Introduction

Phlebotomus sand flies are vectors of medically important pathogens, such as Leishmania, the causative agent of leishmaniasis1, and arthropod-borne viruses (Toscana virus, Naples virus, and Sicilian virus)2. Phlebotomus ariasi Tonnoir, 1921 is the predominant sand fly species in the French Cevennes region3, and one of the two proven vectors, with Phlebotomus perniciosus Newstead, 1911, of leishmaniasis, which is caused by Leishmania infantum in the South of France4. Sand flies are abundant in suburban and rural environments and are often close to human and domestic animal populations4,5. Phlebotomus ariasi is found resting in houses, animal sheds, caves and weep holes in walls, near roads, and in villages. This species has a wide geographic distribution, including many countries of the Western Mediterranean region, such as Algeria, France, Italy, Morocco, Portugal, Spain, and Tunisia6,7,8,9,10,11,12,13,14,15,16.

During the last 10 years, the risk of emergence or re-emergence of leishmaniasis17 and phlebovirus infections18 has increased in France, probably linked to the recent extension of the vector distribution. However, the biology and ecology of Ph. ariasi, one of the main vectors of L. infantum, remain poorly known.

Only few studies have been performed on this species. Analysis of cuticular hydrocarbons highlighted the presence of two Ph. ariasi populations (sylvatic and domestic) in the Cevennes region19. Studies based on isoenzyme data20,21, random amplified polymorphic DNA22, and mitochondrial cytochrome b (cytb) gene sequences11 showed differences among Ph. ariasi populations in known leishmaniasis foci in Europe. Finally, two microsatellite loci and other genetic markers were used to understand Ph. ariasi expansion in Europe during the Pleistocene glacial cycles23.

However, no population genetics study tackled the influence of geographic (spatial organization) together with environmental (altitude and hillside) factors in the distribution of sand flies. Therefore, the aims of this work were to study the structure of Ph. ariasi populations at a local scale, and the impact of environmental factors (geographical distance, altitude, and hillside) on their spatial organization. For this purpose, sampling was performed in a well-documented area (Roquedur, Hérault, France), in which sand fly populations were already ecologically24 and morphometrically25 described, and where human and canine leishmaniasis caused by L. infantum are endemic5,26. The analysis was carried out using 11 previously described microsatellite loci for Ph. ariasi27.

Results

Genotyping

In total, 1,253 sand flies were genotyped using the 11 loci described in the Supplementary Table S1. Sand fly DNA samples in which more than six loci could not be amplified were removed from the analyses (n = 54 individuals, Group “/” in Supplementary Table S1).

Bayesian clustering

Discriminant analysis of principal components (DAPC) identified 40 clusters with a mean assignment probability PAss = 0.8568. Twenty individuals were grouped in one strongly differentiated cluster (cluster 21 with PAss = 1) (Fig. 1). Moreover, two outliers (one from cluster 2 and one from cluster 28) were close to cluster 21. Bayesian Analysis of Population Structure (BAPS) found 28 clusters (probabilities for number of clusters = 0.93641). A cluster of 22 individuals (BAPS cluster 28) included the 20 individuals from DAPC cluster 21 and the two outliers highlighted by DAPC. The optimal number of clusters found by STRUCTURE HARVESTER (used to visualize the results of STRUCTURE analysis) was two with ΔK = 16.03 (the second biggest ΔK was 2.55). Surprisingly, the clusters found by STRUCTURE did not match the DAPC or BAPS results. These two clusters grouped individuals from several stations with no obvious relation with any ecological or geographical parameter, and with a very small average assignment probability of individuals to their cluster (PAss = 0.53683).The partition found by STRUCTURE and STRUCTURE harvester with K = 2 is probably meaningless.

Figure 1
figure 1

Genetic variation among the captured Phlebotomus ariasi individuals. Scatter plot of individuals based on the first two axes (created from the optimum 13 principal components) of the DAPC. The inset shows the amount of variation represented by the discriminant analysis eigenvalues. Points and ellipses are colored according to the groups defined by the DAPC. Misassigned individuals (outliers) are indicated.

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Cytb fragment sequencing of seven individuals from cluster 21 (# 12, 17, 18, 19, 856, 872, and 874) and one of the two outliers (# 23, cluster 2) showed 99–100% similarity with Ph. ariasi (GenBank accession number: KP685539.1, and sequences in Supplementary File 1). Therefore, the taxonomic status of the 22 outliers could not be elucidated. These 22 individuals (Group A in Supplementary Table S1) were all males captured in three stations (ST02, ST11 and ST12). Neither particular environmental condition nor specific morphological character was recorded for these individuals compared with the other sand fly specimens. The 22 outliers were homozygous for allele 204 at the Aria1 locus. This allele was not found in the other 1,177 individuals (Group B in Supplementary Table S1). The dendrogram (NJTree) seems to exclude two subsamples A from all other subsamples, while a third one appeared fully included within group B (Fig. 2). This structuring appears to be robust since the same tree was obtained with 6 (Aria2, Aria3, Aria4, Aria5, Aria13, and Aria14, loci selected after Linkage Disequilibrium (LD) and F-statistics analyses, see below) or 10 loci (excluding Aria1).

Figure 2
figure 2

Dendrogram (NJTree) of the genetic relationship between subsamples defined as combinations of group A or B and sampling station (ST) based on Cavalli-Sforza and Edwards chord distance calculated with six loci (Aria2, Aria3, Aria4, Aria5, Aria13, and Aria14). Outlier individuals (Group A) are indicated in red. The same tree was obtained also using 10 loci (excluding Aria1).

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Except when specified otherwise, the 22 individuals of Group A were removed from the analyses to prevent Wahlund effects.

Locus selection for the analyses of Group B sand flies

The Aria1 locus was almost monomorphic (HS = 0.06) and was removed from the data. Using regression approach we detected a marginally not significant Short Allele Dominance (SAD) for the Aria14 locus (p value = 0.0507). As SAD results from a preferential amplification of the shortest allele in heterozygous individuals28, the Aria14 microsatellite profile of each homozygous individual was checked again following recommendation suggested in De Meeûs et al.29, corrected, and then analyzed using GENEMAPPER 4.0. After correction, SAD could not be detected on this locus any longer (p value = 0.0731).

The Micro-Checker analysis suggested stutter artifacts at five loci (Aria2, Aria3, Aria10, Aria14, and Aria15). The unilateral exact binomial test indicated that the proportion of significant stuttering was not significantly higher than the expected proportion under the null hypothesis for Aria2 (p value = 0.2078), Aria3 (p value = 0.2078) and Aria14 (p value = 0.5819). Nevertheless, it was marginally not significant for Aria10 (p value = 0.0503) and highly significant for Aria15 (p value = 9.728e−06). Therefore, alleles close in size were pooled, avoiding pooling together only rare alleles (i.e., presence of at least one frequent allele in the pool) for these two loci as recommended29. As the Aria10 locus became monomorphic after pooling, this locus was removed from the analysis. For Aria15, alleles 117, 119 and 121 were pooled with allele 123; allele 127 was pooled with allele 125; and allele 131 was pooled with allele 129. However, after correction, the Micro-Checker analysis and the unilateral exact binomial test (p value = 9.728e−06) highlighted that stuttering still affected this locus. Therefore, Aria 15 was also removed from the analysis.

On the remaining 9 loci, the linkage disequilibrium (LD) analysis indicated that 7 locus pairs (out of 11) displayed a significant LD (19.4%). Three of these locus pairs (Aria11 and Aria 12, Aria 11 and Aria 13, Aria 12 and Aria14) remained significant after Benjamini and Yekutieli adjustment. Aria11 and Aria12 displayed outlier profiles with strongly negative FIS (Fig. 3), above average FST (Fig. 4), large FIS and FST variance (Figs. 3, 4), and strong LD. These results suggested the non-neutrality of these loci. Therefore, Aria11 and Aria12 were also removed from the analysis.

Figure 3
figure 3

Deviation from the genotypic proportions expected for panmixia as measured by FIS in Phlebotomus ariasi for each microsatellite locus and averaged across loci. For each locus, the 95% CI values for subsamples obtained with the jackknife over subsamples is represented with dashes. The 95% CI values for the average, with 10 and 6 loci, were obtained by 5,000 bootstraps over loci. The results of tests for panmixia, short allele dominance, and number of subsamples with stuttering for each locus are also indicated.

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Figure 4
figure 4

Effect of subdivision (FST value) in Phlebotomus ariasi for each microsatellite locus and averaged across loci. For each locus, the 95% CI value calculated with the jackknife over subsamples is represented with dashes. The 95% CI of average was obtained by 5,000 bootstraps over loci.

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To check the result stability, additional DAPC analyses were performed by successively removing the following loci Aria1, Aria10, Aria11, Aria12, and Aria15. As long as Aria1 was kept, the obtained pattern was the same as in Fig. 2 (data not shown). With the remaining six loci (Aria2, Aria3, Aria4, Aria5, Aria13, and Aria14), DAPC and BAPS provided no clear structure. This suggested that the distinction between group A (the 22 outliers) and group B (the 1,177 remaining individuals) only relied on the Aria1 locus (Supplementary Table S1).

Linkage disequilibrium and F-statistics with the six remaining loci

With the remaining six loci (Aria2, Aria3, Aria4, Aria5, Aria13, and Aria14), the proportion of significant LD tests was 13.3% (two locus pairs), but none of these tests remained significant after Benjamini and Yekutieli adjustment. There was still a rather small but significant heterozygote deficit in subsamples (average FIS = 0.059, p value = 0.0001). Variation across loci appeared pronounced (Fig. 3), but the relationship between FIS and FST was not significant (Spearman's ρ = 0.2319, p value = 0.3292). There was no relationship between the number of missing data at each locus and the FIS values (ρ = − 0.029, p value = 0.5403). The jackknife method showed that the loci standard error of FIS was 0.055 and was 27 times bigger than that for FST (0.002). The Micro-Checker analysis suggested the presence of null alleles at four loci (Aria2, Aria3, Aria4, and Aria14). According to the criteria described in De Meeûs30 (see “Methods” section), null alleles only explained partly (if any) the observed heterozygote deficit.

To check the result stability again, the FIS computed for the dataset that included also Group A (complete dataset) was compared with the one obtained without it (Group B) (Supplementary Table S1). The FIS was bigger in the complete dataset than in Group B alone (FIS = 0.079 and FIS = 0.057, respectively), and the difference was significant (p value = 0.0486 unilateral Wilcoxon signed rank test). This translated into a Wahlund effect when Group A individuals were included in the data. These individuals were thus kept excluded.

Regression approach

A Principal Component Analysis (PCA) was undertaken with PCAGen (developed by J. Goudet, freely available at https://www2.unil.ch/popgen/softwares/pcagen.htm). The first two axes were significant using the broken stick criterion but not by permutation testing (p value = 0.2187 for axis 1 and p value = 0.1045 for axis 2) (see “Methods” section). Axis 1 and axis 2 represented 31% and 23% of the total inertia, respectively. We undertook two generalized linear model (glm) with the coordinates of subsamples at these axes (see “Methods” section). For the first axis, the minimum model, after a stepwise procedure, was: axis1 ~ hillside + latitude + longitude + latitude:hillside. For this axis, none of the included variables played a significant role (p value > 0.05 for all tests). For axis 2, the minimum model was: axis2 ~ hillside + altitude + latitude + longitude. Altitude and hillside played a significant role (p values < 0.001) and represented 51% and 15% of the total deviance, respectively (Table 1).

Table 1 Minimum model obtained for the regression of PCA axis 2 coordinates of Phlebotomus ariasi sampling sites.
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Isolation by altitude distance

As showed above, latitude and longitude had a weak effect on the genetic structure. Geographic parameters were thus removed from the model and isolation by altitudinal distance was tested using the Mantel test for each hillside separately (South–East and North–West, two tests).

When individuals were grouped by altitude levels (see Table 2 and “Methods” section), isolation by altitude distance was significant for the Northern (p value = 0.00605) and marginally non-significant for the Southern hillside (p value = 0.07345). When combined with the generalized binomial procedure31, computed with the MultiTest V132, isolation by altitude appeared highly significant (p value = 0.0054).

Table 2 Sampling stations (ST) in the study area.
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Effective population sizes and migration

The average effective population size (Ne) was 69 individuals (range: 39 to 98) across subsamples and methods (see “Methods” section). Ne was not correlated with altitude (Spearman's ρ = 0.2216, p value = 0.7864). There was no effect of hillside (Kruskal–Wallis p value = 0.6242). Nei's unbiased estimator of genetic diversities33 was HS = 0.52 and HT = 0.524 for the subsamples and total sample, respectively, with Meirmans and Hedrick's GST’’ = 0.01734, and then Nem = (1 − GST’’)/4 GST’’ = 14 (immigrants per generation in each subpopulation assuming an island model of migration). The correlation between Nei's GST and HS was strongly negative (rho = − 0.94, p value = 0.0083). Therefore, according to Wang’s criterion35, it is more accurate using FST. FST with the ENA correction for null alleles and 95% confidence intervals (95% CI) after 5,000 bootstraps over loci were computed with FreeNA36. This provided FST = 0.014 (95% CI = [0.006, 0.023]), with a corresponding Nem = 18 (95% CI [10, 40]).

Discussion

Locus selection

In this study, using 11 microsatellite markers, we investigated the genetic structure of Ph. ariasi populations in 1,253 individuals collected in 17 stations in the South of France (Supplementary Table S1). Different analyses (Micro-Checker, LD, FIS and FST variance) allowed identifying loci with technical problems and/or loci that may not be neutral concerning natural selection. Consequently, we removed five of the eleven loci for various reasons including the presence of incurable stutter artifacts (Aria10 and Aria15), absence of polymorphism (Aria1), or non-neutral evolution (Aria11 and Aria12). One locus with SAD could be corrected.

Presence of outlier individuals

The Bayesian analyses (DAPC and BAPS) for all remaining loci (Aria2, Aria3, Aria4, Aria5, Aria13, and Aria14) confirmed the 22 outliers (Group A). These outliers were morphologically similar to Ph. ariasi and displayed 99–100% cytb sequence similarities. A previous study, based on chromatographic analysis of cuticular components, provided evidence that there are two distinct Ph. ariasi populations in our study area: one predominantly sylvatic, and the second one domestic19. However, in our study, there was no geographical distribution difference between individuals from Group A and Group B.

This finding could reflect the existence of cryptic species, as already suspected or reported for other sand fly species (e.g., Lutzomyia longipalpis37,38, Lutzomyia umbratilis39, and Sergentomyia bailyi40). However, the distinction between Group A and Group B mainly depends on a single genetic marker (Aria1), which is fixed for different alleles in each group, while the other markers display a rather weak, though significant, signal. This result is supported by the dendrogram as the same tree was obtained with 6 or 10 loci (excluding Aria1). Additional molecular and biological studies will be necessary to test the cryptic species hypothesis (vector competence, interbreeding, hosts and niche preferences, behavior, etc.). As the taxonomic status of Group A individuals could not be elucidated, they were removed from the analyses.

Genetic structuring in ecotypes

We observed an important and significant heterozygote deficit, instead of the heterozygote excess expected for dioecious populations with random mating41. We found no evidence of Wahlund effect with the LD-based method described by Manangwa et al.42. Consequently, the heterozygote deficit could (non-exclusively) be explained by (1) null alleles; (2) allelic dropout; or (3) positive assortative mating. In the last case, this would require a mutual attraction of sexual partners based on a sufficient proportion of the genome to allow the hitchhiking of microsatellite markers, which are theoretically non-coding DNA sequences.

The glm approach showed a strong influence of altitude and hillside, and a weak (if any) influence of geographic distances on the genetic data. The proportion of deviance (67%) explained by altitude and hillside suggested the existence of ecotypes in this Ph. ariasi population.

The very strong migration rate estimated in our study (more than 25% of the effective subpopulation size) is hard to reconcile with the emergence of genetically distinct ecotypes. Different scenarios can explain ecotype structuring despite the strong migration rate: (1) the death of most immigrants before they can reproduce (unlikely scenario), and (2) significant assortative mating that can also explain the heterozygote deficit observed in our data (see above). The rate of codominant assortative mating based on genes homogeneously distributed in the genome can be approximated with the same equation as for selfing, as described in Hartl et al.43 (page 272), with the equation a ≈ 2FIS/(1 + FIS). With a FIS = 0.057, the assortative mating in our population would be ≈ 0.11 (≈ 11% of zygotes produced).

It is worth noting that previous research on the same species in the same study area highlighted differences in wing phenotypes according to altitude and hillside25. It has been demonstrated that wing configuration is associated with wing beating frequency and mate recognition44,45. These features would lead to a preferential choice of partners that could explain assortative mating and ecotype structuring. More studies are necessary to investigate the link between phenotypic (wing configuration) and genetic (microsatellite) diversity.

Ecological and epidemiological consequences of this genetic structuration

This structuring has undoubtedly implications on the ecology and evolution of sand flies. This structure might favor the global stability of their populations. Indeed, local environmental changes would have no or low effect on the overall population because the ecotypes would be affected independently. The high levels of migration rates associated with the well-known low capacity of flying of sand flies would help to colonize and recolonize at small-scale environments46. It also could explain the global but slow (compared with that of invasive mosquitoes, such as Aedes albopictus) geographical expansion of sand flies observed in France17.

This particular population structure can also influence Leishmania transmission. Previous studies suggested the existence of different imbricated Leishmania transmission cycles at very small scales47,48. However, due to the low flying capacities of sand flies46,49, migrants are expected to disperse mainly over short distances. Therefore, the spread and increase of L. infantum cases in France might be mainly due to the movement of infected hosts.

This study demonstrates for the first time that Ph. ariasi presents a genetic structure in ecotypes. These data highlight the necessity to consider sand fly populations at small and specific scales to determine their ecology and its impact on Leishmania transmission. This structure may explain the long-term stability of sand fly populations.

Not many papers compare available clustering algorithms to date and it is thus hard to really understand when and why such algorithms will converge or diverge in the best partition they offer. For some attempt comparisons see: Latch et al.50, Kaeuffer et al.51, Frantz et al.52, Blair et al.53, Bohling et al.54, Manangwa et al.42.

This type of study needs to be extended to other sand fly species. Indeed, the large diversities of sand fly populations worldwide may correlate with different ecological vector capacities and sensitivity to control measures.

Methods

Study area

The field study was performed in the South of France, on the “massif de l’Oiselette” hill situated between the “Hérault” (Ganges, Hérault) and “Arre” (Le Vigan, Gard) valleys. Sand flies were sampled along a 14 km transect from the “Saint Julien de la Nef” to “Le Vigan” villages, including “Roquedur-le-haut” (at 601 m above sea level) (Fig. 5; Table 2). This region has a Mediterranean sub-humid climate55, and is characterized by the presence of plant species typical of scrubland habitats5.

Figure 5
figure 5

Localization (A), map (B) and profile (C) of the study area. Red dots and numbers indicate the sampling stations. A1 (Northern hillside, 100–300 m), A2 (Northern hillside, 300–500 m), A3 (Northern hillside, > 500 m), A4 (Southern hillside, 300–500 m), and A5 (Southern hillside, 300–100 m). The map came from three sources ([A] Google Earth, [B] SAFER and [C] adapted from Rioux et al.3 "Creative Commons license") that were combined under Adobe Illusrator.

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The study area was divided in two hillsides with a South–East and North–West orientation, like in previous works performed in this area3,24,25. Stations were selected based on the paper of Rioux et al.3, first to allow the comparison with the data obtained 30 years ago in terms of species distribution, density and abundance24, second because these stations were distributed along the 14 km transect and represented the altitude and hillside diversity necessary for our population genetics study. Station 7 and Station 8 were not present in Rioux et al.3. These two stations are transitional between the two hillsides, and were thus added in the present study to obtain information on the consequences of transitional ecosystems. The stations were grouped according to altitude and hillside (see Fig. 5; Table 2). This area is characterized by the presence of various domestic animals, such as chicken, sheep, ducks, geese, horses, rabbits, cats, dogs, and also many different wild animals (wild boars, foxes, rodents, lizards, birds, etc.). These animals represent potential sand fly hosts. Moreover, cases of canine leishmaniasis were observed during the collection period (J.P. personal observation).

Sand fly collection and identification

Sand flies were collected monthly using 3,589 sticky traps (20 × 20 cm white paper covered with castor oil, as described by Alten et al.56 and Ayhan et al.57) between May and September 2011. Seventeen localities (sampling stations) were sampled (Fig. 5; Table 2) with a mean of 189 sticky traps per station, in various biotopes, inside and around human dwellings and animal sheds, close to the vegetation, and in wall crevices. Each trap was collected after 2 days.

In total, 1,253 sand flies were captured and transferred individually into 1.5 mL Eppendorf tubes with 96% ethanol and labeled. Prior to mounting, the sand fly head, genitalia and wings were removed. Heads and genitalia were cleared in Marc-André solution (chloral hydrate/acetic acid) and mounted in chloral gum46. Each individual specimen was identified on the basis of the morphology of the pharynges and/or male genitalia or female spermathecae, using the keys of Abonnenc46, Lewis6 and Killick-Kendrick et al.58.

DNA extraction

Sand fly DNA was extracted using the Chelex method59 described in Prudhomme et al.27. Extraction was performed at the UMR “Unité des Virus Emergents” (UVE, IHU Méditerranée Infection, Marseille, France). Each entire sand fly was ground using a Mixer Mill MM300 (QIAGEN, Venlo, Netherlands) with one 3-mm tungsten bead in 200 μL Eagle’s Minimal Essential Medium at a frequency of 30 cycles s−1 for 3 min. A volume of 140 μL of each sample was then used for DNA purification by adding the Chelex resin suspension59 or by using the Eppendorf epMotion 5075 working station and the Macherey–Nagel NucleoSpin 96 Virus kit.

DNA amplification and genotyping

Based on the microsatellite position in the sequence and the repeated pattern structure, 11 of the most polymorphic loci were selected among the previously described 16 microsatellite markers for Ph. ariasi27. Each 25 μL reaction mix included 1 pmol of forward (labeled with the fluorochrome FAM, ATT0565 or HEX) and reverse primers, 5 ng of sand fly DNA sample, 6 pmol of dNTP mix, 2.5 μL of 10X buffer, and 0.25 μL of Taq polymerase (ROCHE DIAGNOSTICS, 5 UI/μL). DNA was amplified in a thermal cycler using the following conditions: initial denaturation step at 95 °C for 10 min, followed by 40 cycles at 95 °C for 30 s, the specific annealing temperature of each locus27 for 30 s, 72 °C for 1 min, and a final extension step at 72 °C for 10 min. For genotyping, 1 μL of PCR product was added into a standard loading mix (0.5 μL of the internal GeneScan 500LIZ dye size standard and 12.5 μL of Hi-Di formamide) (both from APPLIED BIOSYSTEMS) and sequenced on an ABI Prism 3130 Genetic Analyzer (APPLIED BIOSYSTEMS) automated sequencer. Profiles were read and analyzed using GENEMAPPER 4.0 [APPLIED BIOSYSTEMS, Foster City (CA)].

DNA from a subset of Ph. ariasi samples was used to check the species identification (see “Results” section) by amplifying a cytb gene fragment using the primers N1N-PDR [5′-CA(T/C) ATT CAA CC(A/T) GAA TGA TA-3′] and C3B-PDR [5′-GGT A(C/T)(A/T) TTG CCT CGA (T/A)TT CG(T/A) TAT GA-3′], according to a previously published protocol60,61 and the following conditions: initial denaturation at 94 °C of 3 min; 5 cycles of denaturation (94 °C for 30 s), annealing (40 °C for 60 s), and extension (68 °C for 60 s), followed by 40 cycles of denaturation (94 °C for 60 s), annealing (44 °C for 60 s) and extension (68 °C for 60 s), and a final extension (68 °C for 10 min). Direct sequencing in both directions was performed by Eurofins Genomics.

Data analysis

Microsatellite raw data were formatted for CREATE62 that allowed their transformation into the formats needed for the different analyses. Samples with more than 50% of missing data were removed from the analyses.

Bayesian clustering

Several Bayesian clustering analyses were carried out. To validate the sand fly species identification, the first analysis was based on the 11 selected loci and included all sand fly samples. Then, to study the organization of Ph. ariasi populations, data were analyzed at different scales: hillside, altitude, and station. This analysis was performed with the loci selected after Linkage Disequilibrium (LD) and F-statistics analyses (see “Results” section).

As the clustering method accuracy can vary depending on the statistical properties of specific software programs and datasets29,42,63, three different Bayesian clustering methods were used. First, a discriminant analysis of principal components (DAPC)64 was performed using the adegenet65 package for R66. This was followed by a Bayesian Analysis of Population Structure (BAPS; admixture model67,68, maximum number of clusters: 35, repetition: 50; that is freely available at https://www.helsinki.fi/bsg/software/BAPS/), and a STRUCTURE (version 2.3.4) analysis69 (burning period: 10,000, number of clusters from 1 to 35, with the admixture model). STRUCTURE HARVESTER vA.270 was used to visualize the STRUCTURE analysis results, examine the ad hoc ΔK statistic, and determine the optimal number of clusters.

Finally, a dendrogram (NJTree) was built with MEGA 771 from a Cavalli-Sforza and Edward's chord distance (DCSE)72 matrix as recommended73, between subsamples defined as combinations of the group obtained by Bayesian clustering and sampling stations. Because null alleles were suspected to occur, DCSE was computed with the INA correction with FreeNA36, after recoding missing data into null homozygotes following authors' recommendation.

Linkage disequilibrium and F-statistics

LD between each locus pair was tested with the G-based permutation test with 10,000 randomizations. This test was performed with FSTAT 2.9.4, an updated version of FSTAT 2.9.374 available at https://www.t-de-meeus.fr/ProgMeeusGB.html. This procedure is the most powerful for testing LD across different subsamples32. There are as many p values as locus pairs. Then, the False Discovery Rate (FDR) correction for multiple non-independent tests described by Benjamini et al.75 was applied with R 3.5.166.

Wright's F statistics76 were estimated with the Weir and Cockerham's unbiased estimators77. Significant departure from 0, for F statistics, was tested by randomizing alleles between individuals within subsamples (deviation from the local random mating test) or individuals between subsamples within the total sample (population subdivision test). The p value corresponded to the number of times a statistic measured in randomized samples was as big as (or bigger than) the observed one (unilateral tests). For local panmixia, the statistic used was f (Weir and Cockerham's FIS estimator). To test for subdivision, the G-based test78 was used. According to De Meeûs et al.32, the G-based test is the most powerful procedure when combining tests across loci.

The 95% confidence intervals (CI) of F-statistics were computed using the jackknife over populations method for each locus or 5,000 bootstraps over loci for the averages, as described in De Meeûs et al.79. Parameter estimates, testing, jackknife and bootstrap computations were done with FSTAT 2.9.4.

The determination procedure described by De Meeûs30 and Manangwa et al.42 was used to discriminate demographic from technical causes of significant homozygote excess and LD. In the case of null alleles, FIS and FST artificially increase and a positive correlation is expected between the statistics, FIS standard error is at least twice that of FST, and a positive correlation is also expected between FIS and the number of missing data (putative null homozygotes). Correlations were tested with the unilateral Spearman’s rank correlation test in R 3.5.166. The frequency of null alleles was assessed with Micro-Checker v2.2.380 using Brookfield's second method81.

The presence of stutter artifacts at each locus in each subsample was evaluated with Micro-Checker v2.2.380. A unilateral exact binomial test was used with R 3.5.166 to determine whether the observed proportion of significant stutter artifacts was greater than the expected 5% under the null hypothesis.

Short Allele Dominance (SAD) was assessed with the method described by Manangwa et al.42. The correlation between allele size and FIT was tested with the unilateral Spearman’s rank correlation test in R 3.5.166. In the case of SAD, a negative correlation is expected between allele size and FIT42.

In the case of significant stutter artifacts or SAD, the incriminated loci were corrected using the method described by De Meeûs et al.29. Stuttering was addressed by pooling alleles close in size. To avoid a spurious increase of heterozygosity, each pooled group contained at least one frequent allele (e.g., with p value ≥ 0.05). SAD was addressed by going back to the chromatograms of homozygous individuals and trying to find a larger size micro-peak that could have been missed in the first reading.

In some instances, FIS values were compared between subsample groups using the Wilcoxon signed rank test for paired data (the locus was the pairing unit).

Role of environmental factors on sand fly structuring

A principal component analysis (PCA) was done with PCAGen 1.2.1 (developed by J. Goudet, freely available at https://www2.unil.ch/popgen/softwares/pcagen.htm). The significance of the first axes was tested using the broken stick criterion82 and 10,000 permutations of individuals across subsamples. The metric of each axis divided by the total genetic diversity corresponded to Weir & Cockerham's Theta (FST estimator) (Goudet’s personal communication). The coordinates of subsamples for each significant axis were used as the response variable of generalized linear models (glm). General models were as follows: axis i ~ latitude + longitude + altitude + hillside + latitude: hillside + longitude:hillside + altitude:hillside (where i corresponded to the significant axes; latitude and longitude are the latitudinal and longitudinal GPS coordinates in decimal degrees; altitude is the altitude in meters; hillside: south–east or north–west; and "X:Y" represents the interaction between the explanatory variables X and Y). All glm's were done using R version 3.5.166, with the package rcmdr (R-commander)83,84. Model selection was performed using a forward stepwise model selection procedure and the Akaike Information Criterion85.

The influence of different factors (including interactions) was tested by examining the differences between models (complete, additive, and with one variable) with analyses of variances. As the entry order of explanatory variables matters in R analyses, the mean partial R2 of each variable was calculated across all possible models.

Isolation by distance

Geographic distances were computed with Genepop version 4.7.086 using the Euclidian distance computed with the Universal Transverse Mercator (UTM) coordinates (Table 2). Genetic distances were estimated with the Cavalli-Sforza and Edwards chord distance72 DCSE, the most powerful method to detect isolation by distance with microsatellite markers in most situations87. Due to the presence of missing data, data were converted into the FreeNA format to compute DCSE between each station pair with the ENA correction36 that provides a very good correction for the isolation by distance regression slope in the case of null alleles87. As these station pair ended into a non‐squared matrix that could not be handled by Genepop, the relationships between genetic and geographic distances were tested with a Mantel test (104 permutations)88 in FSTAT 2.9.4. As the Mantel test in FSTAT is bilateral by default, the p value was halved in the case of positive slope, or computed as: “1-(1-bilateral p values)/2” in the case of negative slope, to obtain unilateral p values for a positive slope.

The relationships between genetic and altitudinal distances were also tested in the conditions described above. To avoid any interaction, the analyses for the South and North hillsides were done separately. The two p values obtained were then combined with the generalized binomial test31, with MultiTest32, taking into account all tests as recommended when the number of combined tests k < 489.

Effective population sizes and migration

Four different methods were used to estimate the effective population sizes (Ne). The results obtained with these methods were averaged and weighted by the number of times a usable value (after removal of “infinity” results) was obtained. To obtain a range of possible Ne values, the same approach was performed for the minimum and maximum values obtained with each method.

First, NeEstimator v290 was used with the LD method91,92 that applies a correction for missing data93 and the molecular co-ancestry method94. The LD method uses several threshold allele frequencies (0.05, 0.02, 0.01, and all alleles) to compute Ne. The average across the different values obtained with these frequencies was computed.

The intra- and inter-loci correlation method was also used to compute Ne95 using Estim v2.296 (available at: https://www.t-demeeus.fr/ProgMeeusGB.html).

Finally, the heterozygote-excess method (expected in dioecious populations) described by Balloux41 was used for each locus that displayed heterozygote excess, as follows: Ne = [− 1/(2 FIS)] −FIS/(1 + FIS).

To determine the number of immigrants, the standardized differentiation index described by Meirmans and Hedrick was computed to correct for polymorphism excess: GST" = n × (HT − HS)/[(n × HT − HS) × (1 − HS)]34, where HT and HS are Nei's unbiased estimates of genetic diversity in the total sample or within subsamples, respectively33, and n is the number of subsamples. Genetic diversities were estimated with FSTAT 2.9.4. Assuming an island model, this value was then used to obtain an approximation for the number of immigrants within subpopulations as Nem = (1 − GST")/(4 × GST"). However, according to Wang’s criterion35 if Nei’s GST33 is negatively correlated with HS, it is wiser to use FST for this computation.