Use of high-resolution melting for the rapid diagnosis of thrips (Insecta: Thysanoptera) in legume crops in South Florida

Abstract

Thrips (Order: Thysanoptera) are significant agricultural pests that cause severe economic losses globally through direct feeding damage and their role as vectors of orthotospoviruses. South Florida’s warm climate and diverse cropping systems, particularly legume crops such as snap bean (Phaseolus vulgaris L.) and lablab (Lablab purpureus), provide favorable environment for thrips infestations. Three thrips species, Megalurothrips usitatus (Bagnall), Thrips palmi Karny, and Frankliniella insularis (Franklin) (Thysanoptera: Thripidae), are prevalent in the region, with M. usitatus being an invasive pest that poses a significant threat to leguminous crops. Accurate species identification is critical for effective integrated pest management strategies. However, morphological identification is challenging due to the small size, subtle diagnostic traits, and high phenotypic similarity among thrips species, particularly in immature stages. This study aimed to develop a rapid, reliable molecular identification tool for these thrips species using high-resolution melting (HRM) analysis, a post-polymerase chain reaction (PCR) technique that differentiates DNA sequences based on their unique melting profiles. HRM analysis enables species-specific identification by detecting genetic variations and single nucleotide polymorphisms in PCR amplicons. We optimized and validated an HRM-based assay for the precise identification of M. usitatus, T. palmi, and F. insularis using both adult and larval stages collected from leguminous crops in South Florida. The assay demonstrated high specificity, sensitivity, and reproducibility, providing a robust molecular diagnostic tool for species identification. This method has the potential to enhance pest management efforts by facilitating early detection and targeted management, reducing the economic impact of thrips on legume production in the region.

Introduction

Thrips (Order: Thysanoptera) are serious agricultural pests that cause severe economic losses worldwide (Lewis 1997, Shipp et al. 2000, Park et al. 2007). They damage a wide variety of plants by piercing cells and extracting their contents, leading to silvery discoloration, and distorted growth of leaves, flowers, and fruits (Welter et al. 1990, Shipp et al. 1998, Mound et al. 2022). Thrips can also act as vectors of orthotospoviruses, further amplifying their economic impact (German et al. 1992, Riley et al. 2011). Their small size, high reproductive rates, and adaptability make thrips particularly challenging to control, requiring integrated management approaches (Demirozer et al. 2012, Gao et al. 2012, Reitz et al. 2020).

The agricultural ecosystem in South Florida, characterized by warm temperatures and a diverse cropping system, provides a favorable environment for numerous thrips species (Garrick and Liburd 2018). Legumes crops, especially snap bean (Phaseolus vulgaris L.) and lablab (Lablab purpureus (L.) Sweet) are particularly susceptible to thrips infestations, which can significantly affect yield and quality. Florida leads the United States in fresh-market snap bean production, with ∼36,200 acres harvested annually, contributing over $100 million to the state’s agricultural economy (USDA NASS 2024). Recently, a major threat to legumes emerged in the region: Megalurothrips usitatus (Bagnall) (Thysanoptera: Thripidae), an invasive thrips species that feeds and oviposit on legume foliage and blooms, with both larvae and adults feeding on flowers, leaves, and pods (Chang 1988). This feeding causes malformation, that impair pollination, reduces photosynthesis, and lower yield and marketability (Soto-Adames 2020, Cambero Campos et al. 2023, Tang et al. 2023). Native to Asia, M. usitatus was first reported in continental America in South Florida in 2020 (Soto-Adames 2020), and has since been documented in Costa Rica (Rodríguez-Arrieta et al. 2023), Cuba (Suris 2021), Guatemala (Racancoj et al. 2023), Honduras (Orozco 2022), Mexico (Cambero Campos et al. 2023), Nicaragua (FAO 2022), and Puerto Rico (Cabrera-Asencio and de Jensen 2023). Since establishing in Florida, M. usitatus has become a major pest of snap bean and in severely infested fields, complete crop loss has been reported (Adhikari et al. 2023). Alongside M. usitatus, 2 other non-native and long-established thrips species, Thrips palmi Karny (Thysanoptera: Thripidae) and Frankliniella insularis (Franklin) (Thysanoptera: Thripidae), are commonly found in legume crops in South Florida (Ivey et al. 2024, 2025). Notably, F. insularis closely resembles M. usitatus in appearance, which may complicate field identification (Ivey et al. 2024).

Accurate species identification is crucial for effective pest management, and control strategies are often species-specific, particularly for species with known insecticide resistance (Bielza 2008). However, species-level identification is challenging due to their minute size, morphological similarities, polymorphism, and sexual dimorphism (Brunner et al. 2004, Hoddle et al. 2008, Brunner and Frey 2010, Rugman-Jones et al. 2010). Moreover, morphological keys are generally available only for adults and some second-instar larvae, while first instar-larvae, prepupae, and pupae are particularly difficult to identify, even for trained specialists (Vierbergen et al. 2010, Cluever and Smith 2017).

To address the limitations of morphological identification, molecular tools such as DNA barcoding have been increasingly adopted for thrips diagnosis (Ribeiro-Junior et al. 2024). DNA barcoding typically targets a standardizing fragment of the mitochondrial cytochrome oxidase I (COI) gene, enabling the development of reference libraries such as BOLD and GenBank (Hebert et al. 2003). While these methods improve accuracy and enable identification across life stages, they typically rely on sequencing, which increases cost, turnaround time, and dependence on specialized infrastructure and curated reference databases. These constraints limit their routine application in large-scale surveys, regulatory diagnostics, and rapid decision-making contexts, highlighting the need for alternative molecular tools that are booth accurate and operationally efficient. One such approach is high-resolution melting (HRM) analysis, an advanced post-polymerase chain reaction (PCR) technique that enables the identification of a set of known targets in a single reaction based on sequence variation (Wittwer et al. 2003, Winder et al. 2011). This technique is particularly valuable in context where diagnostic protocols must be rapid and cost-effective, while also being specific, sensitive, accurate, and reproducible, such as in quarantine and pest management application (Frey et al. 2006). HRM relies on the principle that under standard conditions, the melting temperature of a stretch of double-stranded DNA (eg, a PCR amplicon) is entirely dependent upon its DNA sequence, and even a single nucleotide difference can change that melting profile. During HRM, PCR amplicons are labeled with an intercalating dye (that fluoresces when bound; for example, SYBR Green or LC Green) and slowly heated in precise increments, while fluorescence is measured. As the double-stranded amplicon melts (denatures) the dye is released, and fluorescence decreases. The result is a melt curve that is sequence-specific and can be used to distinguish closely relates species (Wittwer et al. 2003, Liew et al. 2004).

In the absence of rapid and precise tools for identifying thrips species in South Florida, we developed an HRM-based assay for identification of 3 thrips species commonly found in local legume crops: M. usitatus, T. palmi, and F. insularis. This assay enables quick and accurate differentiation of these species, supporting both pest management and regulatory efforts. The assay was validated using both adult and immature larval stages of thrips to ensure reliable identification across developmental stages. To complement the molecular work, specimens used in this assay were slide-mounted for morphological reassessment and taxonomic verification.

Materials and Methods

Specimen Collection and Preliminary Identification

Adults and larvae of M. usitatus, T. palmi, and F. insularis were collected from lablab and snap bean fields in Miami-Dade County, Florida, United States, between December 2022 and January 2023 (Table 1). Specimens were collected by sampling twenty random flowers into 95% ethanol and stored at −20 °C in the Laboratory of Vegetable Entomology at the Gulf Coast Research and Education Center, University of Florida, Wimauma, United States, until further analysis. Adult thrips specimens were initially separated into morphospecies based on body color for preliminary species identification, comprising M, usitatus (10 adults), F. insularis (12 adults), and T. palmi (10 adults). Because thrips larvae cannot be reliably identified to species based on morphology alone, larval specimens were not assigned to species a priori. Instead, DNA from larval specimens were directly subjected to molecular identification using Sanger sequencing of the COI region. The resulting sequences were compared with references sequences to confirm species identity. Subsequently, DNA from each target species were included in the HRM assay to evaluate the performance of the method across life stages. A total of 10 larvae per species were tested, and their identities were further validated by comparison with HRM diagnostic profiles generated by the assay.

Table 1.

Thrips species collected from snap bean (Phaseolus vulgaris) and lablab (Lablab purpureus) over 2 months (December 2022 and January 2023) at 4 sites within the HAA, South Florida, including sample ID, host plant, collection coordinates, insect stage, and GenBank accession numbers for individual COI sequences

Sample ID	Host	Coordinates	Species	Insect Stage	GenBank [COI]
FL1.02	Lablab purpureus	25°28'03"N 80°29'37"W	Megalurothrips usitatus	Adult	PQ375910
FL1.09	Lablab purpureus	25°28'03"N 80°29'37"W	Megalurothrips usitatus	Adult	PQ375917
FL1.18	Lablab purpureus	25°28'03"N 80°29'37"W	Megalurothrips usitatus	Adult	PQ375923
FL1.19	Lablab purpureus	25°28'03"N 80°29'37"W	Megalurothrips usitatus	Adult	PQ375924
FL1.20	Lablab purpureus	25°28'03"N 80°29'37"W	Megalurothrips usitatus	Adult	PQ375925
FL1.22	Lablab purpureus	25°28'03"N 80°29'37"W	Megalurothrips usitatus	Adult	PQ375927
FL2.03	Lablab purpureus	25°26'51"N 80°30'24"W	Megalurothrips usitatus	Adult	PQ375934
FL17.01	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Megalurothrips usitatus	Adult	PQ375993
FL17.09	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Megalurothrips usitatus	Adult	PQ376001
FL17.17	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Megalurothrips usitatus	Adult	PQ376009
FL7.01	Lablab purpureus	25°29'50"N 80°31'28"W	Megalurothrips usitatus	Larva	PQ375981
FL7.03	Lablab purpureus	25°29'50"N 80°31'28"W	Megalurothrips usitatus	Larva	PQ375982
FL7.05	Lablab purpureus	25°29'50"N 80°31'28"W	Megalurothrips usitatus	Larva	PQ375983
FL7.06	Lablab purpureus	25°29'50"N 80°31'28"W	Megalurothrips usitatus	Larva	PQ375984
FL7.07	Lablab purpureus	25°29'50"N 80°31'28"W	Megalurothrips usitatus	Larva	PQ375985
FL31.01	Phaseolus vulgaris	26°35'15"N 81°16'27"W	Megalurothrips usitatus	Larva	PQ406367
FL31.02	Phaseolus vulgaris	26°35'15"N 81°16'27"W	Megalurothrips usitatus	Larva	PQ406368
FL31.03	Phaseolus vulgaris	26°35'15"N 81°16'27"W	Megalurothrips usitatus	Larva	PQ406369
FL31.04	Phaseolus vulgaris	26°35'15"N 81°16'27"W	Megalurothrips usitatus	Larva	PQ406370
FL31.05	Phaseolus vulgaris	26°35'15"N 81°16'27"W	Megalurothrips usitatus	Larva	PQ406371
FL9.06	Lablab purpureus	25°27'44"N 80°32'42"W	Frankliniella insularis	Adult	PQ406390
FL9.07	Lablab purpureus	25°27'44"N 80°32'42"W	Frankliniella insularis	Adult	PQ406391
FL10.01	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406406
FL10.02	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406407
FL11.01	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406423
FL11.02	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406424
FL11.12	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406432
FL11.13	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406433
FL11.14	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406434
FL11.15	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Adult	PQ406435
FL21.01	Lablab purpureus	25°28'03"N 80°29'37"W	Frankliniella insularis	Adult	PQ406441
FL21.10	Lablab purpureus	25°28'03"N 80°29'37"W	Frankliniella insularis	Adult	PQ406441
FL29.01	Phaseolus vulgaris	25°26'51"N 80°30'24"W	Frankliniella insularis	Larva	PQ406456
FL29.04	Phaseolus vulgaris	25°26'51"N 80°30'24"W	Frankliniella insularis	Larva	PQ406457
FL29.07	Phaseolus vulgaris	25°26'51"N 80°30'24"W	Frankliniella insularis	Larva	PQ406458
FL29.10	Phaseolus vulgaris	25°26'51"N 80°30'24"W	Frankliniella insularis	Larva	PQ406459
FL29.14	Phaseolus vulgaris	25°26'51"N 80°30'24"W	Frankliniella insularis	Larva	PQ406461
FL29.16	Phaseolus vulgaris	25°26'51"N 80°30'24"W	Frankliniella insularis	Larva	PQ406462
FL10.15	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Larva	PQ406415
FL10.16	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Larva	PQ406416
FL10.17	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Larva	PQ406417
FL10.18	Lablab purpureus	25°31'39"N 80°32'34"W	Frankliniella insularis	Larva	PQ406418
FL27.09	Phaseolus vulgaris	25°27'44"N 80°32'42"W	Thrips palmi	Adult	PQ406353
FL12.02	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Adult	PQ406302
FL12.03	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Adult	PQ406303
FL12.05	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Adult	PQ406304
FL19.01	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Thrips palmi	Adult	PQ406314
FL19.05	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Thrips palmi	Adult	PQ406318
FL19.21	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Thrips palmi	Adult	PQ406333
FL22.01	Lablab purpureus	25°31'39"N 80°32'34"W	Thrips palmi	Adult	PQ406334
FL24.01	Phaseolus vulgaris	25°27'44"N 80°32'42"W	Thrips palmi	Adult	PQ406348
FL30.02	Phaseolus vulgaris	25°36'32"N 80°28'55"W	Thrips palmi	Adult	PQ406356
FL12.05	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406304
FL12.06	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406305
FL12.08	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406306
FL12.09	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406307
FL12.11	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406308
FL12.12	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406309
FL12.13	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406310
FL12.14	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406311
FL12.16	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406312
FL12.19	Lablab purpureus	25°26'51"N 80°30'24"W	Thrips palmi	Larva	PQ406313

DNA Extraction, PCR and Sequencing

DNA was extracted from individual thrips adults and larvae using the simple, non-destructive HotSHOT method (Truett et al. 2000), resulting in a final volume of 80 µl. Total DNA extracted was used as a template for PCR amplification of the COI gene region, using the primers C1-J-1718 (Simon et al. 1994) and HCO-2198 (Folmer et al. 1994). PCR reactions were carried out in 25 µl total volumes containing 4 µl of DNA template, 12.5 µl of GoTaq Master Mix (Promega), 2.0 µl of bovine serum albumin (BSA; Thermo Scientific), and 0.25 µl of each primer (at a working concentration of 10 µM). Thermal cycling conditions included an initial denaturation step at 95 °C for 5 min, followed by 40 cycles of denaturation at 95 °C for 30 s, annealing at 47 °C for 30 s, and extension at 72 °C for 45 s. A final extension step was performed at 72 °C for 10 min. To verify successful amplification, 5 µl of each PCR product was subjected to electrophoresis on a 1% agarose gel stained with GelRed (Biotium). PCR products were subsequently cleaned using the Wizard PCR Preps DNA purification system (Promega, Madison, WI, United States) and directed-sequenced in both forward and reverse senses (GENEWIZ, South Palinfield, NJ, United States). The sequence chromatograms were carefully analyzed, checking forward and reverse reads for parity, using Geneious software version 2023.0.4. (Dotmatics, Boston, MA, United States. Available at: https://www.geneious.com). The obtained DNA sequences were subsequently assigned to a species based on morphological identification of the extracted specimens, and sequences were compared with those of other thrips species using BLASTn searches of the GenBank database.

Morphological Identification

Following non-destructive DNA extraction, adult specimens were retrieved and slide-mounted for detailed morphological analysis (Supplementary Figs S1–S3). Each extracted specimen was identified using the taxonomic keys of Mound and Kibby (1998), based on diagnostic traits such as antennal segmentation, wing venation, and setal arrangement. All adult specimens were examined under a compound microscope to ensure accurate species determination. Voucher specimens have been deposited in the Laboratory of Vegetable Entomology at the University of Florida’s Gulf Coast Research and Education Center (UF-GCREC) Wimauma, FL.

HRM Assay Design

COI sequences obtained in this study were aligned with those of 428 sequences retrieved from GenBank, using Geneious version 2023.0.4, resulting in a 448 bp alignment of 490 sequences. The alignment was used to design sets of PCR primers flanking a stretch of DNA that harbored a total of 7 single nucleotide polymorphisms (SNPs), which collectively were able to discriminate F. insularis, M. usitatus and T. palmi (Fig. 1). Only SNPs located within the amplified HRM target region were considered informative for HRM-based species discrimination. For each potential primer set, the melting temperature of the species-specific amplicons was predicted via simulations in the uMelt software (v. Quartz) (Dwight et al. 2011), to ensure ample interspecific variation (∼1 °C) between species, and allow unambiguous diagnosis. This process resulted in a single pair of PCR primers, LT119F (5′-GGATGAACAGTTTATCCACC-3′) and LT204R (5′-CCMGCTAAATGKARAGAAAA-3′) (Table 2), that produced an 85 bp amplicon with a distinct melting peak for each species. The reverse primer LT204R incorporated degenerate nucleotide positions (M = A/C, K = G/T, R = A/G) to accommodate interspecific sequence variation at the primer-binding site while maintaining amplification efficiency and HRM specificity. All primers were commercially synthesized by Sigma-Aldrich (St. Louis, MO, United States).

Fig. 1.

Multiple sequence alignment of partial cytochrome c oxidase subunit I (COI) gene fragment from Frankliniella insularis, Megalurothrips usitatus, and Thrips palmi, showing polymorphic nucleotide positions used for HRM assay design. Colored boxes indicate variable nucleotide sites. Nucleotide color code: adenine (A) = red, thymine (T) = green, cytosine (C) = purple, and guanine (G) = yellow. The annealing regions of the forward (119F) and reverse (204R) primers are indicated above the alignment.

Table 2.

Primers used for the initial amplification and sequence the mitochondrial COI region, and primers designed for HRM assay of thrips species collected in South Florida

Name	Sequence 5'-3'	Source
C1-J-1718	GGAGGATTTGGAAATTGATTAGTTCC	Simon et al. (1994)
HCO-2198	TAAACTTCAGGGTGACCAAAAAATCA	Folmer et al. (1994)
LT119F	GGATGAACAGTTTATCCACC	This study
LT204R	CCMGCTAAATGKARAGAAAA	This study

Degenerated bases in primer sequence nucleotide code (M = A/C, K = G/T, R = A/G).

HRM was conducted using a LightCycler 480 Instrument II (Roche, Pleasanton, CA). A protocol was developed whereby HRM analysis was performed immediately following the generation of amplicons via PCR. The fluorescent dye EvaGreen (Biotium, Hayward, CA) was incorporated into the PCR step at a concentration that has been shown to facilitate complete saturation of the resulting dsDNA amplicon without inhibiting its amplification (Radvanszky et al. 2015). Reactions were performed in 5 µl volumes, comprised of 2 µl of AccuStart II PCR SuperMix 2X (Quantabio, Beverly, MA), 0.25 µl of EvaGreen Dye 1X, 0.05 µl of each forward and reverse primers (0.1 mM), 0.5 µl of DNA extract (1-10 ng/µl), and 2.15 µl of HPLC water. Thermal cycling conditions were an initial denaturation at 94 °C for 2 min, 40 cycles of 94 °C for 15 s, 60 °C for 15 s, and 72 °C for 15 s, then a hold temperature of 72 °C for 2 min, followed by the melt analysis starting at 65 °C and rising in 0.1 degree increments up to 80 °C. The resulting melt curves were automatically calculated by the LightCycler software. During the development phase of the assay, the fidelity of the primer pair was checked at the end of the amplification stage, by electrophoresing representative qPCR products on a 2% agarose gel to confirm the presence of a single 85 bp amplicon (data not shown).

Statistical Analysis

One-way analysis of variance (ANOVA) was conducted to determine whether there was significant difference in the average T_m values between species using JMP Version 19.0.5. (JMP Statistical Discovery LLC, Cary, NC, United States). Also, all experiments and measurements were performed in triplicates.

Results

Molecular Identification

A total of 32 adult thrips specimens and 30 larvae, collected from lablab and snap bean crops, were successfully sequenced (Table 1). The resulting COI sequences were submitted to GenBank, and their accession numbers are listed in Table 1. BLASTn analysis of these sequences against the GenBank database revealed high similarity (>99%) with reference sequences of M. usitatus (eg, NC070094), F. insularis (eg, ON935526), or T. palmi (eg, AB597604), confirming the identity of each specimen.

HRM Diagnosis

Melt curves were obtained for a total of 62 samples using the primer LT119F/LT204R (Table 2). The derivative melt curve plot (change in fluorescence against change in temperature [-d/dT]) revealed clear and consistent species-specific melt peaks corresponding to M. usitatus, F. insularis, and T. palmi (Fig. 2). Likewise, the normalized fluorescence curves showed clustering based on the target amplification (Fig. 3), confirming the specificity and diagnostic potential of the assay. All negative tests and no-template Controls (NTC) were negative for amplification and melting.

Fig. 2.

Melting peaks obtained from HRM analysis of Megalurothrips usitatus, Frankliniella insularis and Thrips palmi using LT119F/LT204R primer pair targeting the COI gene. Each sample was analyzed in triplicate across three independent runs. ANOVA indicated significant differences in mean T_m among species (P < 0.0001), allowing clear molecular discrimination among the three thrips species.

Fig. 3.

Normalized standard melting curves generated by HRM analysis for Megalurothrips usitatus, Frankliniella insularis, and Thrips palmi. Each curve represents the characteristic fluorescence decrease pattern corresponding to the amplicon dissociation profiles of the COI region. ANOVA indicated significant differences in mean T_m among species (P < 0.0001), allowing clear molecular discrimination among the three thrips species.

The ANOVA test revealed significant differences in melting temperatures among species (P < 0.0001). Frankliniella insularis exhibited the highest T_m (76.80 ± 0.02 °C), followed by T. palmi (76.18 ± 0.02 °C) and M. usitatus (75.65 ± 0.05 °C). The low within-sample variability observed across replicates indicates that HRM profiles are highly reproducible and allow reliable discrimination among the 3 species despite relatively small differences in melting temperature.

Discussion

Accurate identification of thrips species is essential for effective pest management and biosecurity strategies, particularly in regions where multiple invasive species co-occur, since management choices are often species-specific. Misidentification can lead to inappropriate control measures, reduced efficacy, and increased economic losses. Given that closely related thrips species often differ in host preference, reproductive capacity, insecticide susceptibility, and virus transmission potential, reliable diagnostic tools are essential for guiding timely and targeted pest management decisions. Megalurothrips usitatus poses an increasing threat to legume production in Florida, and its presence alongside F. insularis and T. palmi, complicates pest management efforts (Adhikari et al. 2023, Ivey et al. 2024, 2025). Herein, we have developed a quick, reliable, and economic method for identifying these 3 species, to facilitate monitoring and aid their management.

Traditional thrips identification relies on detailed morphological analysis, which involves labor-intensive slide-mounting and detailed microscopic analysis by trained taxonomic experts (Mound and Kibby 1998, Mound et al. 2022). Furthermore, identification of first-instar larvae, pre-pupae, and pupal stages remain particularly challenging, even for thrips taxonomic specialists (Vierbergen et al. 2010, Cluever and Smith 2017). These difficulties can lead to misidentification, which may result to inappropriate management and failed quarantine efforts, underscoring the need for reliable diagnostic tools for both pest and invasive species identification. The preparation of a single specimen for identification can take several hours, and processing large batches of field-collected samples can several days (Mehle and Trdan 2012). These challenges are further compounded by the limited number of qualified taxonomists, restricting timely identification. Considering labor and expertise, sending a single thrips specimen to a taxonomist for morphological identification can cost over $100, depending on the provider and service level (UC Riverside Entomology Museum). These limitations highlight the need for more efficient, accessible, and scalable diagnostics alternatives.

In this context, the HRM assay developed in this study, based on species-specific variation in a short COI fragment, requires only basic molecular biology skills and access to standard qPCR equipment. Results can be generated quickly, and the entire process, from DNA extraction to results, can be completed in under 4 h. This includes ∼30 to 45 min for rapid, non-destructive DNA extraction, 1.5 to 2 h for PCR amplification, and 20 to 30 min for high-resolution melt analysis and interpretation. With per-sample cost (reagents, consumables, labor and overhead) estimated at <$1, this represents a substantial savings in both time and resources compared to traditional taxonomic methods (Rugman-Jones and Stouthamer 2017 (Rugman-Jones and Stouthamer 2017).

The assay was optimized using specimens from South Florida and validated based on COI sequence variation observed among target species collected from 4 sites within the Homestead Agricultural Area (HAA) over a 2-month sampling period. The primer pair LT119F/LT204R, designed in this study, reliably amplified all 3 target species and produced distinct and reproducible melting profiles. This enables the rapid differentiation of M. usitatus, F. insularis, and T. palmi without the need for sequencing, providing an efficient diagnostic tool. Given that the presence of M. usitatus in the United States is confined to South Florida, the assay is particularly well-suited for regulatory and research application in this area. However, the expansion of this method to other regions should be preceded by evaluating local genetic diversity/variation to ensure that diagnostic precision is maintained. If not, the introduction of additional intraspecific variation (characteristic of the mitochondrial COI), could result in altered melt profiles, and mis-interpretation of assay results.

Tools like the one developed herein, are critical in high throughput monitoring scenarios, where decisions need to be made quickly and accurately to prevent spread or to guide site-specific pest control measures. However, as with any species-specific assay, the utility of this method is context dependent. The HRM assay is designed to distinguish between a defined set of 3 sympatric species based on known interspecific variation. That said, if an unexpected fourth thrips species was assayed (ie, one not included in the assay design), it would most likely either not amplify due to primer mismatch, or produce an anomalous melt profile. Either outcome could be flagged and followed up with sequencing to determine whether a new taxon is present. This reveals an advantage of HRM over alternative DNA-based diagnostic methods. Since HRM is necessarily conducted on individual specimens, it can identify the potential presence of a non-target taxon, perhaps a newly invading species. Further, if that taxon was revealed by the production of an unexpected melt curve, the assay can easily be modified to accommodate this additional melt profile. Alternatively, primers may need to be redesigned to accommodate the additional interspecific variation in the DNA sequences.

The HRM method works well if we are seeking accurate data on the abundance of each target species, are interested in what else might be present in a sampled field/commodity, and/or have a reasonable number of specimens that need to be processed. However, if the number of specimens per sampling event increases significantly, and/or the need is only to identify presence/absence of the target species in a sample, alternative approaches may make more economic sense. One such approach would be to design a unique fluorescently labeled hydrolysis probe for each of the target taxa, that could subsequently be combined into a single diagnostic reaction (ie, multiplexed) (Heid et al. 1996). Although it would be harder to design and optimize (combining up to 6 primers and 3 probes in a single reaction), such an assay could facilitate the identification of all 3 target species within a bulk sample extraction (eg, multiple thrips specimens from a single location) (Gómez-Marco et al. 2015). However, we would sacrifice abundance data on those target species, and, unless every specimen in a bulk extraction was a non-target, there would be no indication of the presence of additional thrips species.

Overall, the HRM assay presented herein, provides a scalable, cost-effective, and rapid diagnostic tool for species-level identification of thrips species in Florida legumes. It reduces the expense and time needed for conventional molecular and morphological identification. It fills a critical gap for pest detection and monitoring in agricultural biosecurity, reducing dependence on expert taxonomists while enhancing the speed and accuracy of decision-making in pest management and regulatory frameworks.

Author Contributions

Felipe Barreto da Silva (Conceptualization [lead], Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Project administration [equal], Software [lead], Validation [lead], Visualization [lead], Writing—original draft [lead]), Bruno Rossitto De Marchi (Conceptualization [supporting], Funding acquisition [supporting], Methodology [supporting], Supervision [supporting], Visualization [supporting], Writing—review & editing [supporting]), Paul Rugman-Jones (Conceptualization [supporting], Data curation [supporting], Formal analysis [supporting], Investigation [supporting], Methodology [supporting], Software [supporting], Supervision [supporting], Validation [supporting], Visualization [supporting], Writing—review & editing [supporting]), Rafaela Gomes Ruschel (Data curation [supporting], Formal analysis [supporting], Methodology [supporting], Software [supporting], Validation [supporting], Writing—review & editing [equal]), Jessica Chitwood-Brown (Software [supporting], Validation [supporting], Writing—review & editing [supporting]), Thomas Skarlinsky (Conceptualization [supporting], Methodology [supporting], Visualization [supporting], Writing—review & editing [supporting]), Felipe Soto-Adames (Conceptualization [supporting], Methodology [supporting], Visualization [supporting], Writing—review & editing [supporting]), and Hugh A. Smith (Conceptualization [supporting], Data curation [supporting], Formal analysis [supporting], Funding acquisition [lead], Investigation [supporting], Methodology [supporting], Project administration [lead], Resources [lead], Software [supporting], Supervision [lead], Validation [supporting], Visualization [supporting], Writing—review & editing [supporting])

Supplementary Material

Supplementary material is available at Journal of Economic Entomology online.

Funding

This research was funded with support from US Department of Agriculture, Animal and Plant Health Inspection Service (USDA APHIS), Agreement No. APP-20507/AP22PPQS&T00C104.

Conflicts of Interest

The authors declare no conflicts of interest.