populations (Table S1) in contrast to the patterns observed in China, India, and African nations (e.g.,22,33,34,39,57). All Australian populations analysed for their corn or rice mitochondrial haplotypes via mitogenome assemblies of whole genome sequencing data therefore contrasted with the invasive populations from SEA where in some countries (e.g., Myanmar, Vietnam) FAW with the C-strain mtCOI haplotypes made up approximately 50% of the populations examined (see Table S1 for C- and R-strains mitogenome proportions, see also Fig. 1 ‘C-strain’ and ‘R-strain’ Maximum Likelihood cladograms).Figure 1Maximum Likelihood cladograms of unique Spodoptera frugiperda C-strain and R-strain partial mitochondrial genomes based on concatenation of the 13 PCGs (11,393 bp) using IQ-Tree with 1000 UFBoot replications. Individuals in clades I, II, III, and IV (C-strain) and in Clades I, II, V (R-strain) that are in the same colour scheme (i.e., green, orange, blue, or pinks) shared 100% nucleotide identity. Mitogenome haplotypes from native individuals for both C- and R-strains are in khaki green colour. Red and dark grey dots at branch nodes represent bootstrap values of 87–100% and 74–86%, respectively. Bootstrap values  Hetexp; see60) could likewise indicate recent mixing of distinct populations from SEA that suggest multiple introductions (e.g.,33,39 cf.46,47,61,62; i.e., due to a recent bottleneck from a recent western Africa founder event).Table 1 Population genetic differentiation via pairwise FST estimates between Spodoptera frugiperda populations from the invasive ranges of Africa (Uganda, Malawi, Benin), South Asia (India), East Asia (China (Cangyuan (CY), Xinping (XP), YuanJiang (YJ)), South Korea), Southeast Asia (Malaysia (Johor, Kedah, Penang States), Laos, Vietnam, Myanmar), and Pacific/Australia (Papua New Guinea (PNG), Australia—Kununurra (Western Australia, WA), Northern Territory (NT), Strathmore, Walkamin, Burdekin, Mackay (Queensland, Qld), Wee Waa (New South Wales, NSW).Full size tableThe observed heterozygosity excess detected in all invasive range populations could be further explained as due to population sub-structure and isolation breaking through periodic migration. Significant numbers of loci (ca. 30%) were also shown to not be in Hardy–Weinberg equilibrium (HWE) especially for the Malaysian (i.e., Kedah), but also Australian (i.e., Wee Waa, NT, Kununurra), Chinese (e.g., XP), South Korean, and Malawian populations. Taken as a whole, genetic diversity results from this study therefore suggested that the invasive Asian (i.e., SA, SEA, EA) FAW populations exhibited signatures of recent mixing of previously separated populations. Simulated patterns of moth migration of various invasive FAW populations such as between Myanmar and China (e.g.,41,42,55) and to Australia54 are incompatible with the population genomic data, which suggests these were likely discrete and non-panmictic FAW populations with the most probable explanation being due to multiple origins of founding populations.Genetic differentiation analysisEstimates of pairwise genetic differentiation (FST) between populations varied significantly (Table 1) and extended to between populations within a country (e.g., Mackay vs. rest of Australia; Kedah vs. rest of Malaysia). Of interest are the pairwise estimates between different Australian FAW populations from Kununurra (Western Australia), Northern Territory, Queensland (Strathmore, Walkamin, Burdekin, Mackay) and New South Wales (Wee Waa) that represented the most recently reported invasive populations in this study, and predominantly showed significant differentiation amongst themselves (with the exception of the two Queensland populations of Mackay and partially for Walkamin) and with other SEA/SA/EA countries. The majority of non-significant population genetic differentiation estimates were in SEA where the presence of FAW was reported earlier, i.e., since 2018 (e.g.,63,64 or as early as 200865,66; see also33), while across Asia (e.g., China) since 2016 but also potentially pre-2014 (16,67; see also33).Interestingly, significant genetic differentiation was observed between populations from Yunnan province in China and populations from Myanmar, Laos, and Vietnam. Penang and Johor (Malaysia) populations were not significantly differentiated from other SE Asian populations, nor with Ugandan and Malawian populations from east Africa. Individuals from Benin and Mackay (Queensland, Australia) showed non-significant genetic differentiation with all populations except with Kedah, and for Mackay also surprisingly with the Wee Waa population from New South Wales. The South Korean population exhibited significant genetic differentiation with SE Asian population except with Mackay, India and the Yuanjiang (YJ) population in Yunnan Province. Finally, the Kedah population, being one of the earliest collected samples from Malaysia and having been maintained as a laboratory population, showed strong differentiation with all populations (and lowest nucleotide diversity, π = 0.237; Table 2) further supporting unique, non-African, introduction events in SEA. Strong genetic differentiation suggested there was limited gene flow to breakdown sub-structure between populations, and the FST estimates from these invasive populations therefore failed to support a west-to-east spread pathway for the FAW. This observation instead suggested the widespread presence of genetically distinct FAW populations, likely due to independent introductions and therefore also highlighting likely biosecurity weaknesses especially in East Asia (e.g., China, South Korea) and SEA (e.g., Malaysia).Table 2 Population statistics for Spodoptera frugiperda populations from Southeast Asia (i.e., Malaysia (MYS; Johor, Kedah, Penang), Laos, Vietnam, Myanmar), East Asia (i.e., South Korea), and Pacific/Australia (i.e., Papua New Guinea (PNG), Australia).Full size tableThe genetic diversity of Australian populations identified surprisingly complex sub-structure patterns given the short time frame of population detections across different northern Australian regions. Significant genetic differentiation between, e.g., Kununurra (WA), Northern Territory (NT), Queensland (e.g., Strathmore, Burdekin), and Wee Waa (NSW) populations suggests these populations likely derived from separate establishment events. The WA Kununurra population was not significantly differentiated from the Johor State (Malaysia), India and the Cangyuan (CY) China populations, suggesting a potential south-eastern route from SA/SEA into north-western Australia. Contrasting this, Walkamin and Mackay populations showed non-significant genetic differentiation with the Madang (PNG) population, suggesting a potential second pathway for SEA individuals to arrive at the north-eastern region of Australia. Significant genetic differentiation between WA, NT, and Qld populations suggested that at least during the early stage of pest establishment in northern Australia, there was limited gene flow to homogenise the unique genetic background carried by these distinct individuals, some of which exhibited also distinct insecticide resistance profiles48,49.PCAWe selected specific populations to compare using Principal Component Analysis (PCA) as examples to support evidence of independent introductions, as seen from Fig. 3a between China (CY, YJ, XP) populations vs. Myanmar, in Fig. 3b (within Malaysian populations between those collected from Penang and Johor States vs. Kedah State), in Fig. 3c for between China and East Africa (e.g., Uganda, Malawi), and where Benin and India individuals that grouped with either China or east Africa; and in Fig. 3d between China, Malaysia (Kedah State), and Australia (NT, NSW)). Genetic variability between Australian populations (e.g., Strathmore (QLD) vs. NT and NSW) was also evident (Fig. 3d).Figure 3Principal component analysis (PCA) showing variability between selected FAW populations from their invasive ranges. (a) China and Myanmar; (b) Kedah and Johor/Penang populations from Malaysia, (c) China and east African (Uganda/Malawi) populations, (d) Australia (Strathmore, Qld/Northern Territory + New South Wales), China, and Malaysia (Kedah) populations, (e) Australia (Strathmore, Qld) and PNG (Madang Province) populations, (f) Lao PDR/Vietnam and South Korea populations, (g) China and SE Asian (Lao PDR/Vietnam/Myanmar/Philippines/Malaysia) and Pacific/Australia (PNG) populations, and (h) Australia, China and Malaysia (Kedah) populations. Note the overall population genomic variability between countries (e.g., a, c–g) and within countries (e.g., Malaysia (b), Australia (d)). Populations with similar genomic variability are also evident, e.g., for Strathmore (e) and South Korea (f); and for Madang (e) and Lao PDR/Vietnam (f), further supporting potential different population origins of various FAW populations across the current invasive regions. The Southeast Asian and Chinese populations are overall different (g), Australia’s FAW populations showed similarity with both Southeast Asia and China (g, h).Full size imagePCA also showed that differences existed between FAW populations from the Madang Province in PNG and with the Strathmore population from Qld (Fig. 3e). The SEA FAW populations from Lao PDR/Vietnam also exhibited diversity from the South Korean population (Fig. 3f), with the South Korean and Strathmore populations largely exhibiting similar diversity patterns, while the Madang population shared similarity with Laos and Vietnam populations. Plotting all SEA populations against China clearly showed that populations from SEA were distinct from the Chinese FAW populations (Fig. 3g), while in Australia, individuals from various populations shared similarity with both Chinese and SEA FAW. Despite the connectedness of the landscape between SEA and China, SEA largely appeared to have their own FAW populations, with FAW in SEA and in China differing in their genome compositions overall as shown via PCA.PCA further enabled visualisation of genetic diversity amongst Australia FAW populations, suggesting that arrival and establishment of FAW likely involved separate introduction events that followed closely after each other and over a short timeframe. While it had been anticipated that the southward spread of FAW from SEA would necessarily lead to Australia FAW and PNG FAW to share similar genetic backgrounds, the Madang Province FAW population appeared to be different from the Strathmore (Qld) population, with the Madang population being more similar to Lao PDR/Vietnam populations, and the Strathmore population more similar to FAW from South Korea.DivMigrate analysisDirectionality of gene flow between African, South Asia (Indian), East Asia (China) and SE Asian populations were predominantly from China to east African and SE Asian populations (e.g., Figs. 4a, b, S-1; see also Table 3), while movements of FAW in Laos and Vietnam (i.e., the Indochina region) were predominantly with other SEA countries (e.g., with Myanmar and East Africa; Figs. 4c, d, S-2; see also Table 3) but with no directional movements to the three Yunnan populations (CY, XP, YJ). Migration directionality with other SE Asian populations (e.g., Johor (JB; Fig. S-3) and Penang (PN, Fig. S-4)) showed that these two populations (but especially the Johor population) were predominantly source populations for Uganda, Malawi, Philippines, Vietnam, and PNG (Fig. S-3). Bidirectional migration between Myanmar and Laos PDR populations were also detected with the Johor population from Malaysia (Fig. S-3). When India was selected as the source population, bidirectional migration events were detected with Myanmar and with the Cangyuan (CY) populations (Fig. S-5) while unidirectional migration events from India to Uganda and Malawi and to Laos were detected, and the China Yuanjian (YJ) population showed unidirectional migration to India. Unidirectional migration events from CY and YJ populations to the PNG Madang population were detected, while bidirectional migration events between PNG and Myanmar, Laos PDR, Philippines, Vietnam, and with Uganda and Malawi were also detected (Fig. S-6). No migration events were detected between the West African Benin population and with the South Korean population.Figure 4Source populations are CY (a) and XP (b). (c, d) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) migration between countries in Africa and South Asia/East Asia/SE Asia. Migration rates between populations are as provided in Table 3. (c) Vietnam (VNM) as the source population identified an incidence of unidirectional migration from Malaysia (MYS) Johor state (JB) to Vietnam, while bidirectional migration events were detected from Vietnam to other SE Asian (e.g., Philippines (PHL), Lao PDR (Lao), Myanmar (MMR)), to Pacific/Australia (i.e., Papua New Guinea (PNG)), as well as to east Africa (Uganda (UGA), Malawi (MWI)). (d) Lao PDR (LAO) as source population identified bidirectional migration events between various SEA populations and east African populations, while unidirectional migration events were identified from India (IND) and China (CHN) Yunnan populations (CY, YJ) to Laos PDR. No migration events were evident from SE Asian populations to China.(a, b) DivMigrate analyses with edge weight setting at 0.453 showing unidirectional (yellow arrow lines) and bidirectional (blue arrow lines) gene flow between countries in Africa and South Asia/East Asia/SE Asia. Significant migration rates (at alpha = 0.5) are in red and as provided in Table 3. Incidences of unidirectional migration were predominantly detected from China (CHN) Yunnan populations (CY, XP) to SE Asian populations (e.g., Myanmar (MMR), Laos PDR (Lao), Philippines (PHL)) and to east African populations (e.g., Uganda (UGA), Malawi (MWI)) (a, b).Full size imageTable 3 DivMigrate matrix showing effective migration rates calculated using GST from source to target invasive populations.Full size tableAdmixture analysisAdmixture analyses involving all Australian, Southeast Asian and South Korean populations from this study; and native populations from the Americas and Caribbean Islands, and invasive populations from Africa (Benin, Uganda, Malawi), India, and China33, provided an overall complex picture of population structure that reflected the species’ likely introduction histories across its invasive ranges.Admixture analysis that excluded New World, African and Indian populations identified four genetic clusters (i.e., K = 4) to best describe these invasive populations from SEA, and EA (i.e., China, South Korea), and Pacific/Australia (Fig. 5a). At K = 4, Australian populations from NT and NSW, YJ population from China, South Korean, and Malaysia’s Kedah population, each showed unique admixture patterns (i.e., some individuals from NT and NSW populations lacked cluster 3; most of YJ (but also some CY and XP) individuals lacked clusters 1 and 2; South Korean (e.g., MF individuals) lacked cluster 2; Malaysia’s Kedah population lacked evidence of admixture (i.e., reflecting its laboratory culture history) and was made up predominantly by individuals that belonged to cluster 4. Populations from China also differed from most populations from SEA due to the overall absence of genetic cluster 4. Taken as a whole, establishment of the FAW populations in China, Malaysia, vs. other SE Asian populations, and between Australian populations (e.g., NT/NSW cf. WA/Qld), likely involved individuals from diverse genetic background (i.e., multiple introductions). At K = 4, the majority of Australian populations appeared to contain genetic clusters similar to China (i.e., cluster 3) and to SEA (i.e., cluster 2).Figure 5Admixture and corresponding CV plots for FAW populations from: (a) Australia, China, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, and Vietnam, and (b) Benin, China, India, South Korea, Lao PDR, Myanmar, Malaysia, Philippines, PNG, Tanzania, and Vietnam. Optimal ancestral genetic clusters are K = 4 for both admixture plots. Boxed individuals have unique admixture patterns at K = 4 when compared with other populations. China FAW lacked Cluster 2 (navy blue colour; present in almost all SEA and Australian FAW), while in NSW and NT some individuals lacked cluster 3. South Korea ‘MF’ population generally lacked cluster 2, while Kedah (Malaysia) showed distinct (cluster 4) pattern for all individuals. The overall same observations are evident in the admixture plot in (b), with African FAW generally exhibiting admixture patterns similar to SEA populations than to Chinese FAW. With the exception of Kedah (Malaysia) and some Chinese FAW individuals, all FAW in the invasive range showed evidence of genomic admixture (i.e., hybrid signature). The figures were generated using the POPHELPER program  and further manipulated in Microsoft PowerPoint for Mac v16.54.Full size imageOverall admixture patterns at best K = 4 in China and SEA remained unchanged when analysed together with African and Indian individuals (Fig. 5b; excluded Australia). Benin individuals were either similar to China or to SEA, while eastern African populations (e.g., Uganda, Malawi) were similar to Southeast Asian populations from e.g., Vietnam, Laos, and is in agreement with the phylogenetic inference (Fig. 3) that identified these African individuals as having loci that were derived from Southeast Asian populations.Genome-wide SNP loci demonstrated that invasive FAW populations from SEA and Australia exhibited admixed genomic signatures similar to that observed in other invasive populations33,34. While the current invasive populations in Africa and Asia likely arrived already as hybrids as suggested by Yainna et al.68, the Malaysia Kedah State population was potentially established by offspring of a non-admixed female. Distinct admixture patterns in Malaysian FAW populations between Kedah and Johor/Penang states therefore suggested that establishment of these populations was likely as separate introduction events. As reported also in Tay et al.33, the Chinese YJ population appeared to have admixed signature that differed from XP and CY populations, and suggested that the YJ population could have a different introduction history than the XP and CY populations. Similar multiple genetic signatures based on lesser nuclear markers by Jiang et al.39 also supported likely multiple introductions of China Yunnan populations. More