Detecting and quantifying rare sex in natural populations

Detecting and quantifying rare sex in natural populations | 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 Detecting and quantifying rare sex in natural populations View ORCID Profile Tymoteusz Pieszko , View ORCID Profile Jerome Kelleher , Christopher G. Wilson , View ORCID Profile Timothy G. Barraclough doi: https://doi.org/10.1101/2025.06.03.657731 Tymoteusz Pieszko a Department of Biology, University of Oxford , 11a Mansfield Road, Oxford OX1 3SZ, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tymoteusz Pieszko For correspondence: tymoteusz.pieszko{at}stx.ox.ac.uk Jerome Kelleher b Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford , Oxford OX3 7LF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jerome Kelleher Christopher G. Wilson a Department of Biology, University of Oxford , 11a Mansfield Road, Oxford OX1 3SZ, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Timothy G. Barraclough a Department of Biology, University of Oxford , 11a Mansfield Road, Oxford OX1 3SZ, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Timothy G. Barraclough Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract The distinction between sexual and asexual reproduction is fundamental to eukaryotic evolution. Testing theories about the evolution of reproductive modes first requires knowing whether sex is present or absent in a population. While this seems straightforward, the literature on asexuality reflects a history of shifting claims and uncertainty regarding reproductive mode, especially where sex is potentially rare or cryptic. Here, we develop a new framework to explore the challenges in detecting and quantifying sexual reproduction from population genomic data, based on genetic simulations. We first show that commonly calculated population genetic statistics do not reliably distinguish sexual and obligate asexual scenarios if asexuality is accompanied by sex-independent homologous recombination, as emerging evidence suggests is often the case. We then present a new method to quantify the relationship between evolutionary trees and mode of reproduction by exhaustively classifying local trees for pairs of diploid individuals using ancestral recombination graphs (ARGs). This approach accurately distinguishes signatures of genetic exchange and homologous recombination, although uncertainty remains due to unavoidable biases in the steps needed to reconstruct trees from genome data. We introduce a new statistic and simulation models to account for common reconstruction biases. Our approach offers the potential for improved quantitative inference of reproductive modes that is extendable and applicable to a broad range of eukaryotes. Significance Statement Determining how often, if at all, organisms have sex has implications across biology. Studies often use population genomic data to interrogate the private life of putative asexuals, but the answers prove surprisingly inconclusive. We develop a framework to explore the challenges in detecting and quantifying rates of sex. New simulation models show how sex-independent recombination, which occurs widely across a range of asexual eukaryotes, causes genetic patterns to resemble sexual populations, even when sex is absent. An approach based on ancestral recombination graphs (ARGs) and classification of local trees accounts for these problems and quantifies remaining uncertainty due to inevitable reconstruction biases. Our framework will enable improved inferences of reproductive mode across a wide range of eukaryotes. Introduction Sex is a defining feature of eukaryotic life, and fundamental questions about its benefits, costs and prevalence remain of considerable interest in evolutionary biology ( Hartfield & Keightley, 2012 ; Maynard Smith, 1978 ; Otto, 2021 ). Testing hypotheses about the evolution of reproductive mode depends critically on the ability to detect the presence or absence of sex reliably and, if sexual and asexual reproduction co-occur, to estimate the frequency of sex accurately. Because direct observations in the wild are often infeasible (but see Neiman et al., 2018 ), a more common approach has been to use DNA sequence data to infer the rates of different modes of reproduction ( Schurko et al., 2009 ; Tibayrenc et al., 1991 ), first from genetic markers (e.g., Burt et al., 1996 ; Kuhn et al., 2021 ) and now chiefly with whole genome data ( Brandt et al., 2021 ; Freitas et al., 2023 ; Vakhrusheva et al., 2020 ). Sexual reproduction has important consequences for genetic variation ( Otto, 2003 ) and therefore patterns of sequence evolution were long predicted to provide clear evidence of reproductive modes ( Schurko et al., 2009 ; Tibayrenc et al., 1991 ; Maynard Smith, 1999 ; Normark et al., 2003 ). In some taxa, this approach yielded insights that were later validated ( Eitel et al., 2011 ; Signorovitch et al., 2005 ). However, numerous other cases, including of agricultural ( Lee et al., 2024 ; Yildirir et al., 2020 ) and medical ( Carpenter et al., 2012 ; Cooper et al., 2007 ; Poxleitner et al., 2008 ; Bradic & Carlton, 2018 ) importance, remain ambiguous. The literature on asexual animals alone demonstrates the complexities in applying these methods, with numerous revised claims and uncertainty that spans decades of study ( Freitas et al., 2023 ; Vakhrusheva et al., 2020 ; Boyer et al., 2021 ; Mark Welch et al., 2008 ; Molinier et al., 2025 ; Schwander et al., 2011 ). Classically-predicted features of obligate asexuality ( Normark et al. 2003 ) have failed to materialise in genomic surveys ( Jaron et al., 2021 ; Nowell et al., 2018 ), while sequence-based evidence for the presence of cryptic sex sometimes contradicts the described natural history ( Wilson et al., 2024 ). Before we can confidently assess which reproductive modes are present, we need to understand why this remains such a difficult task. One fundamental problem is that many tests do not formally separate the two main components of sexual reproduction—crossover recombination and genetic exchange mediated by the syngamy of haploid gametes ( Fig. 1A ). It is now clear, however, that homologous recombination between diploid chromosomes also occurs during asexual reproduction via a range of mechanisms, such as gene conversion, crossing-over, and break-induced replication associated with mitotic or meiotic pathways ( Jinks-Robertson & Petes, 2021 ; Engelstädter, 2017 ). We group such sex-independent mechanisms of recombination using the term ‘asyngamous recombination’ ( Wilson et al., 2024 ). The main evidence for asyngamous recombination comes from the loss of heterozygosity (LOH) ( Fig. 1B and C ), observed even in lab-reared populations where the contribution of sex can be excluded. LOH is widespread across a range of asexual eukaryotes, including plants such as pineapple ( Chen et al., 2019 ), fungi such as Saccharomyces cerevisiae ( Sui et al., 2020 ), Candida albicans ( Forche et al., 2011 ) and Aspergillus nidulans ( Cardoso et al., 2010 ), diatoms ( Bulankova et al., 2021 ), and parthenogenetic animals ( Flynn et al., 2017 ; Houtain et al., 2024 ; Simion et al., 2021 ). Some forms of asyngamous recombination additionally result in reciprocal exchanges between the homologs ( Fig. 1D ) ( Jinks-Robertson & Petes, 2021 ; Mateus et al., 2022 ). Yet, most genetic tests for sex in wild populations still assume that asyngamous recombination is negligible (e.g., Vakhrusheva et al., 2020 ; Ali et al., 2016 ; Nicoll et al., 2024 ; Stoeckel et al., 2021 ; Tsai et al., 2008 ), whereas others consider the evolutionary consequences of asyngamous recombination but assume obligate asexuality in the study system (e.g., Jaron et al., 2021 ; Jaron et al., 2022 ; Kershenbaum et al., 2023 ; Flot et al., 2013 ). There is hence an apparent mismatch between the bulk of theory, which assumes asexually-produced offspring only differ by new mutations, and growing evidence that asexual reproduction involves homologous recombination. Download figure Open in new tab Fig. 1. Immediate effects of sexual and asexual reproduction on sequence diversity across a diploid chromosome arm. (A) Sexual reproduction with crossover recombination. (B) Mitotic parthenogenesis with gene conversion. Gene conversion during mitotic parthenogenesis results in localised loss of heterozygosity (LOH). (C) Meiotic parthenogenesis (central fusion automixis) with crossover recombination. Crossovers during central fusion automixis may result in long-range LOH or reciprocal recombination with a change of linkage (D). Another key issue is the limited consideration given to stochasticity and inference error. For instance, it has long been recognised that different regimes of DNA transmission are expected to result in distinct genealogical relationships ( Birky Jr, 1996 ; Koufopanou et al., 1997 ). In diploids, clonal transmission should group homologs from different individuals as two symmetric sub-trees corresponding to allelic copies 1 and 2 ( Birky Jr, 1996 ; Lam et al., 2011 ; Mark Welch & Meselson, 2000 ). However, past applications of these approaches have not always accounted for the various reasons why trees might deviate from patterns compatible with strict asexuality. While some studies treated any such deviations as strong evidence of genetic exchange ( Vakhrusheva et al., 2020 ; Debortoli et al., 2016 ; Laine et al., 2022 ; Signorovitch et al., 2015 ; Chen et al., 2018 ), inferred trees will vary for additional biological and technical reasons, including non-neutral evolution, biases in the mutation process, gene and genome duplications and loss, and reconstruction artefacts ( Wilson et al., 2024 ; Auxier & Bazzicalupo, 2019 ; Steenwyk et al., 2023 ; Wilson et al., 2018 )—alongside asyngamous recombination as outlined above. Here, we investigate the detection and quantification of sex in eukaryotic diploids by developing new individual-based models. Our models incorporate recognized mechanisms of asexual reproduction and asyngamous recombination ( Neiman et al., 2014 ), enabled by efficient and flexible forward-time simulations in SLiM4 ( Haller et al., 2019 ; Haller & Messer, 2023 ). We first show that common population genetic statistics do not distinguish between sexual and obligate asexual scenarios under a realistic range of asyngamous recombination rates. We then develop a new tree-based approach that captures genome-wide heterogeneity using ancestral recombination graphs (ARGs). Increasingly recognised as a natural framework to disentangle reticulate evolution ( Nielsen et al., 2025 ; Lewanski et al., 2024 ; Brandt et al., 2024 ), ARGs describe evolutionary histories of genome regions delimited by recombination breakpoints ( Wong et al., 2024 ) which can be represented as local trees ( Fig. 2 ). Specifically, we decompose the problem by exhaustively classifying local trees for pairs of diploid individuals according to reproductive mode, and introduce a novel tree-based statistic that succinctly captures evidence for a genome-wide asexual history. Our results highlight, however, that even the most powerful current approaches are affected by unavoidable technical biases. Taken together, our work establishes a simulation-based framework for inferring the presence and rate of sexual reproduction from population genomic data and quantifies the inherent uncertainty in tests of reproductive modes. Download figure Open in new tab Fig. 2. Patterns of sequence diversity are reflected in a heterogenous genealogy. In this example, the genealogy of two diploid individuals (blue and orange, with chromosomes 0, 1 and 2, 3, respectively) changes due to asyngamous recombination. The resulting patterns of diversity, with loss of heterozygosity (LOH) in one (blocks II and IV) or both (blocks V and VI) individuals, ancient (e.g., block II) or more recent (e.g., block VI), are best represented as the underlying ancestral recombination graph (ARG) with local trees that describe non-recombining histories. Results Simulated scenarios of mixed reproduction We constructed models of mitotic parthenogenesis (referred to as ‘MP’) and automixis via central fusion (‘CF’). The models incorporate asyngamous recombination in the form of gene conversion (MP) and crossover recombination (CF). In the MP model, each breakpoint initiates a gene conversion tract with only a local effect that always results in LOH. In the CF model, crossovers affect all the downstream positions of the chromosome but only 50% of recombinant regions result in LOH, with a further 25% resulting in a change of linkage. Hence, while ‘central fusion’ sensu stricto describes a specific cytological pathway (Stenberg and Saura, 2009), our model applies to any form of abortive meiosis where ploidy is maintained via co-segregation of non-sister chromatids ( Beukeboom & Pijnacker, 2000 ; Eisman & Kaufman, 2007 ; Hiruta et al., 2010 ; Terwagne et al., 2022 ). Full model descriptions and validation with analytical results ( Engelstädter, 2017 ) can be found in Methods and SI Appendix, section A. We ran simulations using a Snakemake pipeline ( Mölder et al., 2021 ) described in Methods and SI Appendix, section B. We simulated scenarios where a 1 Mb chromosome arm evolves neutrally in populations of N = 1,000 individuals reproducing sexually with probability σ and asexually with probability 1 − σ under both modes in turn. We explored parameter space covering scenarios of obligate asexuality ( σ = 0), rare sex (0 < σ ≤ 10 −3 , i.e., , where N is the population size) ( Bengtsson, 2003 ; Hartfield et al., 2018 ) and frequent sex (10 −3 < σ ≤ 10 −0.5 , where 10 −0.5 ≈ 0.316) to reflect the zone of uncertainty for studies aiming to test for the presence and rate of sex. The per-site per-generation rate of asyngamous recombination varied between 0 and 10 −6 . This rate can alternatively be expressed in terms of the loss of heterozygosity (LOH) rate per site γ ( Kopčak & Hartfield, 2024 ) and we follow this convention hereafter, letting γ vary from 0 to 0.005 under MP (eq. 7 in SI Appendix, section A) and from 0 to 0.16 under CF (eq. 6). Unless stated otherwise, we present results based on the MP model; we discuss the differences between the models in SI Appendix, section C. Commonly calculated statistics are often uninformative about reproductive mode As expected from previous theory ( Tibayrenc et al., 1990 ; Hartfield et al., 2016 ), the statistics of H I (individual-level heterozygosity), F IS (inbreeding coefficient) and r 2 (calculated for SNVs < 1,000 bp apart as a measure of linkage disequilibrium decay) tended towards values predicted for a fully sexual, randomly mating population as sexual reproduction became more frequent ( Fig. 3 : H I tended towards 4 N e μ = 0.002; F IS tended to 0 for an outbred population; r 2 at 1,000 bp tended to low values). In the absence of asyngamous recombination (bottom rows of the heatmaps in Fig. 3 ), the transitions between asexuality and sex-driven values were sharpest at σ ≈ 0.001 (i.e., ), which we define as the transition from rare to frequent sex on the basis of earlier studies ( Bengtsson, 2003 ; Hartfield et al., 2018 ). In the absence of asyngamous recombination and sex (bottom left), the statistics tended towards values expected under clonal reproduction (high H I and r 2 , F IS ≪ 0) ( Birky Jr, 1996 ; Tibayrenc et al., 1990 ). Download figure Open in new tab Fig. 3. Variation in H I , F IS and r 2 for SNVs < 1,000 bp apart across the simulated space of the sex rate σ and the LOH rate γ ; MP model. For each scenario, mean statistic values were obtained for samples of 100 random individuals and averaged across 100 replicates. Introducing increasing rates of asyngamous recombination, however, led to a wide range of H I , F IS and r 2 values under both obligate asexuality and rare sex ( Fig 3 ). For instance, under obligate asexuality, H I varied between <0.001 and 0.007 ( Fig. 3A )—values that are within the range of estimates from sexual (e.g., Mackintosh et al., 2019 ; Romiguier et al., 2014 ) and predominantly asexual groups ( Jaron et al., 2021 ; Nowell et al., 2018 ). F IS varied between strongly negative values at the lower end of the simulated γ range (≈ −0.4) to strongly positive values at its higher end (≈ 0.4; Fig. 3B ), which would normally be interpreted as evidence of inbreeding. Finally, there was considerable LD decay (SNVs < 1,000 bp apart) with r 2 ≤ 0.11 for γ ≥ 5 × 10 −4 ( Fig. 3C ). Under CF, the patterns were qualitatively similar but reflected the higher overall rates of LOH (SI Appendix, Fig. S1). Very high rates ( γ > 2.4 × 10 −2 ) led to less LD decay (SI Appendix, Fig. S1C), as there was little diversity left. An appealing approach to test for sex is to compare population genetic metrics in a target population with either a theoretical sexual expectation (e.g., Vakhrusheva et al., 2020 ) or patterns in related populations known to reproduce sexually (e.g., Freitas et al., 2023 ; Phan Thi et al., 2025 ), with the assumption that overlapping values are sufficient to reject the target’s obligate asexual status. However, our results indicate that for each of the statistics, there exists a range of γ where these metrics are comparable in value between populations with frequent sex and their obligate asexual counterparts ( Fig. 3 ; SI Appendix, Fig. S1). This range appears very narrow for H I and F IS ( γ ≈ 5 × 10 −4 ; Fig. 3 ; SI Appendix, Fig. S1), as previously noted by Vakhrusheva et al. (2020) , whose analytical model predicts F IS ≈ 0 only if γ is almost precisely , a scenario they consider unlikely. Using a likelihood analysis based on our numerical approach, we explored whether sampling stochasticity could broaden the conditions for observing F IS ≈ 0 under obligate asexuality. Under MP, the results were consistent with the analytical model, with realistic sample sizes ( n = 5, 10 or 15) yielding high support for the scenario of (Akaike weights A w = 64%, 91% and 96%, respectively; SI Appendix, Fig. S2). However, under CF, support for was lower ( A w = 40%, 55% and 59%, respectively; SI Appendix, Fig. S3), with up to 57% A w support ( n = 5) for scenarios where γ varied 10-fold around . We hence conclude that, with small sample sizes and especially in the presence of automictic crossing-over, observing F IS ≈ 0 is not unlikely even in the absence of sexual reproduction. The γ range where σ had little effect on calculated values was wider for LD decay, spanning scenarios of γ ≥ 5 × 10 −4 ( Fig. 3C ; Fig. 1SC). While recognising that asyngamous recombination creates LD decay, some authors have interpreted certain characteristics of the decay curves as specifically indicating sex, such as the steepness of decay ( Freitas et al., 2023 ; Vakhrusheva et al., 2020 ) or a specifically ‘logarithmic-exponential’ shape ( Phan Thi et al., 2025 ) if r 2 is plotted as a function of inter-site distance. To evaluate these verbal arguments, we plotted r 2 curves ( n = 10, a maximum of 500 SNVs sampled per simulation) across the MP and CF scenarios (SI Appendix, Fig. S4, S5). Especially under MP, the shape of the curves was remarkably similar under frequent sex and a range of obligate asexual scenarios ( γ between 5 × 10 −5 and 1.6 × 10 −3 ), with LD falling to r 2 ≈ 0.16 even within the first 20 kb (for γ = 5 × 10 −4 ), rendering these properties uninformative. Taken together, we conclude that classical population genetic statistics cannot be used to infer cryptic or rare sex when the cytological basis of asexuality is incompletely understood and asyngamous recombination has not been quantified. Tree composition analysis We present a new method for quantitative tree-based analysis of reproductive history. This approach divides the 18 possible two-individual, four-tip coalescent trees ( Wakeley, 2008 ) into the following categories: (1) Category CL: compatible with clonality (4 trees; blue in Fig. 4A ). Trees whose topologies are compatible with asexual descent without recombination (clonality). In CL trees, homologs from different individuals cluster together ( Birky Jr, 1996 ). (2) Category AR: compatible with asyngamous recombination (6 trees; green in Fig. 4A ). Trees whose topologies are compatible with asyngamous recombination in an asexual background. Asyngamous recombination can only result in a subset of topological changes that bring homologs together within asexual lineages ( Birky Jr, 1996 ). (3) Category SX: specific to sex (8 trees; orange in Fig. 4A ). Trees whose topology is sex-specific, i.e., can only be generated through sexual reproduction or other forms of genetic exchange. When trees for more than 2 individuals are considered, these include ‘allele sharing’ patterns used by earlier studies ( Vakhrusheva et al., 2020 ; Laine et al., 2022 ; Signorovitch et al., 2015 ; Vastrade et al., 2022 ). Download figure Open in new tab Fig. 4. Systematic analysis of tree composition. (A) Coalescent trees for individuals A and B (with alleles 0, 1 and 2, 3, respectively) include 4 CL (blue), 6 AR (green) and 8 SX (orange) trees. (B)-(E) p ( CL ), p ( AR ) and p ( SX ) across the space of sex rates σ and LOH rates γ ; MP model. Note that the colour scale is capped at 1 in the p ( CL ) and p ( AR ) heatmaps, and at 0.5 in the p ( SX ) heatmaps. (B) True values. (C) Heterozygosity bias of β = 0.1. (D) IQ-TREE inference. (E) SINGER inference. We propose that the contributions of asexual and sexual reproduction to a genealogy can be quantified as the relative spans of local CL, AR and SX trees— a summary we refer to as the ‘tree composition’. Given a genealogy for n individuals (2 n tips), our approach enables exhaustive classification of signatures by aggregating them across all the possible relationships (SI Appendix, Fig. S6). This pairwise decomposition moves beyond the use of trees as a categorical signature (where patterns such as ‘allele sharing’ are used as a 0-1 indicator of sex) while avoiding the combinatorial impracticality of attempting to classify all possible trees for more than two individuals. In contrast to past studies that used arbitrarily delimited genome regions (e.g., entire scaffolds) to reconstruct trees ( Laine et al., 2022 ), an ARG framework explicitly accounts for the varying genomic span of local non-recombining trees. Below, by sampling two-individual ARGs from simulation output (see Methods), we explore the variation in the expected tree composition across the simulated space of σ and γ . Tree composition discriminates asexual and sexual scenarios in true ARGs Across the parameter space examined, the tree composition of true ARGs (i.e., without reconstruction error) reflected the rates of the underlying generative processes ( Fig. 4B ; see SI Appendix, Fig. S7A for the CF results). As σ increased, the proportion of SX trees p ( SX ) tended towards the value expected under outcrossing ( Wakeley, 2008 ), with a steep transition at the boundary between rare and frequent sex. This transition was shifted to higher σ at the higher end of γ . Under obligate asexuality without recombination (bottom left of the heatmaps in Fig. 4B ), only category CL trees were represented; p ( CL ) decreased, and p ( AR ) increased with γ (left-most columns). We conclude that tree composition expectations distinguish scenarios of obligate asexuality, rare sex and frequent sex, assuming that local trees can be accurately estimated. Heterozygosity bias in phased data increases the proportion of CL trees Implementing our method (or any analysis of diploid copies) requires phasing of haplotypes within individuals, but with short-read data, this is only possible for relatively heterozygous regions of the genome ( Martin et al., 2016 ). This introduces a potential ‘heterozygosity bias’, i.e., that regions with high H I are more likely to be included. For instance, among published genomic studies on asexual animals, the proportion of the reference assembly recovered in phased segments is < 10% ( Brandt et al., 2021 ; Vakhrusheva et al., 2020 ; Laine et al., 2022 ) and only occasionally higher (21%; Öztoprak et al., 2025 ). To evaluate this issue, we added a ‘heterozygosity bias’ to our pipeline, which we denote by β , representing the proportion of genomic regions with the highest mean H I that are sampled. Based on estimates from real datasets, we modelled the effect of a bias of β = 0.1 across parameter space. Adding the heterozygosity bias increased p ( CL ) at the expense of p ( AR ) across the simulated scenarios ( Fig. 4C ; SI Appendix, Fig. S7B). Under frequent sex ( σ = 10 −0.5 ), p ( CL ) is expected to approach (≈ 0.22), but the bias increased this approximately two-fold ( p ( CL ) ≈ 0.45), as would be consistent with a rate of sex two orders of magnitude lower ( σ = 10 −3 ), or even with obligate asexuality (when γ = 5 × 10 −4 ). The impact on p ( SX ) was more subtle. In regions of parameter space with high H I (approx. H I ≥ 4 N e μ), p ( SX ) decreased; on the other hand, p ( SX ) increased in the regions with low H I (SI Appendix, Fig. S8). Taken together, a realistic bias can drastically change the expected relative proportions of CL and AR trees, which could lead to overestimates of the contribution of clonality to the reproductive history. Reconstruction artefacts increase the proportion of SX trees True genealogies are unknown for natural populations and must be inferred from sequence data. To investigate how reconstruction errors might impact or bias estimates of the tree composition, we inferred trees from simulated datasets using two approaches: a phylogenetic approach using equally sized genomic windows for the maximum likelihood inference of local trees (IQ-TREE v2; Fig. 4D ; SI Appendix, Fig. 7C) ( Minh et al., 2020 ) and an ARG reconstruction method that identifies recombination breakpoints and infers local trees under the assumption of the sequentially Markovian coalescent (SINGER; Fig. 4E ; SI Appendix, Fig. 7D) ( Deng et al., 2024 ). We chose these methods as contrasting approaches with different strengths and weaknesses (SI Appendix, section D), and applied them in combination with a heterozygosity bias ( β = 0.1) to reflect the typical data currently available. Under obligate asexuality, both methods erroneously inferred SX trees, with maximum p ( SX ) of 0.17 and 0.21 for IQ-TREE and SINGER, respectively ( Fig. 4D and E ). Since p ( SX ) increased with γ , the mis-inference of SX trees may be attributed to asyngamous recombination, acting via mixed signal from CL and AR trees, mutational stochasticity in AR regions, or both. While IQ-TREE over- or under-estimated p ( SX ) across the simulations (SI Appendix, Fig. S9), SINGER inference consistently resulted in overestimates, but less so with increasing σ (SI Appendix, Fig. S10), consistent with an increasing match to SINGER’s assumption of a sexual population model ( Deng et al., 2024 ). Taken together, we conclude that the two inference methods interact with asyngamous recombination to introduce different biases to tree composition. In both cases, however, the recovery of SX trees from phased genomic data, even at frequencies otherwise consistent with intermediate rates of sex ( Fig. 4B ), cannot be taken as a signature strongly incompatible with obligate asexual reproduction. A tree-derived statistic that is robust to sampling artefacts Under obligate asexuality, all four external branches of CL trees should have equal lengths, reflecting the number of generations to the most recent common ancestral individual. If CL-topology trees are generated through sexual exchange, however, coalescence times should vary between the two external subtrees across the genome, as they do not all necessarily reflect inheritance from the same common ancestral individual. To quantify the contributions of the two types of CL trees to a genealogy and discriminate between reproductive scenarios, we devised a simple statistic that measures the degree of asymmetry between external branch lengths. Specifically, we define Δ m (for ‘difference in mutation counts’) as: Where is the mean absolute difference in mutation counts m between the branches of the two external subtrees, is the mean absolute difference in m within subtrees, and m̄ is the mean number of mutations on external branches. For example, given tree I, and ( Fig. 5A ). The expected value of Δ m averaged across all CL trees is therefore 0 under obligate asexuality and > 0 if sexual reproduction contributed to the genealogy ( Fig. 5A ). The degree of the deviation from 0 should be proportional to the degree of variation in the coalescence times of internal nodes. Download figure Open in new tab Fig. 5. A tree-based statistic Δ m to distinguish obligate asexual and sexual scenarios. (A) E(Δ m ) depends on the degree of asymmetry in internal node times; example of a type I tree ( Fig. 4A ). (B)-(E) Δ m across the space of sex rates σ and LOH rates γ ; MP model. (B) True values. (C) Heterozygosity bias of β = 0.1. (D) IQ-TREE inference. (E) SINGER inference. As previously, two important considerations are whether (1) Δ m unambiguously distinguishes obligate asexual and sexual scenarios and (2) it is robust to the artefacts identified above. To assess these conditions, we calculated Δ m across the simulated parameter space ( Fig. 5B ; see SI Appendix, Fig. S11 for the CF results). According to the expectation, Δ m was ≈ 0 under obligate asexuality and increased with σ , reaching 0.55 when σ = 10 −2 and γ = 5 × 10 −6 ( Fig. 5B ). This relationship was not monotonic, however, and Δ m values decreased at the higher end of simulated σ as well as with increasing γ (down to ≈ 0.20 and 0.1 when γ = 5 × 10 −3 ). Heterozygosity bias had little impact on Δ m across the parameter space ( Fig. 5C ). Finally, inference error introduced either by IQ-TREE ( Fig. 5D ) or SINGER ( Fig. 5E ) resulted in inflated Δ m values under obligate asexuality, especially at the higher end of γ (reaching 33% and 18% of the maximum observed value, respectively), an effect that was nonetheless more modest than the impact of inference error on p ( SX ) (39% and 48% inflation, respectively). Taken together, these results indicate that Δ m is a promising tool to distinguish between asexual and sexual scenarios under realistic assumptions of data quality and sampling error. Predictability and discriminability of different scenarios To explore how robustly the metrics can predict the rate of sex, considering levels of stochasticity across σ scenarios, we performed two additional analyses. First, for each summary statistic and γ scenario, we fitted a 2 nd -degree polynomial regression with statistic values for individual simulations as the predictor and σ as the response variable (Table S3). The rate of sex σ was consistently strongly predicted by p ( SX ) across all γ scenarios, with the R 2 coefficient ranging from 0.65 to 0.72. For other statistics, the strength of this relationship depended on γ ; for example, H I and F IS predicted σ strongly except when . Interestingly, Δ m showed considerable variability ( R 2 < 0.43), a result we attribute to the short chromosome length used in our simulations. Second, we examined how confidently the statistics can discriminate between particular σ scenarios. For focal scenarios of σ = 0.0 and 10 −3 in turn, we assessed how often statistic values across the studied range of σ fell outside the 95% interval under the focal scenario—that is, how often they rejected the focal scenario (SI Appendix, Fig. S12 and S13). Given a focal scenario of σ = 0.0 (SI Appendix, Fig. S12A), the statistics varied in their ability to reject alternative scenarios when σ > 0, but with a clear-cut tendency for p ( SX ) to perform best irrespective of the value of γ . However, even true p ( SX ) failed to discriminate obligate asexuality from very rare sex ( σ ≤ 10 −4 ), where, due to stochastic under-representation, > 86% of replicated ARGs contained no SX trees. Even more striking was the inability of the statistics—except H I and to a lesser extent F IS —to discriminate σ = 10 −3 from σ 95% of replicated values for scenarios of σ < 10 −3 fell within the 95% interval for σ = 10 −3 . This result likely reflects several factors, including the stochastic under-representation of SX trees when σ = 10 −3 and their mis-inference when σ < 10 −3 . Discussion Estimating the rates of sexual and asexual reproduction is a prerequisite for answering a range of fundamental evolutionary questions. Interpreting the genomic landscape as an outcome of an inferred reproductive history could shed light on deep links between reproductive mode and microevolutionary patterns ( Feretzaki & Heitman, 2013 ; Morran et al., 2011 ), macroevolutionary trends ( Barraclough, 2019 ), and a range of ecological, genetic, and life history peculiarities ( Wilson et al., 2024 ; Jeffries et al., 2025 ). However, while jointly modelling multiple forces that shape genome-wide variation has become a gold standard in many branches of evolutionary research (e.g., Laetsch et al., 2023 ), the same is not true for reproductive history analysis. As a first step towards addressing this issue, we focus on homologous recombination that operates independently of the sexual pathway, which remains largely overlooked in current approaches to quantifying sex from sequence data (e.g., Vakhrusheva et al., 2020 ; Ali et al., 2016 ; Nicoll et al., 2024 ; Stoeckel et al., 2021 ; Tsai et al., 2008 ). Using simulation models of asexuality, we found that ‘asyngamous’ recombination confounds sequence-based statistics commonly used to support the presence of sex (e.g., Freitas et al., 2023 ; Vakhrusheva et al., 2020 ; Phan Thi et al., 2025 ). Specifically, under intermediate rates of asyngamous recombination, these statistics take values characteristic of outcrossing populations even when sex is rare or absent. Estimates of the per-site rate of LOH across asexual systems are consistently on the order of 10 −5 -10 −4 (Table S2), broadly aligning with the values required to mimic sex-like patterns of variation. For example, a recent laboratory evolution study ( Houtain et al., 2024 ) estimated a per-site LOH rate of 3.24 × 10 −4 in the bdelloid rotifer Adineta vaga , a representative of a widespread and abundant group where no sexual reproduction, males or hermaphrodites have ever been observed ( Wilson et al., 2024 ; Birky Jr, 2010 ). We find that this value is consistent with γ required for F IS ≈ 0 in our simulated scenarios ( Fig. 3 ; SI Appendix Fig. S2, Fig. S3), potentially explaining close compatibility with the Hardy-Weinberg expectation observed in a wild A. vaga population ( Vakhrusheva et al., 2020 ). Although it might seem improbable that asyngamous recombination would take exactly those values required to confound tests of sex, there are biological reasons why this might be expected. The generation of LOH has two contrasting consequences for fitness. On the one hand, exposure of recessive deleterious mutations leads to loss of fitness and inbreeding depression ( Charlesworth & Willis, 2009 ). On the other, LOH can help fix beneficial mutations and counteract Muller’s ratchet by overwriting deleterious mutations ( Flot et al., 2013 ; Kopčak & Hartfield, 2024 ; Mandegar & Otto, 2007 ). To maximise fitness benefits, the spontaneous rate of asyngamous recombination and LOH might be expected to be selected in a similar way to recombination rate in sexual organisms ( Dapper & Payseur, 2017 ), or via programmed distortion of chromatid segregation ( Blanc et al., 2023 ; Lacy et al., 2024 ). Concurrently, observed levels of LOH might be biased towards intermediate values through selection against excess homozygosity ( Flynn et al., 2017 ). The majority of ARG inference methods are rooted in a simple model of a sexual population with crossover recombination ( Li & Stephens, 2003 ; McVean & Cardin, 2005 ). Here, we show that ARG-based methods can be extended to systems with non-canonical reproductive biology. Specifically, we present tools to simulate ARGs for populations with mixed sexual and realistically modelled asexual reproduction, and to interrogate such reproductive histories. Our results confirm that the tree composition of a true ARG is the best predictor of the rate of sex and most clearly distinguishes scenarios across the studied range of σ . In addition, our simulation models provide a powerful basis to develop ARG-based methods for evolutionary inference in asexual diploids, including processes such as demography ( Fan et al., 2025 ; Osmond & Coop, 2024 ; Wohns et al., 2022 ) and selection ( Hejase et al., 2022 ; Stern et al., 2019 ). Nevertheless, we caution that the accuracy of inference from haplotype-resolved segments is still limited by technical artefacts, which cause inferred genealogies to be unrepresentative of the true ARG. We find that a bias towards heterozygous regions of the genome, inherent to read-based haplotype phasing, on the one hand, and asyngamous recombination, on the other, result in overrepresentation of tree-level signatures of clonality and sex, respectively. These results have implications for tree-based inferences of reproductive mode, whether reporting evidence of long-term asexuality ( Brandt et al., 2021 ; Öztoprak et al., 2025 ) or cryptic sex ( Vakhrusheva et al., 2020 ; Laine et al., 2022 ), and such datasets would be interesting to revisit with our quantitative approach to tree composition analysis. Importantly, other biases associated with the sequencing, assembly and phasing procedures, as well as limitations to inference itself ( Lin et al., 2024 ), will need to be considered beyond the simple biases simulated here. We anticipate that further advances in obtaining long-range phased blocks ( Lin et al., 2022 ; Porubsky et al., 2021 ) and the development of nonparametric ARG reconstruction methods ( Kelleher et al., 2019 ; Rasmussen & Guo, 2023 ; Speidel et al., 2019 ; Zhang et al., 2023 ) will be critical to fully benefit from an ARG-based approach. Bearing these limitations in mind, we introduce a new statistic Δ m that appears more robust to biases and could be extended to unphased data without explicit tree inference ( Mackintosh & Setter, 2024 ). Simulating genome-wide genealogies offers a powerful tool for the exploration and inference of evolutionary scenarios ( Lauterbur et al., 2023 ). Integration of our approach with statistical frameworks that leverage multiple sources of information, such as Approximate Bayesian Computation ( Johri et al., 2022 ), supervised machine learning ( Schrider & Kern, 2018 ) and convolutional neural networks ( Whitehouse et al., 2024 ; Korfmann et al., 2023 ), could help address several outstanding problems. For instance, the status of putative ‘ancient asexuals’ ( Judson & Normark, 1996 ) remains contentious, with claims of cryptic sex in the well-documented absence of males ( Vakhrusheva et al., 2020 ; Wilson et al., 2024 ; Laine et al., 2022 ; Birky Jr, 2010 ), and conversely, claims of long-term asexuality in their presence ( Öztoprak et al., 2025 ; Taberly, 1988 ). Our results provide a guide for future studies to (1) explicitly account for asyngamous recombination, (2) address technical and methodological limitations and (3) quantitatively and exhaustively classify signatures that can be explicitly linked to asexual and sexual reproduction in turn. To this aim, we provide the building blocks that will help disentangle the idiosyncrasies of different systems of reproduction and recombination. Methods We used tskit v0.5.8 ( Wong et al., 2024 ; Kelleher et al., 2016 ) to process and analyse ARGs, and matplotlib v3.9.2 ( Hunter, 2007 ) and seaborn v0.13.2 ( Waskom, 2021 ) for visualisation in Python. The simulation pipeline and post-simulation code are available at https://github.com/TymekPieszko/SexSigns_2025 . Simulation models of asexuality We implemented both the MP and CF models in scenarios where a single diploid chromosome of length L evolved in populations conforming to Wright-Fisher assumptions (discrete, non-overlapping generations, constant population size), in which offspring are produced sexually by hermaphroditic parents with probability σ and asexually with probability 1 − σ (see Table S1 for definitions of all simulation parameters). Parthenogenesis is accompanied by asyngamous recombination: gene conversion with an initiation rate r GC in the MP model and crossover recombination at rate r CO in the CF model (see SI Appendix, section A for descriptions of the recombination routines). The length of gene conversion tracts is geometrically distributed with mean λ and can be set independently of the tract initiation rate. Sexual reproduction is accompanied by crossover recombination at rate r using the standard machinery of SLiM4. To validate the CF model, we followed Engelstädter (2017) in assuming that crossovers occur between all four chromatids without interference (SI Appendix, Fig. S14) and used the equilibrium individual-level heterozygosity Ĥ I as an indicator of concordance with the analytical model ( Engelstädter, 2017 ) (see SI Appendix, section A for a summary). To validate the MP model, we used Engelstädter’s (2017) suggestion for deriving the expected Ĥ I (SI Appendix, section A). In both cases, we obtained close concordance between analytical and simulated values after 200,000 simulated generations (each recombination scenario in 30 replicates, other parameters as in Table S1), except at low recombination rates ( γ ≤ 5 × 10 −6 ; SI Appendix, Fig. S15 and S16). Note that results obtained though simulation do not always reach a mutation-LOH equilibrium due to finite run times (including in our pipeline; see below), but equilibria are unlikely to be encountered in nature for the same reason. Simulation pipeline We ran our simulations in a pipeline written using the workflow management system Snakemake v8.13.0 ( Mölder et al., 2021 ). In summary, the pipeline consisted of three major steps. First, we used the MP and CF models to run simulations with tree-sequence recording of output ( Haller et al., 2019 ) across the space of sex rates σ and LOH rates γ (see Table S1 for all parameter values used). Each scenario was run for 5,000 generations and replicated 100 times. Second, after ensuring complete coalescence of the ARGs using pyslim v1.0.4 ( Haller et al., 2019 ) and adding neutral mutations at a per-site per-generation rate μ = 5 × 10 −7 using msprime v1.3.2 ( Baumdicker et al., 2022 ), we subsampled each ARG to two random individuals and generated files for downstream processing of these target samples. Third, for each two-individual ARG, we re-inferred genealogies using IQ-TREE v2.2.2.6 ( Minh et al., 2020 ) and SINGER v0.1.8-beta ( Deng et al., 2024 ). Details of the pipeline are described in SI Appendix, section B. Calculating population genetic statistics We calculated population genetic statistics using sub-ARGs of 100 random individuals processed as above. We calculated H I using the TreeSequence.diversity method (mode=’site’) and r 2 using the tskit.LdCalculator ( Ralph et al., 2020 ). We used a custom function to calculate F IS for biallelic SNVs. For each 100-individual ARG, we calculated mean H I , F IS and r 2 for SNVs < 1,000 bp apart. We finally calculated mean values across the replicates and plotted heatmaps for the simulated space of σ and γ ( Fig. 3 ; SI Appendix, Fig. S1). To visualise LD decay curves (SI Appendix, Fig. S4 and S5), we calculated mean r 2 values for SNVs binned by distance in windows of 1 kb. F IS likelihood analysis Under the obligate asexual scenarios, we additionally calculated F IS for smaller sub-ARGs ( n = 5, 10 or 15). Assuming normally distributed replicated F IS values, we computed the likelihood of observing F IS = 0 for each scenario and value of n , which we transformed to Akaike information criterion (AIC) values (with three parameters). Finally, for each value of n and set of MP and CF scenarios, we computed Akaike weights of evidence ( A w ) for different values of γ (SI Appendix, Fig. S2 and S3). Tree composition analysis We implemented our approach to classifying two-individual trees ( Fig. 4A ) to be applicable to tree sequences (simulated or output by SINGER) and Newick-format trees (output by IQ-TREE). For each two-individual ARG sampled within the pipeline, we summed the spans of trees falling into the CL, AR and SX categories, which we expressed as proportions of the total: p ( CL ), p ( AR ) and p ( SX ), respectively. We repeated this calculation after applying the heterozygosity bias of β = 0.1 and re-inferring genealogies with IQ-TREE and SINGER in turn. To obtain tree proportions for SINGER genealogies, we summed tree spans across the samples of each MCMC chain. We finally calculated mean p ( CL ), p ( AR ) and p ( SX ) across the replicates and under each condition, and plotted heatmaps for the simulated space of σ and γ ( Fig. 4 ; SI Appendix, Fig. S7). Calculating Δ m We implemented the calculation of Δ m to be applicable to tree sequences (simulated or output by SINGER) and Newick-format trees (output by IQ-TREE). An ARG-wide Δ m value is obtained as the mean of values for target trees (i.e., CL-topology trees with mutations) weighted by their span. A Δ m value for a Newick-format tree is calculated using the branch lengths, a proxy for mutation number if the mutation rate is constant. We calculated Δ m for each two-individual ARG sampled within the pipeline, the same ARGs after applying the heterozygosity bias of β = 0.1, and after re-inferring genealogies with IQ-TREE and SINGER in turn. To obtain Δ m for SINGER genealogies, we averaged values across the samples of each MCMC chain. We finally calculated mean Δ m across the replicates and under each condition, and plotted heatmaps for the simulated space of σ and γ ( Fig. 5 ; SI Appendix, Fig. S11). 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