Population genetics of the nidicolous soft tick Ornithodoros phacochoerus in the context of African swine fever sylvatic cycle

Population genetics of the nidicolous soft tick Ornithodoros phacochoerus in the context of African swine fever sylvatic cycle | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Population genetics of the nidicolous soft tick Ornithodoros phacochoerus in the context of African swine fever sylvatic cycle View ORCID Profile Florian Taraveau , View ORCID Profile David Bru , View ORCID Profile Thomas Pollet , Mélanie Jeanneau , Maxime Duhayon , Elsa Rudo Pires Lameira , Antónia de Andrade , Alberto Francisco , Jacinto Chapala , View ORCID Profile Carlos João Quembo , View ORCID Profile Hélène Jourdan-Pineau doi: https://doi.org/10.1101/2025.10.07.680904 Florian Taraveau 1 UMR ASTRE, CIRAD, INRAE, Campus de Baillarguet , F-34398, Montpellier, France 2 Université Claude Bernard Lyon 1, UMR 5557 Ecologie Microbienne, CNRS, INRAE , VetAgro Sup, F-69622, Villeurbanne, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Florian Taraveau For correspondence: florian.taraveau.cirad{at}gmail.com helene.jourdan{at}cirad.fr David Bru 1 UMR ASTRE, CIRAD, INRAE, Campus de Baillarguet , F-34398, Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David Bru Thomas Pollet 1 UMR ASTRE, CIRAD, INRAE, Campus de Baillarguet , F-34398, Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Thomas Pollet Mélanie Jeanneau 1 UMR ASTRE, CIRAD, INRAE, Campus de Baillarguet , F-34398, Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maxime Duhayon 1 UMR ASTRE, CIRAD, INRAE, Campus de Baillarguet , F-34398, Montpellier, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elsa Rudo Pires Lameira 3 Regional Veterinary Laboratory, Agricultural Research Institute of Mozambique-Central Region Office , Chimoio EN6.CP42, Mozambique 4 Instituto Superior Politécnico de Manica (ISPM), Matsinho campus , Vandúzi district, Manica province, P.O. Box 417, Mozambique 5 Department of Veterinary Tropical Diseases, Faculty of Veterinary Sciences, University of Pretoria , Pretoria, South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antónia de Andrade 3 Regional Veterinary Laboratory, Agricultural Research Institute of Mozambique-Central Region Office , Chimoio EN6.CP42, Mozambique Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alberto Francisco 3 Regional Veterinary Laboratory, Agricultural Research Institute of Mozambique-Central Region Office , Chimoio EN6.CP42, Mozambique Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacinto Chapala 3 Regional Veterinary Laboratory, Agricultural Research Institute of Mozambique-Central Region Office , Chimoio EN6.CP42, Mozambique Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carlos João Quembo 3 Regional Veterinary Laboratory, Agricultural Research Institute of Mozambique-Central Region Office , Chimoio EN6.CP42, Mozambique Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carlos João Quembo Hélène Jourdan-Pineau 3 Regional Veterinary Laboratory, Agricultural Research Institute of Mozambique-Central Region Office , Chimoio EN6.CP42, Mozambique Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hélène Jourdan-Pineau For correspondence: florian.taraveau.cirad{at}gmail.com helene.jourdan{at}cirad.fr Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Ticks of the species Ornithodoros phacochoerus are endophilic soft ticks which infest warthog burrows. Like other Ornithodoros of the O. moubata species complex, O. phacochoerus is a vector of the African swine fever virus and participates in the maintenance of the virus among warthogs in the sylvatic cycle in Southern and Eastern African countries. In this study, the population genetic structure of O. phacochoerus was investigated using sixteen microsatellite markers. After sampling campaigns that took place between 2020 and 2022, we analyzed 684 ticks from 21 warthog burrows and resting sites from Coutada 9 Game Reserve, Macossa District, Mozambique, and 138 ticks from 3 warthog burrows and resting sites from Gorongosa National Park, Gorongosa District, Mozambique. Burrows and resting sites are regularly used by the common warthog Phacochoerus africanus which is thought to be responsible for the movements of O. phacochoerus between sites. After genotyping, the observed genetic variation followed a model of isolation by distance with a structure at the level of the sampling sites (burrows and resting sites). Gorongosa National Park and Coutada 9 Game Reserve appeared to be completely isolated from each other in terms of gene flow, and Coutada 9 Game Reserve showed a clear signature of a bottleneck effect. Effective population sizes within sampling sites were quite low (4.1 individuals), with an estimated migration rate of 35% and a mean dispersal distance of 209 meters per tick generation. These results suggested frequent tick movements between burrows at a small geographic scale, due to warthog movements between the burrows. As both wildlife conservation areas are positive for African swine fever virus, these results reinforce the suspected role of the ticks in the sylvatic cycle of the virus, as infected ticks could be moved from one burrow to another, maintaining the presence of the virus in several sites of the conservation areas. This confirms the importance of maintaining buffer zones around conservation areas, buffer zones that should remain free of any domestic pigs to prevent vector spillover from the sylvatic cycle to the domestic cycle. Background Vector-borne diseases have a major impact on human health, accounting for more than 17% of all human infectious diseases ( World Health Organization 2020 ), but also pose a growing health risk to livestock as the size of livestock production systems increases ( Otranto 2018 ). A vector-borne disease is the result of complex interactions between the transmitted pathogen, the vector, the host and the environment. Therefore, population size, population dynamics, migration patterns and mating system of hematophagous arthropods represent critical information for understanding the epidemiology of vector-borne diseases and, consequently, for vector control programs and disease management strategies. However, much basic information about vector ecology is still unknown because their small size, location and behavior complicate direct observation of their population biology. Population genetics offers a way around these limitations, based on variation in allelic frequency at neutral genetic markers (de Meeûs et al. 2007 ; McCoy 2008 ). In veterinary medicine, ticks are the most important arthropod vector of animal diseases, ahead of mosquitoes. Among these diseases, only a few are caused by viruses. This is the case with African swine fever (ASF), the most complex and devastating emerging infectious disease of pigs. In domestic pigs, infection can cause up to 100% mortality in about 10 days ( Sánchez-Vizcaíno et al. 2015 ; Blome, Franzke, and Beer 2020 ). There is currently no vaccine or treatment for this disease. In the wild, the ASF virus has existed for millions of years in a sylvatic cycle involving warthogs ( Phacochoerus africanus ) and soft ticks (of the Ornithodoros moubata complex) that infest their burrows ( Forth et al. 2020 ; Jori et al. 2023 ). After infection, wild suids show no clinical signs, despite a significant viremia ( Thomson, Gainaru, and Van Dellen 1980 ; Anderson et al. 1998 ). The sylvatic cycle involving soft ticks represents a constant risk of ASF virus spillover to domestic pigs, particularly in the buffer zone of wildlife conservation areas. The link between the sylvatic and the domestic cycles is still poorly understood. Experimental evidence suggests that direct contact transmission between domestic pigs and warthogs is unlikely ( Thomson, Gainaru, and Van Dellen 1980 ). Therefore, either indirect contamination occurs with domestic pigs being potentially infected by warthog fomites, or soft ticks are involved in viral transmission from wild suids to pigs. Although there is no direct evidence of tick movement between warthog burrows and pigpens, some studies have reported the presence of soft ticks in pigpens near conservation areas ( Quembo et al. 2016 ), and soft tick nymphs have also been found in warthogs shot by hunters ( Horak et al. 1983 ). Investigating dispersal patterns of soft ticks in Southern Africa is critical in order to assess their role in ASF virus transmission at the wild-domestic interface. To date, population genetics studies and mitochondrial marker studies have mostly focused on hard ticks and soft ticks that infest bird nests ( Araya-Anchetta et al. 2015 ), although specific markers have been developed for the soft tick Ornithodoros coriaceus ( Kirchoff, Peacock, and Teglas 2008 ). Population genetics on hard ticks suggests that tick dispersal is largely dependent on host type and mobility, with high gene flow (and low genetic differentiation) for tick species that feed on large and/or highly mobile hosts. However, nidicolous hard ticks show a somewhat different pattern, with spatially structured populations driven by host nesting areas ( McCoy, Tirard, and Michalakis 2003 ; Van Oosten et al. 2014 ). Like nidicolous hard ticks, soft ticks of the Ornithodoros moubata complex live in their host’s resting sites: burrows or other resting places. However, unlike hard ticks, they need very short blood meals (less than 2 hours) and are therefore less likely to be dispersed by their host. Even if the presence of soft ticks has been reported on warthogs outside their burrows ( Horak et al. 1983 ), it is not known whether this leads to efficient dispersal. The Ornithodoros moubata complex consists of nine species of soft ticks distributed throughout Eastern and Southern Africa ( Bakkes et al. 2018 ). The presence of these ticks appears to be influenced by several factors, including climate, biome (semiarid, temperate, savannah), altitude, soil type, vegetation, and distribution areas of their host species ( Sonenshine and Roe 2014 ; Bakkes et al. 2018 ; Vial et al. 2018 ; Jourdan-Pineau et al. 2022 ). Among these species, Ornithodoros phacochoerus is found in the Eastern part of South Africa, in Swaziland and in Mozambique ( Bakkes et al. 2018 ). In the latter country, the existence of both sylvatic and domestic cycles has been demonstrated ( Quembo et al. 2016 ; Quembo et al. 2017 ), and ASF virus represents a high risk for pig farming systems ( Penrith et al. 2007 ). Using a set of 19 microsatellite markers recently developed from genomic data of Ornithodoros porcinus and Ornithodoros moubata ( Taraveau et al. 2024 ), we studied the population genetic structure of Ornithodoros phacochoerus in Mozambique. With 24 sites sampled between two wildlife conservation areas, our objective was to evaluate the dispersal pattern of this soft tick to investigate its potential role in ASF virus spillover from the sylvatic cycle to the domestic cycle. This study is the first population genetic study based on genomic markers for soft ticks. Methods Tick sampling Ornithodoros phacochoerus ticks were collected in Mozambique from two different wildlife conservation areas: the Coutada 9 Game Reserve from the district of Macossa, Manica province, and the Gorongosa National Park from the district of Gorongosa, Sofala province ( FIGURE 1 ). Ticks were collected over three sampling campaigns, in October 2020, October 2021, and October 2022. Ticks from Coutada 9 Game Reserve were collected from 83 different sites out of which 21 sites were selected for population genetics (including two sites, site number 5 and 15, which were voluntarily resampled in 2022 from warthog burrows previously sampled in 2020 and which were only used for N e calculation). Pigpens (n=11) located in the buffer zone of the Coutada 9 Game Reserve were also investigated but no tick could be collected in these sites. Ticks from Gorongosa National Park were collected from three different sites which were all kept for population genetic analysis. Download figure Open in new tab FIGURE 1: Location of sampling sites for Ornithodoros phacochoerus in Coutada 9 Game Reserve and Gorongosa National Park, Mozambique. For the sampled sites which were used in population genetics (indicated with blue spots), the identification number of the site is also indicated on the map. Coutada 9 Game Reserve map was provided by Mokore Wildlife. Gorongosa National Park map was provided by Margarida Pedro Victor from Gorongosa National Park staff. There were two different types of sites: “rocks” were shady areas made of rock and mud, where wild herbivores, including warthogs, spent time resting, “burrows” were holes in the ground that were primarily dug by aardvarks ( Orycteropus afer ) and used by warthogs to sleep and rear their young, thus escaping predation ( SUPPLEMENTARY MATERIAL S1 ). One exception was site number G3 in Gorongosa National Park which was a deck under a house in Chitengo camp (“deck house”) which was collected after observation of warthogs using the site for resting. Rocks were selected based on previously sampled areas and the use of camera traps to assess the presence of wildlife. Burrows were visually inspected as they were highly frequent in the environment. Burrows were selected based on a distance of 2km between them. The selection of the genotyped sites was also based on the number of ticks collected (at least 30 ticks in each site if possible). In the sites not selected here, no ticks (16 sites) or few ticks (46 sites) were collected. Location of the sites is shown in FIGURE 1 and type of site (rocks/burrows) is presented in TABLE 1 . View this table: View inline View popup TABLE 1: Effective population size, F IS , and Nei’s estimate of genetic diversity in each site from Coutada 9 Game Reserve. Sampling of the ticks was performed by excavating the soil from the side and the entrance of the burrow or by collecting soil from resting areas, then by spreading this material over a black tarp under sunlight. Ticks were then collected by hand and kept in a tube with some sand until DNA extraction. Each site investigation was performed during 45 minutes and the number of ticks collected was recorded. Tick DNA was shared with the Agricultural Research Institute of Mozambique (IIAM) under Material Transfer Agreement and in compliance with the Nagoya Protocol. DNA extractions For the selected sites in Coutada 9, thirty ticks per site were extracted for DNA. In Gorongosa, due to the little number of sites, all the ticks collected were extracted. Pictures of the ticks were taken to assess their size. Ticks washing and DNA extraction were performed following the method already described in Taraveau et al . ( Taraveau et al. 2024 ). Genotyping Genotyping was performed using 19 microsatellite markers published in Taraveau et al. with the same protocol of DNA amplification ( Taraveau et al. 2024 ). Genotyping was performed by capillary electrophoresis at the GPTR Laboratory (Great Regional Technical Platform of Genotyping, AGAP Institut/CIRAD, Montpellier, France) with an ABI 3500xL Genetic Analyzer (Applied Biosystems, Foster City, CA, USA). Alleles were scored using GeneMapper® v.6 software (Applied Biosystems, Waltham, MA, USA). Allele scoring was performed automatically according to the bin set previously designed for the markers, then manually checked by two different experimenters. In total, based on previous analyses ( Taraveau et al. 2024 ) and results obtained here, sixteen microsatellite markers were kept for further analyses. A total of 824 ticks were genotyped, 482 of which presented full genotypes for all loci, 331 partial genotypes (between 5 and 15 successful loci; mean value: 13 successful loci; SD: 4.4) and 11 ticks were excluded due to amplification in less than 5 loci. Population genetics analysis and statistics For all analyses, CREATE was used to convert the genotyping results into the appropriate format ( Coombs, Letcher, and Nislow 2008 ). Percentage of null allele was calculated using blank proportions for each locus using FreeNa with 10000 bootstraps ( Chapuis and Estoup 2007 ). A one-sided Spearman’s correlation test was also performed in R version 4.2.3 (2023-03-15) ( R Core Team 2023 ) to evaluate if the residual positive F IS in each locus could be explained by the number of blanks used as a signal for null alleles (S=614.95, p-value=0.7245). MicroChecker ( Van Oosterhout et al. 2004 ) was used to evaluate the number of populations affected by the presence of null alleles, stuttering or short allele dominance in each locus. Based on the data collected, loci deviating for HW equilibrium were discarded ( Chapuis and Estoup 2007 ). Fstat version 2.9.4 was used for most basic calculations, including, number of alleles, Nei estimator for genetic diversity ( Nei 1973 ), test for Hardy-Weinberg (HW) equilibrium, F IS , H 0 , and H e ( Goudet 2003 ). Two dimension factorial components analysis were made from genetic distribution using Genetix ( Belkhir et al. 2004 ). Significance of the axis from the factorial components analysis was evaluated using the broken stick test for 16 loci ( Frontier 1976 ). Different levels of population structure could be considered. First at the scale of the two natural reserves, Coutada 9 Game Reserve and Gorongosa National Park. This hierarchical level will be indicated as “parks” for future analysis. The second level of potential structure is the site of collection of the ticks (Burrows and Rocks), this will be indicated as “sites”. For evaluation of relevant population structure level, hierarchical F-statistics were calculated using R version 4.2.3 (2023-03-15) ( R Core Team 2023 ) with the package HierFstat ( Goudet 2005 ) using 1000 randomizations of individuals between sites or of sites between parks (de Meeûs and Goudet 2007 ). Second, population structure between parks and between sites within Coutada 9 Game Reserve was evaluated using a Bayesian approach with STRUCTURE version 2.3 ( Falush, Stephens, and Pritchard 2003 ). In each analysis, the number of population K was fixed to a value from one to the number of parks (K=2) or sites (K=21). The number of MCMC (Markov chain Monte Carlo) repeats was fixed to 50000 for a burn-in of 5000, the admixture model was chosen with uncorrelated allele frequencies. The most likely number of populations K was then selected from the lowest estimated Ln probability Pr(X|K), and assignment of individuals for this K was used for graphical representation in bar chart and in Coutada 9 Game Reserve map. Isolation by distance was calculated between each pair of sites for the Coutada 9 Game Reserve using Genepop on the web version 4.7.5 ( Raymond and Rousset 1995 ). F ST estimates were calculated using FreeNA with the ENA correction method for null alleles and 5000 bootstraps to obtain a 95% confidence interval. Geographic distances were obtained using the Haversine formula with R version 4.2.3 (2023-03-15) to calculate the distance between each pair of sites from their GPS coordinates. The matrix of distances was log transformed and compared with the matrix of genetic distances using F ST /(1-F ST ) in Genepop version 4.7.5 using Isolde. Minimum distance between sites was set at 1km (for log transformation), and 1,000,000 permutations were performed for the Mantel test ( De Meeûs 2021 ). Using the slope b obtained from isolation by distance ( Rousset 1997 ), neighborhood size (N b ) was estimated, N b =1/b, the product of the effective population density per km 2 (D e ), and squared dispersal distance between offspring and parents (σ 2 ) was calculated, D e σ 2 =1/(4πb) ( Watts et al. 2007 ; De Meeûs 2021 ), and the number of migrant entering in a site at each generation from the neighborhood (N e m) was estimated, N e m=1/(2πb), and consequently, the migration rate m=2 D e σ 2 /N e ( Rousset 1997 ). Effective population size (N e ) was calculated using NeEstimator version 2.1 ( Do et al. 2014 ). NeEstimator was used with two different methods: one using all 21 populations from Coutada 9 Game Reserve, and the other using only site number 5 with two temporal replicates (2020 and 2022). First, N e per population was estimated using the co-ancestry model, with Jackknife for confidence intervals ( Nomura 2008 ). Second, N e was also estimated using the linkage disequilibrium method for random mating with a lowest allele frequency value of 0.050 ( Waples and Do 2010 ). Finally, the temporal method was used with Pollak ( Pollak 1983 ), Nei/Tajima ( Nei and Tajima 1981 ), and Jorde/Ryman ( Jorde and Ryman 2007 ) methods, on the site number 5 which was sampled twice in Coutada 9 game reserve ( Waples 2005 ). Two years separated the two sampling, which was evaluated as two generations of ticks between each sampling ( Vial 2009 ). Estimation of the number of sites per km 2 (D sites ) was made based on previous field observations of number of sites effectively used by warthogs and published in the literature: 15 sites/km 2 in the Sengwa Wildlife Research Area (Zimbabwe) ( Cumming 1975 ) and 17 sites/km 2 in Hluhluwe-iMfolozi Park (South-Africa) ( White and Cameron 2009 ), leading to us to use a mean value of D sites =16 sites/km 2 . Effective density (D e ) was calculated D e =N e *D sites leading to an estimation of the dispersion surface between parents and offspring (σ) ( De Meeûs et al. 2019 ). Signature of a bottleneck effect was investigated using the Bottleneck software version 1.2.02 ( Piry, Luikart, and Cornuet 1999 ) using IAM model, SMM model, and TPM models with 70% of SMM and 30 of variance. Significance was evaluated with a Wilcoxon sign rank test with 1000 iterations followed by a generalized binomial procedure over the p-values from all sites using MultiTest V.1.2 with a default precision of 0.0001 to evaluate the significance of the results ( De Meeûs, Guégan, and Teriokhin 2009 ). As described in the literature, a signature of bottleneck is likely when the test is significant for both IAM and TPM models at least while significant p-value only for IAM is likely to indicate small effective population sizes (N e ) ( Ravel et al. 2023 ). From these results, it is possible to evaluate the effective population size just after the bottleneck (N eb ) ( Cornuet and Luikart 1996 ). With a mean gene diversity between 0 and 0.3, 16 loci, 30 individuals per site, and a significant bottleneck signal in both IAM and SMM model, τ 1 and τ 2 were estimated at 1 and 2.5 with an α of 100, 1000, or higher. If the number of generations τ since the bottleneck is known, N eb can be estimated as [τ/(2*τ 2 *N e ); τ/(2*τ 1 *N e )] ( Cornuet and Luikart 1996 ; De Meeûs 2021 ). In Coutada 9 Game Reserve and Gorongosa National Park, τ was estimated to be one generation per year since the opening of the park, time at which warthog populations in both areas were strongly depleted: τ Coutada9 =19, τ Gorongosa =14 (Coutada 9 Game Reserve and Gorongosa National Park personal communications). Rocks and burrows were compared on three parameters (Nei estimator of genetic diversity, F IS , and N e ) with a Kruskal-Wallis test in R version 4.2.3 (2023-03-15) ( R Core Team 2023 ). Relatedness (r) between every pair of ticks within a site was evaluated using SPAGeDI v1.5 ( Hardy and Vekemans 2002 ). Two relatedness indicators were used: Queller and Goodnight indicator ( Queller and Goodnight 1989 ) which takes in consideration alleles frequencies and is frequently used in similar studies for its robustness ( Van Oosten et al. 2014 ) and Lynch and Ritland indicator ( Lynch and Ritland 1999 ) which uses the covariance between genotypes and is robust to investigate population structure. Positive relatedness indicated individual more related than random whereas negative relatedness indicated individuals less related than random. In parallel, each individual was given a score of dispersal opportunity based on its size. This score is an indirect parameter used for matrix comparisons and it does not directly account for the number of blood meals taken by the tick as this number cannot be estimated solely based on the tick size. First stage nymph (size < 1.75 mm) were attributed a score of 0 and the size category “Small”, intermediate nymphs (1.75 mm < size < 5.5 mm) were attributed a score of 1 and the size category “Medium”, and late stage nymphs and adults (5.5 mm < size) were attributed a score of 3 and the size category “Large”. Association between a matrix of the sum of dispersal scores for each pair of ticks and a matrix of relatedness for each pair of ticks was performed using SPAGeDI v1.5 ( Hardy and Vekemans 2002 ) with 10 000 permutations over individuals and loci. Group by group comparison (for sum of dispersal scores) was performed using a Kruskal-Wallis test followed with a Dunn test and boxplot representation was made for Lynch and Ritland indicator of relatedness using R version 4.2.3 (2023-03-15) ( R Core Team 2023 ). Results Quality of the microsatellite markers Three microsatellite markers (ms-61, ms-76, and ms-111) were discarded due to signal of null alleles or stuttering associated with positive F IS , and significant deviation from Hardy-Weinberg equilibrium ( SUPPLEMENTARY MATERIAL 2 , SUPPLEMENTARY MATERIAL 3 ). No signature of short alleles dominance was detected in any of the loci and they were considered independent as previously tested ( Taraveau et al. 2024 ). Analysis were conducted using the sixteen remaining loci. The number of alleles, H E , H O , and deviation from Hardy-Weinberg equilibrium per site are indicated in SUPPLEMENTARY MATERIAL 4 , F ST between sites are presented in SUPPLEMENTARY MATERIAL 5 . Relevant level of population structure Hierarchical F-statistics calculated with HierFstat indicated a significant genetic structure between the two parks (F Park-Total = 0.55; 1000 permutations; p-value = 0.002) and among the sites within the parks (F Site-Park = 0.19; 1000 permutations; p-value = 0.001). For the following results, the subpopulation unit chosen will be the site based on HierFstat results. Due to the number of sites collected, most analysis focused on the Coutada 9 Game Reserve (21 sites and 2 sites re-sampled) rather than on the Gorongosa National Park (3 sites). Using STRUCTURE, there was no evidence of a Wahlund effect within the sites. Between the two parks, STRUCTURE confirmed a clear separation in K=2 subpopulations, one for each park, with clear assignment of the ticks (Ln Pr(X|K)=-11779, Var=155.4). The K number of subpopulations which gave the best results within Coutada 9 Game Reserve was K=7 (Ln Pr(X|K)=-6659, Var=927.1) ( FIGURE 2 ). STRUCTURE repartition of the ticks in K=7 subpopulations was projected on Coutada 9 Game Reserve Map for visual representation of isolation by distance ( FIGURE 3 ). In Coutada 9, most ticks from a same site appeared to be defined by a similar combination of more than one STRUCTURE subpopulation ( FIGURE 2D , FIGURE 3 ). In Coutada 9 Game Reserve, all K between K=3 and K=13 gave results less than 5% worse than the optimal K=7. Download figure Open in new tab FIGURE 2: Sample distribution according to genetic proximity. A and B , two dimensions factorial correspondance analysis of individual ticks. Percentages indicate the part of the differentiation explained by each axis. Each site is represented by one colour. C and D , STRUCTURE plots for the best K number of subpopulations. Each color represents one of the K subpopulations defined and the chance for each tick to belong to this subpopulation. Ticks are grouped by site with the site number under each group. A and C , analysis performed with all 24 sites. B and D , analysis performed with the 21 sites from Coutada 9 Game Reserve. Download figure Open in new tab FIGURE 3: Geographical representation of genetic repartition of the ticks in k=7 subpopulations. STRUCTURE assignation of the ticks for the best K number of subpopulations was used. Each color represents one of the K subpopulations defined by STRUCTURE. For each site, the mean percentage of assignation to each K subpopulation is represented. Factorial components analysis (FCA) were performed over all samples and are presented in FIGURE 2A . There were a significant first axe separating samples from Coutada 9 Game Reserve and Gorongosa National Park (Percentage of inertia according to the broken stick criterion over which first and second axes are significant: I 1 =21.1% and I 2 =14.9%). FCA presented in FIGURE 2B did not provide clear spatial contribution to the genetic distribution within Coutada 9 Game Reserve. Isolation by distance In Coutada 9 Game Reserve, significant isolation by distance was detected among sites with a highly significant signal (b=0.11, Mantel test 1,000,000 permutations, p-value=0.00002, 95% confidence interval=[0.076; 0.14], see SUPPLEMENTARY MATERIAL 6 ). Neighborhood size (N b ) was estimated at 9.0 individuals (95% CI=[7.0; 13.1]), and number of migrants per generation in a site (N e m) was estimated at 1.4 individuals (95% CI=[1.1; 2.1]). Effective population size Effective population size calculation with co-ancestry method gave a mean N e of 3.9 individuals per site (Jackknife for confidence interval: [0.96 ; 11]). Using the linkage disequilibrium method, a mean value of N e was calculated at 4.3 individuals per site (Jackknife for confidence interval: [1.4 ; 17]). A mean value of N e =4.1 will be used for further calculations ( TABLE 1 ). For the temporal method of calculation, site number 5 was used with two years between each sampling, corresponding to two generations of ticks. Results were similar to co-ancestry and linkage disequilibrium method (mean N e =3.9), with values of 3.7 individuals (Pollak method), 4.0 individuals (Jorde/Ryman method), and 4.1 individuals (Nei/Tajima method). Dispersal distance It is difficult to estimate how many burrows are frequently used by warthogs in the environment and how close they are from one another. Using an estimate of 16 burrows per km 2 , the effective density (D e ) was estimated to be 66 ticks/km 2 (95% CI [19; 231]), and the dispersion to be 209 meters/generation (95% CI [98; 466]) based on isolation by distance regression and N e estimations. From previous calculations, the product of population density and dispersal rate was estimated at D e σ 2 =0.71 (95% CI [0.55; 1.0]), leading to a migration rate (m) of 35% per generation (95% CI [14%, 92%]). Bottleneck For bottleneck analysis, we treated separately Coutada 9 Game Reserve and Gorongosa National Park. Results are presented in TABLE 2 . Coutada 9 Game Reserve presented a strong signature of bottleneck with all three models (IAM, TPM, SMM) having significant p-values, and with IAM and TPM models being highly significant. Gorongosa National Park presented as less clear signal probably due to the analysis of only three sites. View this table: View inline View popup Download powerpoint TABLE 2: Signature of bottleneck for three different models of mutation in Coutada 9 Game Reserve and Gorongosa National Park. Effective population size after the bottleneck N eb could be evaluated in Coutada 9 Game Reserve with both IAM and SMM models being significant N eb =[0.93; 2.32] individuals per site. Rocks versus burrows Two types of sites were collected, rocks and burrows. Comparisons between the two revealed no significant differences for F IS or N E but a significant difference in genetic diversity with the burrows presenting a higher level of genetic diversity than the rocks (Kruskal-Wallis test , p value=0.024). We tried to evaluate if rocks sites were part of the isolation by distance model or if they could be dead-ends for the ticks. Isolation by distance was tested when excluding the rocks and was still significant (b=0.15, Mantel test 1,000,000 permutations, p-value=0.00004). Adding back the rocks in the model improved the p-value obtained. Relatedness Adult ticks and late stage nymphs took more blood meals than smaller ticks and had more opportunities to be moved from a site to another by warthogs. Here we wanted to test if ticks with less dispersal opportunities (smaller size) were more related between them than ticks with more dispersal opportunities (larger size). For the two estimators of relatedness tested ( Queller and Goodnight 1989 / Lynch and Ritland 1999 ), there was no significant trend associated with the sum of dispersal opportunities. However, several dispersal scores presented significant differences of relatedness (Queller and Goodnight: Kruskal-Wallis test , p value=2.0×10 -7 ; Lynch and Ritland: Kruskal-Wallis test , p value=0.00026) ( FIGURE 4 and TABLE 3 ). For instance, comparison S-M versus S-S showed no difference in relatedness indicating that nymphs 1 (S) were not more or less related between them than they were with intermediate nymphs (M). Comparison M-M versus S-S on the opposite seemed to indicate that nymphs 1 were more related between them (S-S) than intermediate nymphs between them (M-M) according to both estimators of relatedness. View this table: View inline View popup TABLE 3: Dunn’s test for multiple comparisons between indicators of relatedness and different comparisons of tick size categories. Download figure Open in new tab FIGURE 4: Relatedness between ticks after comparison based on their category size. Discussion This study is the first to investigate the population structure of a tick vector of the African swine fever virus in an endemic area. As observed for other endophilic tick species ( Van Oosten et al. 2014 ), populations of Ornithodoros phacochoerus were expected to be spatially structured between parks, and between sites within parks, and structured in families within sites depending on the stage of development. Population structure At the scale of the two parks studied here, high genetic differentiation was found as previously suspected ( Taraveau et al. 2024 ). Gorongosa National Park and Coutada 9 Game Reserve are separated by 150km (in a straight line between sampled sites), and there are no warthog populations between the two conservation areas as a result of poaching pressure outside of conservation areas and human settlements (Quembo personal communication). This strong barrier to gene flow is supported by the observed distribution of the alleles detected: 44/65 alleles are specific to one of the reserves and the allelic pool is poorer in each of the reserves separately. It is interesting to note that the division of wildlife into smaller fragmented areas (more than 100 km separating Gorongosa National Park and Coutada 9 Game Reserve) probably prevents any gene flow, leading to a loss of genetic diversity within each area. This is a frequent outcome of habitat fragmentation and a common problem in conservation biology ( Hohenlohe, Funk, and Rajora 2021 ), and parasites are no exception. Such strict geographic isolation is likely to have evolutionary consequences leading to identify isolated populations as different species in the future, as it has already happened with the different species of the O. moubata complex. Within each park, the site was the first biological meaningful unit to consider. Ornithodoros phacochoerus are nidicolous ticks and are most commonly found in areas protected from sunlight, with controlled temperature and humidity, such as warthog burrows ( Vial 2009 ). Tick populations in Coutada 9 Game Reserve were strongly structured by sampling sites, confirming that burrows constitute a relevant unit for tick population study. Consequently, the sampling site was considered as our relevant subunit of population, as supported by the analysis performed with HierFstat. During sampling, ticks were also found in areas used for resting during days (rocks), suggesting that there may be multiple spots of tick presence in the environment, even outside of burrows. Similar to the conditions in burrows, rocks are shaded places and seem to convey buffered climatic conditions favorable to soft ticks (unpublished data). We expected higher levels of diversity in rocks than in burrows, since rocks are areas where many warthogs rest during the day and that are used by other mammal species (lot of animal movements, so we expected lot of tick movements). The opposite was found, with burrows having the highest genetic diversity. Warthog behavior in relation to burrows could explain this result, as burrows play a crucial role for warthogs and warthogs spend much time exploring, selecting and using new burrows ( Cumming 1975 ). It is also possible that ticks in rocks are more susceptible to adverse environmental conditions (predation, desiccation), making these areas less favorable to the ticks. Finally, other species may use both burrows (porcupines) and rocks (mostly ungulates), and the role of these other species in the distribution of tick populations remains difficult to assess. Analysis of the digested blood meals could provide information on which of these potential hosts are frequently encountered by ticks and could shape tick population structure. We found a strong signal of isolation by distance between the sites. Isolation by distance indicates that the genetic differentiation between individuals increases with geographical distance. Geographically closer individuals are more similar genetically speaking. Migration rates and distances were also evaluated: per generation, 35% of the effective population size was the result of migration. Migration distance was estimated to be about 200 meters per tick and per generation. Together, these results suggest that burrows are not isolated from each other, but that there are regular exchanges only between geographically close burrows. As very little to no migration occurs between burrows that are far from one another, the ticks from distant burrows are genetically more different from one another than ticks from close burrows. Previous genetic studies of ectoparasites have highlighted the influence of host movements on the migration patterns of their parasites ( Van Oosten et al. 2014 ; Dupraz et al. 2017 ; Moon, Chown, and Fraser 2019 ). Here also, it is highly probable that the density and behavior of warthogs, the main host of Ornithodoros phacochoerus , are shaping the tick population structure. In Coutada 9 Game Reserve, the warthog population is estimated at 4360 warthogs over 2380 km 2 (Mokore Safaris personal communication), or 1.8 warthogs/km 2 . In Gorongosa National Park, at the time of sampling, there were an estimated 5123 warthogs in an area of 1845 km 2 (Gorongosa National Park personal communication), or 2.8 warthogs/km 2 . Warthogs often live in small groups of up to five individuals ( Cumming 1975 ). Burrows are used for sleeping at night, and for shelter from predators, rain, or the midday sun during the day. Newborn warthogs also remain in burrows for the first few weeks of their lives for protection. There are many more burrows available than used (54–57 burrows/km 2 available compared to the 16 burrows/km 2 used by warthogs). Warthogs use multiple burrows over time, sometimes changing burrows daily, or returning to the same burrows from time to time. Some burrows may be abandoned or reused after long periods of time ( Cumming 1975 ). Warthog’s home range have been estimated to be 1.71 km 2 (ranging from 0.64 km 2 to 3.41 km) ( Cumming 1975 ). The home ranges of different warthog groups can overlap and include multiple burrows. As a result, Ornithodoros phacochoerus ticks probably have many opportunities to be moved from one burrow to another within small distances, which is consistent with the migration distance calculated here (200 meters per generation). Since burrows may be used by several different groups of warthogs, ticks may be moved from one group’s home range to another which leads to a pattern where there are no strict separations of groups of ticks based on warthog groups. Warthog densities are important enough to allow frequent passage in the burrows (and consequently, numerous dispersal opportunities for the ticks). Many burrows are abandoned which probably leads, at long term, to the extinction of subpopulations of ticks that occupied these burrows. This is coherent with the sampling observation that many burrows were completely free of ticks. Finally, a migration rate of 35% is quite a high value compared to other tick species. For instance, for Rhipicephalus microplus in New-Caledonia, an island model of migration was detected with a migration rate between adjacent sites estimated at 7% ( De Meeûs 2021 ). On the contrary, in mobile parasites with an isolation by distance pattern, such as the tsetse fly Glossina palpalis , migration rates can be even higher (27% to 55%) ( Bouyer et al. 2009 ; De Meeûs 2021 ). The high migration rate found here is not surprising as a result of frequent warthog movement and small effective population size. Even the movements of a few reproductive ticks have great consequences on gene flow and represent a consequent migration. However as explained, these migrations only take place at small geographic scale, maintaining the overall structuration of the population. Overall, the structure of tick population is coherent with what is known of warthog behavior, and it is likely that tick movements depend essentially on warthogs. The assignation to subpopulation units using STRUCTURE, gave a number of K=7 clusters. Several hypotheses could be made to explain this result. First, the number of subpopulations obtained with STRUCTURE may correspond to areas which are a bit similar genetically speaking as a result of being closer geographically as observed in FIGURE 3 . STRUCTURE subpopulations could then be the result of warthog social behavior, with subpopulations of ticks corresponding to subgroups of warthogs sharing the same area (same home range). As mentioned previously, home ranged are shared between warthog groups, leading to mix patterns as observed here. Alternatively, the number of clusters obtained with STRUCTURE may reveal environmental heterogeneity and constraints, limiting warthog movement. However, with the exception of one site on the other side of the river, there were no strong environmental barriers shaping the environment in Coutada 9 Game Reserve. Other explanations might be looked for by investigating the bottleneck signature. Each of the K=7 STRUCTURE subpopulations could correspond to descendants of one subgroup of ticks which survived the bottleneck. However, it is important to remind here that STRUCTURE doesn’t deal well with populations that follow an isolation by distance model as mentioned in STRUCTURE documentation: “Isolation by distance refers to the idea that individuals may be spatially distributed across some region, with local dispersal. […] The underlying structure model is not well suited to data from this kind of scenario. When this occurs, the inferred value of K, and the corresponding allele frequencies in each group can be rather arbitrary. Depending on the sampling scheme, most individuals may have mixed membership in multiple groups. […] In such situations, interpreting the results may be challenging.” ( Falush, Stephens, and Pritchard 2003 ). The STRUCTURE results presented here give hints of biological events happening in Coutada 9 Game Reserve, but should not be overinterpreted. More analysis would be needed to investigate and confirm these hypotheses. Population size and bottleneck Effective population sizes were calculated for each site. The numbers obtained were quite low, but consistent with the number of adults collected at each site. This suggests that only a few individuals survive until gaining access to reproduction in each generation. This is consistent with the low level of polymorphism observed for the microsatellite markers within a park and the low level of genetic diversity. The low numbers obtained for effective population size could be the result of a of loss of ticks to the environment during warthog movements, predation by other arthropod species ( Samish and Alekseev 2001 ), or natural death of the ticks. In addition, warthogs have been shown to select their burrows rather than using the nearest available burrow ( Cumming 1975 ). High tick infestation in a burrow may be a criterion for warthogs to avoid using a burrow, thereby regulating tick population size. Ornithodoros phacochoerus being a nidicolous species, high levels of inbreeding are expected ( Van Oosten et al. 2014 ). Here, when looking at tick relatedness, it appeared that the sites were not organized into families, with a mean relatedness between pairs of ticks close to 0 and little differences depending on the stage of the ticks (represented by the tick size category). Stage 1 nymphs (size category “Small”) tended be a bit more related between them than other groups. This is probably the result of several stage 1 nymphs being born from the same mother in the burrow, and having not gained any opportunity to migrate as they did not take any blood meal yet. We estimated that there was a migration rate of 35% between sites, and a neighborhood size of 9.0 individuals (which represents the number of individuals with which mating is susceptible to happen for an adult tick). Both these results suggest high gene flow between sites (high migration rates and neighborhood size twice as big as effective population size). We also expect that the 35% migration rate could apply to all stages of ticks (except nymphs 1, which have not yet taken a blood meal), resulting in the mixing of tick families at all stages, thus avoiding excessive inbreeding. In Mozambique, most conservation areas were largely depleted during the second half of the 20 th century due to colonial occupation and subsequent civil war ( Roque et al. 2022 ; Bento et al. 2023 ; Huntley 2023 ). For most mammal species, a strong bottleneck effect was reported. Warthogs were still present in low numbers in Coutada 9 Game Reserve or Gorongosa National Park and no reintroduction occurred for this species in the two reserves, but populations have been maintained through careful protection of the remaining animals (Coutada 9 Game Reserve and Gorongosa National Park personal communications). Bottleneck effects would be expected to affect terrestrial parasites as well as their hosts. This was investigated in this study, and a strong bottleneck signature was detected in the Coutada 9 Game Reserve. With a conservation program resumed in 2002 for Coutada 9 Game Reserve, the effective population size of ticks after the bottleneck event (N eb ) was estimated to be [0.93; 2.32] individuals per burrow. The strength of the signal using IAM, TPM, and SMM models suggested a strong bottleneck event. One hypothesis could be that as the number of warthogs declined, some sites were abbandonned completely, leading to the death of several subpopulations of ticks and to the bottleneck effect observed in Ornithodoros phacochoerus . Nei’s estimate of genetic diversity was quite low within sites, probably as a result of this bottleneck effect and small effective population sizes. The loss of genetic diversity following a bottleneck effect this strong would be almost impossible to recover from, even if populations can grow back to their initial size. All of the results presented here apply mostly to Coutada 9 Game Reserve, as the sampling scheme was much more informative than in Gorongosa National Park. Gorongosa National Park has a wide variety of habitats with strong divisions by rivers and lake and seasonal variations in water levels. Finding warthog burrows was much more difficult in Gorongosa National Park than in Coutada 9 Game Reserve, where most of the vegetation consisted of small to medium sized bushes with frequent cycles of bushfires keeping the vegetation low. It is highly likely that the results observed in Coutada 9 Game Reserve are applicable to most other wildlife conservation areas with warthogs, but more sampling should be done in Gorongosa National Park to confirm these results. Link with African swine fever virus Like other species of the O. moubata complex, Ornithodoros phacochoerus is a vector for the African swine fever virus ( Bernard et al. 2023 ), and both Coutada 9 Game Reserve (Lameira, unpublished data) and Gorongosa National Park ( Quembo et al. 2017 ) have been confirmed to have a sylvatic cycle for African swine fever virus. Based on the information collected in this study, Ornithodoros ticks could play three roles in the epidemiology of the African swine fever virus. 1) Because of the tick migration associated with the frequent movements of warthogs between burrows, it is highly likely that ticks are moving ASF virus from burrows to burrows and from warthog groups to warthog groups, thus being an active actor in the ASF sylvatic cycle. 2) Because of their endophilic behavior and long lifespan ( Vial 2009 ), ticks also have a role as maintenance hosts for the virus within the burrows, possibly for several years, even when the burrows are not used for a long period of time. They are also able to transmit the virus to young warthogs that remain within the burrows and which are the more likely to become viremic after infection, potentially infecting more ticks ( Jori et al. 2023 ). Soft ticks should be able to transmit the virus from the sylvatic cycle to the domestic cycle. Since we were not able to find ticks in pigpens around Coutada 9 Game Reserve, it was not possible to properly tackle this issue. Nonetheless, based on the tick migration distances calculated in this study and estimates of warthog home ranges ( Cumming 1975 ), the viral transmission to pigs by soft ticks would occur if free-ranging pigs or unfenced pigpens were present in close proximity to the conservation areas. This has already been suspected in Gorongosa National Park, where pigs near the park were infected with the same ASF genotype as ticks and warthogs from inside the park ( Quembo et al. 2017 ). Since tick migration distance estimated per generation is limited (200 meters) a buffer zone of half a kilometer without any domestic pigs should be sufficient to prevent Ornithodoros soft ticks to cause any spillover of the African Swine fever virus from wild suids to domestic pigs. The most important message to take away from this study is one that has already been said many times before ( Plowright et al. 2024 ): buffer zones around conservation areas are essential, in part to prevent the spillover of wildlife pathogens to domestic animals. This is true for African swine fever virus, and our results suggest that a buffer zone of half a kilometer without any domestic pigs should be sufficient for the risks associated with Ornithodoros soft ticks. Conclusion The population genetic analysis performed here suggested that Ornithodoros phacochoerus populations were organized at the scale of the site (burrows and rocks) within conservation areas, with gene flows following an isolation by distance model. Strong bottleneck effects were observed in Mozambican conservation areas, and very limited gene flow appeared to occur between distant areas. Effective population sizes within sites were quite low with a mean value of 4.1 individuals gaining access to reproduction per site. Dispersal occurred over short distances (98 to 466 meters around the site) but was quite frequent due to frequent movements of warthogs. Due to the veterinary risk associated with Ornithodoros phacochoerus (vector of the African swine fever virus), our study reinforces previous recommendations: the enforcement of buffer zones around conservation areas is necessary, both for the protection of wildlife and, as suggested in this study, to prevent the spillover of wildlife pathogens into the domestic reservoir. This study supports the use of a buffer zone of at least 0.5 km without any domestic pigs around conservation areas with a sylvatic cycle of African swine fever virus. This recommendation considers only the risk associated with the transmission of the virus by soft ticks. The risk associated with direct contact between warthogs and domestic pigs should be evaluated as already described elsewhere. Author contributions FT coordinated the study, collected the samples in the field, performed the experiments, analyzed the data, and wrote the manuscript. DB performed the experiments, and analyzed the data. TP obtained the funding, and coordinated the study. MJ, MD, and EL collected the samples in the field, and performed the experiments. AA performed the experiments. AF and JC collected the samples in the field. CQ obtained the funding, coordinated the study, provided the material, and collected the samples in the field. HJ-P obtained the funding, conceived and coordinated the study, provided the material, collected the samples in the field, and performed the experiments. All authors read and corrected the final manuscript. Data, scripts and codes availability Data supporting the findings presented here are available within this paper and its supplementary material, genotyping dataset for the populations studied here will be available on Dryad repository shortly after publication. Supplementary material Download figure Open in new tab SUPPLEMENTARY MATERIAL 1: Pictures of two “rocks” sites and two “burrows” sites that were sampled for Ornithodoros ticks. A . and B . rocks. C . and D . burrows. Download figure Open in new tab SUPPLEMENTARY MATERIAL 2: Mean F IS at each locus for the 24 sites considered. F IS calculated using Weir and Cockerham estimation (Smallf), Jackknifing over populations for confidence interval. Mean value is indicated with a black square and confidence interval are indicated with upper and lower lines. Calculations performed using Fstat. Loci ms-61, ms-76, and ms-111 were eliminated. View this table: View inline View popup Download powerpoint SUPPLEMENTARY MATERIAL 3: Quality of the nineteen microsatellite loci View this table: View inline View popup Download powerpoint SUPPLEMENTARY MATERIAL 4: Population genetic parameters in each site. The number of ticks with successful DNA extraction and amplification is indicated (n). Number of alleles in each site in total for the 16 loci (N alleles ), expected (H E ) and observed (H O ) heterozygosity, and deviation from Hardy-Weinberg (HW) equilibrium. Download figure Open in new tab SUPPLEMENTARY MATERIAL 5: F ST estimates between sites. A . Value calculated using FreeNA with the ENA correction method for null alleles, 5000 bootstraps. B . and C . Lower and upper values of 95% confidence interval. First column and first line contain the site number. Each colored cell contains the F ST for the pair of sites compared. Nuancing was made: yellow for small value of F ST , green for high values of F ST . Download figure Open in new tab SUPPLEMENTARY MATERIAL 6: Graphical representation of isolation by distance between sites in Coutada 9 Game Reserve. F ST /(1-F ST ) against ln transformation of the distance between sites in km. Conflict of interest disclosure The authors of this preprint declare that they have no financial conflict of interest with the content of this article. Funding This study was supported and financed by the Ecology and Evolution of Infectious Diseases Program, grant no. 2019-67015-28981 from the USDA National Institute of Food and Agriculture, in a project entitled “Unraveling the effect of contact networks & socio-economic factors in the emergence of infectious diseases at the wild-domestic interface” ( https://www.asf-nifnaf.org/ ). Acknowledgements The authors would like to thank the students from the IIAM who helped for sample sorting and DNA extractions, Edna Manuel Meque, Isaque António Francisco, Matias José Dina, Neidy Alfredo Lopes, Albertina Jaime Chabissa and Luisa Duarte Madeira, as well as Estelle Pineau who helped for tick sampling and DNA extractions. We would like to thank Mokore and Western camps which provided help and shelter during sampling in Coutada 9 Game Reserve. We would like to thank Gorongosa National Park for permission to conduct research (scientific permit PNG/DSCI/C235/2022), we are grateful for the staff for their logistical support. We also would like to thank Ronan Rivallan from the AGAP Institut for the great help on the GPTR platform. Many thanks to Thierry De Meeûs from the Intertryp unit, CIRAD, IRD, for his formation in population genetics and his great advices in the analysis of the genotyping results. 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