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Development of microsatellite markers for colony delineation of the invasive Asian subterranean termite (Blattodea: Rhinotermitidae) in South Florida and Taiwan

  • Cheng-Lung Tsai EMAIL logo , Hou-Feng Li EMAIL logo , Yung-Hao Ching , Ming-Der Lin , Guan-Yu Chen , Aaron Mullins , Rudolf H. Scheffrahn , Thomas Chouvenc and Nan-Yao Su
Published/Copyright: December 16, 2024
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Florida Entomologist
From the journal Florida Entomologist Volume 107 Issue 1

Abstract

The Asian subterranean termite, Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae), is a major pest of wooden structures. Native to Southeast Asia, it has successfully invaded various regions worldwide. Developing a molecular technique for colony delineation is critical to evaluate the efficacy of subterranean termite baiting systems against C. gestroi. This study has assessed the robustness of 19 newly developed microsatellite loci for colony delineation of the invasive C. gestroi in both South Florida and Taiwan. Our results suggest that at least nine microsatellite markers, each with two alleles per locus, can accurately distinguish all C. gestroi colonies with little genetic variation in South Florida. Conversely, only five microsatellite loci are needed to delineate C. gestroi colonies in Taiwan. Additionally, differences in colony breeding systems may affect genetic differentiation among C. gestroi colonies. Our study provides a practical genetic method to accurately identify colony affiliation of foragers, which will help in the field evaluation of baiting systems in areas invaded by C. gestroi.

Resumen

La termita asiática, Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae), es una plaga importante de las estructuras de madera. Originaria del sudeste asiático, ha invadido con éxito varias regiones del mundo. El desarrollo de una técnica molecular para la delimitación de colonias es fundamental para evaluar la eficacia de los sistemas subterráneos de cebo para controlar termitas C. gestroi. Este estudio evaluó la utilidad de 19 loci de microsatélites recientemente desarrollados para la delimitación de colonias de la especie invasora C. gestroi tanto en el sur de Florida como en Taiwán. Nuestros resultados sugieren que, en el sur de Florida, al menos nueve marcadores de microsatélites, cada uno con dos alelos por locus, pueden distinguir con precisión todas las colonias de C. gestroi las que presentan poca variación genética. Por el contrario, sólo se necesitan cinco loci de microsatélites para delimitar las colonias de C. gestroi en Taiwán. Además, las diferencias en los sistemas de reproducción de las colonias pueden afectar la diferenciación genética entre las colonias de C. gestroi. Nuestro estudio proporciona un método genético práctico para identificar con precisión la afiliación de colonias de recolectores, lo que ayudará en la evaluación de campo de los sistemas de cebo en áreas invadidas por C. gestroi.

Keywords: Coptotermes gestroi ; invasion history; genetic variability; termite management
Palabras clave: Coptotermes gestroi ; historia de invasión; variabilidad genética; manejo de termitas

1 Introduction

The increasing prevalence of international transportation has led to a rise in the biological invasion of pathogens and pests into non-native regions, resulting in significant negative impacts on local ecosystems and environments (Nghiem et al. 2013; Sax et al. 2005). Social insects, such as ants, bees, and termites, have become successful invaders, causing considerable damage in newly colonized areas (Chapman and Bourke 2001; Holway et al. 2002). Managing invasive social insects, particularly through colony elimination strategies, is crucial to mitigating their spread through unintentional human-mediated activities.

The Asian subterranean termite, Coptotermes gestroi (Wasmann) (Blattodea: Rhinotermitidae), is a primary structural pest native to Southeast Asia. It has successfully invaded various regions, including South America (Brazil), North America (southern Mexico, South Florida, and some islands of the West Indies), Marquesas Islands, Mauritius, Reunion Islands (Indian Ocean), and Taiwan (https://www.cabi.org/isc/) (Jenkins et al. 2007; Li et al. 2009; Rust and Su 2012; Tsai and Chen 2003; Yeap et al. 2011). Both C. gestroi and the Formosan subterranean termite, Coptotermes formosanus Shiraki, are responsible for a significant proportion of the estimated annual losses exceeding US $32 billion attributed to subterranean termites (Su et al. 2017). Additionally, C. formosanus is ranked as one of the 100 worst global invasive species (Lowe et al. 2000).

Colonies of Coptotermes termites are initiated by a monogamous pair of reproductives (Vargo 2019), and subsequent budding can produce neotenic reproductives from the original colony. The relatively simple breeding system of Coptotermes increases the probability of using microsatellites to examine their colony structure successfully. However, inbreeding may also increase genetic homogeneity among colonies with a recent invasion history, making colony delineation difficult. Over the past two decades, C. gestroi has become one of the most destructive wood pests in the metropolitan regions of South Florida, coexisting with C. formosanus due to anthropogenic activities and nuptial flight dispersal (Chouvenc et al. 2016). The colony elimination system using termite baits has been widely employed to control C. formosanus in the USA (Su 2019), which was initially validated through the mark-recapture method (Su 1994), and later by molecular methods (Husseneder and Grace 2001).

Microsatellite markers have been extensively used to investigate the breeding systems, population genetic structure, and invasion origins of social insects (Ascunce et al. 2011; Cheng et al. 2013; Huang et al. 2013; Husseneder et al. 2012, 2013), including two Coptotermes species (Husseneder and Grace 2001; Vargo et al. 2003; Yeap et al. 2011). Previous studies have verified the robustness of microsatellites in delineating C. formosanus colonies (Su et al. 2016; Tseng et al. 2021). Another study also employed microsatellite loci to confirm C. gestroi reinfestation within two years after baiting treatment (Lin et al. 2021), highlighting the potential of microsatellite loci for distinguishing colonies. However, in our earlier experiment using the microsatellite primers from Yeap et al. (2009), only one to two alleles per locus were available, which was insufficient for colony delineation of C. gestroi populations in South Florida. There is a need to develop more polymorphic microsatellite markers from genomic sequences for C. gestroi populations in South Florida due to their young invasion history and relative homogeneous genetic variability (Eyer et al. 2021; Kaczmarczyk-Ziemba 2020).

The genetic variability of an invasive species in a newly colonized region can be influenced by factors such as the duration of the infestation, the source population of the invaders, the size of the propagule, and the number of introductions. Generally, genetic bottlenecks result in reduced genetic variability in the initially invaded population (Eyer and Vargo 2021; Husseneder et al. 2012; Tsutsui et al. 2000). Multiple invasions by genetically differentiated populations may contribute to an increase in genetic variation. Investigating the genetic variability of C. gestroi populations using microsatellites would enhance our understanding of genetic differences between Florida (invasion history of ∼30 years) and Taiwan (invasion history of ∼110 years) (Li et al. 2009, 2010; Lin et al. 2021; Su et al. 1997). Pertinent information would contribute to the development and usage of microsatellites in delineating C. gestroi colonies in the future.

In this study, we successfully developed 19 novel microsatellite markers based on the genomic sequences of two Coptotermes species. These loci were used to assess and compare the robustness of colony delineation for C. gestroi populations in South Florida and Taiwan. The aims of this study are: (1) to investigate the genetic variability of microsatellite loci of C. gestroi populations in South Florida and Taiwan; and (2) to compare the robustness of the newly developed microsatellite loci for colony delineation of C. gestroi populations.

2 Materials and methods

2.1 Specimen collection

Fifteen C. gestroi workers collected from an incipient colony in the laboratory were decapitated using sterile 100 μl tips after cold treatment, and the heads were used for genomic sequencing. We used seven C. gestroi specimens and one C. formosanus sample to determine the PCR amplification success of the new microsatellite loci (Table S1). Subsequently, we utilized 24 C. gestroi specimens from the University of Florida Termite Collection at the Fort Lauderdale Research and Education Center, including samples from the following counties Hillsborough (one individual), Lee (one), Palm Beach (three), Broward (seven), Miami-Dade (ten), and Monroe (two), to conduct the polymorphism test (Table S1).

To evaluate the robustness of newly developed microsatellite markers in delineating C. gestroi colonies, we conducted a comparative analysis between South Florida and Taiwan. In South Florida, four colonies from Fort Lauderdale (Broward county) and one distant colony from Miami (Miami-Dade county) were collected for this study (Table 1; Figure 1A). The geographic distances between the colonies were measured using Google Maps (https://www.google.com/). Each colony in Fort Lauderdale was spaced over 850 m apart, while the colony in Miami was located approximately 34 km away from those in Fort Lauderdale. Additionally, three 1-yr old incipient laboratory colonies from the Fort Lauderdale Research and Education Center also were utilized to confirm the robustness of these new microsatellite loci. We used 20 workers per colony for the genotypic analyses. In Taiwan, three distinct C. gestroi colonies were collected from the Xiaping Tropical Botanical Garden in Nantou county (Table 1; Figure 1B; Chiu et al. 2016). The geographic distance between these colonies was more than 165 m. Ten workers from each colony were sampled for subsequent molecular analyses.

Table 1:

Collection information, sample numbers, and breeding system of Coptotermes gestroi used in this study.

Location Site Colony GPS-Latitude GPS-Longitude Samples Breeding system
Florida, USA Fort Lauderdale FL1 26.118438 °N 80.174075 °W 20 Simple family
FL2 26.110364 °N 80.174844 °W 20 Simple family
FL3 26.106418 °N 80.190717 °W 20 Extended family
FL4 26.086663 °N 80.177399 °W 20 Extended family
Miami FL5 25.784316 °N 80.136504 °W 20 Simple family
Subterranean termite laboratory Lab1 – – 20 Simple family
Lab2 – – 20 Simple family
Lab3 – – 20 Extended family
Nantou, Taiwan Xiaping Tropical Botanical Garden TW1 23.775828 °N 120.671675 °E 10 Simple family
TW2 23.772751 °N 120.673221 °E 10 Simple family
TW3 23.774981 °N 120.672890 °E 10 Simple family
Figure 1: Collection localities of Coptotermes gestroi colonies in South Florida and Taiwan. (A) Four colonies were collected from Fort Lauderdale, with an additional distant colony from Miami; (B) three colonies were collected from Xiaping Tropical Botanical Garden, Nantou County, Taiwan. The map was modified from Chiu et al. (2016).
Figure 1:

Collection localities of Coptotermes gestroi colonies in South Florida and Taiwan. (A) Four colonies were collected from Fort Lauderdale, with an additional distant colony from Miami; (B) three colonies were collected from Xiaping Tropical Botanical Garden, Nantou County, Taiwan. The map was modified from Chiu et al. (2016).

2.2 DNA Extraction and genomic sequencing

For PacBio sequencing, genomic DNA was extracted from 15 C. gestroi worker heads using phenol/chloroform. Initially, the DNA concentration was pre-checked using a NanoDrop (Thermo Fisher Scientific, USA), with OD ratios of 1.8 and 2.0 for 260/280 nm and 260/230 nm, respectively. For high-throughput sequencing, DNA quantity was determined using the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, USA). DNA integrity was profiled using the Femto Pulse system and the genomic DNA 165 kb Kit (Agilent, USA), showing a sequence size range of 5–50 kb and a major distribution around 21 kb.

Shotgun library construction was conducted using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences, USA). Genomic DNA was sheared using the Megaruptor® (Diagenode, USA). The SMRTbell adaptor-ligated library was subjected to gel size selection at 5–30 kb (mainly at 10 kb) using the BluePippin Size-Selection system (Sage Science, USA). SMRT sequencing was conducted on the Sequel system with the Sequel Sequencing Kit 3.0 and SMRT Cell 1 M V3 LR kit (Pacific Biosciences, USA), featuring 20-h movie runs and a 2-h pre-extension. The polymerase reads were processed with SMRTlink 9.0 to generate subreads and circular consensus reads (CCS). The CCS parameters included a minimum read length of 10 kb, at least three full passes, and a minimum predicted accuracy of 99 %. The PacBio sequencing reads of C. gestroi have been deposited in GenBank under BioProject accession number PRJNA938258.

For the microsatellite test, genomic DNA was extracted from the legs or heads of C. gestroi workers using the hot sodium hydroxide and Tris (HotSHOT) method, which was modified from Meeker et al. (2007). The protocol was as follows: 1) the tissue was ground in a 1.5 ml tube with 100 μl of 50 mM NaOH, then incubated at 95 °C for 20 min; 2) added 10 μl (10 % of the total volume) of 1 M Tris buffer (pH = 8) to the mixture; and 3) mixed the solution by a vortex mixer for 10 s, then centrifuged for 10 min. Finally, the supernatant was transferred to a new 1.5 ml tube. DNA samples were preserved at −20 °C for molecular analyses.

2.3 Development of microsatellite loci

The draft genome of C. formosanus (Itakura et al. 2020) and the filtered CCS reads of C. gestroi were used to screen microsatellite repeats for colony delineation. The accession numbers of the C. formosanus genomic sequences from GenBank are provided in Table S2. We conducted an analysis using Krait v.1.3.3 (Du et al. 2018) to understand the distribution of microsatellites in the C. gestroi genome, which would contribute to the development of more microsatellites for relevant studies of C. gestroi in the future. Then, we randomly selected sequencing reads of more than 10,000 bp to search for microsatellite repeats using MSATCOMMANDER v.0.8.2 (Faircloth 2008). The minimum repeat motifs for mono, di, tri, tetra, penta, and hexanucleotides were set at 20, 20, 10, 10, 10, and 10, respectively. We selected the tri and tetranucleotide repeats as candidates of microsatellites as they were easier for scoring microsatellite alleles than the common dinucleotide repeats. The primers were designed using Primer3 with default settings (Untergasser et al. 2012). A total of 112 putative primer sets, namely 52 pairs from the C. formosanus genome and 60 sets from the C. gestroi genome, were designed in this study. We added the M13 primer (5′-CACGACGTTGTAAAACGAC-3′) to the 5′ end of each forward primer (Schuelke 2000). The M13 primer was alternatively labeled with fluorescent dyes, which anneal to tails incorporated into the forward primer sequences for subsequent fragment analyses.

We used seven C. gestroi samples and one C. formosanus sample to assess the success rate of PCR amplification (Table S1). The PCR assays were conducted in a volume of 10 μl, including 3.6 μl of ddH2O, 5 μl of 2× PCR Mix (GeneDirex Inc., Taiwan), 0.2 μl for each of forward and reverse primers, and 1 μl of template DNA. The PCR conditions were as follows: initial denaturation at 95 °C for 5 min, 29 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, followed by 10 cycles at 95 °C for 30 s, 50 °C for 30 s, 72 °C for 30 s, and then 72 °C for 20 min as a final extension. The PCR products were checked using 4.5 μl mixed with 1 μl of 6X EZ-Vision DNA Dye (Amresco Inc., Solon, Ohio, USA). Then, the PCR products were subjected to electrophoresis on a 2 % agarose gel at 170 V using a Sub-Cell Model 96 cell. The gel was imaged using a Canon EOS M50 digital camera (Canon, Louisiana, USA). The successfully amplified microsatellite markers were used in subsequent polymorphism tests.

2.4 Microsatellite genotyping and genetic analysis

Twenty-four C. gestroi samples from six counties (Broward, Miami-Dade, Palm Beach, Monroe, Hillsborough, and Lee) were used to evaluate the polymorphism of microsatellite loci (Table S1). Each locus was amplified in a 10 μl reaction volume, consisting of 3.2 μl of ddH2O, 5 μl of 2× PCR Mix (GeneDirex Inc., Taiwan), 0.25 μl for forward and reverse primers, 0.3 μl of 1 μM M13 primer labeled with fluorescent dyes FAM, NED, PET or VIC (Applied Biosystems, Foster City, California, USA), and 1 μl of template DNA. The fluorescent labeled dye used for each microsatellite marker is listed in Table 2. We mixed the PCR products of four fluorescent labeled primers with 5 μl ddH2O for microsatellite genotyping. The PCR products were then mixed with GeneScan™ 500 LIZ™ dye Size Standard (Applied Biosystems, Foster City, California, USA) and analyzed using an ABI 3730XL DNA Analyzer (Applied Biosystems, Foster City, California, USA). We checked and scored the microsatellite alleles using Geneious Prime v. 2021.1.1 (Biomatters Ltd, Auckland, New Zealand) and Microsatellite Analysis software (available on Thermo Fisher Cloud). All the microsatellite genotypic data are provided in Table S3. For subsequent analyses, the populations Hillsborough, Lee, Palm Beach, and Monroe were excluded because the representative samples of these groups were less than three.

Table 2:

Characteristics of 19 microsatellite loci and primers developed for Coptotermes gestroi.

Locus Repeat motif Primer sequence (5′→3′) Ta (°C) Allele range Fluorescent label for forward primer
AAC38198 AAC F: [M13]TGACACTGACACAGGCTGCT 60.68 235–255 NED
R: TCAAACTGCTGCTTTCATCG 60.13
AAT39727 AAT F: [M13]CGTTACAGTCGAATGGCTGA 59.86 152–173 NED
R:CCACGTAAACTTTCGACTCTTGT 59.74
AAAC310 AAAC F: [M13]CCCCAACGTAACAGAATACACA 59.78 168–172 FAM
R:TTTTTCCCACAGGTTATGTTCC 60.09
AACT53130 AACT F: [M13]TTGGTTTTTCTGGTGGTGGT 60.25 248–268 FAM
R:TCTGAGGACCCTGTTAGCTTC 58.54
AGT17373 AGT F: [M13]TTCTTTCAGCGGTCTCTGACT 59.22 178–268 VIC
R:TGCAGTAACCGCACTTATTATTCT 59.29
AGT31996 AGT F: [M13]CAGCCTAAAAAGAACGCTTGA 59.66 254–257 PET
R:CGGATATGGCACTTAAAACGA 59.97
AGT75997 AGT F: [M13]AATAATTTGCGGTTGCCTCA 60.46 255–282 PET
R:AGGGAAGGTTTCGCTTTCTG 60.73
AGTT16349 AGTT F: [M13]GCCTCACGATAGGGACAAGT 59.17 233–281 VIC
R:GCCCTCGATTTCTACGTGAC 59.7
ATT03508 ATT F: [M13]GGCTGTAAAAGCGACAGTCC 59.88 239–257 PET
R:GATCGCGTTTGGAATTTGAC 60.46
ATT77391 ATT F: [M13]TGAGTGCAATGTCTGCAGGT 60.47 177–201 NED
R:AGACCACCGAGCCTAATGC 60.23
ATGT304 ATGT F: [M13]TGTCACTGACTGGGTGCTGT 60.37 123–157 NED
R:ACACAAATTGGCTGTGCTTG 59.76
CATT39307 CATT F: [M13]ACAGCTCACTGAAAGCCGTAA 60.07 178–222 FAM
R:GGACAACTGGCTTTGGAAGA 60.23
CATT63910 CATT F: [M13]TGATTTCATCCACCAAATGTGT 60.1 224–244 VIC
R:AAGCTACCTAGACCGCATGG 59.36
CTT54255 CTT F: [M13]TCTCGGAAACTGCTAGCGTAA 60.16 227–263 PET
R:CCGCCAGGCTTTAAAAGTTA 59.38
GAT66353 GAT F: [M13]CAGTGTTGTATGCCCCATGA 60.39 239–269 VIC
R:GCACGTCCCAGTTCAAAAAC 60.54
GATA234 GATA F: [M13]GTGGTGGAGTCTGGCATGAA 60 216–232 NED
R:AACATGCAGACAGACCAGCA 59.9
GATA291 GATA F: [M13]CCTTCCAGGCATTGCTTTAG 59.84 146–192 FAM
R:TGAGACATGGGTGCATCATT 59.93
GTT00404 GTT F: [M13]TGGGTTCCATCTGTGCTGTA 60.11 215–248 FAM
R:GAACCCACCAGGCAAACTAA 59.97
GTT54407 GTT F: [M13]GGCTTCCACGTAATTCCTACC 59.85 205–244 VIC
R:ACCCAATGGCAAAACGTAAC 59.73

  1. Ta, annealing temperature.

We examined the scoring errors, including allele dropout, stuttering, and null alleles, using Micro-Checker v.2.2.3 (Van Oosterhout et al. 2004), with a standard (Dunn-Sidak) adjusted 95 % confidence interval and 1,000 repetitions. For each locus, the allele size, number of alleles per locus (Na), observed heterozygosity (HO), expected heterozygosity (HE), and Hardy–Weinberg equilibrium were analyzed using GenAlEx v.6.5 (Peakall and Smouse 2006). The loci AAAC310 and AGT31996 were excluded from the principal coordinate analysis (PCoA) because they were missing in the Taiwan population. To estimate genetic differentiation among Fort Lauderdale, Miami, and Taiwan, we used Wright’s fixation index (F ST), Jost’s estimate of differentiation (Dest), and Hedrick’s standardized G ST for a small number of populations (G″ST) using GenAlEx v.6.5 (Peakall and Smouse 2006).

2.5 Colony delineation and breeding system

We conducted genotypic differentiation for all pairs of colonies using the exact G-test on the Genepop v. 4.7.5 website (https://genepop.curtin.edu.au/). The combinations of microsatellite loci tested for colony delineation were as follows: (A) six loci with a single allele per locus – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, and GATA234; (B) six loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, and GTT54407; (C) 12 loci with one or two alleles per locus – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, GATA234, AAT39727, AACT53130, AGT31996, CTT54255, GATA291, and GTT54407; (D) seven loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, and AGT17373; (E) eight loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, and AGTT16349; (F) nine loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, AAC38198, and AGTT16349; (G) eleven loci with 2–4 alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, CATT39307, AAC38198, AGTT16349, and AGT75997; (H) thirteen loci with 2–4 alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, CATT39307, GTT00404, ATT77391, AAC38198, AGTT16349, and AGT75997. The Markov chain parameters were set as the default values. Statistical support was obtained through permutations of multilocus genotypes between each pair of colonies using Fisher’s method (P < 0.05) and applying Bonferroni corrections for multiple comparisons. If each pair of colonies was significantly differentiated, they were considered different colonies (Vargo 2003; Husseneder et al. 2005).

We examined the frequencies and classes of worker genotypes by performing G-test for goodness-of-fit between observed and expected genotypes across all loci for each colony to determine the breeding system of C. gestroi colonies – simple family (a colony initiated by a single monogamous pair of reproductives) or extended family (a colony headed by numerous neotenics descended from a single monogamous pair) (Husseneder et al. 2005; Vargo 2003; Vargo et al. 2003; Vargo and Husseneder 2009). A colony was identified as a simple family when the genotypes of workers were consistent with the expected genotypes of a single monogamous pair of parents and the obtained genotype frequencies did not significantly deviate from Mendelian ratios according to the G-test. A colony was recognized as an extended family when the genotypes were inconsistent with the expected genotypes of a single monogamous pair of parents, the workers endowed no more than four alleles at a locus, three classes of homozygotes, and the genotype frequencies of workers significantly deviated from the expected values in simple family.

2.6 Population genetic structure

We conducted two genetic clustering methods to examine the genetic differences among the presumed colonies. First, relationships among colonies were examined using PCoA based on a standardized covariance matrix of pairwise genetic distances in GenAlEx v.6.5 (Peakall and Smouse 2006). Then, we used the model-based Bayesian clustering software STRUCTURE v 2.3.4 (Pritchard et al. 2000) to examine the possible origin of a given termite. We employed the admixture model and correlated allele frequency among populations for the analyses (Falush et al. 2003). The number of potential clusters (K) was set from one to five for Floridian C. gestroi colonies; and from one to three for incipient laboratory colonies and Taiwanese colonies. Ten replicates were executed for each K, with a 100,000 burn-in followed by 1,000,000 Markov Chain Monte Carlo (MCMC) replications. We determined the optimal K value using StructureSelector (Li and Liu 2018), which was calculated using the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods, with a threshold value of 0.8 (Puechmaille 2016). The clustering results were generated through the CLUMPAK server (Kopelman et al. 2015).

3 Results

3.1 Genome data, microsatellite distribution, and PCR amplification

PacBio sequencing yielded an N50 of 7,106 for subreads, with a total length of 38,791,654 bp and an average subread length of 5,459 bp. We obtained 27,544 CCS reads from the C. gestroi genome, featuring a minimum predicted accuracy of 99 % and lengths ranging from 91 bp to 20,975 bp. Based on the published genome size of C. formosanus, which is 875.84 Mb (Itakura et al. 2020), these CCS reads of C. gestroi were estimated to provide approximately 0.23 X genome coverage. The high-accuracy CCS reads were directly used to design microsatellite loci.

Of the CCS reads, 24,511 sequences were predominantly distributed between 5,000 bp and 10,000 bp. Furthermore, a total of 23,116 reads exhibited repeat motifs suitable for the development of new microsatellite markers. These repeat motifs primarily consisted of tetranucleotide (45.1 %) and trinucleotide (33.6 %) repeats (Figure 2). The proportions of the remaining nucleotide repeats were 14.2 % for dinucleotide repeats, 4.31 % for mononucleotide repeats, 2.7 % for pentanucleotide repeats, and 0.06 % for hexanucleotide repeats. Of the 112 primer sets tested, we successfully amplified the target C. gestroi microsatellites using 48 primer pairs, including 18 out of 52 sets from the C. formosanus genome and 30 out of 60 sets from the C. gestroi genome (Table S2). Of these 48 primer sets, 19 microsatellite loci exhibiting polymorphism among counties were used to delineate C. gestroi colonies. Notably, the loci AAAC310 and AGT31996 were absent in the Taiwanese population. In addition, several Floridian samples were not successfully amplified for the polymorphism test, although most were collected between 2018 and 2020.

Figure 2: Distribution of microsatellites in the genomic sequencing reads of Coptotermes gestroi. Repeat motif – mono: mononucleotide, di: dinucleotide, tri: trinucleotide, tetra: tetranucleotide, penta: pentanucleotide, hexa: hexanucleotide.
Figure 2:

Distribution of microsatellites in the genomic sequencing reads of Coptotermes gestroi. Repeat motif – mono: mononucleotide, di: dinucleotide, tri: trinucleotide, tetra: tetranucleotide, penta: pentanucleotide, hexa: hexanucleotide.

3.2 Allele frequency and Hardy–Weinberg equilibrium test

In South Florida, a total of 34 and 46 alleles were found across 19 microsatellite loci from the colonies in Fort Lauderdale and Miami, respectively (Table 3). The number of alleles per locus ranged from one to four, with an average of 1.8 alleles per locus in Fort Lauderdale and 2.4 in Miami. In Taiwan, a total of 32 alleles were discovered across 17 microsatellite loci, owing to the absence of the loci AAAC310 and AGT31996. The number of alleles per locus ranged from one to three, with an average of 1.9 alleles per locus. In each population, the monomorphic microsatellite loci were as follows: Fort Lauderdale – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, and GATA234; Miami – CTT54255; and Taiwan – AGTT16349, ATT03508, ATT77391, CATT39307, and GTT54407 (Table 3). Neither null allele nor scoring errors were detected from any of the loci.

Table 3:

Characterization of 19 microsatellite loci in Coptotermes gestroi samples collected from South Florida and Taiwan.

Locus N Na HO HE HWE Probability
FL MIA TW FL MIA TW FL MIA TW FL MIA TW FL MIA TW
AAC38198 9 8 8 2 3 3 0.222 0.500 0.625 0.444 0.539 0.461 0.134 0.620 0.647
AAT39727 8 9 9 2 2 2 0.125 0.000 0.444 0.117 0.198 0.494 0.850 0.003b 0.764
AAAC310 9 10 N/A 1 2 N/A 0.000 0.000 N/A 0.000 0.180 N/A – 0.002b N/A
AACT53130 6 9 7 2 2 2 0.833 1.000 0.571 0.486 0.500 0.408 0.080 0.003b 0.290
AGT17373 8 8 9 2 3 2 0.375 0.125 0.444 0.305 0.227 0.444 0.514 0.001b 1.000
AGT31996 10 10 N/A 2 2 N/A 1.000 1.000 N/A 0.500 0.500 N/A 0.002b 0.002b N/A
AGT75997 10 8 9 2 4 2 0.500 1.000 0.556 0.455 0.609 0.475 0.754 0.001c 0.613
AGTT16349 9 9 9 2 3 1 0.667 0.111 0.000 0.494 0.204 0.000 0.294 0.000c –
ATT03508 10 11 9 1 3 1 0.000 0.273 0.000 0.000 0.574 0.000 – 0.005b –
ATT77391 7 9 9 3 2 1 0.286 0.111 0.000 0.255 0.105 0.000 0.978 0.860 –
ATGT304 9 11 9 1 2 2 0.000 0.000 0.444 0.000 0.165 0.494 – 0.001c 0.764
CATT39307 10 8 9 2 4 1 0.200 0.625 0.000 0.180 0.555 0.000 0.725 0.476 –
CATT63910 7 5 9 1 2 2 0.000 0.000 0.333 0.000 0.320 0.500 – 0.025a 0.317
CTT54255 10 11 9 2 1 2 0.400 0.000 0.667 0.420 0.000 0.494 0.880 – 0.294
GAT66353 10 9 9 1 2 2 0.000 0.333 0.556 0.000 0.500 0.401 – 0.317 0.249
GATA234 9 9 9 1 2 2 0.000 0.000 0.222 0.000 0.198 0.346 – 0.003b 0.284
GATA291 8 9 9 2 2 3 0.500 0.000 0.444 0.469 0.198 0.426 0.850 0.003b 0.268
GTT00404 7 9 9 3 3 3 0.571 0.111 0.222 0.520 0.204 0.494 0.730 0.000c 0.010a
GTT54407 9 9 9 2 2 1 0.333 0.000 0.000 0.278 0.198 0.000 0.549 0.003b –

  1. N, number of individuals examined (one individual per colony); Na, number of alleles; HO, observed heterozygosity; HE, expected heterozygosity; FL, Fort Lauderdale; MIA, Miami; TW, Taiwan; and the probability test of Hardy–Weinberg equilibrium (HWE) by Chi-Square (X2): aP < 0.05, bP < 0.01, cP < 0.001.

In the Hardy–Weinberg equilibrium test (Table 3), the microsatellite locus (AGT31996) in Fort Lauderdale and 14 loci (AAT39727, AAAC310, AACT53130, AGT17373, AGT31996, AGT75997, AGTT16349, ATT03508, ATGT304, CATT63910, GATA234, GATA291, GTT00404, and GTT54407) in Miami significantly deviated from the Hardy–Weinberg equilibrium. In Taiwan, only the locus GTT00404 significantly deviated from the Hardy–Weinberg equilibrium.

3.3 Genetic differentiation of C. gestroi colonies

The PCoA plots, based on standardized covariance of the genetic distance matrix, separated the C. gestroi samples into three clusters, indicating significant genetic differentiation among Fort Lauderdale, Miami, and Taiwan samples (Figure 3A). Axis 1 and Axis 2 accounted for 40.74 % and 33.36 % of the total variance, respectively. Notably, one sample from Miami clustered with the Fort Lauderdale group. Significant genetic differentiation was observed between Fort Lauderdale and Miami (F ST = 0.45, Dest = 0.66, G″ST = 0.86, P < 0.01), Fort Lauderdale and Taiwan (F ST = 0.54, Dest = 0.98, G″ ST = 0.99, P < 0.01), and Miami and Taiwan (F ST = 0.50, Dest = 0.93, G″ST = 0.98, P < 0.01). Furthermore, the STRUCTURE result also suggested three groups for the C. gestroi samples corresponding to Fort Lauderdale, Miami, and Taiwan, with the exception of a sample from Fort Lauderdale that showed admixture with the Miami group (Figure 3C).

Figure 3: Genetic structure and principal coordinate analysis (PCoA) of Coptotermes gestroi populations from South Florida and Taiwan based on 17 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. The collection information for each population is labeled beside their representative cluster. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 3) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.
Figure 3:

Genetic structure and principal coordinate analysis (PCoA) of Coptotermes gestroi populations from South Florida and Taiwan based on 17 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. The collection information for each population is labeled beside their representative cluster. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 3) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.

For the Floridian colonies, the PCoA analysis approximately separated C. gestroi samples into four clusters, namely FL1, FL2, FL3, and FL5, which accounted for 42.31 % of the total genetic variation (21.84 % and 20.47 % for Axis 1 and Axis 2, respectively) (Figure 4A). Notably, nearly 50 % of the samples from FL4 were scattered among the other colonies. However, the MedMedK and MaxMedK analyses of STRUCTURE results supported the existence of three clusters for the Floridian C. gestroi samples (Figure 4B and C).

Figure 4: Genetic structure and principal coordinate analysis (PCoA) of Coptotermes gestroi colonies from South Florida based on 19 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. Each colony is represented by different shapes and colors. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 3) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.
Figure 4:

Genetic structure and principal coordinate analysis (PCoA) of Coptotermes gestroi colonies from South Florida based on 19 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. Each colony is represented by different shapes and colors. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 3) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.

In incipient colonies, the PCoA analysis primarily grouped samples with their representative colonies, although a few samples showed admixture with others (genetic variation: 51.39 %; Axis 1 – 32.65 %, Axis 2 – 18.74 %) (Figure 5A). The STRUCTURE analysis suggested one cluster for the laboratory colonies (Figure 5B and C). For the Taiwanese colonies, the PCoA analysis delineated the C. gestroi samples into three distinct genetic clusters (genetic variation: 67.47 %; Axis 1 – 46.87 %, Axis 2 – 20.60 %) (Figure 6A), which was corroborated by the STRUCTURE analysis (Figure 6B and C).

Figure 5: Genetic structure and principal coordinate analysis (PCoA) of the incipient laboratory colonies of Coptotermes gestroi based on 19 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. Each colony is represented by different shapes and colors. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 1) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.
Figure 5:

Genetic structure and principal coordinate analysis (PCoA) of the incipient laboratory colonies of Coptotermes gestroi based on 19 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. Each colony is represented by different shapes and colors. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 1) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.

Figure 6: Genetic structure and principal coordinate analysis (PCoA) of Coptotermes gestroi colonies from Taiwan based on 17 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. Each colony is represented by different shapes and colors. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 3) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.
Figure 6:

Genetic structure and principal coordinate analysis (PCoA) of Coptotermes gestroi colonies from Taiwan based on 17 microsatellite loci. (A) PCoA plot of C. gestroi individuals based on the standardized covariance of the genetic distance matrix. Each colony is represented by different shapes and colors. (B) Assumed genetic clusters (K) determined by the median of medians (MedMedK) and the maximum of medians (MaxMedK) methods. (C) Results of genetic clustering (K = 3) obtained from STRUCTURE analyses. Different colors represent the possible genetic clusters. Each bar stands for one individual.

3.4 Colony delineation

Our results indicated that the six microsatellite loci (AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, and GATA234), each possessing a single allele in Fort Lauderdale, did not differentiate the Floridian C. gestroi colonies and the incipient laboratory colonies (Table 4A). However, these loci were effective in identifying the three distinct colonies in Taiwan. Six microsatellite loci with two alleles per locus successfully delineated most colonies from South Florida, the incipient laboratory colonies, and those in Taiwan, with the exception of FL2 and FL3 (Table 4B). Twelve microsatellite loci with one or two alleles per locus showed identical results to the six loci with two alleles per locus, failing to distinguish identities between colonies FL2 and FL3 (Table 4C). Similarly, seven or eight microsatellite loci with two alleles per locus were still insufficient to differentiate conies FL2 and FL3 (Table 4D, E). In contrast, nine microsatellite loci with two alleles per locus clearly delineated all C. gestroi colonies from South Florida, the laboratory, and Taiwan (Table 4F). Further analyses showed that eleven microsatellite loci with two alleles per locus, as well as those loci with 2–4 alleles per locus, were capable of distinguishing all the colonies used in this study (Table 4G, H).

Table 4:

Colony delineation of Coptotermes gestroi from South Florida (five colonies), the laboratory (three colonies), and Taiwan (three colonies) using eight combinations of microsatellite loci with variations of alleles. A: six loci with a single allele per locus – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, and GATA234; B: six loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, and GTT54407; C: 12 loci with one or two alleles per locus – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, GATA234, AAT39727, AACT53130, AGT31996, CTT54255, GATA291, and GTT54407; D: seven loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, and AGT17373; E: eight loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, and AGTT16349; F: nine loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, AAC38198, and AGTT16349; G: eleven loci with 2–4 alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, CATT39307, AAC38198, AGTT16349, and AGT75997; H: thirteen loci with 2–4 alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, CATT39307, GTT00404, ATT77391, AAC38198, AGTT16349, and AGT75997; ✓: Significant genetic differentiation, P < 0.05.

Table 4: Colony delineation of Coptotermes gestroi from South Florida (five colonies), the laboratory (three colonies), and Taiwan (three colonies) using eight combinations of microsatellite loci with variations of alleles. A: six loci with a single allele per locus – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, and GATA234; B: six loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, and GTT54407; C: 12 loci with one or two alleles per locus – AAAC310, ATT03508, ATGT304, CATT63910, GAT66353, GATA234, AAT39727, AACT53130, AGT31996, CTT54255, GATA291, and GTT54407; D: seven loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, and AGT17373; E: eight loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, and AGTT16349; F: nine loci with two alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, AAC38198, and AGTT16349; G: eleven loci with 2–4 alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, CATT39307, AAC38198, AGTT16349, and AGT75997; H: thirteen loci with 2–4 alleles per locus – AAT39727, AACT53130, AGT31996, CTT54255, GATA291, GTT54407, AGT17373, CATT39307, GTT00404, ATT77391, AAC38198, AGTT16349, and AGT75997; ✓: Significant genetic differentiation, P < 0.05.

The results of the breeding system analyses revealed that three Floridian colonies (FL1, FL2, and FL5) were simple families (Tables S4, S5, S8), while the other two colonies (FL3 and FL4) were extended families (G-test, P < 0.05; Tables S6, S7). Similarly, the incipient laboratory colonies Lab1 and Lab2 were simple families, whereas Lab3 was unexpectedly classified as an extended family (G-test, P < 0.05; Tables S9-S11). All three Taiwanese colonies (TW1, TW2, and TW3) were simple families (Tables S12-S14).

4 Discussion

The invasive sources and amounts of genetic variation significantly influence the efficacy of using microsatellites for colony delineation in the invasive C. gestroi. Previous studies indicated that C. gestroi has been settled in South Florida for ∼30 years and in Taiwan for ∼110 years (Li et al. 2010; Su et al. 1997). Theoretically, the maturation of Coptotermes colonies requires 4–8 years (Chouvenc and Su 2014; Su and Tamashiro 1987), implying that C. gestroi may have established populations for 3–7 generations in South Florida and 13–27 generations in Taiwan. Although our results suggest that a longer invasion history with more genetic variation aids colony delineation, it is important to note that South Florida and Taiwan have different invasive origins, with the Floridian C. gestroi originating from Malaysia/Singapore and the Taiwanese C. gestroi from the Philippines (Jenkins et al. 2007; Li et al. 2009). Our results also demonstrate the impacts of anthropogenic activities on the genetic variability of C. gestroi colonies. The greater allele diversity in Miami might logically be attributed to its proximity to the port, which presumably increases the import and export of C. gestroi colonies from various locations, thereby introducing genetic variation. Further studies are required to explore the influence of invasion history and invasion numbers on the genetic variability of microsatellites for colony delineation.

Our study can serve as a reference for using microsatellites for colony delineation of invasive C. gestroi colonies with recent invasion history. We found that at least nine microsatellite loci with two alleles per locus are needed to confirm colony affiliation of Floridian C. gestroi populations (Table 4F), although six loci with two alleles have confirmed their robustness in delineating the incipient laboratory colonies (Table 4B, C). Moreover, thirteen microsatellite markers with 2–4 alleles per locus may provide more genetic variation for C. gestroi colony identification in South Florida if the genetic variation among colonies is quite low (Table 4H). On the other hand, six monomorphic microsatellite loci in Fort Lauderdale were found to possess two alleles in Taiwan (Table 3), except for the missing locus AAAC310. These five loci, along with other combinations of microsatellite markers, have been proven to be robust in distinguishing the three C. gestroi colonies in Taiwan (Table 4). Our study also provides data on the abundance and types of microsatellites present in our C. gestroi genomic sequences (Figure 2), which could assist future studies if more microsatellites are required.

The breeding system investigation of C. gestroi helps us to address the population genetic structure of field colonies. In Fort Lauderdale, the colonies FL3 and FL4 are extended families (Tables S6–S7), which likely complicates their colony delineation. The genotype frequencies of extended families increase homogeneity that would significantly decrease their genetic variation for colony affiliation recognition from others. In this study, it is interesting that the incipient colony Lab3 is an extended family. The 1-yr old incipient laboratory colonies are established by a monogamous pair of reproductives after collection during the nuptial flight. Therefore, it is possible that the queen might have copulated with other males when they were kept in the same container before moving to new vials.

Foraging range, nuptial flight, and anthropogenic activities determine the distribution range of Coptotermes colonies. Previous studies have shown that mature colonies of C. formosanus can forage up to 100 m (Husseneder and Grace 2001; Su 1994; Tamashiro et al. 1987), which can serve as a reference for identifying colony affiliation. Additionally, with the aid of wind, the geographic range of nuptial flights of Coptotermes is generally less than 1 km (Messenger and Mullins 2005). Therefore, colony FL5 from Miami is definitely a distinct colony from those in Fort Lauderdale since their geographic distance is approximately 34 km. However, colony FL5 was found to have a genetic composition similar to that of the colonies from Fort Lauderdale, indicating the possibility of movement by anthropogenic activities. The collection site of FL5 is located near a port, which serves as a distribution center for cargo and wooden products, facilitating the import and export of C. gestroi colonies from foreign countries or localities across South Florida. Furthermore, colony FL4, which is isolated by a river and located around 2.5 km away from other colonies in Fort Lauderdale, has a different origin. It is thus required to explore the detailed population genetic structure of C. gestroi to clearly understand the impacts of human-mediated activities on the transportation of C. gestroi colonies in South Florida.

The prevalence of international trade has increased the risk of invasion by harmful alien pests, resulting in ecological destruction and economic losses in invaded regions (Nghiem et al. 2013). The transportation of cargo and wooden products has heightened the invasive risks posed by termite species such as Reticulitermes flavipes (Kollar) and C. formosanus (Blumenfeld et al. 2021; Perdereau et al. 2013). Given that the Taiwanese and Hawaiian C. gestroi originate from the Philippines (Li et al. 2009), it is possible that their alleles may differ from those in South Florida. Human-mediated activities may complicate the genetic structure of the original invasive colonies through gene flow and/or hybridization, potentially resulting in a complex genetic composition and alterations to ecological features (Chouvenc et al. 2015; Yang et al. 2012). Accumulating genetic data from these regions would aid in monitoring and preventing the invasion of C. gestroi colonies with differentiated genetic compositions into South Florida.

To conclude, the present results validate the robustness of colony delineation using new microsatellite loci in South Florida, incipient laboratory colonies, and Taiwan. The extended families in C. gestroi colonies with recent invasion history may complicate the genetic composition, increasing the difficulty of colony delineation. However, after testing multiple microsatellite loci, we propose that at least nine loci with two alleles are sufficient to distinguish all tested colonies from South Florida, the laboratory, and Taiwan. This study demonstrates a feasible approach to delineating C. gestroi colonies with a recent invasion history, which can further benefit their management in the future.

Corresponding author: Cheng-Lung Tsai, Entomology and Nematology Department , Ft. Lauderdale Research and Education Center, University of Florida, Ft. Lauderdale, FL 33314, USA; Department of Entomology, National Chung Hsing University, 145 Xingda Rd., Taichung City 40227, Taiwan, E-mail: ; and Hou-Feng Li, Department of Entomology, National Chung Hsing University, 145 Xingda Rd., Taichung City 40227, Taiwan, E-mail:

Funding source: Corteva Agriscience

Funding source: National Institute of Food and Agriculture

Award Identifier / Grant number: FLA-FTL-005865

Acknowledgments

The authors thank Dr. De-Fen Mou, Dr. Brian Bahder, Dr. Braham Dhillon, and Dr. Jun Zhao (Entomology and Nematology Department, Fort Lauderdale Research and Education Center, University of Florida, USA) for providing experimental equipment and technical assistance. The first author is grateful to Dr. Sang-Bin Lee and Dr. Joseph Velenovsky (Entomology and Nematology Department, Fort Lauderdale Research and Education Center, University of Florida, USA) for their insights on termite biology and sharing of experiences. We are grateful to Dr. Mei-Er Chen and Dr. Wen-Bin Yeh (Department of Entomology, National Chung Hsing University, Taichung, Taiwan) for supplying PCR amplification equipment and the 96-well gel running system for PCR products examination. Special thanks to Dr. Chia-Hung Hsieh (Department of Forestry and Nature Conservation, Chinese Culture University, Taipei, Taiwan), Dr. Shu-Ping Tseng (Department of Entomology, National Taiwan University, Taipei, Taiwan), and Wen-Jun Lin and Yi-Ning Chiu (Department of Entomology, National Chung Hsing University, Taichung, Taiwan) for their analytical assistance. We also acknowledge Dr. Mei-Yeh Lu, Ms. Jeng-Yi Li, and the High Throughput Sequencing Core at the Biodiversity Research Center, Academia Sinica, for their support with the PacBio sequencing. Additionally, the authors acknowledge the Genomics Center for Clinical and Biotechnological Applications of National Core Facility for Biopharmaceuticals, Taiwan (MOST 110-2740-B-A49A-501) for sequencing.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: CLT, HFL, YHC, MDL, and NYS conceived and designed the experiment. NYS acquired funding. HFL, AM, RHS, and TC collected and identified the specimens. CLT, YHC, MDL, and GYC designed the microsatellite primers. CLT conducted the experiment. CLT analyzed the data and wrote the manuscript. All authors read and approved the manuscript.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare no conflict of interest.

  6. Research funding: This work was supported in part by the USDA National Institute of Food and Agriculture, Hatch project number FLA-FTL-005865. Additional funding was provided by Corteva Agriscience.

  7. Data availability: The PacBio sequencing reads of C. gestroi have been deposited in GenBank under BioProject accession number PRJNA938258.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/flaent-2024-0031).

Received: 2023-08-27
Accepted: 2024-07-24
Published Online: 2024-12-16

© 2024 the author(s), published by De Gruyter on behalf of the Florida Entomological Society

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  32. Dr. Charles W. O’Brien: True Pioneer in Weevil Taxonomy and Publisher
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https://doi.org/10.1515/flaent-2024-0031
Keywords for this article
Coptotermes gestroi; invasion history; genetic variability; termite management
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Distribution and dispersal of adult spotted wing drosophila, Drosophila suzukii (Diptera: Drosophilidae), in organically grown strawberries in Florida
  4. A comparison of the capture of non-target arthropods between control methods and monitoring traps of Anastrepha ludens in citrus agroecosystems
  5. Development of microsatellite markers for colony delineation of the invasive Asian subterranean termite (Blattodea: Rhinotermitidae) in South Florida and Taiwan
  6. Biology and life table of Oligonychus punicae Hirst (Trombidiformes: Tetranychidae) on three host plants
  7. Relative captures and detection of male Ceratitis capitata using a natural oil lure or trimedlure plugs
  8. Evaluation of HOOK SWD attract-and-kill on captures, emergence, and survival of Drosophila suzukii in Florida
  9. Rearing Neoseiulus cucumeris and Amblyseius swirskii (Mesostigmata: Phytoseiidae) on non-target species reduces their predation efficacy on target species
  10. Response of male Bactrocera zonata (Diptera: Tephritidae) to methyl eugenol: can they be desensitized?
  11. Monitoring of coccinellid (Coleoptera) presence and syrphid (Diptera) species diversity and abundance in southern California citrus orchards: implications for conservation biological control of Asian citrus psyllid and other citrus pests
  12. Topical treatment of adult house flies, Musca domestica L. (Diptera: Muscidae), with Beauveria bassiana in combination with three entomopathogenic bacteria
  13. Laboratory evaluation of 15 entomopathogenic fungal spore formulations on the mortality of Drosophila suzukii (Diptera: Drosophilidae), related drosophilids, and honeybees
  14. Effect of diatomaceous earth on diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae), larval feeding and survival on cabbage
  15. Bioactivity of seed extracts from different genotypes of Jatropha curcas (Euphorbiaceae) against Spodoptera frugiperda (Lepidoptera: Noctuidae)
  16. Assessment of sugarberry as a host tree of Halyomorpha halys (Hemiptera: Pentatomidae) in southeastern USA agroecosystems
  17. The importance of multigeneration host specificity testing: rejection of a potential biocontrol agent of Nymphaea mexicana (Nymphaeaceae) in South Africa
  18. Endophytic potential of entomopathogenic fungi associated with Urochloa ruziziensis (Poaceae) for spittlebug (Hemiptera: Cercopidae) control
  19. The first complete mitogenome sequence of a biological control agent, Pseudophilothrips ichini (Hood) (Thysanoptera: Phlaeothripidae)
  20. Exploring the potential of Delphastus davidsoni (Coleoptera: Coccinellidae) in the biological control of Bemisia tabaci MEAM 1 (Hemiptera: Aleyrodidae)
  21. Behavioral responses of Ixodiphagus hookeri (Hymenoptera; Encyrtidae) to Rhipicephalus sanguineus nymphs (Ixodida: Ixodidae) and dog hair volatiles
  22. Illustrating the current geographic distribution of Diaphorina citri (Hemiptera: Psyllidae) in Campeche, Mexico: a maximum entropy modeling approach
  23. New records of Clusiidae (Diptera: Schizophora), including three species new to North America
  24. Photuris mcavoyi (Coleoptera: Lampyridae): a new firefly from Delaware interdunal wetlands
  25. Bees (Hymenoptera: Apoidea) diversity and synanthropy in a protected natural area and its influence zone in western Mexico
  26. Temperature-dependent development and life tables of Palpita unionalis (Lepidoptera: Pyralidae)
  27. Orchid bee collects herbicide that mimics the fragrance of its orchid mutualists
  28. Importance of wildflowers in Orius insidiosus (Heteroptera: Anthocoridae) diet
  29. Bee diversity and abundance in perennial irrigated crops and adjacent habitats in central Washington state
  30. Comparison of home-made and commercial baits for trapping Drosophila suzukii (Diptera: Drosophilidae) in blueberry crops
  31. Miscellaneous
  32. Dr. Charles W. O’Brien: True Pioneer in Weevil Taxonomy and Publisher
  33. Scientific Notes
  34. Nests and resin sources (including propolis) of the naturalized orchid bee Euglossa dilemma (Hymenoptera: Apidae) in Florida
  35. Impact of laurel wilt on the avocado germplasm collection at the United States Department of Agriculture, Agricultural Research Service, Subtropical Horticulture Research Station
  36. Monitoring adult Delia platura (Diptera: Anthomyiidae) in New York State corn fields using blue and yellow sticky cards
  37. New distribution records and host plants of two species of Hypothenemus (Coleoptera: Curculionidae: Scolytinae) in mangrove ecosystems of Tamaulipas, Mexico
  38. First record of Trichogramma pretiosum parasitizing Iridopsis panopla eggs in eucalyptus in Brazil
  39. Spodoptera cosmioides (Lepidoptera: Noctuidae) as an alternative host for mass rearing the parasitoid Palmistichus elaeisis (Hymenoptera: Eulophidae)
  40. Effects of biochar on ambrosia beetle attacks on redbud and pecan container trees
  41. First report of Diatraea impersonatella (Lepidoptera: Crambidae) on sugarcane (Saccharum officinarum L.) in Honduras
  42. Book Reviews
  43. Kratzer, C. A.: The Cicadas of North America
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