Genetic characterization of fall armyworm (Spodoptera frugiperda) in Ecuador and comparisons with regional populations identify likely migratory relationships

Authors: Rodney N. Nagoshi aff001;  Benjamin Y. Nagoshi aff002;  Ernesto Cañarte aff003;  Bernardo Navarrete aff003;  Ramón Solórzano aff003;  Sandra Garcés-Carrera aff003
Authors place of work: Center for Medical, Agricultural and Veterinary Entomology, United States Department of Agriculture-Agricultural Research Service, Gainesville, Florida, United States of America aff001;  University of South Florida, Tampa, Florida, United States of America aff002;  National Institute of Agriculture Research (INIAP), Quito, Ecuador aff003
Published in the journal: PLoS ONE 14(9)
Category: Research Article
doi: 10.1371/journal.pone.0222332


Fall armyworm, Spodoptera frugiperda (J. E. Smith), is an important agricultural pest native to the Americas that has recently been introduced into the Eastern Hemisphere where it has spread rapidly through most of Africa and much of Asia. The long-term economic consequences of this invasion will depend on how the species and important subpopulations become distributed upon reaching equilibrium, which is expected to be influenced by a number of factors including climate, geography, agricultural practices, and seasonal winds, among others. Much of our understanding of fall armyworm movements have come from mapping genetically defined subpopulations in the Western Hemisphere, particularly in North America where annual long-distance migrations of thousands of kilometers have been documented and modeled. In contrast, fall armyworm mapping in much of the rest of the hemisphere is relatively incomplete, with the northern portion of South America particularly lacking despite its potential importance for understanding fall armyworm migration patterns. Here we describe the first genetic description of fall armyworm infesting corn in Ecuador, which lies near a likely migration conduit based on the location of regional trade winds. The results were compared with populations from corn habitats in select locations in the Caribbean and South America to investigate the possible migratory relationship between these populations and was further assessed with respect to prevailing wind patterns and the distribution of locations with climate favorable for fall armyworm population establishment and growth.


Earth sciences – Seasons – Autumn – People and places – Geographical locations – South America – Ecuador – Peru – Biology and life sciences – Genetics – Heredity – Genetic mapping – Haplotypes – Psychology – Behavior – Animal behavior – Animal migration – Zoology – Organisms – Eukaryota – Plants – Grasses – Maize – Social sciences – Research and analysis methods – Animal studies – Experimental organism systems – Model organisms – Plant and algal models – Physical sciences – Physics – Classical mechanics – Mechanical stress – Thermal stresses


The noctuid moth Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), commonly called fall armyworm, is native to the Western Hemisphere. It is the primary insect pest of corn in the southeastern United States, the Caribbean, and South America and can cause significant economic damage in several other crops including sorghum, millet, cotton, and rice [1].

Fall armyworm is a tropical pest not known to diapause and so unable to survive severe winters. In North America known winter populations are limited to southern Texas and southern Florida [2], yet infestations annually extend thousands of kilometers northward as far as Canada [3]. This is due to an extensive annual migration beginning in the spring that modeling shows depends on three factors: 1) Seasonal air transport systems that direct northward migration, 2) Extensive corn acreage along the migratory route that support high density fall armyworm populations, and 3) Climate conditions favorable to fall armyworm development in the infested regions [4]. These characteristics are a consequence of the capabilities exhibited by many Spodoptera species of wind-based long-distance flights at high altitude that are limited to nocturnal hours [5]. Continuous flights of several hundred kilometers per night are possible under optimal conditions, but suitable staging areas are needed along the migratory route for rest and feeding during the day [5]. The North American migration occurs over several generations, hence the need for extensive corn acreage to support high density populations.

Long distance migration of fall armyworm in other regions has not yet been demonstrated. Whole genome variation comparisons suggest fall armyworm is a single breeding population across the entire hemisphere, consistent with frequent and general migration [6, 7], though there are indications of regional genetic heterogeneity in South America [8]. We have found using methods that are less sensitive to low frequencies of genetic introgressions that fall armyworm can be subdivided into two geographically separated groups [9]. One is the “TX-type” that include populations in that overwinter in Texas (and migrate to central North America), Mexico, Trinidad and Tobago, and South America. The other is the “FL-type” found in Florida, which migrate to the eastern United States, and is present in most of the islands surveyed in the Caribbean [3, 10, 11]. This pattern has been consistently observed for over 10 years and so represents the equilibrium distribution of fall armyworm in the Western Hemisphere.

The broad host range exhibited by fall armyworm is in part due to the presence of two subpopulations that differ in their host plant preferences [12, 13]. Historically designated as host strains their phylogenetic relationship remains unclear. They are morphologically indistinguishable except for evidence of wing size differences between strains in South America [14, 15], and there have been reports of strain differences in female pheromone constituents, mating behaviors, and physiology [1624]. However, fall armyworm exhibits substantial variability between geographical populations independent of strain differences that can make identifying strain diagnostic traits problematic [25].

The strains were originally identified by genetic marker differences between larval specimens collected from rice versus corn, which gave rise to the designation rice-strain (RS) and corn-strain (CS) [13]]. Subsequent studies showed a stronger preference of RS to pasture grasses and millet, with rice being more variable, while the CS group is preferentially found in corn, cotton, and sorghum [26, 27]. The correspondence between markers and host plant has been observed throughout the Western Hemisphere but is not absolute, with on average about 20% of larvae from corn expressing RS markers and sporadic observations of more substantial disagreements [2830]. Despite this variability, genetic markers remain the method of choice for strain identification.

Most useful for our studies have been genetic markers derived from portions of the mitochondrial Cytochrome Oxidase Subunit I (COI) and the sex-linked Triosephosphate isomerase (Tpi) genes [28, 31]. One segment of COI (COIA) includes a segment commonly used for DNA barcoding and which we typically use to confirm species identification [32]. A second segment, COIB, carries polymorphisms instrumental in distinguishing both strains and the FL-type/TX-type groupings [9, 33, 34]. The nuclear Tpi gene is also used as a marker of strain identity and may be more accurate than COIB [28]. Because of its location as a nuclear marker (as opposed to the mitochondrial COI), the Tpi marker can be used both in combination with COI and separately to explore the possibility of hybridization between strains [34, 35].

Fall armyworm recently invaded Africa, where it rapidly spread throughout the continent and has subsequently been found in India and southeastern Asia [3639]. The potential economic consequences are significant and there is much interest in using the Western Hemisphere information to better understand how fall armyworm is moving in the Eastern Hemisphere and its eventual distribution at equilibrium. Both will be largely determined by seasonal wind patterns, regional and local environmental conditions, and the availability of host plants, particularly corn. With respect to climate, the situation in Africa will likely reflect that of South America more than North America.

The northern portion of South America has not been studied using our suite of markers yet represents a potentially important region for understanding fall armyworm migration patterns. The two American continents are linked by two obvious pathways for natural migration between the continents for flying insects. One is the land connection comprised of Central America and the Isthmus of Panama, and the other is the chain of islands in the Caribbean Sea known as the Greater and Lesser Antilles. Our previous studies indicated that large population movements in the Caribbean were unlikely with little mixing observed between fall armyworms from the two continents, but we have no data from Central America or Panama.

We are in the process of extensive surveys of fall armyworm in Ecuador, which is the closest location we have analyzed to date to the Isthmus of Panama and Central America. We present in this study the first genetic description of fall armyworm from cornfields in Ecuador, which was then compared with those from select corn-growing locations in the Caribbean and South America to investigate possible migratory relationships. The results were further assessed within the context of prevailing wind patterns during the collection period and the distribution of climate favorable for fall armyworm growth and establishment. We further demonstrate that COIB can differentiate fall armyworm from other Spodoptera species, eliminating the need to sequence COIA and thereby simplifying the genetic analysis.


Species identification using COIB

The mitochondrial COI and nuclear Tpi genes contain polymorphisms useful for the analysis of fall armyworm populations (Fig 1). The 5’ segment of the COI locus, COIA, contains the barcode region capable of species and host strain identification [32], while the more 3’ segment, COIB, produce haplotypes that also identify strain and differentiate between two geographically separated populations [9]. To streamline the analysis by COI we tested whether COIB could substitute for COIA for species identification.

<h2>Diagrams of the COIB, <i>Tpi</i> gene segments.</h2>
Fig. 1.

Diagrams of the COIB, Tpi gene segments.

A. The COI gene showing the locations of the COIA segment frequently used for DNA barcoding and the COIB segments used in this study. The mCOI1164D site defines COI-based strain identity and together with polymorphisms at mCOI1287R describe the corn-strain h1-4 haplotypes. Black arrows indicate locations of primers used for PCR and DNA sequencing. B. Map of a section of the Z-linked Tpi gene that includes exon4 (fourth exon from the start of the coding region), exon5 and the intervening intron. The gTpi183Y site defines Tpi-based strain identity with the empirically observed sequence chromatographs shown indicating the C, T, C/T heterozygote forms found in this study. The Tpie4i4 contains a portion of exon4 and intron4.

Sequence information from the COIB region were obtained for 13 Spodoptera species from GenBank. These were found to share a 259-bp COIB segment (designated COIB259). Despite the short length this sequence was sufficient to distinguish fall armyworm of both strains from the other Spodoptera species by Neighbor-joining analysis (Fig 2). Spodoptera cosmiodes (Walker) and S. descoinsi ((Lalanne-Cassou & Silvain) were identical in their COIB259 sequence, while S. albula (Walker) and S. dolichos (F.) appear to be closely related. A total of 143 specimens from Manabi, Ecuador were analyzed by COI sequencing with 22 COIB259 haplotypes observed. One Ecuador haplotype clustered with the consensus rice-strain COIB259 while the remainders were most similar to the consensus corn-strain haplotype (Fig 2).

<h2>Phylogenetic tree inferred by using neighbor-joining method and Tamura-Nei model [<em class="ref">40</em>] with no outgroup.</h2>
Fig. 2.

Phylogenetic tree inferred by using neighbor-joining method and Tamura-Nei model [40] with no outgroup.

The sequences for the different Spodoptera species were obtained from GenBank. Corn-strain (S. frugiperda CS) and rice-strain (S.frugiperda RS) are consensus reference sequences (S1 Fig).

Strain-identity based on specific COI and Tpi polymorphisms

The two strains can also be distinguished by polymorphic sites in the COI and Tpi genes (Fig 1). The site COIB1164 is strain-specific with a T1164 indicating rice-strain (COI-RS) and an A1164 or G1164 diagnostic of the corn-strain (COI-CS). The results from this single polymorphic site were consistent with the phylogenetic comparisons as the single Ecuador specimen clustering as COI-RS expressed T1164, while the remaining Ecuador haplotypes clustering with the consensus COI-CS sequence expressed either A1164 or G1164. Overall, the COI-CS haplotype predominated at all locations as expected since collections were limited to cornfields (Fig 3A).

<h2>Comparisons of <i>COI</i> and <i>Tpi</i> haplotype frequencies of fall armyworm in Ecuador.</h2>
Fig. 3.

Comparisons of COI and Tpi haplotype frequencies of fall armyworm in Ecuador.

A. Frequency of the COI strain diagnostic markers based on analysis of the COIB segment. Number of specimens indicated above columns. B. Frequency of the Tpi strain diagnostic markers from the TpiE4 exon segment. Estimated number of chromosomes tested indicated above columns.

The COI strain data were confirmed by the Tpi marker, where strain identification is defined by the gTpi183Y polymorphism in exon4 (Fig 3B). The Z-chromosome-linked Tpi gene is present in two copies in males (ZZ) and one copy in females (ZW), which means that a portion of the larval collections, which were not sexed, have the potential to be heterozygous for Tpi. As a result, three gTpi183Y sequencing chromatographs are observed that derive from homozygosity (ZZ) or hemizygosity (ZW) for C183 (TpiC or corn-strain), T183 (TpiR or rice-strain), or the presence of both C and T (TpiH or hybrid). The number of TpiC and TpiR chromosomes were estimated and both Ecuador locations were over 95% TpiC (Fig 3B).

COI-h haplotype proportions

Near the COIB1164 strain-diagnostic marker is a polymorphic site, COIB1287, that is partially strain-specific. The COI-RS T1164 variant is always associated with A1287, while the corn-strain A1164 or G1164 polymorphisms can be found with either A1287 or G1287. As a result, COI-RS is identified by the combination T1164A1287 while COI-CS includes four variants designated the COI-h haplotypes, A1164A1287 (h1), A1164G1287 (h2), G1164A1287 (h3), and G1164G1287 (h4) (Fig 1A). The relative proportions of h2 and h4 as measured by the metric (h4-h2)/(h2+h4) vary with location in such a way that they subdivide fall armyworm populations in the Western Hemisphere into two groups (FL-type and TX-type) [33]. Examination of specimens from Ecuador were of the TX-type, consistent with locations in South America and different from that found in the Greater Antilles and Florida (Fig 4).

<h2>Frequency distributions of the COIB h-haplotypes, h1-4, for Ecuador and select locations in the Western Hemisphere.</h2>
Fig. 4.

Frequency distributions of the COIB h-haplotypes, h1-4, for Ecuador and select locations in the Western Hemisphere.

Numbers indicate the number of COI-CS specimens. Data for Florida (FL), Puerto Rico (PR), Dominican Republic (DR), Bolivia (Bol), Trinidad-Tobago (TT) and Peru (Per) were from Nagoshi et al. (2016) [33]. Ecuador specimens are from Portoviejo (Ecu-Pv) and Tosagua (Ecu-To).

Haplotype network comparisons of Tpi variation

Network analysis was used to compare the genetic variation in the Tpi gene of Ecuador fall armyworm with those from four regional collection sites. The Tpie4i4 haplotypes identified from Ecuador were similar in sequence to each other, with no more than two mutations difference between one haplotype and the most closely related variant (Fig 5). There was considerable overlap in the haplotypes found in Bolivia, Peru, and Trinidad-Tobago with those in Ecuador and with each other, again with little variation between them. This was in marked contrast with the collections from Puerto Rico. Seven haplotypes unique to Puerto Rico were identified that each differed by at least eight mutations from those at the other locations. These results suggest that the Puerto Rico population is effectively isolated from the other locations and is in the process of genetic divergence. In contrast, there appears to be significant interactions between the fall armyworms in Ecuador, Peru, Bolivia, and Trinidad and Tobago despite the thousands of kilometers that separate each of these locations.

<h2>Haplotype network analysis using the Tpie4i4 segment of the <i>Tpi</i> gene illustrating the genetic differences between the haplotypes found in Ecuador, Peru, Bolivia, Trinidad-Tobago, and Puerto Rico.</h2>
Fig. 5.

Haplotype network analysis using the Tpie4i4 segment of the Tpi gene illustrating the genetic differences between the haplotypes found in Ecuador, Peru, Bolivia, Trinidad-Tobago, and Puerto Rico.

Kilometers in parenthesis indicate distance from Manabi, Eucador. Circle area proportional to number of haplotypes.


The characterization of fall armyworm from Ecuador provides an important data point from a region in the Western Hemisphere where fall armyworm has not been studied extensively with our suite of molecular markers. Ecuador is of particular interest because of its location relative to potential intercontinental migration pathways and air transport systems that are likely driving fall armyworm migration in South America. The South American Low Level Jet is the predominant lower altitude wind system in the region, forming an easterly trade wind from the equatorial Atlantic that is deflected sharply south-southeastward by the higher elevations of the Andes Mountain Range [41]. This occurs just south of Ecuador and most likely directs migration of fall armyworm populations to the southern and eastern portions of the continent. With respect to the survey locations, these observations suggest that populations in Trinidad and Tobago could contribute to fall armyworm in Ecuador, which in turn acts as a migratory source for Peru. However, seasonal winds in Ecuador during two periods of high fall armyworm levels are generally of low velocity and inconsistent direction (Fig 6). Such conditions raise the possibility that the fall armyworm populations in Ecuador could be relatively isolated from the rest of the continent.

<h2>Maps indicating wind conditions in Ecuador and surrounding regions.</h2>
Fig. 6.

Maps indicating wind conditions in Ecuador and surrounding regions.

A. Map of vector mean winds Ecuador and surrounding areas. Climatology of monthly vector mean winds from March and September at the 925-mb pressure-height from NCEP-NCAR Reanalysis data using the 1981–2010 base period. TT, Trinidad-Tobago; Ecu, Ecuador; Per, Peru. B. HYSPLIT forward projections of 12-h overnight trajectories at 500 m AGL based on historical wind data. Average monthly trajectories for selected months are displayed as a frequency distribution from Ecuador (Ecu: -0.78, -80.22), Peru (Per: -12.08, -76.93), Trinidad and Tobago (TT: 10.65, -61.43).

Our findings of strong similarities in the COIB (Fig 5) and Tpi (Fig 6) haplotype compositions of fall armyworms from Ecuador, Peru, Bolivia, and Trinidad and Tobago are consistent with extensive mixing of the fall armyworm from these four locations, while those from Puerto Rico appear to be isolated. This result has important implications if true as Puerto Rico is only 100 km further from Ecuador than Trinidad and Tobago based on direct flight comparisons. It suggests that despite the known ability of fall armyworm to migrate thousands of kilometers per season in North America, the approximately 800 km of water separating Puerto Rico from the South American mainland (compared to only 50 km for Trinidad and Tobago) is a significant barrier preventing regular migration of fall armyworm from one site to the other.

To identify the most likely drivers of population movements in this region we examined two environmental factors that are known to significantly impact fall armyworm migration and dispersion patterns. These are wind vectors and the distribution of habitats with suitable climates. Substantial populations of fall armyworm were observed in cornfields in Ecuador during the spring and fall seasons. Wind vectors during these periods in most of Ecuador and Peru exhibit low velocity and inconsistent direction (Fig 6A), which are not compatible with the wind-dependent long-distance migration behavior observed in North America. This can be visualized by HYSPLIT analysis where we projected the direction and distance of dispersion promoted by wind vectors averaged for the months of March and September over a single 12-hour nocturnal flight period (Fig 6B). Dispersion projections from Trinidad and Tobago show a strong westward bias consistent with the strong prevailing wind vectors there, which contrasted with the more localized distributions originating from Ecuador and Peru.

More favorable to fall armyworm movements were the extensive areas with supportive habitats for fall armyworm populations (Fig 7). The CLIMEX analysis was set to show shading in map location with values equal or greater than 33, with an ecoclimatic index (EI) value of 30 or above considered highly favorable [42]. The EI climate suitability map describes a contiguous pathway of favorable habits from Trinidad and Tobago that extends through the eastern sections of Ecuador, Peru, and Bolivia. From these observations, if the widely dispersed fall armyworm from Trinidad and Tobago, Ecuador, Peru, and Bolivia represent a single interbreeding population as suggested by their genetic similarities, then movement between populations is likely to be dominated by short flights to adjacent favorable habitats rather than the long-distance wind-directed migration observed in North America. This migratory behavior should also be compatible with host plant availability as agricultural activity in the region, including corn production, largely overlap the areas of favorable climate for fall armyworm [43]. Less certain are the population densities that can be supported. The large migratory populations observed and modeled in North America are generally associated with extensive acreages of contiguous and high intensity corn plantings [4], conditions that tend to optimize population density and may not be typical of corn production in the northern region of South America. While speculative, these observations when considered in total suggests that fall armyworm movements in this region of South America may be associated with smaller migratory populations covering shorter distances in more variable directions.

<h2>Map indicating the distribution of habitats in Ecuador and surrounding regions based on climate suitability for the development of fall armyworm.</h2>
Fig. 7.

Map indicating the distribution of habitats in Ecuador and surrounding regions based on climate suitability for the development of fall armyworm.

Map is based on CLIMEX calculations of the Establishment Index (EI) with higher values indicating more favorable climate.

In summary, it is important to note that these studies are limited to the fall armyworm infesting corn as collections from other host plants, particularly those associated with the rice-strain have been difficult to obtain in this region. Based on this sampling we conclude that the COI and Tpi compositions of fall armyworm found in corn in Ecuador are consistent with populations in the northern regions of South America extending from Trinidad and Tobago to the east, Peru to the south west, and Bolivia to the south undergoing substantial mixing due to migration and population dispersion. This pattern is consistent with the seasonal wind patterns consisting of strong eastward trade winds over Trinidad and Tobago that are deflected southward by the Andes mountain range in Peru as well as a favorable distribution of suitable habitats that could support localized movements of fall armyworm from Ecuador into eastern Peru and Bolivia. Evidence of substantial introgression of fall armyworm populations from Central America or the Greater Antilles into Ecuador have yet to be observed.


Specimen collections and DNA preparation

Ecuador specimens were obtained in 2018 as larvae from corn plants from two cantons in Manabi province, Portoviejo (March and September) and Tosagua (March) (Table 1). Collections from Peru, Puerto Rico, Trinidad and Tobago, Bolivia, multiple locations in Florida, and the Dominican Republic were described previously (Table 1). For these collections, the data for COIB were previously reported [33, 44], while data for Tpi were from this study. No endangered or protected species were involved in this study. We obtained permission from private farmers for access and data collection in their fields.

Tab. 1.

Source information for fall armyworm collections.

<h2>Source information for fall armyworm collections.</h2>

Collected specimens were stored either air-dried or in ethanol at room temperature. A portion of each specimen was excised and homogenized in a 5-ml Dounce homogenizer (Thermo Fisher Scientific, Waltham, MA, USA) in 800 μl Genomic Lysis buffer (Zymo Research, Orange, CA, USA) and incubated at 55°C for 5–30 min. Debris was removed by centrifugation at 10,000 rpm for 5 min. The supernatant was transferred to a Zymo-Spin III column (Zymo Research, Orange, CA, USA) and processed according to manufacturer’s instructions. The DNA preparation was increased to a final volume of 100 μl with distilled water. Genomic DNA preparations of fall armyworm samples from previous studies were stored at -20°C. Species identity was initially determined by morphology and confirmed by sequence analysis of the COIB region.

PCR amplification and DNA sequencing

PCR amplification for all segments was performed in a 30-μl reaction mix containing 3 μl 10X manufacturer’s reaction buffer, 1 μl 10mM dNTP, 0.5 μl 20-μM primer mix, 1 μl DNA template (between 0.05–0.5 μg), 0.5-unit Taq DNA polymerase (New England Biolabs, Beverly, MA). The thermocycling program was 94°C (1 min), followed by 33 cycles of 92°C (30 s), 56°C (45 s), 72°C (45 s), and a final segment of 72°C for 3 min. Typically 96 PCR amplifications were performed at the same time using either 0.2-ml tube strips or 96 well microtiter plates. All primers were obtained from Integrated DNA Technologies (Coralville, IA) and are mapped in Fig 1. Amplification of the COIB segment typically used the primer pair 924F (5’-TTATTGCTGTACCAACAGGT-3’) and 1303R (5’- CAGGATAGTCAGAATATCGACG-3’). When necessary nested PCR was used in which the first PCR was performed using primers 891F (5’-TACACGAGCATATTTTACATC-3’) and 1472R (5’-GCTGGTGGTAAATTTTGATATC-3’) followed by a second PCR using the internal primers 924F and 1303R. Amplification of the Tpi exon-intron segment used the primers 412F (5’- CCGGACTGAAGGTTATCGCTTG -3’) and 1140R (5’- GCGGAAGCATTCGCTGACAACC-3’) to produce a variable length fragment due to insertion and deletion mutations. Nested PCR was again used when needed with the first PCR done with primers 634F (5’-TTGCCCATGCTCTTGAGTCC-3’) and 1166R (5’-TGGATACGGACAGCGTTAGC-3’) and the second PCR using the internal primers 412F and 1140R.

For fragment isolations, 6 μl of 6X gel loading buffer was added to each amplification reaction and the entire sample run on a 1.8% agarose horizontal gel containing GelRed (Biotium, Hayward, CA) in 0.5X Tris-borate buffer (TBE, 45 mM Tris base, 45 mM boric acid, 1 mM EDTA pH 8.0). Fragments were visualized on a long-wave UV light box and manually cut out from the gel. Fragment isolation was performed using Zymo-Spin I columns (Zymo Research, Orange, CA) according to manufacturer’s instructions. The University of Florida Interdisciplinary Center for Biotechnology (Gainesville, FL) and Genewiz (South Plainfield, NJ) performed the DNA sequencing.

DNA alignments and consensus building were performed using MUSCLE (multiple sequence comparison by log-expectation), a public domain multiple alignment software incorporated into the Geneious Pro 10.1.2 program (Biomatters, New Zealand, [45]. Phylogenetic trees were graphically displayed in a neighbor-joining (NJ) tree analysis also included in the Geneious Pro 10.1.2 program [46]. Phylogenetic networks were estimated by the TCS statistical parsimony algorithm ([47]) incorporated in the software program PopArt [48].

Characterization of the CO1 and Tpi gene segments

The genetic markers are all single nucleotide substitutions. Sites in the COI gene are designated by an "m" (mitochondria) while Tpi sites are designated "g" (genomic). This is followed by the DNA name, number of base pairs from the predicted translational start site (COI), 5’ start of exon (Tpi), or 5’ start of the intron (TpI4) and the nucleotides observed using IUPAC convention (R: A or G, Y: C or T, W: A or T, K: G or T, S: C or G, D: A or G or T).

The COI markers are from the maternally inherited mitochondrial genome. The COIB segment was amplified by CO1 primers 891F and 1472R (Fig 1A). Overlapping COIB segments for various Spodoptera species were obtained from GenBank and included, S. abula (HQ177287), S. cosmiodes (HQ177295), S. descoinsi (HQ177306), S. dolichos (HQ177313), S. eridania (Stoll in Cramer)(HQ177321), S. exempta (Walker)(HQ177334), S. exigua (Hübner)(HQ177339), S. latisfscia (Walker)(HQ177354), S. littoralis (Boisduval)(HQ177364), S. mauritia (Boisduval)(HQ177382), S. ornithogalli (Guenée)(HQ177392), S. praefica (Grote)(HQ177407), S. litura (F.)(HQ177375). These sequences had in common a 259-bp segment designated COIB259, which was used for species identification by phylogenetic analysis.

The larger COIB296 segment was used for all other COI analyses (Fig 1A). Sites mCOI1164D and mCOI1287R are diagnostic for strain identity in Western Hemisphere populations where there is a single rice-strain, T1164A1287, and four corn-strain configurations, A1164A1287 (h1), A1164G1287 (h2), G1164A1287 (h3), G1164G1287 (h4) [33].

Variants in the Tpi e4 exon segment can also be used to identify host strain identity with results generally comparable with the CO1 marker [28]. The gTpi183Y site is on the fourth exon of the predicted Tpi coding region and was PCR amplified using the Tpi primers 412F and 1140R (Fig 1B). The C-strain allele (TpiC) is indicated by a C183 and the R-strain (TpiR) by T183 [28]. The Tpi gene is located on the Z sex chromosome that is present in one copy in females and two copies in males. Because the genomic DNA was directly sequenced, males heterozygous for Tpi alleles will simultaneously display both alternatives at polymorphic sites, which if different are easily identified by overlapping sequencing chromatographs. Heterozygosity at site Tpi183 was limited to C/T and was denoted as TpiH.

The Tpie4i4 segment includes a 52 bp portion of the e4 exon followed by approximately 172 bp of the TpI4 intron, the latter of which is of variable length due to frequent insertions and deletions (indels). The segment was sequenced with primer 891F for the initial sequencing reaction and 1140R for 2nd strand sequence confirmation when needed in cases of ambiguity. This segment was chosen for analysis because it empirically had the most consistent sequence quality with the given primers. A variable but often high percentage of specimens were heterozygous for frameshift mutations in the intron that could be identified by overlapping chromatographs immediately after the polymorphism. These were not further analyzed.

Calculation of haplotype numbers

The mitochondrial COI markers are calculated directly as the number of specimens exhibiting the COI haplotypes divided by total specimens. Because Tpi is a sex-linked nuclear gene, the number of Tpi genes present will differ by sex. Specimens with a TpiC or TpiR haplotype could be either a homozygous male or hemizygous female. We assumed a 1:1 sex ratio for the larval specimens so that the average number of Tpi genes per specimen is given as 1.5 as calculated: (2 in males + 1 in females)/2. Based on this reasoning the number of TpiC and TpiR specimens were multiplied by 1.5 to estimate the number of chromosomes carrying each marker. In comparison, TpiH specimens are heterozygous and so carried one of each marker. The estimated number of TpiC chromosomes was calculated by 1.5TpiC + TpiH, and TpiR chromosomes = 1.5TpiR + TpiH.

CLIMEX climate suitability analysis

CLIMEX is a dynamic simulation model that estimates the potential geographical distribution and relative abundance of a species according to climate [42]. In principle, extrapolating locations with favorable climate based on the biological parameters of a species should provide a reasonable estimate of geographical distribution, and for pests identify locations at high risk of infestation. CLIMEX projections for fall armyworm has been performed at continental and global scales and while there were a few variations in the biological parameters for fall armyworm used between studies these generated only modest differences in the distribution maps [4951]. We performed a CLIMEX suitability analysis focused on the northern portion of South America, using fall armyworm parameters from du Plessis et al (2018) [49] (Table 2). Climate information was imported from Climond ( [52], using historical data from 1961–1990 at a resolution of 10’.

Tab. 2.

CLIMEX parameter values used for modelling fall armyworm [46].

<h2>CLIMEX parameter values used for modelling fall armyworm [<em class="ref">46</em>].</h2>

The Ecoclimatic Index (EI) is calculate from the annual Growth Index (GI) and the Stress Index (SI). The GI combines the minimum limit, optimum lower, optimum upper, and maximum limit for fall armyworm temperature and moisture indices calculated on a weekly basis, then averaged for the year to provide a measure of population growth potential. This is counterbalanced by the SI, which is a measure of unfavorable conditions focused primarily on temperature and precipitation. The EI integrates the GI and SI and is presented on a 0–100 scale, where 100 represents 100% suitability throughout the year (as would occur in an incubator). EI values greater than 30 are considered to be favorable for the long-term survival of the species in the region [42].

For this study, the Compare Locations (1 species) function in the CLIMEX program was used with the Grid Data simulation file. The species’ known distribution data was imported from CABI’s Invasive Species Compendium ( The location component was set to CM:10 South America. No climate change scenario or irrigation components were set. An EI map was created from the simulation, with the map specifications set at 0.16 diameter circles, showing zeros with three legend items.

HYSPLIT air trajectory projections

Air transport trajectories for select locations were estimated using the Hybrid Single Particle Lagrangian Integrated Trajectory Model at the Air Resources Laboratory (ARL) READY web site run by NOAA ( [53]. Projections were made for air transport conditions averaged for the 30-day period from March 1–30, 2018 and September 1–30, 2018. Fall armyworm migrates nocturnally so the duration of continuous flight was limited to a 12-hour period beginning at dusk, with a starting altitude of 500 m AGL and a maximum altitude of 1500 m AGL [4]. The pathways of the projections from each location were averaged and displayed as a frequency distribution with percentages reflecting the proportion of trajectories entering a given grid.

Supporting information

S1 Fig [tif]
Consensus sequences for -CS and -RS.


1. Andrews KL. Latin-American Research on Spodoptera frugiperda (Lepidoptera, Noctuidae). Fla Entomol. 1988;71(4):630–53. doi: 10.2307/3495022

2. Luginbill P. The fall armyworm. US Dept Agric Tech Bull. 1928;34:1–91.

3. Nagoshi RN, Meagher RL, Hay-Roe M. Inferring the annual migration patterns of fall armyworm (Lepidoptera: Noctuidae) in the United States from mitochondrial haplotypes. Ecology and Evolution. 2012;2(7):1458–67. doi: 10.1002/ece3.268 22957154

4. Westbrook JK, Nagoshi RN, Meagher RL, Fleischer SJ, Jairam S. Modeling seasonal migration of fall armyworm moths. Int J Biometeorol. 2016;60(2):255–67. doi: 10.1007/s00484-015-1022-x 26045330

5. Westbrook JK. Noctuid migration in Texas within the nocturnal aeroecological boundary layer. Integr Comp Biol. 2008;48(1):99–106. doi: 10.1093/icb/icn040 21669776

6. Belay DK, Clark PL, Skoda SR, Isenhour DJ, Molina-Ochoa J, Gianni C, et al. Spatial genetic variation among Spodoptera frugiperda (Lepidoptera: Noctuidae) sampled from the United States, Puerto Rico, Panama, and Argentina. Annals of the Entomological Society of America. 2012;105(2):359–67.

7. Clark PL. Population variation of Spodoptera Frugiperda (J.E. Smith) in the Western Hemisphere. [Ph.D. dissertation]. Lincoln, NE: University of Nebraska; 2005.

8. Martinelli S, Barata RM, Zucchi MI, Silva-Filho MDC, Omoto C. Molecular variability of Spodoptera frugiperda (Lepidoptera: Noctuidae) populations associated to maize and cotton crops in Brazil. J Econ Entomol. 2006;99(2):516–26.

9. Nagoshi RN, Silvie P, Meagher RL. Comparison of haplotype frequencies differentiate fall armyworm (Lepidoptera: Noctuidae) corn-strain populations from Florida and Brazil. J Econ Entomol. 2007;100(3):954–61. doi: 10.1603/0022-0493(2007)100[954:cohfdf];2 17598561

10. Nagoshi RN, Meagher RL, Flanders K, Gore J, Jackson R, Lopez J, et al. Using haplotypes to monitor the migration of fall armyworm (Lepidoptera:Noctuidae) corn-strain populations from Texas and Florida. J Econ Entomol. 2008;101(3):742–9. doi: 10.1603/0022-0493(2008)101[742:uhtmtm];2 18613574

11. Meagher RL, Nagoshi RN. Differential feeding of fall armyworm (Lepidoptera: Noctuidae) host strains on meridic and natural diets. Ann Entomol Soc Am. 2012;105(3):462–70. doi: 10.1603/An11158

12. Pashley DP. Host-associated genetic differentiation in fall armyworm (Lepidoptera, Noctuidae)—a sibling species complex. Ann Entomol Soc Am. 1986;79(6):898–904.

13. Pashley DP, Sparks TC, Quisenberry SS, Jamjanya T, Dowd PF. Two fall armyworm strains feed on corn, rice and bermudagrass. Louisiana Agriculture Magazine. 1987;30:8–9.

14. Cañas-Hoyos N, Marquez EJ, Saldamando-Benjumea CI. Differentiation of Spodoptera frugiperda (Lepidoptera: Noctuidae) corn and rice strains from central Colombia: A wing morphometric approach. Ann Entomol Soc Am. 2014;107(3):575–81.

15. Cañas-Hoyos N, Marquez EJ, Saldamando-Benjumea CI. Heritability of wing size and shape of the rice and corn strains of Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae). Neotrop Entomol. 2016;45(4):411–9. doi: 10.1007/s13744-016-0393-y 27044394

16. Groot AT, Marr M, Heckel DG, Schofl G. The roles and interactions of reproductive isolation mechanisms in fall armyworm (Lepidoptera: Noctuidae) host strains. Ecol Entomol. 2010;35:105–18.

17. Groot AT, Marr M, Schofl G, Lorenz S, Svatos A, Heckel DG. Host strain specific sex pheromone variation in Spodoptera frugiperda. Front Zool. 2008;5.

18. Lima ER, McNeil JN. Female sex pheromones in the host races and hybrids of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). Chemoecology. 2009;19(1):29–36.

19. Pashley DP. Quantitative genetics, development, and physiological adaptation in host strains of fall armyworm. Evolution. 1988;42(1):93–102. doi: 10.1111/j.1558-5646.1988.tb04110.x 28563847

20. Pashley DP, Martin JA. Reproductive incompatibility between host strains of the fall armyworm (Lepidoptera: Noctuidae). Ann Entomol Soc Am. 1987;80:731–3.

21. Rios-Diez JD, Saldamando-Benjumea CI. Susceptibility of Spodoptera frugiperda (Lepidoptera: Noctuidae) strains from central Colombia to two insecticides, methomyl and lambda-cyhalothrin: A study of the genetic basis of resistance. J Econ Entomol. 2011;104(5):1698–705. doi: 10.1603/ec11079 22066201

22. Rios-Diez JD, Siegfried B, Saldamando-Benjumea CI. Susceptibility of Spodoptera frugiperda (Lepidoptera: Noctuidae) strains from central Colombia to Cry1Ab and Cry1Ac entotoxins of Bacillus thuringiensis. Southwest Entomol. 2012;37(3):281–93.

23. Schöfl G, Dill A, Heckel DG, Groot AT. Allochronic separation versus mate choice: Nonrandom patterns of mating between fall armyworm host strains. Am Nat. 2011;177(4):470–85. doi: 10.1086/658904 21460569

24. Schöfl G, Heckel DG, Groot AT. Time-shifted reproductive behaviours among fall armyworm (Noctuidae: Spodoptera frugiperda) host strains: evidence for differing modes of inheritance. J Evolution Biol. 2009;22(7):1447–59.

25. Unbehend M, Hanniger S, Vasquez GM, Juarez ML, Reisig D, McNeil JN, et al. Geographic variation in sexual attraction of Spodoptera frugiperda corn- and rice-strain males to pheromone Lures. Plos One. 2014;9(2).

26. Juárez ML, Murúa MG, García MG, Ontivero M, Vera MT, Vilardi JC, et al. Host association of Spodoptera frugiperda (Lepidoptera: Noctuidae) corn and rice strains in Argentina, Brazil, and Paraguay. J Econ Entomol. 2012;105(2):573–82. doi: 10.1603/ec11184 22606829

27. Murúa MG, Nagoshi RN, Dos Santos DA, Hay-Roe M, Meagher RL, Vilardi JC. Demonstration using field collections that Argentina fall armyworm populations exhibit strain-specific host plant preferences. J Econ Entomol. 2015;108(5):2305–15. doi: 10.1093/jee/tov203 26453719

28. Nagoshi RN. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann Entomol Soc Am. 2010;103(2):283–92. doi: 10.1603/An09046

29. Nagoshi RN, Meagher RL. Behavior and distribution of the two fall armyworm host strains in Florida. Fla Entomol. 2004;87(4):440–9.

30. Nagoshi RN, Meagher RL. Seasonal distribution of fall armyworm (Lepidoptera: Noctuidae) host strains in agricultural and turf grass habitats. Environ Entomol. 2004;33(4):881–9.

31. Levy HC, Garcia-Maruniak A, Maruniak JE. Strain identification of Spodoptera frugiperda (Lepidoptera: Noctuidae) insects and cell line: PCR-RFLP of cytochrome oxidase C subunit I gene. Fla Entomol. 2002;85(1):186–90.

32. Nagoshi RN, Brambila J, Meagher RL. Use of DNA barcodes to identify invasive armyworm Spodoptera species in Florida. J Insect Sci. 2011;11:154 available online: 431 doi: 10.1673/031.011.15401 22239735

33. Nagoshi RN, Fleischer S, Meagher RL, Hay-Roe M, Khan A, Murua MG, et al. Fall armyworm migration across the Lesser Antilles and the potential for genetic exchanges between North and South American populations. Plos One. 2017;12(2):e0171743. doi: 10.1371/journal.pone.0171743 28166292

34. Nagoshi RN, Meagher RL, Nuessly G, Hall DG. Effects of fall armyworm (Lepidoptera: Noctuidae) interstrain mating in wild populations. Environ Entomol. 2006;35(2):561–8.

35. Nagoshi RN, Fleischer S, Meagher RL. Demonstration and quantification of restricted mating between fall armyworm host strains in field collections by SNP comparisons. J Econ Entomol. 2017;110(6):2568–75. doi: 10.1093/jee/tox229 29126215

36. Nagoshi RN, Dhanani I, Asokan R, Mahadevaswamy HM, Kalleshwaraswamy CM, Sharanabasappa, et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. Plos One. 2019;14(5).

37. Nagoshi RN, Goergen G, Du Plessis H, van den Berg J, Meagher R. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci Rep-Uk. 2019;9.

38. Shylesha AN, Jalali SK, Gupta A, Varshney R, Venkatesan T, Shetty P, et al. Studies on new invasive pest Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) and its natural enemies. Journal of Biological Control. 2018;32(3):145–51. doi: 10.18311/jbc/2018/21707

39. Ganiger PC, Yeshwanth HM, Muralimohan K, Vinay N, Kumar ARV, Chandrashekara K. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr Sci India. 2018;115(4):621–3.

40. Tamura K, Nei M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial-DNA in humans and chimpanzees. Molecular Biology and Evolution. 1993;10(3):512–26. doi: 10.1093/oxfordjournals.molbev.a040023 8336541

41. Nagoshi RN, Fleischer S, Meagher RL, Hay-Roe M, Khan A, Murua MG, et al. Fall armyworm migration across the Lesser Antilles and the potential for genetic exchanges between North and South American populations (vol 12, e0171743, 2017). Plos One. 2017;12(3).

42. Kriticos DJ, Maywald GF, Yonow T, Zurcher EJ, Herrmann NI, Sutherst RW. CLIMEX Version 4: Exploring the effects of climate on plants, animals and diseases. Canberra: CSIRO; 2015.

43. USDA-FAS. Northern South America—Crop Production Maps: United States Department of Agriculture; 2018.

44. Nagoshi RN, Meagher RL, Jenkins DA. Puerto Rico fall armyworm has only limited interactions with those from Brazil or Texas but could have substantial exchanges with Florida populations. J Econ Entomol. 2010;103(2):360–7. doi: 10.1603/ec09253 20429449

45. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12):1647–9. doi: 10.1093/bioinformatics/bts199 22543367

46. Saitou N, Nei M. The Neighbor-Joining method—a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution. 1987;4(4):406–25. doi: 10.1093/oxfordjournals.molbev.a040454 3447015

47. Clements MJ, Kleinschmidt CE, Maragos CM, Pataky JK, White DG. Evaluation of inoculation techniques for fusarium ear rot and fumonisin contamination of corn. Plant Disease. 2003;87(2):147–53. doi: 10.1094/PDIS.2003.87.2.147 30812919

48. Leigh JW, Bryant D. POPART: full-feature software for haplotype network construction. Methods Ecol Evol. 2015;6(9):1110–6.

49. du Plessis H, van den Berg J, Ota N, Kriticos DJ. Spodoptera frugiperda. CSIRO-InSTePP Pest Geography [Internet]. 2018.

50. Early R, Gonzalez-Moreno P, Murphy ST, Day R. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. Neobiota. 2018;(40):25–50.

51. Ramirez-Cabral NYZ, Kumar L, Shabani F. Future climate scenarios project a decrease in the risk of fall armyworm outbreaks. J Agr Sci-Cambridge. 2017;155(8):1219–38.

52. Kriticos DJ, Webber BL, Leriche A, Ota N, Macadam I, Bathols J, et al. CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol Evol. 2012;3(1):53–64.

53. Stein AF, Draxler RR, Rolph GD, Stunder BJB, Cohen MD, Ngan F. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. B Am Meteorol Soc. 2015;96(12):2059–77. doi: 10.1175/Bams-D-14-00110.1

Článek vyšel v časopise


2019 Číslo 9