Population genomics of fall armyworm in Brazil and Argentina revealed by Genotyping-by- Sequencing and implications for pest management

doi:10.21203/rs.3.rs-2105384/v1

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Population genomics of fall armyworm in Brazil and Argentina revealed by Genotyping-by- Sequencing and implications for pest management

https://doi.org/10.21203/rs.3.rs-2105384/v1

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The fall armyworm (FAW), Spodoptera frugiperda, is a significant pest of many crops in the world and it is native to the Western Hemisphere, where the species has shown the ability to rapidly evolve resistance to transgenic plants and insecticides. Despite the importance of this species, there is a gap in the knowledge regarding the migration pattern of FAW in South America. Here, we examined the genetic diversity of FAW populations across a wide agricultural area of Brazil and three locations from Argentina using a Genotyping-by-Sequencing (GBS) approach. We also characterized samples by their host strain based on mitochondrial and Z-linked genetic markers. The GBS methodology enabled us to discover 3309 SNPs, including neutral and outlier markers. The 37 mitochondrial haplotypes did not reveal clusters based on geography, whereas GBS data revealed significant genetic structure between Brazil and Argentina, and also among the Argentinian ecoregions. Populations inside Brazil showed little genetic differentiation indicating high gene flow among locations and confirming that structure is related to the presence of corn and rice strains. Outlier analysis indicated 456 loci putatively under selection, including genes possibly related to resistance evolution. This study provides clarification of the population genetic structure of FAW in South America and highlights the importance of genomic research to understand the risks of spread of resistance genes.

Population structure

Spodoptera frugiperda

genetic diversity

population genomics

SNPs

host strains

reduced representation sequencing

Identifying population structure and patterns of gene flow featured by insect pest populations is critical for understanding outbreak patterns and for predicting the spread of resistance alleles^1,2. Gene flow is a result of effective dispersal and it can be indirectly estimated with molecular markers using population genetic models to compare genotype frequencies^3,4. Gene flow can be affected by both geographic distances and environmental factors⁵. In the case of insects, the dispersion can be greatly biased by human-aided movements, when specimens are passively transported by contaminated cargo^6,7.

The fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), is a major agricultural pest that can feed on several different hosts⁸. In Brazil, FAW is one of the primary pests of corn and significantly impacts other important crops, including soybean and cotton⁹. A particular concern is the demonstrated capacity of FAW to rapidly evolve resistance to insecticides and transgenic crops, which can significantly impact the effectiveness of FAW control strategies¹⁰. The spread of such resistance traits is dependent on the migratory behavior of FAW and it is therefore important for effective pest management to delineate the pattern and magnitude population movement across national boundaries.

FAW has a known migratory movement in North America, where populations from Texas and Florida make annual migrations northwards to recolonize areas in the north of USA and Canada¹¹. This behavior reflects the inability of FAW to survive freezing temperatures, which limits winter populations to the most southern locations in the states of Texas and Florida. Less is known about migration patterns in South America, which experiences a significantly different climate than North America. Much of South America features suitable climatic conditions for FAW reproduction year-round, which will presumably reduce the need and benefits of large-scale migration¹². In addition, the agricultural landscape and practices in Brazil greatly favors large pest populations since irrigation enables continuous cultivation of host plants in many locations, while in areas with more restricted planting window FAW is well-adapted to exploit weed hosts during the off-season¹³.

However, there is evidence that much of the FAW infestations in Argentina could be derived from migrant populations. Climate suitability modeling of South America shows that conditions suitable for persistent FAW populations are absent from most of Argentina, including the agricultural areas that exhibit seasonal FAW infestations^12,14. This suggests an annual southward migration from more suitable locations, with the neighboring countries of Bolivia, Paraguay, and Brazil being the prime candidates for the migratory sources.

One approach to study migration is through the use of molecular markers that can identify the genetic structure of populations in a geographical area and can be used to extrapolate the levels of gene flow and genetic differentiation between groups. A complicating factor for FAW is that the species can be subdivided into two groups called host strains that differ in their distribution on plant hosts in the field, with the C-strain preferentially found in corn and the R-strain in pasture grasses and, to a lesser extent, rice^15,16. The host strains are morphologically indistinguishable and so can only be identified through the use of molecular markers, with the most commonly used being polymorphisms in the mitochondrial cytochrome oxidase subunit I (COI) and the Z-chromosome linked triosephosphate isomerase (Tpi) genes^17,18.

The C-strain population as defined by the COI marker consists of four haplotypes (COI-h1, COI-h2, COI-h3, and COI-h4) that were used to differentiate the North American overwintering populations in Florida and Texas¹⁹. However, this distinction is based solely on the relative frequencies of the COI haplotypes, with no other indication that these geographical populations are genetically differentiated. Given the extensive migration that occurs annually in North America and the resultant opportunities for mixing between populations, the level of genetic structure exhibited by FAW on this continent is likely to be limited.

Previous genetic studies of FAW populations from South America have employed a range of molecular markers, such as microsatellites^20,21, AFLP^22,23, RAPD⁹, mitochondrial genes^24,25 and SNPs²⁶. At this time, the most consistent evidence of population structure is that indicative of the two host strains, although some level of genetic structure has been observed for populations within Paraguay and within Brazil^9,21,26. Overall, it remains unclear whether and to what extent geographically separated FAW in South America exhibit genetic differentiation, which in turn provides no new insight into FAW migratory behavior on this continent.

To better address this issue, we used a variation of the commonly used restriction-site-associated DNA sequencing methodology that potentially provides a more efficient way of identifying single nucleotide polymorphisms (SNPs) for population studies. Genotyping-by-sequencing (GBS) is a simple and highly multiplexed method that enables reduced representation of the genome, while generating a large number of SNPs markers using high-throughput platforms of next generation sequencing (NGS)²⁷. GBS data allows us to resolve patterns of genetic diversity and spatial structure at very fine scale²⁸, and it was utilized in this study to evaluate patterns of gene flow and genetic structure of FAW populations across Brazil and Argentina (Fig. 1, Table 1). Based on a genome-wide SNPs analysis, a novel understanding of genetic structure and gene flow of FAW was revealed. We additionally examined the information of host strains to discover informative loci putatively under selection. According to the results, we discuss some practical implications of our findings to the FAW management in South America.

Fall Armyworm Host Strains

We characterized strain composition of the fall armyworm populations using diagnostic polymorphisms in the COIB and Tpi regions. A higher percentage of the samples collected from corn fields were identified as C-strain using the Tpi marker (95%) than COI (86%), consistent with other studies suggesting that Tpi is the most accurate strain marker¹⁸. The AR03 population in Argentina (Santa Fe) had the lowest agreement between markers (7%). Tpi-hybrids (TpiH) were found only in Brazilian locations, mainly in MA02 population, which also had many R-strain samples (Fig. 2).

Polymorphisms in the COI region subdivides the COI-CS group into four haplotypes. The COI-h2 haplotype averaged 82% of COI-CS samples (158/192), similar to previously reported by Arias et al., (2019). The second most common haplogroup was COI-h1 (17%), followed by COI-h4, with only two individuals from Brazil. No COI-h3 was present in the 228 individuals.

The haplotype network analysis with the COI sequences resulted in 37 haplotypes based on 34 polymorphic sites. Samples showed high haplotype diversity estimated by Hd: 0.7888, and low nucleotide diversity π: 0.00765. The two main clusters were grouped as COI-RS and COI-CS, subdivided in COI-h1 and COI-h2 (Supplementary Fig. S1). COI haplotype network showed that samples were grouped according to host strain rather than geography. Haplotype 1 (H1) was the most frequent in Brazil, and the only haplotype present in the 11 samples from AR01.

SNPs discovery

The GBS library generated a total of 187,842,557 reads that were retained after demultiplexing and quality checking (mean per sample = 795,943; standard deviation = 332,148.8). A total of 3309 loci were retained in 228 individuals from 15 locations across Brazil and Argentina. Mean coverage per-sample was 23.3 x (min = 8.7x, max = 45.1x). Estimates of genetic diversity were similar across locations and are summarized in Supplementary Table S1.

Gene flow and population genetic structure

Pairwise F_ST calculations (Fig. 3A) revealed high levels of genetic differentiation among the three populations from Argentina, which is a strong indicative of reduced gene flow among these locations. On the other hand, Brazilian populations belonging to C-strain had very low F_ST values (ranging from 0.000 to 0.006). Likewise, Brazilian R-strain populations MA02 and RS had pairwise F_ST = 0. BIC analysis from DAPC resulted in the smallest value k = 4 (Fig. 3B), separating the three FAW locations from Argentina and grouping all samples from Brazil.

Outlier Detection

When considering the 15 FAW populations, we identified 209 outlier SNPs in common by at least two of the three tests performed, whereas by comparing R- and C-strains we found 293 outliers in common by at least two of the three tests performed (Fig. 5A and 5B). Altogether, we identified a total of 456 outlier loci putatively under selection (13.8%), and the remaining 2853 SNPs were considered to be neutral. By analyzing only neutral markers, we found a significant F_ST value of 0.067 between Brazil and Argentina samples, and the DAPC indicated three clusters, grouping the AR02 (Buenos Aires) with the Brazilian populations (Fig. 4A). The outlier loci in turn resulted in five clusters: AR01, AR02, AR03, C-strain-Brazil, and R-strain-Brazil (including TpiH from MA02 population) (Fig. 4B). Alternatively, these groups could also be identified by bar plots of DAPC membership coefficients (Fig. 4C and 4D).

Annotation of outlier loci

For outlier analyses, 456 SNP loci putatively under selection were compared to the annotations of the reference genome ZJU_Sfru_1.0 and 306 of these were within predicted protein coding genes of FAW. After Blast2GO analysis, 220 proteins were successfully mapped, and 94 were annotated (Supplementary Table S2). We found many outlier loci within genes possibly involved in binding functions and associated with the cell membrane (Fig. 5C). Among the 94 loci with GO IDs, SNP_1055 was annotated as an ABC transporter C subfamily member 13, and the mutation was putatively under selection when comparing different locations, rather than related to the presence of two host strains. Regarding detoxifying activity, the locus SNP_3077 was in a gene similar to cytochrome P450 CYP314A1 (this locus was detected as outlier when comparing host strains), and the locus SNP_1559 was in a gene similar to an annotated esterase FE4-like. Another mechanism of insect defense involves chitin processing, and here we had five loci related to cuticle proteins or chitin binding (SNP_574, SNP_1209, SNP_1853, SNP_2799, SNP_3139).

Analysis of outliers comparing host strains resulted in 68 outlier SNPs (23% of 293) in the sex chromosome Z, which concentrated over twice the number of outliers compared to the autosomal chromosomes (Fig. 5D). The locus SNP_421 located at chromosome 3 was within a gene similar to an odorant receptor and could be related to sensorial stimulus to either host perception or male mating preferences. The locus SNP_3298 was in a gene that had 100% identity to a GPI-anchored glycoprotein. Besides this locus, another 188 loci were successfully blasted with more than 90% similarity, but had no GO ID associated with them (Supplementary Table S3), including one locus identified as a cytochrome P450 (SNP_423), one cadherin (SNP_1010), one zinc carboxypeptidase (SNP_1352), one GABA receptor (SNP_719), and one UDP-glucosyltransferase (SNP_2719). The frequencies of each polymorphism for some candidate genes under selection were also calculated for the Brazil locations (Fig. 6).

GBS has been used successfully in insect pests to reveal insights about gene flow and coancestry²⁹, spatial and temporal genetic structure^30,31, and incursions of invasive pests^1,32. Here we associated the informative data set provided by GBS with molecular markers used for host strain identification to better explain the patterns of FAW population structure in Brazil and Argentina and to identify candidate genes under selection, either because of differentiation among populations or between host strains.

By assessing a large number of samples in Brazil, we confirmed that FAW collected in corn fields were predominantly C-strain, with less than 4% of samples featuring both the COI-RS and TpiR diagnostic markers, and the few specimens featuring discordant genotypes likely represent vestiges of interbreeding events that occurred in the past. Based solely on diagnostic polymorphisms in COI and Tpi regions, MA02 and AR03 populations showed increased levels of strain hybridization, and we were able to describe the level of gene flow of these locations by relying also on SNP data set. Furthermore, our mtCOI sequencing detected no COI-h3 haplotype, which confirms that populations in South America are not significantly interbreeding with those from Florida and Caribbean^33,34.

GBS data revealed high levels of gene flow and low genetic differentiation between MA02 and RS population, which was composed by pure R-strain samples. Since these two populations were the most geographically distant locations sampled in Brazil, apart over 3200 km, we presumed that MA02 was composed by R-strain specimens with recent events of hybridization. Altogether, both COI haplotype network and pairwise F_ST distance analysis confirmed that the Brazilian populations are structured by host strains, rather than by geographical ecoregions.

Long distance migration enables FAW populations to travel from Southern Texas and Florida up to Canada, a distance of nearly 2500 km, in less than three months^11,35. Therefore given its strong flight performance, we can hypothesize that FAW is also performing long distance migration within Brazil sufficient to keep populations homogeneous within each host strain.

As for Argentinian populations, a novel pattern of genetic structure was revealed based on GBS data, but not with COI haplotype analysis. Several studies comparing populations from Brazil and Argentina in the past showed strong similarities between the two countries^7,22,36,37, which would be expected if the great majority of Argentina FAW are derived from seasonal migrations from Brazil. However, our data indicated significant genetic structure between countries as well as among provinces inside Argentina. This result is consistent with observations of mating incompatibility between populations collected in northern Argentina compared to those from the Pampas region, indicating pre-reproductive isolating barriers between geographically separated populations³⁸. GBS data for another important Noctuidae pest in the Americas, the sugarcane borer (SCB), showed a similar pattern of genetic structure between Argentina and Brazil populations, and among populations within Argentina^6,39. We hypothesize that Argentina likely contains one or more endemic FAW populations that exhibit significant geographical isolation and that additional studies are required to better investigate the fine scale genetic structure of FAW as well as identify locations capable of supporting permanent FAW populations in this country.

In order to explain how selection pressures might be affecting FAW populations in South America, we examined the putative annotations of genes containing outlier SNPs. Host strain outliers were mostly concentrated on the sex chromosome Z, suggesting that the selection pressures are acting upon specific regions of the FAW genome. This result corroborates with previous study where the preponderance of strain specific SNPs were Z-linked⁴⁰, and is consistent with the proposal that strain divergence is being driven primarily by Z-chromosome functions⁴¹. We believe that by reducing complexity of the genome, the GBS method was able to capture a fairly large number of polymorphisms in the Z-chromosome, and thereby discriminate between the R- and C-strains. It is possible that previous research based on nuclear SSR markers²¹ that did not differentiate the host strains lacked sufficient coverage of the sex chromosome.

Other functional annotations revealed proteins that were likely involved in binding activities and that were present in or related to the cell membrane. Mutations in Cry receptor genes have been reported in numerous lepidopteran species to be the most common mechanism of resistance against Bt toxins⁴², and here we found outlier SNPs in genes likely coding for Cry receptors such as GPI-anchored glycoprotein, cadherin and zinc carboxypeptidase. We also found outlier SNPs in genes possibly coding many important enzymes such as cytochrome P450 CYP314A1⁴³, esterase FE4-like⁴⁴, JHAMT⁴⁵, and also proteins related to cuticle and chitin, which may be an indication of response to management with insecticides⁴⁶. Two noteworthy outlier SNPs associated with host strains were in genes possibly encoding an odorant receptor and a UDP-glucuronosyltransferase. Odorant receptors function in insects olfaction process, which is indispensable for host selection for feeding and oviposition⁴⁵. UDP-glucuronosyltransferase, in turn, appears to be associated with C-strain ability to detoxify DIMBOA⁴⁷, a toxic compound produced by corn plants but not rice. In conclusion, our work strongly suggest that positive selection is affecting allele frequencies at the level of populations and host strains.

From the Insect Resistance Management (IRM) perspective, resistance evolution is one of the most challenging problems in the sustainable control of FAW⁴⁸. Therefore understanding patterns of gene flow and consequent risks for spread of field-evolved resistance alleles are crucial for effective management. Our SNP data set poses a challenging scenario in Brazil, where locations presented high levels of gene flow across all ecoregions and low genetic structure within host strains. Positive selection was also related to a higher degree of FAW strains differentiation in Brazil, and this is plausible given that C-strain is less susceptible to insecticides and Bt crops⁴⁹. Moreover, both pairwise F_ST distances and DAPC showed genetic structure between FAW populations of Brazil and Argentina, and the most important IRM implication of this result is that a plan can be anticipated and designed after a resistant population is reported in either country.

In conclusion, by combining classic molecular markers for FAW host strain identification, and genome-wide SNPs identified with GBS, we obtained more resolution of population structure than previously reported. The genetic structure and pattern of FAW in Argentina and Brazil reinforces the importance of phytosanitary barriers between countries for effective FAW management in each location. In agreement with this issue, outlier analysis suggested that positive selection is associated with field management and host strain divergences. Taking all this into consideration, current GBS data proved to be useful for population genomics research in South America and it may be applied to other geographies where the species has been recently introduced.

Sampling and DNA extraction

Fall armyworm caterpillars were manually collected in fields from 12 locations in Brazil under the SISBIO license number 58435. Field samples from 3 locations of Argentina were also included (AR01-AR03 – Table 1). Specimens were collected between June 2018 and January 2021. Larvae were transferred to trays containing artificial diet and incubated in laboratory conditions until become moths. Random moths were placed in 1.5 mL polypropylene microcentrifuge tubes with 98% ethanol and stored at -20ºC. Species identification was confirmed by morphology and sequencing of the COI gene (see below). We extracted DNA from the legs of random adults using CTAB-based method⁵⁰. DNA quality and quantification were assayed by agarose electrophoresis gela (1% w/v) stained with SYBR Safe DNA Gel Stain (Invitrogen, Carlsbad, CA, USA). DNA concentrations were adjusted to approximately 30 ng/µL.

Table 1

Fall armyworm (*Spodoptera frugiperda*) sampling information. N refers to the number of samples successfully sequenced after GBS processing, totaling 228 individuals.
Location Code	Country	Locality (City, State)	Collection Date	Latitude	Longitude	Host	N
AR01	Argentina	Metán - Salta	22-Feb-18	-25.5	-64.97	Corn	11
AR02	Argentina	America - Buenos Aires	24-Jan-18	-35.48	-62.97	Corn	3
AR03	Argentina	San Justo - Santa Fe	27-Feb-18	-30.78	-60.58	Corn	15
BA02	Brazil	São Desidério - Bahia	26-Jun-18	-12.44	-45.64	Corn	19
BA03	Brazil	Barreiras - Bahia	19-Jul-18	-11.78	-45.78	Corn	18
DF	Brazil	Planaltina - Distrito Federal	8-Jun-18	-15.87	-47.4	Corn	20
GO	Brazil	Rio Verde - Goiás	22-Jun-18	-17.75	-51.04	Corn	24
MA01	Brazil	São Luís - Maranhão	12-Dec-18	-2.59	-44.21	Corn	10
MA02	Brazil	São Luís - Maranhão	19-Dec-18	-2.58	-44.21	Corn	13
MT01	Brazil	Campo Novo do Parecis - Mato Grosso	25-Jun-18	-13.97	-57.98	Corn	24
MT02	Brazil	Campo Novo do Parecis - Mato Grosso	1-Jul-18	-14.27	-57.76	Corn	21
PR	Brazil	Toledo - Paraná	20-Jun-18	-24.67	-53.76	Corn	9
RS	Brazil	Alegrete – Rio Grande do Sul	13-Jan-21	-30.02	-55.71	Rice	3
SC	Brazil	Chapecó – Santa Catarina	14-Dec-20	-27.09	-52.64	Corn	15
SP	Brazil	Taquarituba - São Paulo	14-Jul-18	-23.54	-49.22	Corn	23

Host strains identification

For the 228 specimens whose reads were successfully retained after GBS processing, we did polymerase chain reaction (PCR) amplifications of the COI and TpiEI4 regions to characterize host strains based on diagnostic polymorphisms at mCOI1164, mCOI1287 and gTpi183 positions using the same primer sequences and procedures as described elsewhere⁷. Samples featuring both C and T nucleotides at the gTpi183 position were identified as hybrids (TpiH). The COI sequences were used to confirm species identification as well. Sequences were aligned with Clustal W⁵¹ using MEGA 7⁵². Populations were analyzed for descriptive statistics such as nucleotide diversity (π), number of haplotypes, and haplotype diversity (Hd) using DnaSP 6⁵³. Popart software was used to generate haplotype map and network⁵⁴. The genetic data presented in this study are publicly available on GenBank (BioProject PRJNA847933, and Accessions ON704174 - ON704629).

GBS library sequencing and data processing

The steps of the GBS library preparation were done according to methodology described elsewhere⁵⁵ with the following modifications. Genomic DNA was digested with restriction enzymes NsiI-MseI (New England Biolabs - NEB, Ipswich, MA, USA). The barcoded NsiI adapters and a common MseI adapter were ligated to the digested DNA of each sample. Barcoded DNA fragments from all samples were pooled in a single tube and amplified by PCR. The libraries were single-end sequenced to 150 nucleotides on a single lane using the Illumina NextSeq500/550 sequencing kit v2 (Illumina, Inc. San Diego, CA, USA) at the Genome Investigation and Analysis Laboratory of University of São Paulo.

Sequencing quality of GBS libraries was evaluated using FastQC⁵⁶. The 3’end of raw reads were trimmed to 90 bp and were inspected for adaptor sequences removal. We performed demultiplex using process-radtags in STACKS v.1.42⁵⁷. Reads could be assigned to each individual based on the sequence of the barcodes. Samples with less than 200,000 sequences and/or unexpected GC contents were removed for further analysis. Reads were aligned to the Spodoptera frugiperda reference genome ZJU_Sfru_1.0, under Bioproject PRJNA590312⁵⁸, using the algorithm BWA-MEM of the software BWA 0.7.17⁵⁹. Alignment files were processed with SAMtools⁶⁰ and Picard (http://broadinstitute.github.io/picard). We identified SNPs using freebayes 1.3.4⁶¹ with --standard_filters option. Filtering was performed using VCFtools v0.1.12a⁶² and BCFtools 0.1.12⁵⁹. We retained bi-allelic SNPs that passed the following criteria: minor allele frequency ≥ 0.01, read depth ≥ 5X, mapping quality ≥ 20, maximal amount of missing data per locus = 10%. Variants were separated by a minimum distance of 150 bp and r² threshold of 0.6. We performed an additional filter to remove six samples with more than 25% of missing data, and SNPs identified at sexual chromosome W present only in females, mitochondrial genome or in unanchored contigs of the reference genome. Results were stored in variant call format (VCF).

Genetic Differentiation Analysis

Genetic diversity was estimated by calculating the observed heterozygosity H_O, expected heterozygosity H_E, nucleotide diversity π, the inbreeding coefficients (F_IS), and the pairwise Fixation Index (F_ST) using hierfstat package^63,64. We tested for significant differences in heterozygosities and F_IS using confidence intervals calculated based on 1000 bootstrap resamples. F_ST relations were illustrated by heatmaps generated by the RColorBrewer R package. Genetic structure was additionally explored through the Discriminant Analysis of Principal Components (DAPC) using adegenet^65,66 for R software⁶⁷. Sampling locations were considered as priori groupings and all loci were included. The DAPCs were performed separately for neutral and outlier loci putatively under positive selection.

Outlier SNPs detection and annotation

We searched for loci putatively under selection in the set of 15 populations and in the set of strains, where samples were identified as R- or C- strains using the Tpi marker as previously described⁷. TpiH individuals were not included in this analysis. Outlier SNPs were identified using three methods. The fsthet package⁶⁸ generates smoothed quantiles for the F_ST–heterozygosity relationship from the empirical distribution, and outliers were selected with a 95% confidence level threshold. The pcadapt package⁶⁹ considers population structure determined by PCA, and outliers were identified using a false discovery rate (FDR) of 0.05. The program FLK⁷⁰ assumes that SNPs were polymorphic in an ancestral population, and uses a population tree to build a null model of the behavior of F_ST. SNPs with estimated ancestral minor allele frequency < 0.05 were removed. We retained candidate markers identified by at least two methods and the remaining loci were considered as neutral. We compared the sequences containing outliers with the annotation files of the reference genome to identify loci present in encoding genes. We generated a fasta file containing the protein sequences to run Blast2Go⁷¹ software using blastp with Insecta Taxa, InterPro Scan, GO mapping and functional annotation. The GO terms for each gene were submitted to Web Gene Ontology Annotation Plot (WEGO) for summarization⁷².

Acknowledgements

We are truly thankful to Janaína Marques Mondego for helping with collections in Maranhão locations. A.A.-P. thanks São Paulo Research Foundation (FAPESP 2018/00036-9) for a post-doctoral scholarship.

Data Availability

Illumina reads will be available after publication as FASTQ files in the NCBI Sequence Read Archive (SRA) repository, accession PRJNA847933 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA847933?reviewer=nac1b0jr7e3tb4r1hgtsbcej7s).

The DNA sequences used for host strain identification are deposited into GenBank (accession numbers ON704174 - ON704629). Any additional data are available from the corresponding author upon request.

Author Contributions

T.K.I., M.I.Z. and J.F. designed the study. T.K.I., J.F. and M.G.M. collected field specimens. T.K.I., E.M.G.C., A.A.-P. and C.E.A.B. generated the molecular data. T.K.I., E.M.G.C., A.A.-P. and R.N.N. analyzed the results. T.K.I. and M.I.Z. drafted the manuscript. All authors participated in writing and reviewing the manuscript.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

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Population genomics of fall armyworm in Brazil and Argentina revealed by Genotyping-by- Sequencing and implications for pest management

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