# Microsatellite loci support recent speciation of Ambylomma maculatum s.l., Koch, 1944 (Acari: Ixodidae) morphotypes II and III

Bailee Dorsey, Thierry de Meeûs, Michelle E J Allerdice, Christopher D

Paddock, William L Nicholson, Bryan N Ayres, Samantha M Wisely, Bruce H

Noden, Lorenza Beati

► To cite this version:

Bailee Dorsey, Thierry de Meeûs, Michelle E J Allerdice, Christopher D Paddock, William L Nicholson, et al.. Microsatellite loci support recent speciation of Ambylomma maculatum s.l., Koch, 1944 (Acari: Ixodidae) morphotypes II and III. Acarologia, 2025, 65 (2), pp.519-533. 10.24349/w6sh-ilur. hal-05067146

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Acarologia

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# Microsatellite loci support recent speciation of *Amblyomma maculatum* s.l., Koch, 1944 (Acari: Ixodidae) morphotypes II and III

Bailee Dorsey IDª, Thierry de Meeûs IDᵇ, Michelle E. J. Allerdice IDᶜ, Christopher D. Paddock IDᶜ, William L. Nicholson IDᶜ, Bryan N. Ayres IDᶜ, Samantha M. Wisely IDᵈ, Bruce H. Noden IDᵉ, Lorenza Beati IDª

## Original research

### ABSTRACT

In the United States, the *Amblyomma maculatum sensu lato* (s.l.) group is represented by two, distinct allopatric clusters of hard ticks identified as morphotype II in the eastern US and morphotype III in the southwestern states. Previous studies have shown that the two entities have evolved very recently from a common ancestor, are morphologically distinguishable in the adult stage, and are reproductively incompatible. Because of the recent divergence of the two morphotypes, gene sequences usually analyzed for a phylogenetic species definition are insufficiently informative to assign them to separate species. Microsatellite loci are predominantly non-coding sequences, selectively neutral, and usually have higher mutation rates than mitochondrial and coding nuclear gene sequences. An estimate of their divergence time based on the analysis of 8 previously developed microsatellite loci confirmed the recent speciation hypothesis, dating the radiation to approx. 230-460 years ago. A strong bottleneck signature suggests the establishment of these populations after a 1000-fold reduction in the population size of origin.

**Keywords** *Amblyomma maculatum* sensu lato; population structure; speciation; ticks

### Introduction

The Gulf Coast tick, *Amblyomma maculatum* Koch, 1844 (Acari: Ixodidae) belongs to the *A. maculatum* species group, which also includes *Amblyomma tigrinum* Koch, 1844 and *Amblyomma triste* Koch, 1844 (Kohls 1956; Estrada-Peña et al. 2005). In the United States, the geographic range of *A. maculatum* has expanded northward during the past two decades. Through most of the 20th century, the recognized U.S. distribution of *A. maculatum* included a relatively narrow, but contiguous region lying approximately 200 km inward from the southeast coast, and extending from Texas to South Carolina (Cooley and Kohls 1944; Kohls 1956; Paddock and Goddard 2015). Established populations now exist in many northeastern states, including Connecticut, Delaware, New Jersey and New York, as well as several inland central states, including Arkansas, Illinois, Kansas, and Oklahoma (Trout et al. 2010; Florin et al. 2014; Paddock and Goddard 2015; Sonenshine 2018; Phillips et al. 2020; Molaei et al. 2021; Bajwa et al. 2022; Ramírez-Garofalo et al. 2022). The range expansion has been ascribed in

Received 28 March 2024

Accepted 02 May 2025

Published 12 May 2025

Corresponding author

Lorenza Beati ID:
lorenzabeati@georgiasouthern.edu

Academic editor

McCoy, Karen

https://doi.org/10.24349/w6sh-ilur

ISSN 0044-586X (print)

ISSN 2107-7207 (electronic)

<figure><img src="image_2.png" /></figure>

Dorsey B. et al.

Licensed under
Creative Commons CC-BY 4.0

<footnote>ª The U.S. National Tick Collection, ICPS, Georgia Southern University, Statesboro, Georgia, U.S.A.</footnote>

<footnote>ᵇ Intertryp, Univ Montpellier, Cirad, IRD, Montpellier, France.</footnote>

<footnote>ᶜ Rickettsial Zoonoses Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, U.S.A.</footnote>

<footnote>ᵈ Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, U.S.A.</footnote>

<footnote>ᵉ Department of Entomology and Plant Pathology, Oklahoma State University, Oklahoma, U.S.A.</footnote>
part to climate change (Sonenshine 2018; Alkishe and Peterson 2022), and phoretic expansion on migratory birds to reestablished grassland habitats in the eastern United States (Paddock and Goddard 2015; Ramírez-Garofalo *et al.* 2022). Indeed, *A. maculatum* s.l. is known to parasitize avian hosts as immatures and to be carried by migratory birds in the U.S. and Canada (Cooley and Kohls 1944; Keirans and Durden 1998; Teel *et al.* 1998; Kinsey, Durden and Oliver 2000; Estrada-Peña *et al.* 2005; Mukherjee *et al.* 2014; Bajwa *et al.* 2022; Flenniken *et al.* 2022; Scott, McKeown and Scott 2023). Nadolny and Gaff (2018) stated that birds and large to medium sized animals, including dogs traveling with their owners, are contributing to the range expansion of *A. maculatum*. Cattle movements across the U.S. have also been mentioned as a possible source of newly established populations (Semtner and Hair 1973; Teel *et al.* 2010; Trout *et al.* 2010).

Ticks identified as *A. triste*, one of the other recognized taxa in the *A. maculatum* species group, were described recently from archival specimens obtained from birds and mammals in southern Arizona, western Texas, and northern Mexico (Guzmán-Cornejo *et al.* 2006; Mertins *et al.* 2010). These ticks were identified based on their adult tibiotarsal armature, ornamentation, and punctation patterns reminiscent of *A. triste*. Nevertheless, a subsequent morphological examination of field-collected, host-seeking specimens obtained from multiple sites in southern Arizona (Allerdice *et al.* 2017) and northern Mexico (Delgado de la Mora *et al.* 2019), as well as *A. triste* from Argentina, *A. maculatum* s.l. from Peru, and *A. maculatum* s.s. from the eastern U.S. identified four distinct morphotypes: morphotype I corresponded to *A. triste* sensu stricto (s.s.) from Argentina and Uruguay, morphotype II to *A. maculatum* s.s. from the eastern U.S., morphotype III to the ticks from Arizona and northern Mexico, and morphotype IV to the Peruvian specimens (Lado *et al.* 2018). Morphotypes III and IV had intermediary morphological features between *A. triste* s.s. and *A. maculatum* s.s. and differed from each other. This biogeographic distribution does not preclude the possibility that real *A. triste* specimens could sporadically be imported into the U.S. on migratory birds, particularly because *A. triste* s.s. has now been detected as far north as Colombia (Ossa-López *et al.* 2022). Nevertheless, all observations indicate that contemporary field-collected specimens obtained in Arizona and northern Mexico differ markedly from *A. triste* s.s. (Lado *et al.* 2018). More recently, specimens of morphotype III have been collected from multiple sites in southern New Mexico (Hecht *et al.* 2020) and west Texas (Paddock *et al.* 2020).

Molecular phylogenetic analyses (Lado *et al.* 2018) based on both mitochondrial and nuclear (ITS2) datasets revealed that the *A. maculatum* group consists of four well-resolved lineages: *A. tigrinum*, *A. triste* s.s. (morphotype I), the Peruvian clade (morphotype IV), and a last clade clustering *A. maculatum* s.s from the eastern U.S (morphotype II), the southwestern U.S., and Mexico (morphotype III). Because morphotypes II and III did not segregate into two mutually exclusive lineages, the study could not confirm that these were in fact two different species. This study also showed that the *A. maculatum* group radiated and speciated very recently (Lado *et al.* 2018) compared to other *Amblyomma* groups with similar geographical distributions (Beati *et al.* 2013).

Crossbreeding experiments were subsequently performed to further investigate if morphotypes II and III were two distinct species (Allerdice *et al.* 2020). This study showed that while the two morphotypes could easily crossbreed and generate live offspring, F1 hybrid females produced non-viable eggs. This result supported the existence of two different species. *Rickettsia parkeri* Lackman, 1965, a pathogenic spotted fever group *Rickettsia* species, has been detected extensively in morphotypes I, II, and III of the *A. maculatum* tick group (Nava *et al.* 2008; Paddock *et al.* 2010; Delgado-de la Mora *et al.* 2017; Allerdice *et al.* 2017; Hecht *et al.* 2020; Paddock *et al.* 2020). A recent molecular analysis of *R. parkeri* isolates from the Western Hemisphere revealed a genotype unique to morphotype III that was not found in morphotype II, providing additional support for a separate evolutionary history of the two morphotypes (Allerdice *et al.* 2021).

Microsatellite markers are known to mutate rapidly compared to the coding gene sequences used commonly for taxonomic reassessments. Because of their high mutation rates, these
<figure><img src="image_3.png" /><figcaption>**Figure 1** Sampling locations of *Amblyomma maculatum* s.l. in the U.S.; AZ: Arizona; GA: Georgia; FL: Florida; and OK: Oklahoma. In Arizona (magnified in the embedded rectangle), San Pedro Riparian National Conservation Area (SPR) is in Cochise County, while the other four sites are in Santa-Cruz County: Sycamore Canyon (SC); Cave Canyon (CC); Gardner Canyon (GC); and Atascosa Spring Bear Valley Ranch (ASBVR). In the east: Berry College Quarry (BCQ) in Floyd County, Georgia (GA); MacArthur Agro-Ecology Research Center (MAERC), Highlands County, Florida (FL); and Tall Grass Prairie Preserve (TGP), Osage County, Oklahoma (OK).</figcaption></figure>

markers can provide sensitive tools not only for population genetic structure studies, but also for species delimitation, particularly for recent or ongoing speciation events (De Meeûs et al. 2010; Manangwa et al. 2019), which appears to be the case for the *A. maculatum* species group (Lado et al. 2018). Microsatellite markers have been developed for several tick species and genera and have successfully supplied information on population structure, dispersal patterns, mating behavior, tick-host associations, and taxonomy (De Meeûs et al. 2002; McCoy, Tirard and Michalakis 2003; Hasle, Røed and Leinaas 2008; Labruna et al. 2009; Kempf et al. 2011; Ogrzewalska et al. 2014; Velez et al. 2023). In this study, eight previously selected microsatellite loci (Allerdice 2021) were tested on natural populations of the *A. maculatum* group collected in 2016 from the eastern and western U.S. populations of morphotypes II and III. The main goal was to verify if the analysis of microsatellite loci would provide support for the occurrence of two species. In addition, these markers were used for a preliminary study of the genetic structure of the group, an evaluation of the divergence time of the two morphotypes, and to estimate their respective per generation dispersal distances.

# Material and methods

## Samples

Male and female *A. maculatum* s.s. (morphotype II) and morphotype III ticks were sampled from various sites across the U.S. from May to July 2016 by dragging the vegetation or inspecting cattle. Collection sites are shown in Figure 1, with additional information in Table 1.

## Selection and characterization of microsatellite markers

The methods used to select microsatellite loci are described elsewhere in detail (Allerdice 2021). Because of the important stuttering issues encountered in another tick species when trying to score dinucleotide loci (De Meeûs et al. 2021), only tri- and tetranucleotide tandem repeat loci, with no more than 1-2 copies/genome were used. Eight loci were selected based on high polymorphism and the apparent absence of null alleles (Allerdice 2021). Of these, four
<table><caption>**Table 1** Sampling sites with State, County, and site code. Lon = Longitude; Lat = Latitude; M = males; F = females; MT = morphotype according to Lado *et al.* (2018). Site abbreviations are outlined in Figure 1. * indicates samples collected from cattle.</caption><thead><tr><th>State</th><th>County</th><th>Site</th><th>Lon</th><th>Lat</th><th>M</th><th>F</th><th>MT</th></tr></thead><tbody><tr><td>Georgia</td><td>Floyd</td><td>BCQ</td><td>-85.2</td><td>34.29</td><td>12</td><td>15</td><td>II</td></tr><tr><td>Florida</td><td>Highlands</td><td>MAERC</td><td>-81.22</td><td>27.18</td><td>22</td><td>18</td><td>II</td></tr><tr><td>Oklahoma</td><td>Osage</td><td>TGP</td><td>-96.43</td><td>36.84</td><td>2*</td><td>24*</td><td>II</td></tr><tr><td rowspan="5">Arizona</td><td>Cochise</td><td>SPR</td><td>110.13</td><td>31.54</td><td>33</td><td>18</td><td>III</td></tr><tr><td>Santa-Cruz</td><td>SC</td><td>110.79</td><td>31.38</td><td>3</td><td>2</td><td>III</td></tr><tr><td>Santa-Cruz</td><td>CC</td><td>110.82</td><td>31.71</td><td>9</td><td>6</td><td>III</td></tr><tr><td>Santa-Cruz</td><td>GC</td><td>110.75</td><td>31.38</td><td>2</td><td>2</td><td>III</td></tr><tr><td>Santa-Cruz</td><td>ASBVR</td><td>111.18</td><td>31.41</td><td>6</td><td>2</td><td>III</td></tr></tbody></table>

were trinucleotide repeats (L12, L19, L20, and L53) and four were tetranucleotide repeats (L74, L91, L103, and L112) (Table 2).

## Genotyping

DNA was extracted from samples following a previously described method that allows for the simultaneous preservation of tick exoskeletons (Beati and Keirans 2001). PCR amplifications were performed with Type-it Microsatellite PCR kit (Qiagen, Valencia, CA, USA). Temperature gradients were conducted with pooled DNA to optimize PCR temperatures. PCR primer sequences, fluorescent dyes used for genotyping, and amplification conditions are listed in Table 2. Each reaction consisted of 6.25µL Type-it multiplex PCR Master Mix, 3.75µL molecular grade water (Thermo Scientific, Waltham, MA), 0.625µL each of the forward and reverse primer, 2.5µL Q-Solution, and 1.25µL genomic DNA sample for a total volume of 12.5µL per reaction. Primers were synthesized at the Centers for Disease Control and Prevention Genetic Core Facility (Atlanta, GA).

Plates for capillary electrophoresis were prepared following the protocol of the Keck Biotechnology Resource Laboratory (Yale University, New Haven, CT - https://medicine.yale.edu/keck/dna/technologies/analysis/). Each well of a MicroAmp Optical 96-Well Reaction Plate (Applied Biosystems, Waltham, MA) held 2µL of fluorescent-tagged PCR product and 8µL of molecular grade H₂O (Thermo Scientific, Waltham, MA). Plates were shipped overnight to the Keck Biotechnology Resource Laboratory for fragment analysis. The dye standard

<table><caption>**Table 2** *Amblyomma maculatum* microsatellite loci used in this study with locus name, motif, and number of repeats (in parentheses), 5'-3' primer sequences, fluorescent dyes used for genotyping, amplicon sizes in base pairs (bp), and optimal annealing temperature (Tm) in C°.</caption><thead><tr><th>Locus</th><th>Motif</th><th>Primer sequence (5'®3')</th><th>Dye</th><th>Size (bp)</th><th>Tm</th></tr></thead><tbody><tr><td>L12</td><td>ATT (6)</td><td>GTAATAAGATGGCGGCAGGC</td><td>HEX</td><td>426-495</td><td>57</td></tr><tr><td>L19</td><td>CGG (6)</td><td>TGTCCAGCCTTGTCTTTGTCC</td><td>6-FAM</td><td>237-270</td><td>59</td></tr><tr><td>L20</td><td>AAC (27)</td><td>GGGACGCGTAGTAAGCAAGG</td><td>6-FAM</td><td>186-255</td><td>57</td></tr><tr><td>L53</td><td>TGC (10)</td><td>AATTCGGCTTCCGTTTAGGG</td><td>6-FAM</td><td>255-291</td><td>56</td></tr><tr><td>L74</td><td>ATGC (10)</td><td>TCGCTTGGCACACTCTGACG</td><td>6-FAM</td><td>272-396</td><td>55</td></tr><tr><td>L91</td><td>TGCG (4)</td><td>TCGCAGAATACGTGGTTTCGG</td><td>6-FAM</td><td>240-320</td><td>55</td></tr><tr><td>L103</td><td>AAAT (5)</td><td>CGCCTACCAACCTTCTCAGG</td><td>HEX</td><td>386-418</td><td>58</td></tr><tr><td>L112</td><td>AATC (7)</td><td>CGTCAATAAACGAGTGCCC</td><td>6-FAM</td><td>262-302</td><td>56</td></tr><tr><td></td><td></td><td>GGATCAGAGTTGGACACCGC</td><td></td><td></td><td></td></tr><tr><td></td><td></td><td>TGTTTCGGACTGTTAAGCCC</td><td></td><td></td><td></td></tr></tbody></table>
GeneScan™ 500 ROX (Applied Biosystems, Waltham, MA) was added there, before capillary electrophoresis on an Applied Biosystems 3730XL sequencer (Applied Biosystems, Waltham, MA). Allele sizes were scored with Geneious Prime 2022.1.1 (https://www.geneious.com). Raw data were cured following procedures described elsewhere (De Meeûs et al. 2004; Manangwa et al, 2019, De Meeûs et al. 2021; De Meeûs and Noûs 2022). CREATE (Coombs, Letcher and Nislow 2008) was used to convert the raw data spreadsheet into appropriate input files for genetic analyses.

## Genetic data analyses

Deviation from panmictic expectations and the effect of subdivision were assessed using Wright's *F*-statistics (Wright 1965): *F*<sub>IS</sub>, which measures inbreeding of individuals relative to the subpopulation, *F*<sub>ST</sub>, which measures inbreeding of subpopulations compared to total population and *F*<sub>IT</sub>, which results from the combined effect of both previous parameters and measures inbreeding of individuals relative to inbreeding in the total sample. These were computed with Weir and Cockerham's unbiased estimators (Weir and Cockerham 1984). The significant deviation of these statistics from their expected values under the null hypothesis of no inbreeding was tested by randomizing alleles between individuals within each subsample (for panmixia) and individuals between subsamples (for subdivision), with 10,000 permutations. To test for subdivision among subsamples, we used the *G*-statistic as described in (Goudet *et al.* 1996). We computed 95% confidence intervals with 5000 bootstraps over loci. Estimates and randomization tests were undertaken with Fstat 2.9.4 4 (Goudet 1995; Goudet 2003). We also computed 95% confidence intervals for *F*<sub>IS</sub> per locus and subsample with 5000 bootstraps over individuals using Genetix 4.05 (Belkhir *et al.* 2004). To obtain a confidence interval for each locus over all subsamples, we computed the averages, weighted by the number of genotyped individuals and the genetic diversity of each subsample.

We assessed the quality of the data (sampling and loci) and the relevant geographic scale of a tick population by determining the geographic level of subdivision and evaluating whether amplification or selective issues affected any of the chosen loci. We compared the *F*<sub>IS</sub> measured when subsample units corresponded to sites, counties (sites ignored), states (counties ignored), regions and the two morphotypes, respectively. Goudet *et al.* (1994) showed that, when individuals from genetically distant units are pooled, *F*<sub>IS</sub> should increase (i.e., Wahlund effect). The comparisons were undertaken with one-sided Wilcoxon signed rank tests for data paired by loci.

We tested for the existence of linkage disequilibrium (LD) between each locus pair with the *G*-based test over all subsamples as described in De Meeûs, Guégan and Teriokhin (2009) with Fstat 2.9.4 (Goudet 1995; Goudet 2003), with 10,000 randomizations. Because there are as many non-independent tests as pairs of loci (in this case, 28), we corrected *p*-values for the false discovery rate of *k* tests using the Benjamini and Yekutieli correction procedure (BY) (Benjamini and Yekutieli 2001). This was done using the command p-adjust in R (R-Core-Team 2022).

Positive *F*<sub>IS</sub> values are sometimes due to the undetected presence of null alleles in some loci. Null alleles may never be identified as such, if they are so rare that they never appear as homozygous null genotypes (i.e., missing data). Therefore, we used FreeNA (Chapuis and Estoup 2007) to compute null allele frequencies with the EM algorithm (Dempster, Laird and Rubin 1977). Next, we regressed the *F*<sub>IS</sub> obtained at each locus and its 95%CI with the estimated frequency of null alleles *F*<sub>IS</sub> ~ *p*<sub>nulls</sub>, where *p*<sub>nulls</sub> was the null allele frequency averaged over subsamples and for each locus. This regression was used to compute the determination coefficient *R*<sup>2</sup> (proportion of the variance of *F*<sub>IS</sub> explained by null alleles) and the intercept *F*<sub>IS_0</sub> and its 95% CI, which we interpreted as the *F*<sub>IS</sub> that would be observed in absence of null alleles.
Effective population sizes

We estimated effective population sizes ($N_e$) with five different methods: the $F_{IS}$ based method of De Meeûs and Noûs (2023); the LD method (Waples 2006), adjusted for missing data (Peel et al. 2013); the coancestry method (Nomura 2008); the one and two loci correlation method (1L2L) (Vitalis and Couvet 2001a; Vitalis and Couvet 2001b); and the sibship frequencies method (Wang 2009). For the $F_{IS}$ based method, we computed $N_e$ for each locus in each county (assigning “infinite” to values of $F_{IS} \ge 0$) with Fstat and averaged those over loci in each county. For the LD and coancestry methods, we used NeEstimator (Do et al. 2014). For the 1L2L method, we used Estim (Vitalis and Couvet 2001c). We then computed the averages across subsamples for each method and recorded the maximum and minimum obtained values (minimax). The number of usable values (outputs other than 0 or “infinite”) for each method was used to compute the weighted average across methods and the weighted average minimum and maximum values. A detailed justification for using these different methods can be found in De Meeûs and Noûs (2023).

Population structure within and between subsamples

Genetic differentiation was calculated with the ENA algorithm (Chapuis and Estoup 2007) as $F_{ST-FreeNA}$ to correct for the effect of null alleles, if any, and the 95% CI was obtained with 5000 bootstraps over loci. Due to high mutation rates, microsatellite loci display high levels of polymorphism. Consequently, $F_{ST}$ will reflect not only migration but also mutation rates, resulting in maximum values that are lower than the theoretical 1. To correct for high levels of polymorphism, we used the Meirmans method (Meirmans, 2006). This resulted in a subdivision measure that reflects only migration. The dataset, recoded by RecodeData (Meirmans 2006), was used to compute the maximum $F_{ST}$, and $F_{ST-ENA-max}$ with FreeNA. Finally, we calculated $F_{ST-ENA}'=F_{ST-ENA}/F_{ST-ENA-max}$, a value normally immune to both the effects of null alleles and mutation rates.

To calculate the number of immigrants ($N_e m$) within each morphotype, we used $N_e m = (1-F_{ST-ENA}')/(4F_{ST-ENA}')$ (De Meeûs et al. 2007), assuming an Island model, with its 95%CI. The obtained results were used to compute the immigration rate as $m = N_e m/N_e$ and the corresponding 95%CI (based on $F_{ST}$ CI) and minimax range (based on $N_e$ estimates). The geographic distances between sites ($D_{geo}$) were calculated using the GPS coordinates of each site and the R package ‘geosphere’ (Hijmans, Williams and Vennes 2019). We used these distances to evaluate dispersal distances between relevant pairs of populations as $\delta = m D_{geo}$.

To evaluate divergence between the two morphotypes, we used the following formula adapted to two islands: $N_e m = (1-F_{ST-ENA}')/(8F_{ST-ENA}')$ (see equation 6 in (Rousset 1997)). We computed the average genetic divergence ($F_{ST_FreeNA}$'), and the average geographic distance between each county pair and between eastern counties and Arizona counties. The significance of genetic differentiation was tested with the *G*-based test as outlined above. We also evaluated the necessary time required to obtain the observed differentiation between two completely isolated populations with the formula: $g = -2N_e \ln(1-F_{ST-ENA}')$ (Hedrick, 2005, equation 9.13a).

A neighbor-joining tree (NJTree) (Saitou and Nei 1987) was built in NJTree with MEGA (Kumar, Stecher and Tamura 2016). As recommended by Takezaki and Nei (1996), the reconstruction was based on a Cavalli-Sforza and Edwards chord distance ($D_{CSE}$) matrix after correction for null alleles (Cavalli-Sforza and Edwards 1967; Chapuis and Estoup 2007).

Isolation by distance

We used Rousset’s model (Rousset 1997) to evaluate isolation by distance between all counties and morphotypes, and between sites within each morphotype: $F_R = a+b\ln(D_{geo})$, where a and b are the intercept and the slope of the regression, respectively, $F_R = F_{ST-ENA}'/(1-F_{ST-ENA}')$ and $\ln(D_{geo})$ the natural logarithm of the geographic distance between two counties (in meters). For these computations, we used 5000 bootstraps over loci. Significance is recognized if all slopes (average and 95%CI) are above 0 as computed in Excel.
## Dispersal distances

We computed the effective population density in Arizona as $D_e=N_e/S$. We assessed the surface of the sampled Arizona area (S) in two ways: the minimum surface was defined by the polygon delimited by the GPS coordinates of all sites in Google Earth Pro, which resulted in $S_{polygonAZ}=1962$ km² and the maximum surface was calculated using the distance between the two most distant Arizona sites as the diameter of a disc, the surface of which was $S_{max}=7915$ km². We subsequently used the approximation of Séré *et al.* (2017) to estimate the dispersal distance per generation $\delta$ (approximated as $\delta \approx 2[1/(4\pi bD_e)]^{1/2}$). We compared $\delta$ estimates to the one obtained from $N_e m$ measured between subsample pairs.

## Bottleneck signatures

We hypothesized that the establishment of *A. maculatum* in the different U.S. sites occurred from a few individuals. Therefore, we tested for the genetic signature of a bottleneck in each county with the software Bottleneck 1.2.02 (Piry, Luikart and Cornuet 1999) and the Wilcoxon signed rank test as recommended by Cornuet and Luikart (1996). We used the three available models: IAM, TPM (with default options), and SMM. A true bottleneck signature can be assessed if it is significant for at least the IAM and the TPM models (De Meeûs 2021). We combined the *p*-values obtained for Arizona counties (two tests) and eastern counties (three tests) separately with the generalized binomial procedure (Teriokhin, De Meeûs and Guegan 2007), computed with MultiTest V 1.2 (De Meeûs, Guégan and Teriokhin 2009). As recommended for series with less than four tests (De Meeûs 2014), the optimal number of tests recommended for this procedure (*k*': number of smallest required *p*-values of the series) was set to *k* (i.e. all tests of the series). Details about this method can be found in the MultiTest V 1.2 user's manual (http://t-de-meeus.fr/Multitest.html)

## Results

Estimates of $F_{IS}$ did not differ significantly among any pair of sampling designs (sites, counties, states, region or morphotypes). Nevertheless, in terms of subdivision ($F_{ST}$), the level “county” appeared to matter (see below in *Genetic differentiation between eastern and western morphotypes* and Figure 3). Therefore, unless otherwise specified, the counties were considered as subpopulation units for the remaining analyses.

### Quality testing of microsatellite loci

Only two locus pairs (7%) appeared in significant LD. None remained significant after BY correction. Overall, as expected for small dioecious populations, there was a significant heterozygote excess (Figure 2). Nevertheless, it varied considerably across loci (Figure 2). According to De Meeûs (2018) and Manangwa *et al.* (2019) null alleles could account for such variation.

### Effective population sizes

In the western counties of Arizona, the average effective population size was $N_e=84$ with a minimax=[59, 110]. Assuming there were no other counties harboring ticks in Arizona but Cochise and Santa-Cruz, the total effective population size in this area could be considered as the double of $N_e$, thus $N_{e-T}=2N_e=169$ with a minimax=[118, 220]. Eastern counties displayed slightly higher and more variable values: $N_e=175$ with a minimax=[60, 337].

### Genetic differentiation between eastern and western morphotypes

There were different degrees of genetic divergence between the counties and clades as illustrated by the NJTree (Figure 3). Subdivision was strong ($F_{ST-ENA}'=0.5702$ in 95%CI=[0.3257,
<figure><img src="image_4.png" /><figcaption>**Figure 2** Average (solid circles) and 95% confidence intervals (dashes) of F<sub>IS</sub> per locus, over all loci (All), and the intercept of the regression F<sub>IS-0p</sub> nulls and its 95%CI (No null). Two-sided *p*-values for panmixia.</figcaption></figure>

0.8327]) and very significant (*p*-value=0.0004) between the two morphotypes, translating into a very low number of immigrants (*N*<sub>e</sub>*m*=0.09, 95%CI=[0.0251, 0.2588]). Using the effective population sizes computed above, it was compatible with a split occurring about 234 generations ago with a 95%CI=[108, 504], and a minimax=[38, 1259]. If we consider a two year generation time, the estimates must be doubled.

**Genetic differentiation within morphotypes**

Figure 3 shows that there was a highly significant differentiation among eastern counties (*p*-value< 0.002) and a marginally not significant one (*p*-value< 0.0574) in Arizona, although the levels of subdivision and subsequent numbers of immigrants, overlap.

<figure><img src="image_5.png" /><figcaption>**Figure 3** Neighbor-joining tree (NJTree) illustrating relationships between counties and between the two morphotypes of *Amblyomma maculatum* (East: OK, GA, FL; and West: AZ). The tree is based on a Cavalli-Sforza and Edwards chord distance matrix. Average number of immigrants (*N*<sub>e</sub>*m*) with their 95% confidence intervals (95%CI), obtained after 5000 bootstraps over loci, are provided within and between each zone.</figcaption></figure>
Isolation by distance

As illustrated in Figure 4, a significant isolation by distance appeared to occur among the Arizona sites. The test could not be performed for the East, because of the small number of sites. The small number of eastern points seemed; however, to align with the regression (Figure 4).

<figure><img src="image_6.png" /></figure>

**Figure 4** Rousset's genetic distances ($F_R=F_{ST-ENA}/(1-F_{ST-ENA})$) as a function of the natural logarithm of geographic distances between counties and sites within the two *A. maculatum* morphotypes. Results of Rousset's regression are also provided with the average slope (solid line) and its 95%CI (dotted lines) for Arizona. $F_{R-s}$ and $F_{R-i}$ are the regressions for the lower and upper limits of the 95%CI. Eastern counties (empty circles) were added to the regression with their 95%CI (black dashes), but did not contribute to the regression model.

Dispersal distances

Using estimates of *N*<sub>e</sub>*m* between sites, the calculated average dispersal was δ=22 km per generation with 95%CI=[8, 52] (with the same minimax) in Arizona. Within eastern sites, dispersal was similar but with higher variance with δ=19 km per generation with 95%CI=[11, 163] and a minimax=[6, 473].

Effective population densities in Arizona

The isolation by distance slope (b) generated for Arizona (Figure 4) was used to infer a neighborhood of Nb=1/b=167 individuals with 95%CI=[100, 333] (Rousset 1997), which appeared to be very close to the total effective population size computed above (N<sub>e-T</sub>=169). The number of immigrants exchanged between neighboring subpopulations was N<sub>e</sub>*m*=1/2πb=27 in 95%CI=[16, 53] per generation (Rousset 1997). The use of N<sub>e-T</sub> resulted in small effective population density estimates of D<sub>e</sub>=0.086 (95%CI=[0.060, 0.112]) individuals per km², with S<sub>polygonAZ</sub>, and D<sub>e</sub>=0.021 (95%CI=[0.015, 0.028]) with S<sub>max</sub>. These computations resulted in an estimate of dispersal distances of δ=25 (95%CI=[19, 35]) with a minimax=[17, 42] km per generation (S<sub>polygonAZ</sub>), or δ=50 (95%CI=[39, 71]) with a minimax=[34, 84] km per generation (S<sub>max</sub>). These values are very close to those obtained with N<sub>e</sub>*m* between pairs of counties.
Dispersal distances of 20-50 km per year (or every 2 years for a 2-year life cycle) appears,
therefore, to be a robust estimate.

Bottleneck signatures

A significant bottleneck signature was found in each zone and each county with highly
significant *p*-values for both the IAM and the TPM models; they were not significant for
the SMM model. If we refer to Cornuet and Luikart (1996), with a *H*<sub>S</sub> > 0.7, eight loci
and around 30-40 individuals per county, we estimate that bottleneck detection required a
α=N<sub>e-post</sub>/N<sub>e-pre</sub>=1000-fold population drop and parameter τ=g/(2N<sub>e-post</sub>)=1, where N<sub>e-post</sub> and
N<sub>e-pre</sub> are the effective population sizes before and after the bottleneck, respectively, and g is
the number of generations after the bottleneck. Assuming that the bottleneck coincided with the
separation between the two clades (i.e., 234 generations, 95% CI = [108, 504]), we computed
an effective population size post bottleneck (N<sub>ePBN</sub>) = 117 (95% CI = [54, 252]), which fits
our N<sub>e</sub> estimates (see above). This would indicate colonization events from a small number of
ticks, 90 to 300 years ago, from a large ancestral population with N<sub>e-pre</sub>>100,000 individuals.

Discussion

Here we found that the newly developed microsatellite markers of *A. maculatum* were informative at a U.S. scale. The markers showed none of the technical problems frequently occurring in ticks (De Meeûs *et al.* 2021). This study confirms that microsatellite markers can effectively replace occasionally noninformative mitochondrial and nuclear markers (Estrada-Peña *et al.* 2005; Lado *et al.* 2018) commonly used to delimit taxa, particularly for species that have evolved recently. Other genome-wide approaches (SNPs, for instance) are valid alternatives, but are still more costly than microsatellite analyses either because genomic sequencing requires important financial investment in equipment or expensive outsourcing. In addition, microsatellite loci can be amplified even from small amounts of genomic DNA, while SNP analyses require DNA samples of high quality and quantity.

The observed heterozygote excess was consistent with what is expected in small dioecious
populations undergoing random mating (De Meeûs and Noûs 2023). In terms of population
structure, we found an important subdivision between ticks from Arizona and those from the
eastern United States. This subdivision appeared consistent with the existence of two species,
which we named the Arizona and the Eastern clades, corresponding to the previously described
morphotypes III and II, respectively. Lado *et al.* (2018) hypothesized ongoing or extremely
recent speciation events driven by exposure to drastically different habitats (Cuervo *et al.* 2021).
The observation that these two clades produce viable, albeit infertile, F1 offspring (Allerdice
*et al.* 2020) further supports a recent radiation. The relatively recent origin of the two clades is
confirmed by our estimate of the number of generations since divergence (234, 95%CI=(108,
504)). The length of the life cycle of *A. maculatum* s.s. is known to vary from one to two years
depending on habitat and environmental conditions, with longevity increasing in the drier areas
of Texas compared to relatively more humid localities in Oklahoma (Teel *et al.* 2010). Nothing
is yet known about the life cycle length of the Arizona populations, but we can speculate that it
would be similar to that observed in arid conditions. With a two-year life cycle, 234 generations
would translate into 468 years. These are naturally approximations that must be considered
cautiously. Nevertheless, they point to a recent common ancestor and divergence that may be
driven by anthropogenic influences.

In each clade, we identified significant bottleneck signatures indicating that both clades
likely originated from a relatively small number of colonizing ticks. Provided that the
population of origin of the seeding ticks has not been submitted to important disturbances since
the bottleneck event, we may predict that it should display a high effective population size of at
least 100,000 individuals. We know that *A. maculatum* s.s. is found in Colombia (Ossa-López
*et al.* 2022) and morphotype III in Mexico and that ticks of the group are often found on
migratory birds flying northwards to the U.S. (Mukherjee *et al.* 2014). A better understanding of the population structure of *A. maculatum* s.l. in South and Central America could help to identify the origin of the U.S. populations.

The variance of dispersal distance estimates was much higher in the East than in Arizona. This may result from differences in the number of available sites (3 in the East and 5 in Arizona), the distances between sites (100 km max in AZ, 875 km min in the East), or may reflect ecological disparities. More eastern sites should therefore be sampled to clarify which hypothesis offers the most likely explanation. Nonetheless, in both clades, dispersal distances were high (between 20 and 60 km per generation). In Arizona, this suggests strong connectivity between the two counties, which would support a role of birds or wide-ranging mammals as regular hosts. Movement of cattle and dogs (Paddock and Goddard 2015; Nadolny and Gaff 2018) can cause similar displacements, but the frequent occurrence of such randomly directed events would probably have resulted in the progressive obliteration of the signature of isolation by distance. Because isolation by distance was significant, we consider anthropogenic movements of hosts to be less relevant in dispersing morphotype III ticks than wild animals.

The low densities of ticks found in Arizona (ie, a neighborhood size of approximately 167 individuals) suggest that these populations face difficult environmental conditions. Suitable habitats in Arizona are fragmented and confined generally to narrow riparian corridors within and among the Madrean Archipelago, an area surrounded by deserts. Indeed, consistently large and denser populations of morphotype III ticks have so far only been collected close to water (Lynn *et al.* 2024). It is premature to speculate on the future of such fragmented populations under climate change: either they (and their hosts) will progressively adapt to increasing aridity or face extinction. Estimates of population densities in the East will require sampling additional sites.

The Arizona and Eastern clades of *A. maculatum* ticks transmit *R. parkeri* (Paddock *et al.* 2004; Yaglom *et al.* 2019), an emerging pathogen that, following the important increase of its vectors' distribution, has now been detected in regions of the US far beyond its historically recognized range (Phillips *et al.* 2020, Molaei *et al.* 2021). This pathogen has been detected in *A. triste*, *A. maculatum* s.s. and morphotype III (Nava *et al.* 2008; Paddock *et al.* 2010; Delgado-de la Mora *et al.* 2017; Allerdice *et al.* 2017; Hecht *et al.* 2020; Paddock *et al.* 2020). Different genotypes of *R. parkeri* have been described, and some of them appear to be specifically associated with different tick morphotypes (Allerdice *et al.* 2021). The importance of determining the taxonomic status of *A. maculatum* s.l. populations in the U.S. is therefore not only of systematic interest but also public health relevance. Not only do the two morphotypes carry different genotypes of *R. parkeri*, but they are also characterized by different phenologies, as described recently (Lynn *et al.* 2024). This implies that disease risk assessments will also have to be tailored to the distinct life cycles of each morphotype.

Finally, because *A. triste* and *A. tigrinum* are very closely related to *A. maculatum* (Estrada-Peña *et al.* 2005; Lado *et al.* 2018), one would expect that they might share some microsatellite loci. It would certainly be interesting to test these markers on all clades identified in Lado *et al.* (2018) because species delimitations in this group of ticks could be improved by a reassessment of structure among South American populations.

## Acknowledgements

We would like to thank Drs. Scott Harrison and Lance Durden, for their constructive comments about B.D.'s thesis, source of this publication. We would also like to acknowledge David Delgado-de la Mora, Jésus Delgado-de la Mora, and Jésus Licona-Enrîquez, for their contributions during field work. Dr Scot E. Dowd at MrDNA kindly helped us overcome bioinformatic issues. The editor's constructive critical review is also acknowledged.
ORCID

Bailee Dorsey https://orcid.org/0009-0003-0384-5201

Thierry de Meeûs https://orcid.org/0000-0001-8807-241X

Michelle E. J. Allerdice https://orcid.org/0000-0003-2758-3245

Christopher D. Paddock https://orcid.org/0000-0003-1615-8526

William L. Nicholson https://orcid.org/0000-0002-2215-6837

Bryan N. Ayres https://orcid.org/0000-0002-9846-9645

Samantha M. Wisely https://orcid.org/0000-0003-1748-4518

Bruce H. Noden https://orcid.org/0000-0002-0096-370X

Lorenza Beati https://orcid.org/0000-0002-4848-9701

References

Alkishe A., Peterson A.T. 2022. Climate change influences on the geographic distributional potential of the spotted fever vectors *Amblyomma maculatum* and *Dermacentor andersoni*. PeerJ, 10: e13279.
https://doi.org/10.7717/peerj.13279

Allerdice M.E. 2021. Systematics and population structure of *Amblyomma maculatum* group ticks and *Rickettsia parkeri*, an emerging human pathogen in southern Arizona, USA [PhD Thesis]. Mississippi State, Mississippi State University. pp. 152. https://scholarsjunction.msstate.edu/td/5371

Allerdice M.E.J., Beati L., Yaglom H., Lash R.R., Delgado-de La Mora J., Licona-Enriquez J.D., Delgado-de La Mora D., Paddock C.D. 2017. *Rickettsia parkeri* (Rickettsiales: Rickettsiaceae) detected in ticks of the *Amblyomma maculatum* (Acari: Ixodidae) group collected from multiple locations in Southern Arizona. J. Med. Entomol., 54(6): 1743-1749. https://doi.org/10.3201/eid2504.181507

Allerdice M.E.J., Paddock C.D., Hecht J.A., Goddard J., Karpathy S.E. 2021. Phylogenetic differentiation of *Rickettsia parkeri* reveals broad dispersal and distinct clustering within North American strains. Microbiol. Spectr., 9(2): e0141721. https://doi.org/10.1128/Spectrum.01417-21

Allerdice M.E.J., Snellgrove A.N., Hecht J.A., Hartzer K., Jones E.S., Biggerstaff B.J., Ford S.L., Karpathy S.E., Delgado-de La Mora J., Delgado-de La Mora D., Licona-Enriquez J.D., Goddard J., Levin M.L., Paddock C.D. 2020. Reproductive incompatibility between *Amblyomma maculatum* (Acari: Ixodidae) group ticks from two disjunct geographical regions within the USA. Exp. Appl. Acarol., 82(4): 543-557. https://doi.org/10.1007/s10493-020-00057-4

Bajwa W.I., Tsynman L., Egizi A.M., Tokarz R., Maestas L.P., Fonseca D.M. 2022. The Gulf Coast tick, *Amblyomma maculatum* (Ixodida: Ixodidae), and spotted fever group *Rickettsia* in the highly urbanized northeastern United States. J. Med. Entomol., 59(4): 1434-1442. https://doi.org/10.1093/jme/tjac053

Beati L., Keirans J.E. 2001. Analysis of the systematic relationships among ticks of the genera *Rhipicephalus* and *Boophilus* (Acari: Ixodidae) based on mitochondrial 12S ribosomal DNA gene sequences and morphological characters. J. Parasitol., 87(1): 32-48. https://doi.org/10.1645/0022-3395(2001)087%5B0032:A0TSRA%5D2.0.CO;2

Beati L., Nava S., Burkman E.J., Barros-Battesti D.M., Labruna M.B., Guglielmone A.A., Cáceres A.G., Guzmán-Cornejo C.M., León R., Durden L.A., Faccini J.L. 2013. *Amblyomma cajennense* (Fabricius, 1787) (Acari: Ixodidae), the Cayenne tick: phylogeography and evidence for allopatric speciation. BMC Evol. Biol., 13(1): 267. https://doi.org/10.1186/1471-2148-13-267

Belkhir K., Borsa P., Chikhi L., Raufaste N., Bonhomme F. 2004. GENETIX 4.05, logiciel sous Windows ™ pour la génétique des populations. Laboratoire Génome, Populations, Interactions, CNRS UMR 5171, Université de Montpellier II, Montpellier (France). https://kimura.univ-montp2.fr/genetix/

Benjamini Y., Yekutieli D. 2001. The control of the false discovery rate in multiple testing under dependency. Ann. Stat., 29(4). https://doi.org/10.1214/ao斯
1013699998

Cavalli-Sforza L.L., Edwards A.W.F. 1967. Phylogenetic analysis: models and estimation procedures. Evolution, 21(3): 550-570. https://doi.org/10.2307/2406616

Chapuis M.-P., Estoup A. 2007. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol., 24(3): 621-631. https://doi.org/10.1093/molbev/msl191

Cooley R.A., Kohls G.M. 1944. The Genus *Amblyomma* (Ixodidae) in the United States. J. Parasitol., 30(2): 77. https://doi.org/10.2307/3272571

Coombs J.A., Letcher B.H., Nislow K.H. 2008. CREATE: a software to create input files from diploid genotypic data for 52 genetic software programs. Mol. Ecol. Resour., 8(3): 578-580.
https://doi.org/10.1111/j.1471-8286.2007.02036.x

Cornuet J.M., Luikart G. 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics, 144: 2001-2014. https://doi.org/10.1093/genesitics/144.4.2001

Cuervo P.F., Flores F.S., Venzal J.M., Nava S. 2021. Niche divergence among closely related taxa provides insight on evolutionary patterns of ticks. J. Biogeogr., 48(11): 2865-2876. https://doi.org/10.1111/jbi.14245

Delgado-de la Mora J., Sánchez-Montes S., Lincona-Énriquez J.D., Delgado-de la Mora D., Paddock C.D., Beati L., Colunga-Salas P., Guzmán-Cornejo C., Zambrano M.L., Karpathy S.E., López-Pérez A.M., Álvarez-Hernández G. 2019. *Rickettsia parkeri* and *Candidatus Rickettsia andeanae* in ticks of the *Amblyomma maculatum* group, Mexico. Emerg Infect Dis, 25: 836-838. https://doi.org/10.3201/eid2504.181507
De Meeûs, T. 2014. Statistical decision from *k* test series with particular focus on population genetics tools: a DIY notice. Infect. Genet. Evol., 22: 91-93. https://doi.org/10.1016/j.meegid.2014.01.005

De Meeûs, T. 2021. Initiation à la génétique des populations naturelles : applications aux parasites et à leurs vecteurs. 2ème édition revue et augmentée. IRD Editions, Marseille. https://doi.org/10.4000/books.irdeditions.40492

De Meeûs, T. 2018. Revisiting $F_{IS}$, $F_{ST}$, Wahlund effects, and null alleles. J. Hered., 109: 446-456. https://doi.org/10.1093/jhered/esx106

De Meeûs, T., Beati L., Delaye C., Aeschlimann A., Renaud F. 2002. Sex-biased genetic structure in the vector of Lyme Disease, *Ixodes ricinus*. Evolution, 56(9): 1802-1807. https://doi.org/10.1111/j.0014-3820.2002.tb00194.x

De Meeûs T., Chan C.T., Ludwig J.M., Tsao J.I., Patel J., Bhagatwala J., Beati L. 2021. Deceptive combined effects of short allele dominance and stuttering: an example with *Ixodes scapularis*, the main vector of Lyme disease in the U.S.A. Peer Community J., 1. https://doi.org/10.24072/pcjournal.34

De Meeûs T., Guégan J.-F., Teriokhin A.T. 2009. MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data. BMC Bioinformatics, 10(1): 443. https://doi.org/10.1186/1471-2105-10-443

De Meeûs T., Humair P.-F., Grunau C., Delaye C., Renaud F. 2004. Non-Mendelian transmission of alleles at microsatellite loci: an example in *Ixodes ricinus*, the vector of Lyme disease. Int. J. Parasitol., 34(8): 943-950. https://doi.org/10.1016/j.ijpara.2004.04.006

De Meeûs T., Koffi B.B., Barré N., de Garine-Wichatitsky M., Chevillon C. 2010. Swift sympatric adaptation of a species of cattle tick to a new deer host in New-Caledonia. Infect. Genet. Evol., 10, 976-983. https://doi.org/10.1016/j.meegid.2010.06.005

De Meeûs T., McCoy K.D., Prugnolle F., Chevillon C., Durand P., Hurtrez-Boussès S., Renaud F. 2007. Population genetics and molecular epidemiology or how to "débusquer la bête". Infect. Genet. Evol., 7(2): 308-332. https://doi.org/10.1016/j.meegid.2006.07.003

De Meeûs T., Noûs C. 2022. A simple procedure to detect, test for the presence of stuttering, and cure stuttered data with spreadsheet programs. Peer Community J., 2. https://doi.org/10.24072/pcjournal.165 https://doi.org/10.24072/pcjournal.165

De Meeûs T., Noûs C. 2023. A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs. Peer Community J., 3. https://doi.org/10.24072/pcjournal.280

Dempster A.P., Laird N.M., Rubin D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B Methodol., 39(1): 1-38. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

Do C., Waples R.S., Peel D., Macbeth G.M., Tillett B.J., Ovenden J.R. 2014. NEESTIMATOR v2: re-implementation of software for the estimation of contemporary effective population size ($N_c$) from genetic data. Mol. Ecol. Resour., 14(1): 209-214. https://doi.org/10.1111/1755-0998.12157

Estrada-Peña A., Venzal J.M., Mangold A.J., Cafrune M.M., Guglielmone A.A. 2005. The *Amblyomma maculatum* Koch, 1844 (Acari: Ixodidae: Amblyomminae) tick group: diagnostic characters, description of the larva of *A. parvitarsum* Neumann, 1901, 16S rDNA sequences, distribution and hosts. Syst. Parasitol., 60(2): 99-112. https://doi.org/10.1007/s11230-004-1382-9

Flenniken J.M., Tuten H.C., Rose Vineer H., Phillips V.C., Stone C.M., Allan B.F. 2022. Environmental drivers of Gulf Coast tick (Acari: Ixodidae) range expansion in the United States. J. Med. Entomol., 59(5): 1625-1635. https://doi.org/10.1093/jme/tjac091

Florin D.A., Brinkerhoff R.J., Gaff H., Jiang J., Robbins R.G., Eickmeyer W., Butler J., Nielsen D., Wright C., White A., Gimpel M.E., Richards A.L. 2014. Additional U.S. collections of the Gulf Coast tick, *Amblyomma maculatum* (Acari: Ixodidae), from the State of Delaware, the first reported field collections of adult specimens from the State of Maryland, and data regarding this tick from surveillance of migratory songbirds in Maryland. Syst. Appl. Acarol., 19(3): 257. https://doi.org/10.11158/saa.23.12.13

Goudet J. 1995. FSTAT (Version 1.2): A Computer Program to Calculate F-Statistics. J. Hered., 86(6): 485-486. https://doi.org/10.1093/oxfordjournals.jhered.a111627

Goudet J. 2003. FSTAT (version 2.9.4), a program (for Windows 95 and above) to estimate and test population genetics parameters. Available at http://www.t-de-meeus.fr/Programs/Fstat294.zip (update from Goudet 1995). https://doi.org/10.1046/j.1365-294X.2002.01496.x

Goudet J., Raymond M., De Meeûs T., Rousset F. 1996. Testing differentiation in diploid populations. Genetics, 144(4): 1933-1940. https://doi.org/10.1093/genetics/144.4.1933

Guzmán-Cornejo C., Perez T.M., Nava S., Guglielmone A.A. 2006. Confirmation of the presence of *Amblyomma triste* Koch, 1844 (Acari: Ixodidae) in Mexico. Syst. Appl. Acarol., 11(1): 47. https://doi.org/10.11158/saa.11.1.5

Hasle G., Røed K.H., Leinaas H.P. 2008. Multiple paternity in *Ixodes ricinus* (Acari: Ixodidae), assessed by microsatellite markers. J. Parasitol., 94(2): 345-347. https://doi.org/10.1645/GE-1280.1

Hecht J.A., Allerdice M.E.J., Karpathy S.E., Yaglom H.D., Casal M., Lash R.R., Delgado-de La Mora J., Licona-Enriquez J.D., Delgado-de La Mora D., Groschupf K., Mertins J.W., Moors A., Swann D.E., Paddock C.D. 2020. Distribution and occurrence of *Amblyomma maculatum* sensu lato (Acari: Ixodidae) and *Rickettsia parkeri* (Rickettsiales: Rickettsiaceae), Arizona and New Mexico, 2017-2019. J. Med. Entomol., 57(6): 2030-2034. https://doi.org/10.1093/jme/tjaa130

Hedrick P.W. 2005. A standardized genetic differentiation measure. Evolution, 59(8): 1633-1638. https://doi.org/10.1111/j.0014-3820.2005.tb01814.x

Hijmans R.J., Williams E., Vennes C. 2019. Package "geosphere": spherical trigonometry. CRAN, pp. Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (Longitude/latitude) locations.
Keirans J.E., Durden L.A. 1998. Illustrated key to nymphs of the tick genus *Amblyomma* (Acari: Ixodidae) found in the United States. J. Med. Entomol., 35(4): 489-495. https://doi.org/10.1093/jmedent/35.4.489

Kempf F., De Meeûs T., Vaumourin E., Noel V., Taragel'ová V., Plantard O., Heylen D.J.A., Eraud C., Chevillon C., McCoy K.D. 2011. Host races in *Ixodes ricinus*, the European vector of Lyme borreliosis. Infect. Genet. Evol., 11(8): 2043-2048. https://doi.org/10.1016/j.meegid.2011.09.016

Kinsey A.A., Durden L.A., Oliver J.H. 2000. Tick infestation of birds in coastal Georgia and Alabama. J. Parasitol., 86(2): 251-254. https://doi.org/10.1645/0022-3395(2000)086%5B0251:TIOBIC%5D2.0.CO;2

Kohls G.M. 1956. Concerning the identity of *Amblyomma maculatum*, *A. tigrinum*, *A. triste*, and *A. ovatum* of Koch, 1844 (Acarina, Ixodidae). Proc. Entomol. Soc. Wash., 58: 143-147.

Kumar S., Stecher G., Tamura K. 2016. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol. Biol. Evol., 33(7): 1870-1874. https://doi.org/10.1093/molbev/msw054

Labruna M.B., Naranjo V., Mangold A.J., Thompson C., Estrada-Peña A., Guglielmone A.A., Jongejan F., De La Fuente J. 2009. Allopatric speciation in ticks: genetic and reproductive divergence between geographic strains of *Rhipicephalus (Boophilus) microplus*. BMC Evol. Biol., 9(1): 46. https://doi.org/10.1186/1471-2148-9-46

Lado P., Nava S., Mendoza-Uribe L., Caceres A.G., Delgado-de La Mora J., Licona-Enriquez J.D., Delgado-de La Mora D., Labruna M.B., Durden L.A., Allerdice M.E.J., Paddock C.D., Szabó M.P.J., Venzal J.M., Guglielmone A.A., Beati L. 2018. The *Amblyomma maculatum* Koch, 1844 (Acari: Ixodidae) group of ticks: phenotypic plasticity or incipient speciation? Parasit. Vectors, 11(1): 610. https://doi.org/10.1186/s13071-018-3186-9

Lynn G.E., Ludwig T.J., Allerdice M.E., Paddock C.D., Grisham B.A., Lenhart P.A., Teel P.D., Johnson T.L. The natural history of *Amblyomma maculatum* sensu lato, a vector of *Rickettsia parkeri* rickettsiosis, in southern Arizona. Sci. Rep., 14, 28175 (2024). https://doi.org/10.1038/s41598-024-78507-y

Manangwa O., De Meeûs T., Grébaut P., Ségard A., Byamungu M., Ravel S. 2019. Detecting Wahlund effects together with amplification problems: Cryptic species, null alleles and short allele dominance in *Glossina pallidipes* populations from Tanzania. Mol. Ecol. Resour., 19(3): 757-772. https://doi.org/10.1111/1755-0998.12989

McCoy K.D., Tirard C., Michalakis Y. 2003. Spatial genetic structure of the ectoparasite *Ixodes uriae* within breeding cliffs of its colonial seabird host. Heredity, 91(4): 422-429. https://doi.org/10.1038/sj.hdy.6800339

Meirmans P.G. 2006. Using the amova framework to estimate a standardized genetic differentiation measure. Evolution, 60(11): 2399-2402. https://www.jstor.org/stable/4134847 https://doi.org/10.1111/j.0014-3820.2006.tb01874.x

Mertins J.W., Moorhouse A.S., Alfred J.T., Hutcheson H.J. 2010. *Amblyomma triste* (Acari: Ixodidae): new North American collection records, including the first from the United States. J. Med. Entomol., 47(4): 536-542. https://doi.org/10.1093/jmedent/47.4.536

Molaei G., Little E.A.H., Khalil N., Ayres B.N., Nicholson W.L., Paddock C.D. 2021. Established population of the Gulf Coast tick, *Amblyomma maculatum* (Acari: Ixodidae), infected with *Rickettsia parkeri* (Rickettsiales: Rickettsiaceae), in Connecticut. J. Med. Entomol., 58(3): 1459-1462. https://doi.org/10.1093/jme/tjaa299

Mukherjee N., Beati L., Sellers M., Burton L., Adamson S., Robbins R.G., Moore F., Karim S. 2014. Importation of exotic ticks and tick-borne spotted fever group rickettsiae into the United States by migrating songbirds. Ticks Tick-Borne Dis., 5(2): 127-134. https://doi.org/10.1016/j.tttdis.2013.09.009

Nadolny R.M., Gaff H.D. 2018. Natural history of *Amblyomma maculatum* in Virginia. Ticks Tick-Borne Dis., 9(2): 188-195. https://doi.org/10.1016/j.tttdis.2017.09.003

Nava S., Elshenawy Y., Eremeeva M.E., Sumner J.W., Mastropaulo M., Paddock C.D. 2008. *Rickettsia parkeri* in Argentina. Emerg Infect Dis, 14: 1894-1897. https://doi.org/10.3201/eid1412.080860

Nomura T. 2008. Estimation of effective number of breeders from molecular coancestry of single cohort sample: Estimation of effective number of breeders. Evol. Appl., 1(3): 462-474. https://doi.org/10.1111/j.1752-4571.2008.00015.x

Ogrzewalska M., Bajay M.M., Schwarcz K., Bajay S.K., Telles M.P.C., Pinheiro J.B., Zucchi M.I., Pinter A., Labruna M.B. 2014. Isolation and characterization of microsatellite loci from the tick *Amblyomma aureolatum* (Acari: Ixodidae). Genet. Mol. Res., 13(4): 9622-9627. https://doi.org/10.4238/2014

Ossa-López P.A., Robayo-Sánchez L.N., Uribe J.E., Ramírez-Hernández A., Ramírez-Chaves H.E., Cortés-Vecino J.A., Rivera-Páez F.A. 2022. Extension of the distribution of *Amblyomma triste* Koch, 1844: morphological and molecular confirmation of Morphotype I in Colombia. Ticks Tick-Borne Dis., 13(3): 101923. https://doi.org/10.1016/j.tttdis.2022.101923

Paddock C.D., Sumner J.W., Comer J.A., Zaki S.R., Goldsmith C.S., Goddard J., McLellan S.L.F., Tamminga C.L., Ohl C.A. 2004. *Rickettsia parkeri*: a newly recognized cause of spotted fever rickettsiosis in the United States. Clin. Infect. Dis., 38(6): 805-811. https://doi.org/10.1086/381894

Paddock C.D., Fournier P.-E., Sumner J.W., Goddard J., Elshenawy Y., Metcalfe M.G., Loftis A.D., Varela-Stokes A. 2010. Isolation of *Rickettsia parkeri* and identification of a novel spotted fever group *Rickettsia* sp. from Gulf Coast Ticks (*Amblyomma maculatum*) in the United States. Appl. Environ. Microbiol., 76(9): 2689-2696. https://doi.org/10.1128/AEM.02737-09

Paddock C.D., Goddard J. 2015. The evolving medical and veterinary importance of the Gulf Coast tick (Acari: Ixodidae). J. Med. Entomol., 52(2): 230-252. https://doi.org/10.1093/jme/tju022

Paddock C.D., Hecht J.A., Green A.N., Waldrup K.A., Teel P.D., Karpathy S.E., Johnson T.L. 2020. *Rickettsia parkeri* (Rickettsiales: Rickettsiaceae) in the Sky Islands of West Texas. J. Med. Entomol., 57(5), 1582-1587. https://doi.org/10.1093/jme/tjaa059

Peel D., Waples R.S., Macbeth G.M., Do C., Ovenden J.R. 2013. Accounting for missing data in the estimation of contemporary genetic effective population size ($N_e$). Mol. Ecol. Resour., 13(2): 243-253. https://doi.org/10.1111/1755-0998.12049
Phillips V.C., Zieman E.A., Kim C.-H., Stone C.M., Tuten H.C., Jiménez F.A. 2020. Documentation of the expansion of the Gulf Coast tick (*Amblyomma maculatum*) and *Rickettsia parkeri*: first report in Illinois. *J. Parasitol.*, 106(1): 9-13. https://doi.org/10.1645/19-118

Piry, S., Luikart, G., Cornuet, J.M. (1999) BOTTLENECK: A computer program for detecting recent reductions in the effective population size using allele frequency data. *Journal of Heredity*, 90, 502-503. https://doi.org/10.1093/jhered/90.4.502

Ramírez-Garofalo J.R., Curley S.R., Field C.E., Hart C.E., Thangamani S. 2022. Established populations of *Rickettsia parkeri* - infected *Amblyomma maculatum* ticks in New York City, New York, USA. Vector-Borne Zoonotic Dis., 22(3): 184-187. https://doi.org/10.1089/vbz.2021.0085

R-Core-Team. 2022. R: a language and environment for statistical computing. 4.2.2 (2022-10-31 ucrt) ed. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Rousset F. 1997. Genetic differentiation and estimation of gene flow from *F* -statistics under isolation by distance. *Genetics*, 145(4): 1219-1228. https://doi.org/10.1093/genetics/145.4.1219

Saitou N., Nei M. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. *Mol. Biol. Evol.*, 4(4): 406-425. https://doi.org/10.1093/oxfordjournals.molbev.a040454

Scott J.D., McKeown J.T.A., Scott C.M. 2023. Migratory songbirds transport *Amblyomma longirostre* and *Amblyomma maculatum* ticks to Canada. *J. Biomed. Res. Environ. Sci.*, 4(2): 150-156. https://doi.org/10.37871/jbres1659

Semtner P.J., Hair J.A. 1973. Distribution, seasonal abundance, and hosts of the Gulf Coast tick in Oklahoma. *Ann. Entomol. Soc. Am.*, 66(6): 1264-1268. https://doi.org/10.1093/aesa/66.6.1264

Séré M., Thévenon S., Belem A.M.G., De Meeûs T. 2017. Comparison of different genetic distances to test isolation by distance between populations. *Heredity*, 119: 55-63. 10.1038/hdy.2017.26 https://doi.org/10.1038/hdy.2017.26

Sonenshine D.E. 2018. Range expansion of tick disease vectors in North America: implications for spread of tick-borne disease. *Int. J. Environ. Res. Public. Health*, 15(3): 478. https://doi.org/10.3390/ijerph15030478

Takezaki N., Nei M. 1996. Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. *Genetics*, 144(1): 389-399. https://doi.org/10.1093/genetics/144.1.389

Teel P.D., Hopkins S.W., Donahue W.A., Strey O.F. 1998. Population dynamics of immature *Amblyomma maculatum* (Acari: Ixodidae) and other ectoparasites on meadowlarks and northern bobwhite quail resident to the Coastal Prairie of Texas. *J. Med. Entomol.*, 35(4): 483-488. https://doi.org/10.1093/jmedent/35.4.483

Teel P.D., Ketchum H.R., Mock D.E., Wright R.E., Strey O.F. 2010. The Gulf Coast tick: a review of the life history, ecology, distribution, and emergence as an arthropod of medical and veterinary importance. *J. Med. Entomol.*, 47(5): 707-722. https://doi.org/10.1093/jmedent/47.5.707

Teriokhin A.T., De Meeûs T., Guegan J.F. 2007. On the power of some binomial modifications of the Bonferroni multiple test. *Zhurnal Obshchei Biologii*, 68, 332-340. PMID: 18038646

Trout R.T., Steelman C.D., Szalanski A.L., Loftin K. 2010. Establishment of *Amblyomma maculatum* (Gulf Coast Tick) in Arkansas, U.S.A. *Fla. Entomol.*, 93(1): 120-122. https://doi.org/10.1653/024.093.0117

Velez R., De Meeûs T., Beati L., Younsi H., Zhioua E., Antunes S., Domingos A., Ataíde Sampaio D., Carpinteiro D., Moerbeck L., Estrada-Peña A., Santos-Silva M.M., Santos A.S. 2023. Development and testing of microsatellite loci for the study of population genetics of *Ixodes ricinus* Linnaeus, 1758 and *Ixodes inopinatus* Estrada-Peña, Nava and Petney, 2014 (Acari: Ixodidae) in the western Mediterranean region. *Acarologia*, 63(2): 356-372. https://doi.org/10.24349/bvem-4h49

Vitalis R., Couvet D. 2001a. Estimation of effective population size and migration rate from one- and two-locus identity measures. *Genetics*, 157(2): 911-925. https://doi.org/10.1093/genetics/157.2.911

Vitalis R., Couvet D. 2001b. Two-locus identity probabilities and identity disequilibrium in a partially selfing subdivided population. *Genet. Res.*, 77(1): 67-81. https://doi.org/10.1017/S0016672300004833

Vitalis R., Couvet, D. 2001c. ESTIM 1.0: a computer program to infer population parameters from one- and two-locus gene identity probabilities. *Molecular Ecology Notes*, 1(4): 354-356. https://doi.org/10.1046/j.1471-8278.2001.00086.x https://doi.org/10.1046/j.1471-8278.2001.00086.x

Wang J. 2009. A new method for estimating effective population sizes from a single sample of multilocus genotypes. *Mol. Ecol.*, 18(10): 2148-2164. https://doi.org/10.1111/j.1365-294X.2009.04175.x

Waples R.S. 2006. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. *Conserv. Genet.*, 7(2): 167-184. https://doi.org/10.1007/s10592-005-9100-y

Weir B.S., Cockerham C.C. 1984. Estimating F-statistics for the analysis of population structure. *Evolution*, 38(6): 1358. https://doi.org/10.2307/2408641

Wright S. 1965. The interpretation of population structure by F-statistics with special regard to systems of mating. *Evolution*, 19(3): 395. https://doi.org/10.2307/2406450

Yaglom H.D., Casal M., Carson S., O'Grady C.L., Dominguez V., Singleton Jr. J., Chung I., Lodge H., Paddock C.D. 2019. Expanding recognition of *Rickettsia parkeri* rickettsiosis in southern Arizona, 2016-2017. *Vector-Borne Zoonotic Dis.*, 20(2): 82-87. https://doi.org/10.1089/vbz.2019.2491