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High-Resolution Population Genetic Structure of Tawny Crazy Ant (Nylanderia
fulva Mayr: Hymenoptera: Formicidae) from the Origin in South America and
Introduced Regions of the United States
Jocelyn R. Holt ( Jocelyn.Holt@rice.edu )
Rice University
James Montoya Lerma
Universidad del Valle
Luis A. Calcaterra
CONICET and Fundación para el Estudio de Especies Invasivas
Tyler J. Raszick
Texas A&M University
Raul F. Medina
Texas A&M University
Research Article
Keywords: genetic differentiation, high throughput sequencing, SNPs, invasive insect
Posted Date: January 4th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2399319/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
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Abstract
Background
The tawny crazy ant (
Nylanderia fulva
Mayr) is native to South America and was rst reported in the continental United States (US) in 1938. It was not until
the 1990s in Florida and 2000s in Texas that this ant was considered a serious pest in the US. Tawny crazy ant (TCA) is currently considered an invasive pest
in six US states and this ant’s invasion success is attributed in part to a unicolonial nature, multiple queens per nest, natural enemies release in the invasive
range, and ability to detoxify venom from other competitor ant species. A limited number of low-density molecular markers have previously shown little
genetic differentiation among TCA populations across their geographic distribution in the US.
Results
Using High Throughput Sequencing (HTS) we obtained high-density molecular markers (i.e., SNPs) for TCA samples. With 26,657 SNPs we identied genetic
variation among TCA populations in different states across the US (i.e., Texas, Louisiana, Alabama, Mississippi, Georgia, and Florida) and in South America
(i.e., Argentina, Colombia, and Peru).
Conclusion
Our results underscore that for recently introduced invasive species, increasing the number of molecular markers used in population genetic studies can
provide greater resolution. High-resolution information on regional genetic differences can help inform pest management strategies.
Background
Genetic differentiation among insects that are recently introduced may be dicult to detect due to a single introduction event that results in a genetic
bottleneck. In contrast, multiple introductions of genetically differentiated propagules can facilitate detection of population structure. For instance, genetic
variability among ambrosia beetle (
Xylosandrus crassiusculus
Motschulsky) populations was due to multiple introduction events and the identication of
cryptic species [1]. Similarly, invasive populations, particularly those that are small, are likely to experience genetic drift over the course of an invasion event.
Microevolutionary forces, such as selection and genetic drift, can result in genetic differentiation in as quickly as 10 to 50 years [2, 3]. Geographically
widespread and relatively recent introductions of invasive insect species (i.e., 50 years or less) provide an opportunity to characterize the degree of genetic
differentiation among different geographic locations. This information can then be used to increase monitoring at potential points of entry, determine centers
of origin, and determine whether genetically differentiated invasive pest populations vary in traits relevant to their control [4]. In this study, we characterized the
population genetic structure of tawny crazy ant (
Nylanderia fulva
Mayr) in the invaded region of the US and the native origin in South America.
Invasive tawny crazy ant (
Nylanderia fulva
Mayr; hereafter referred to as TCA) was rst identied in Texas in 1938 and later in Florida in 1953 [5, 6]. This
species has a native distribution along the Rio de La Plata basin and the southern portion of the Atlantic Coastal Forest biome [7]. TCA is considered a pest
species in urban, agricultural, and natural/wildland areas in introduced ranges of South America and the US [8–10]. After the initial reports and identication
of TCA in the US, little to no information was recorded about further range expansion or its impacts on urban and natural settings, suggesting that these rst
reported propagules may have failed to establish. TCA was later reported in large numbers in Florida hospitals in the 1990s, and subsequently during a 2002
outbreak in Houston at NASA, and the greater Houston metro area [11, 12]. After the 1990s, TCA expanded their geographic distribution and, in addition to
Texas and Florida, are now reported in Alabama, Georgia, Louisiana, and Mississippi [12]. TCA range expansion was likely facilitated by this ant’s ability to
detoxify re ant venom and displace other ant species from habitats [10, 13], in addition to dietary exibility [14]. Because this ant is a relatively recent invader,
microevolutionary forces may not have had enough time to leave a strong signature of genetic differentiation.
Previous population genetic studies of TCA used low-density molecular markers (sometimes called diagnostic markers; e.g., COI, EF1α-F1, EF1α-F2, CAD, argK,
and microsatellites) and were unable to detect genetic differentiation among TCA populations in the US. The most recent such study used COI and 13
microsatellite markers that characterized TCA in the US as unicolonial [15], and failed to detect differences in population genetic structure from ve different
states (i.e., Texas, Louisiana, Mississippi, Georgia, and Florida). In contrast, within their native range in South America, signicant population genetic structure
was detected [15, 16].
The choice of molecular markers used to detect genetic variation in invasive insects should be informed by the degree to which populations in the invaded
regions are reproductively isolated from the center of origin and by the duration of time since isolation occurred. The combination of reduced gene ow and
decreased effective population sizes, needs to be taken into consideration when characterizing species’ population genetic structure [17]. The use of fewer
(i.e., low-density) molecular markers that are designed for barcoding or diagnostic identication (e.g., COI, ITS) is sucient for detecting greater genetic
differences among species or populations that have a long history of reproductive isolation [18–20]. In instances where there is moderate reproductive
isolation among populations, the use of tens to hundreds of molecular markers (i.e., SSRs, ISSRs) is often sucient to detect genetic variation. In instances
with ongoing gene ow or recent reproductive isolation a higher number of molecular markers is needed [21]. Thus, population genetic studies of recently
invaded species should use high-density molecular markers (e.g., AFLPs, SNPs) to detect genetic variation.
High-density markers are able to detect genetic structure among populations that may have low genetic diversity due to a recent invasion or ongoing gene ow
[22–24]. There are many instances where the use of high-density molecular markers detected genetic differences among populations, where low-density
molecular markers failed to detect variation [24–29]. For example, in native populations of geographically isolated American lobsters (
Homarus americanus
H.
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Milne-Edwards), the use of microsatellite markers failed to detect population genetic structure between the northern and southern range, while the use of 8,144
SNPs (single nucleotide polymorphisms) allowed for the detection of signicant genetic differences between these populations [30]. Similarly, analysis of
18,147 SNPs showed clear delineation of yellow fever mosquito (
Aedes aegypti
L.) populations in Asia and Australia when eight microsatellites failed to
detect these genetic differences [31]. Using high-density molecular markers allows for the identication of genetic structure among populations with recent
gene ow, and identication of biologically relevant genetic differences can be used to inform management practices as was the case for the American lobster
[32].
In this study, we resolved the population genetic structure of TCA in the US and South America. We hypothesized that high-density molecular markers would
allow for the detection of genetic differentiation among ants from different geographic locations. In addition, we hypothesized that analysis of high-density
molecular markers might inform the potential population(s) of origin from the native range in Argentina.
Results
Data Filtering
Approximately 745 million reads were retained after using the process radtags ltering step in STACKS [33, 34]. There were between 4.4 to 12 million reads per
sample. To minimize potential bias due to linkage (i.e., linked alleles), only one SNP per locus was used for data analysis. Most loci met Hardy Weinberg
Equilibrium (HWE), with less than 5% not meeting HWE. This resulted in 96 individuals with 26,657 SNPs among nine geographic locations.
Population Genetic Analysis
When all TCA from South America and the US were analyzed, the number of putative populations estimated by ΔK peaked at ve with a second peak at 11;
suggesting population structure (Fig. 1). Ants from the US and South America clustered in seven genetically differentiated populations according to their
geographic origin (Fig. 2). When only samples from the US were analyzed, the ΔK peaked at six (Supplemental Fig.1) while the optimal BIC values were three
and ve (Supplemental Fig.2). When samples from only Texas were analyzed ΔK of ve and seven was detected (Supplemental Fig.3), however clustering
was not strongly associated with geographic location (Supplemental Fig.4). When only samples from the native range (i.e., Argentina) were analyzed, the ΔK
peaked at three (Supplemental Fig.5).
Population genetic statistics for each collection location are provided in Table 1. The number of private alleles was highest for ants sampled from Argentina
(2120) and Colombia (2294) while ants from Alabama in the US had the lowest number (50) of private alleles or alleles exclusive to a particular population
(Table 1). There were several populations that had moderate levels of genetic differentiation (Table 2). Both Colombia and Peru had high FST values when
compared with other populations (Table 2). The inbreeding coecient was low for the sampled populations (Table 1). When only samples from Argentina
were analyzed, they were grouped into three populations associated with geographic region (Table 4).
A DAPC showed that TCA in both South America and the US had genetically differentiated clusters based mainly upon geographic location (Fig. 3). Ants
collected from Argentina clustered separately from those collected in Colombia and Peru. Within the US, ants collected from each state clustered separately;
although ants collected from Louisiana and Mississippi clustered closest to ants from Texas, which was also supported by low FST values among these
locations indicating little genetic differentiation among these locations (Fig. 3).
An AMOVA on all collection locations for TCA showed that there was no signicant variation among populations despite some moderate to high FST
values among collection locations. The components of variance broken into variation among locations at 0.87% (DF = 7, phi = 0.16,
P
< 0.001) of the
variability, while variation within collection locations accounted for 15.78% (DF = 88, phi = 0.15,
P
> 0.01) of the variability, and variation among all
individual samples was 84.7% (DF = 96, phi = -0.0048,
P
> 0.5). When only samples from the US were evaluated with an AMOVA, there was a lack of
signicant variation detected among locations (Supplemental Table1). Similarly with samples from Argentina, although three clusters were identied
using an AMOVA, these did not have signicant variation detected among locations (Supplemental Table2). This lack of signicance with an AMOVA is
potentially due to the low sample size and a low number of distinct populations [35]. In contrast, pairwise FST values revealed geographic structure for
populations in the US (Table 3).
Calculation of IBD with Genepop did not detect geographic distance as a signicant driver of genetic differentiation when all samples were analyzed (
P
=
0.09400), when only samples from the US were analyzed (
P
= 0.96500), or when only samples from South America were analyzed (
P
= 0.83410). These
results suggest other factors are driving genetic differentiation. A linear regression of the IBD pairwise comparisons indicated FST values were not
signicantly correlated with distance for all samples as this explained only 0.005% variation. When samples were analyzed separately for North and
South America samples, distance explained 36.01% variation for US samples, and 70.76% variation for South American samples, as indicated by the R2
value. When samples from Colombia and Peru were removed from the analysis, IBD was still not signicant (
P
= 0.14500).
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Table 1
Number of TCA samples from US and International collection locations along with summary statistics. The inbreeding coecient (FIS) was
negative or low except for all ants sampled. Among all positions (variant –SNP variation in a gene among individuals in a population resulting in
different alleles and xed –same SNP in a gene for all individuals in a population resulting in no allele variation): n = number of individuals, V =
variant sites that are polymorphic in at least one collection location, % poly = percent polymorphic sites among variant positions: SNPs =
polymorphic sites within a collection location, P = average frequency of the most common allele, Ho = observed heterozygosity, He = expected
heterozygosity, π = average nucleotide diversity, FIS = inbreeding coecient.
Collection Region n V % poly SNPs/Poly-morphic Sites Private Alleles P Ho He πFIS
US
Alabama 2 17301 4.1 5424 50 0.9164 0.1469 0.8861 0.1367 -0.0180
Florida 6 20240 7.6 9751 84 0.905 0.8383 0.1348 0.1443 -0.0379
Georgia 6 22935 7.4 10225 186 0.9018 0.1667 0.1384 0.1518 -0.0316
Louisiana 6 21836 7.9 10468 189 0.9021 0.1614 0.1392 0.1505 -0.0238
Mississippi 6 23137 7.7 10038 231 0.9032 0.1615 0.1350 0.1480 -0.0259
Texas 57 21872 11.3 14931 344 0.9016 0.1604 0.1439 0.1454 -0.0122
South America
Argentina 10 21348 10.8 14123 2120 0.8563 0.1908 0.2010 0.2125 0.0549
Colombia 2 22169 2.65 3841 2294 0.9454 0.1023 0.0706 0.0942 -0.0122
Peru 1 17145 2.0 3024 209 0.9118 0.1764 0.1453 0.1764 0
Total Individuals 96
Table 2
Pairwise FST values among each collection location for TCAs. Samples from South America show genetic differentiation from US
samples. *** = very great (≥ 0.26), ** = great (0.15 - 0.25), * = moderate (0.15 - 0.05), and += little genetic differentiation (≤ 0.05)
(Hartl & Clark 1997).
Collection Region 1
Argentina
2
Colombia
3
Peru
4
Texas
5
Louisiana
6
Mississippi
7
Alabama
8
Georgia
9
Florida
1
Argentina
−
2
Colombia
0.3176*** −
3
Peru
0.1441*0.5723*** −
4
Texas
0.0904*0.3044*** 0.1454*−
5
Louisiana
0.0983*0.4328*** 0.2482** 0.0083+−
6
Mississippi
0.1011*0.4576*** 0.2697*** 0.0098+0.0441+−
7
Alabama
0.0909*0.5501*** 0.3415*** 0.0100+0.0503*0.0633*−
8
Georgia
0.0980*0.4482*** 0.2617*** 0.0100+0.0396+0.0446+0.0591** −
9
Florida
0.1012*0.4291*** 0.2376** 0.0102+0.0348+0.0446+0.0470+0.0412+−
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Table 3
Pairwise FST values among TCAs in the US. Samples show genetic. *** = very great (≥ 0.26), **
= great (0.15 - 0.25), * = moderate (0.15 - 0.05), and += little genetic differentiation (≤ 0.05)
(Hartl & Clark 1997).
Collection Region 1
Texas
2
Louisiana
3
Mississippi
4
Alabama
5
Georgia
6
Florida
1 Texas −
2 Louisiana 0.1789** −
3 Mississippi 0.0611* 0.1785** −
4 Alabama 0.0979* 0.1429* 0.0746* −
5 Georgia 0.0897* 0.1800** 0.0831* 0.0947* −
6 Florida 0.0403+0.1172* 0.0393+0.0362+0.0556* −
Table 4
Pairwise FST values among TCAs in Argentina. Samples show genetic. *** = very
great (≥ 0.26), ** = great (0.15 - 0.25), * = moderate (0.15 - 0.05), and += little
genetic differentiation (≤ 0.05) (Hartl & Clark 1997).
Collection Region 1
Misiones
2
Corrientes & Entre Ríos
3
Buenos Aires
1 Misiones −
2 Corrientes & Entre Ríos 0.1571** −
3 Buenos Aires 0.1923** 0.2087** −
Discussion
Our results show that invasive TCAs within the US belong to at least four genetically differentiated populations, and when substructure is considered, our
results suggest six differentiated populations based upon geographic location. The high-resolution genetic variation among introduced TCAs was possible
due to the increased power of using thousands of randomly generated SNPs.
Our ndings with high-density molecular markers (i.e., 26,657 SNPs) are in contrast to recent research that identied TCAs in the US as a relatively
undifferentiated superclone using microsatellite markers [15]. Our results show that the use of high-density molecular markers can provide greater resolution
among differentiating populations than the use of low-density molecular markers. Several studies have shown the emergence of population genetic structure
when using high-density molecular markers. For example, the genetic characterization of yellow fever mosquito (
Aedes aegypti
L.) populations in California,
using SNPs generated from HTS, identied previously undetected distinct Northern and Southern populations [36]. The two genetically differentiated mosquito
populations suggested independent introduction events and limited admixture [36]. Similarly, greater resolution of the population genetic structure of a coral
reef sh (
Elacatinus lori
D.S. Jordan) along the Belize barrier reef were identied with the use of 2,418 SNPs compared with the use of 89 microsatellite loci
[37]. Thus, when some species are reported as lacking genetic variation, it is important to determine how much of this reporting may be due to the selection of
a limited quantity of diagnostic molecular markers (i.e., low-density molecular markers).
The population genetic variation of TCAs detected by our study, could be the result of several different potential factors. For instance, there could have been
multiple independent propagule introductions into the US, which is supported by a study conducted in a wider geographic region within the native range of the
TCA using COI (Fernández, MB; pers. com.). The differences in population genetic structure detected among TCA in different states aligns with this scenario
(Fig.2). These ndings are also supported by a study that found differences among ultraconserved elements (UCEs) of TCAs from different states [38]. While
transportation of TCA propagules can happen via contaminated soil, wood (i.e., logs or pallets), or vegetation [8], the population structure revealed by our
study suggests that while this happens within a state it is infrequent among states (Fig.2). The great amount of genetic differentiation among ants in the US
and South America suggests historic introduction events of propagules without ongoing gene ow. This inference is based upon our limited sampling of TCAs
from Argentina, which did not capture all the representative genotypes present in the native range.
In addition, differentiation post introduction could also have contributed to the current population structure. TCAs have been in the US for over 20 years, with
propagules reported in 1990s for Florida and the 2000s for Texas. Under the right conditions, microevolutionary forces can generate genetic and phenotypic
differentiation relatively rapidly. For instance, in less than 50 years, soapberry bugs feeding on invasive plants with smaller seeds than their native host plants,
evolved decreased beak lengths in north America [39]. Furthermore, soapberry bugs in Texas and New Mexico were reported as undergoing host-associated
differentiation from Western soapberry to Mexican buckeye in the past approximately 72 years or less (i.e., 1950s to 1960s) [3]. Similarly, invasive spotted-
wing drosophila (
Drosophila suzukii
Matsumura), estimated to have colonized the Hawaiian archipelago in the 1980s, underwent morphological and
population genetic differentiation within approximately 30 years. Flies at higher elevation have larger wing sizes and are genetically different than ies
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occurring at lower elevations across several islands [2]. These examples show that relatively rapid genetic and phenotypic differences among insect
populations can be detected, may be biologically relevant, and should not be dismissed.
Limited morphological differentiation among crazy ant workers makes visual identication of closely related species dicult [16, 38]. Based upon the high
levels of genetic differentiation of ants from Colombia and Peru, it is likely that these are two cryptic species of
Nylanderia
(Table2). In a study of red wood
ants, FST values of 0.2 and above were reported among different species [40]. Similarly, different species of North American Fire Ants were found to have FST
values between 0.2 and 0.442 [41]. Our ndings of great genetic differentiation among ants from Colombia and Peru further supports ndings by Williams et
al (2022) that
Nylanderia fulva
is a species complex. Additional morphological analysis of male ants from these regions could further support our molecular
ndings and aid in determining whether these species have been previously described or are a new addition in the
fulva
/
pubens
species complex.
Insects of different genotypes may interact (i.e., conspecic or interspecic) in invaded regions in ways dissimilar to their native habitats [42, 43], which can
result in admixture or hybridization events [44–46]. In particular, this process might be facilitated by different invasive genotypes (or even species) arriving to
the same novel locations and mating, when these genotypes might not mate in their native region. Two species of invasive re ant (i.e.,
Solenopsis invicta
and
Solenopsis richteri
) were reported to admix in the introduced range of North America [42]. Admixture events could promote genotypes that have enhanced
insecticide resistance or greater tolerance towards biological control agents. For instance, admixture between diamond back moths that are genetically
adapted to consume rice and those genetically adapted to consume corn occurred in conjunction with insect dispersal, which maintained alleles for
insecticide resistance in the surrounding geographic areas [47]. Additionally, hybridization events among different species could result in novel phenotypes.
When the native corn earworm (
Helicoverpa zea
Boddie) mated with invasive cotton bollworms (
Helicoverpa armigera
Hübner) in Brazil, this resulted in hybrid
offspring that were more resistant to pesticide applications than the native corn earworms [48]. Thus, admixture in invaded regions could result in novel
adaptations, which might inuence invasive pest behaviors and subsequent management practices.
Incorporating information on variability of genetic composition can be valuable for managing populations that have different responses to control practices.
In addition, the use of high-density molecular markers can allow for the correlation of pest traits to different populations. For instance, genetically
differentiated populations of green peach aphids (
Myzus persicae
Sulzer), Mediterranean fruit ies (
Ceratitis capitata
Wiedemann), and mountain pine beetles
(
Dendroctonus ponderosae
Hopkins) have all been shown to vary in their susceptibility to pesticides and stress tolerance [49–51]. Recently, the application of
a fungus-like microsporidian pathogen was found effective at decreasing and in some instances eliminating TCA populations [52–54]. Further investigation is
required into the effectiveness of this biopesticide on other TCA populations.
The correlation of genomic information with pest traits can be used to tailor management approaches towards genetically differentiated populations. The
next steps are to correlate these genetic differences with to potential pest traits, which would provide information that can be incorporated into management
practices. Further studies should characterize how these invasive TCA populations differ in traits relevant to pest control. We think that assessing potential
variation among populations in traits such as vector competency, insecticide susceptibility, symbiotic interactions, behavior, etc., are areas relevant to pest
management [4, 36, 55–57].
Conclusion
Our study shows that high-density molecular markers (i.e., thousands of SNPs) revealed population genetic structure among recently invaded TCA propagules.
In addition, we identied two different species from Colombia and Peru within the
fulva
/
pubens
species complex. Identifying genetic differences both in the
introduced and native range is the beginning to understanding the evolutionary ecology of these pests. Continued monitoring of different TCA populations is
recommended, as well as periodic genetic characterization that might identify ongoing changes in pest traits inuenced by microevolutionary forces.
Material And Methods
Sample Collection
TCA worker ants (female and diploid) were collected from across the US (i.e., Alabama, Florida, Georgia, Louisiana, Mississippi, and Texas) (Table1). In
addition, specimens from native (i.e., Argentina) and invasive (i.e., Colombia and Peru) ranges in South America were used to determine potential points of
origin (Table1). To maximize detection of potential genetic variation within each state and country, when possible, worker ants were collected from nests at
least 1 km apart from each other (Supplemental Table3). In addition, when possible, workers were collected from at least three different locations within each
state or country. Ants were preserved in 95% ethanol and labeled with collection site information, collection date and the collector’s name.
DNA Extraction
DNA was extracted from individual worker ants using a Gentra PureGene Kit (QIAGEN, Valencia, CA, USA). The quantity and purity of DNA was checked using
a NanoDrop spectrophotometer (Thermo Fisher Scientic Inc., Waltham, MA, USA) and a PicoGreen dye assay on a NanoDrop Fluorospectrometer (NanoDrop
Technologies, Inc., DE). DNA samples were submitted to Texas A&M University AgriLife Genomics and Bioinformatics Service (TXGen, College Station, TX).
Samples with good quantities and quality of DNA (i.e. 20 + ng/uL DNA concentrations on Nanodrop, when possible samples had a Genomic Quantity Number
(GQN) reading of 2 + on the PicoGreen, and samples that contained average genomic fragment lengths at or above 10000 bp, indicating low shearing)
underwent genomic library preparation. All samples were prepped at the same time and each sample was given a combinatorial dual index with the i5 index
joined to the EcoRI cut site and the i7 index added to each sample pool via PCR following the dual ligation ddRAD and ddRADseq protocols [58, 59]. High-
throughput paired-end sequencing with a read length of 150bp was done using Illumina NovaSeq 6000 S2 with the restriction enzymes EcoRI and NIaIII.
Data Analysis and Single Nucleotide Polymorphism Identication of Ants
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An average of 3.8million reads with phred scores ≥ 24 (an average sequence phred score of 35) were obtained for each individual TCA worker, for a total of
96 samples, with known geographic locations. Sequence reads were ltered for quality using a FastQC version 0.72 tool to summarize all Phred scores, which
met a minimum score of 20 for each sample [60]. Reads were demultiplexed and further ltered using the process radtags program in STACKS version 2.53,
providing both restriction enzymes (--index-index -e ecoRI –renz_2 nlalll) and quality ltering (size of sliding window, -w 0.15; Phred score, -s 20). Reads were
mapped onto the tawny crazy ant (
Nylanderia fulva
) reference genome (NCBI BioProject PRJNA517949 by Kranti Konganti and Aaron Tarone) following the
STACKS protocol [33, 61]. The demultiplexed reads were run rst through the reference map pipeline (comprised of gstacks and populations). The Marukilow
model was used to call variants and genotypes in gstacks. Then the populations program was run with TCA split into nine geographic locations (Argentina,
Colombia, Peru, Texas, Louisiana, Florida, Georgia, Alabama, and Mississippi) and only the rst SNP in a locus was kept (--write-single-snp) [62] and a single
representative for overlapping sites for reference aligned reads was enabled (--ordered-export). Settings for the populations program included ltering loci to
keep those shared by 80% or more of samples within a population (--min-samples-per-pop; -r 0.8), at or above a minimum minor allele frequency of 2% (−min-
maf 0.02), and shared by a minimum of 1 population (--min-populations; -p 1), with a maximum observed heterozygosity of 70% (--max-obs-het 0.7); no
required value was set for the metapopulation, meaning the minimum percentage of individuals across populations required was 0% (--min-samples-overall; -
R) [61, 63–66]. The populations program in STACKS was used to generate population genetic summary statistics (Table1) [33].
Evaluating Genetic Relationships
The data le generated by the populations program in STACKS was used to assess population genetic variation in STRUCTURE version 2.3.4 [67].
STRUCTURE runs (K = 1 − 12) were done for the nine ant collection locations from the introduced and native ranges, plus an additional three potential
populations. Each run had a 10,000 burnin with 10,000 iterations for MCMC (Markov Chain Monte Carlo) and was replicated 10 times for each value of K [36,
68]; taking into consideration the lower sample size of Argentina, these were run for 25000 burnin with 25000 iterations. The number of putative populations in
the data was analyzed with two different programs for all values of K, with Structure Harvester Web with ve runs per value of K from 1 to 10, and with the
packages poppr 2.9.3 [69], ape [70], and magrittr [71] to run a BIC (Bayesian Information Criterion) in R [72–74]. The estimated number of populations was
determined by assessing peak ΔK values [73, 75].
An Analysis of MOlecular VAriance (AMOVA) in R was used to analyze population genetic statistics [76]. The following packages in R were loaded to run 999
permutations: adegenet [77, 78], poppr [69], genepop [79], and ggplot2 [80]. Sample collections were analyzed following the pipeline from the GitHub
repository: Population genetics in R [81, 82]. The missing loci parameter was set to ignore (missing = “ignore”).
Sample clusters based on similarities of shared SNPs was visualized using a DAPC (Discriminant Analysis of Principal Components) with the adegenet
program following the recommended protocols [77, 83].
The software Genepop on the Web version 4.7.5 [79, 84] was used to run a Mantel test that analyzed Isolation by Distance (IBD) [85, 86] among population
pairs. A matrix of FST values generated from STACKS and a matrix of estimated geographic distances in kilometers among populations was constructed in a
.txt le (Supplemental Table4a, 4b, 4c). Option 6. FST and other correlations was selected, with the following parameter settings (Allele identify (F-statistics):
Estimation Ploidy: Diploid, Isolation by distance: 9. Isolation by distance (using Isolde), Isolation by distance parameters: Linear geographic distances, Convert
F-statistics to F/(1-F) statistics: Yes, Minimum distance between samples to be taken in account for regression 0.0001, Number of permutations for Mantel
test: 10000, Please enter 4 random number generator seeds: 5, 13, 37, 75, Output format & Delivery: HTML – Plain Text). The .txt le containing the matrixes
was upload to Genepop on the Web for data analysis. The pairwise comparison output from Isolde for FST values and linear geographic distances was
analyzed in Excel with a linear regression to obtain the slope, intercept, and R2 values of the dataset.
Abbreviations
AFLPs = amplied fragment length polymorphisms
COI = cytochrome oxidase I
DAPC = discriminant analysis of principal components
HTS = high throughput sequencing
IBD = isolation by distance
ITS = internal transcribed spacer
SNPs = single nucleotide polymorphisms
TCA = tawny crazy ant
UCEs = ultraconserved elements
US = United States
Declarations
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Ethics declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable.
Availability of data
Raw data can be found on NCBI under accession number PRJNA892980. Scripts for data analysis are available through GitHub at:
https://github.com/holtjocelyn/tca_pop_gen.git
Competing interests
All the authors declare no competing interests.
Funding
This study was supported by the Texas Invasive Ants Exceptional Item Grant. JRH was supported by an AFRI Competitive Grant from USDA National Institute
of Food and Agriculture (Education and Workforce Development project, accession number: TEX09837) during the writing of this manuscript.
Author Contributions
JRH and RFM wrote the main manuscript text. JRH produced gures and analyzed data. LAC edited the manuscript and provided content. JML edited the
manuscript and provided content. TJR provided bioinformatics assistance, reviewed scripts, and edited the manuscript. All authors reviewed the manuscript.
Acknowledgements
Thank you to the Texas Ecological Research Laboratory for providing access to private land associated with sample collection. We also thank Dr. Spencer
Johnston for assisting with initial genome size assessments, which suggested there was genetic differentiation among TCA from Texas and Florida. We are
thankful to the many individuals who contributed ant samples for analysis. Thank you to Mackenzie Tietjen and Kyle Harrison for advice on data analysis.
Thank you to Michael Dickens for troubleshooting advice for the TAMU Cluster. We also thank our undergraduate researchers Robert Chapa and Jose Torres
for their assistance in maintaining ant colonies and making collection vouchers. Thank you to our colleagues who provided location information for collection
of TCA or TCA specimens: Ed LeBrun, Robert Puckett, and Bryant McDowell. We also thank our colleagues who contributed samples: Robert Puckett, Ed Vargo,
J.A. MacGown, Jessica Warren, Nathalie Baena Andrés Posso-Terranova, Fernando Díaz, Rachel Strecker, Ben Gochnour & Dan Suiter, David Oi, Bryant
McDowell, Crys Wright, J. McMullan, John Gordy, Universidad del Valle - Colombia, J. Vasquez, Gregg Henderson, Wade Sharp, Odair C. Bueno, David Cross,
Melissa Jones, Mackenzie Kjeldgaard, John Warner, Fudd Graham, Dan Devenport. Thank you to Mariana Mateos and Aaron Tarone for their invaluable
insight and feedback. Thank you to María Belén Fernández and Fernando Díaz for valuable observations and comments to the manuscript.
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Figures
Figure 1
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Structure Harvester results for all TCA from South America and the US showing a peak of DK at ve and another peak at 11 putative populations.
Figure 2
Structure plot showing geographically differentiated populations of TCAs. A) The colors show seven genetically differentiated clusters based upon geographic
location. B) Further analysis of only US samples support six genetically differentiated clusters based upon geographic location, for a total of nine population
from South America and the US. The geographic collection location is provided at the top of the structure plot. C) When only samples from Argentina were
analyzed, this revealed three genetically differentiated clusters, which when added with the six from the US, one from Colombia, and one from Peru equals
eleven total clusters that were identied by Structure Harvester.
Page 13/13
Figure 3
DAPC analysis in R. The colored squares represent each geographic region from which samples were collected in South America and the US.
Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.
Supplementalmaterialallguresandtablestcapopgen27Dec2022pdf.pdf
