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Spatial soft sweeps: Patterns of adaptation in populations with long-range dispersal

  • Jayson Paulose,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Department of Physics, University of California, Berkeley, Berkeley, California, United States of America, Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America, Department of Physics, University of Oregon, Eugene, Oregon, United States of America

  • Joachim Hermisson,

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – review & editing

    Affiliations Department of Mathematics, University of Vienna, Vienna, Austria, Max F. Perutz Laboratories, University of Vienna, Vienna, Austria

  • Oskar Hallatschek

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing

    Affiliations Department of Physics, University of California, Berkeley, Berkeley, California, United States of America, Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America


Adaptation in extended populations often occurs through multiple independent mutations responding in parallel to a common selection pressure. As the mutations spread concurrently through the population, they leave behind characteristic patterns of polymorphism near selected loci—so-called soft sweeps—which remain visible after adaptation is complete. These patterns are well-understood in two limits of the spreading dynamics of beneficial mutations: the panmictic case with complete absence of spatial structure, and spreading via short-ranged or diffusive dispersal events, which tessellates space into distinct compact regions each descended from a unique mutation. However, spreading behaviour in most natural populations is not exclusively panmictic or diffusive, but incorporates both short-range and long-range dispersal events. Here, we characterize the spatial patterns of soft sweeps driven by dispersal events whose jump distances are broadly distributed, using lattice-based simulations and scaling arguments. We find that mutant clones adopt a distinctive structure consisting of compact cores surrounded by fragmented “haloes” which mingle with haloes from other clones. As long-range dispersal becomes more prominent, the progression from diffusive to panmictic behaviour is marked by two transitions separating regimes with differing relative sizes of halo to core. We analyze the implications of the core-halo structure for the statistics of soft sweep detection in small genomic samples from the population, and find opposing effects of long-range dispersal on the expected diversity in global samples compared to local samples from geographic subregions of the range. We also discuss consequences of the standing genetic variation induced by the soft sweep on future adaptation and mixing.

Author summary

When a species is spread out over a large geographic range, different regions may adapt to the same selection pressure by acquiring distinct beneficial mutations. The resulting pattern of genetic variation in the population is called a soft sweep. Dispersal strongly influences soft sweep patterns, as it determines how a mutation that arose in one region might spread to others. Although most plant and animal populations experience some amount of dispersal over very long distances, the impact of such long-range dispersal events on soft sweep patterns remains poorly understood. We use computer simulations and mathematical analysis to study patterns of genetic variation in a model of soft sweeps including long-range dispersal. We show that long-range dispersal leaves distinct signatures in the genetic makeup of the population, which can be detected in genetic samples from individuals across the range. Our results are important for correctly interpreting patterns of genetic diversity in populations that have undergone recent adaptation.


Rare beneficial alleles can rapidly increase their frequency in a population in response to a new selective pressure. When adaptation is limited by the availability of mutations, a single beneficial mutation may sweep through the entire population in the classical scenario of a “hard sweep”. However, populations may exploit a high availability of beneficial mutations due to standing variation, recurrent new mutation, or recurrent migration [15] to respond quickly to new selection pressures. As a result, multiple adaptive alleles may sweep through the population concurrently, leaving genealogical signatures that distinguish them from hard sweeps. Such events are termed soft sweeps. Soft sweeps are now known to be frequent and perhaps dominant in many species [6, 7]. Well-studied examples in humans include multiple origins for the sickle cell trait which confers resistance to malaria [8], and of lactose tolerance within and among geographically separated human populations [9, 10].

Soft sweeps rely on a supply of beneficial mutations on distinct genetic backgrounds, which has two main origins. One is when selection acts on an allele which has multiple copies in the population due to standing genetic variation—a likely source of soft sweeps when the potentially beneficial alleles were neutral or only mildly deleterious before the appearance of the selective pressure [3]. In this work, we focus on the other important scenario of soft sweeps due to recurrent new mutations which arise after the onset of the selection pressure. Soft sweeps become likely when the time taken for an established mutation to fix in the entire population is long compared to the expected time for additional new mutations to arise and establish. In a panmictic population, the relative rate of the two processes is set primarily by the rate at which new mutations enter the population as a whole [5].

Most examples of soft sweeps in nature, however, show patterns consistent with arising in a geographically structured rather than a panmictic population [7]. Spatial structure promotes soft sweeps [11]: when lineages spread diffusively (i.e. when offspring travel a restricted distance between local fixation events), a beneficial mutation advances as a constant-speed wave expanding outward from the point of origin, much slower than the logistic growth expected in a well-mixed population. Therefore, fixation is slowed down by the time taken for genetic information to spread through the range, making multi-origin sweeps more likely. However, the detection of such a spatial soft sweep crucially depends on the sampling strategy: the wavelike advance of distinct alleles divides up the range into regions within which a single allele is predominant. If genetic samples are only taken from a small region within the species’ range, the sweep may appear hard in the local sample even if it was soft in the global range.

Between the two limits of wavelike spreading and panmictic adaptation lies a broad range of spreading behaviour driven by dispersal events that are neither local nor global. Many organisms spread through long-range jumps drawn from a probability distribution of dispersal distances (dispersal kernel) that does not have a hard cutoff in distance but instead allows large, albeit rare, dispersal events that may span a significant fraction of the population range [12, 13]. A recent compilation of plant dispersal studies showed that such so-called “fat-tailed” kernels provided a good statistical description for a majority of data sets surveyed [14]. Fat-tailed dispersal kernels accelerate the growth of mutant clones, whose sizes grow faster-than-linearly with time and ultimately overtake growth driven by a constant-speed wave [12, 15]. Besides changing the rate at which beneficial alleles take over the population, long-range dispersal also breaks up the wave of advance [16]: the original clone produces geographically separated satellites which strongly influence the spatial structure of regions taken over by distinct alleles.

Despite its prominence in empirically measured dispersal behaviour and its strong effects on mutant clone structure and dynamics, the impact of long-range dispersal on soft sweeps is poorly understood. Past work incorporating fat-tailed dispersal kernels in spatial soft sweeps [11] relied on deterministic approximations of the jump-driven spreading behaviour of a single beneficial allele [12]. However, recent analysis has shown that deterministic approaches are accurate only in the two extreme limits of local (i.e. wavelike) and global (i.e. panmictic) spreading, and break down over the entire regime of intermediate long-range dispersal [17]. Away from the limiting cases, the correct long-time spreading dynamics is obtained only by explicitly including rare stochastic events which drive the population growth. Deterministic approaches also do not account for the disconnected satellite structure, which has consequences for soft sweep detection in local samples.

Here, we study soft sweeps driven by the stochastic spreading of alleles via long-range dispersal. We perform simulations of spatial soft sweeps in which beneficial alleles spread via fat-tailed dispersal kernels which fall off as a power law with distance, focusing on the regime in which multiple alleles arise concurrently. We find that long-range dispersal gives rise to distinctive spatial patterns in the distribution of mutant clones. In particular, when dispersal is sufficiently long-ranged, mutant clones are discontiguous in space, in contrast to the compact clones expected from wavelike spreading models. We identify qualitatively different regimes for spatial soft sweep patterns depending on the tail of the jump distribution. We show that analytical results for the stochastic jump-driven growth of a solitary allele [17], combined with a mutation-expansion balance relevant for spatial soft sweeps [11], allow us to predict the range sizes beyond which soft sweeps become likely. We also analyze how stochastic aspects of growth of independent alleles, particularly the establishment of satellites disconnected from the initial expanding clone, influence the statistics of observing soft sweeps in a small sample from the large population. We find that long-range dispersal has contrasting effects on the likelihood of soft sweep detection, depending on whether the population is sampled locally or globally.


Model of spatial soft sweeps

We consider a haploid population that lives in a d-dimensional habitat consisting of demes that are arranged on an integer lattice (e.g. square lattice in d = 2). Local resource limitation constrains the deme population to a fixed size , assumed to be the same for all demes. Denoting the linear dimension of the lattice as L, the total population size is . The population is panmictic within each deme. With a rate m per generation, individuals migrate from one deme to another. For each dispersal event, the distance r to the target deme is chosen from a probability distribution with weight J(r), appropriately discretized, with the normalization . The function J(r) is called the jump kernel. The dispersal direction is chosen uniformly at random from the unit sphere in d dimensions. New mutations arise in all demes at a constant rate u per individual per generation. Each new mutation is distinguishable from previous mutations (e.g. due to different genomic backgrounds), but all mutations confer the same selective advantage s. Back mutations are ignored. To minimize the effect of the specific boundary geometry, periodic boundary conditions are assumed.

To focus on the effects of long-range dispersal over local dynamics, we now impose a set of bounds on the individual-based parameters following [11]. In particular, we consider only situations where ; ; (strong selection, and low mutation and migration rates at the deme level). Mutations are also assumed to be fully redundant, i.e. a second mutation confers no additional advantage. The strong selection condition implies that genetic drift within a deme is irrelevant relative to selection: a new mutation, upon surviving stochastic drift and fixing within a deme (which happens with probability 2s) cannot be subsequently lost due to genetic drift. The bounds on mutation and migration rates meanwhile imply that the fixation dynamics of a beneficial mutation within a deme is fast compared to the dynamics of mutation within a deme or of migration among demes. The time to fixation of a beneficial allele from a single mutant individual in the deme, , is a few times 1/s. When and , the fixation time scale is much shorter than the establishment time scales of new alleles arising due to mutation or migration, which are and respectively. Therefore, the first beneficial allele that establishes in a deme, whether through mutation or migration, fixes in that deme without interference from other alleles. Furthermore, the assumption of mutual redundancy means that subsequent mutations that arrive after the first fixation event also have no effect. As a result, the first beneficial allele that establishes in a deme excludes any subsequent ones—a situation termed allelic exclusion [11].

Taken together, these assumptions lead to a simplified model that ignores the microscopic dynamics of mutations within demes. For each deme, we keep track of a single quantity: the allelic identity (whether wildtype or one of the unique mutants that has arisen) that has fixed in the deme. At the deme level, new mutations fix within wildtype demes at the rate , and each mutated deme sends out migrants at rate with the target deme selected according to the dispersal kernel J(r) (the rates explicitly include the fixation probability 2s of a single mutant in a wildtype deme). The first successful mutant to arrive at a wildtype deme, whether through mutation or migration, immediately fixes within that deme. The state of the deme thereafter is left unchanged by mutation or migration events, because of allelic excusion.

When time is measured in units of the expected interval between successive dispersal events per deme, the reduced model is characterized by just three quantities: L; J(r); and the per-deme rate of mutations per dispersal attempt , which we call the rescaled mutation rate of our model. Simulations are begun with a lattice of demes of size Ld all occupied by the wildtype. Each discrete simulation step is either a mutation or an attempted migration event, with the relative rates determined by and the fraction of wildtype sites at that step. Mutation events flip a randomly-selected wildtype deme into a new allelic identity. Migration events first pick a mutated origin and then pick a target deme according to the jump kernel. If the target site is wildtype, it acquires the allelic identity of the origin; otherwise the migration is unsuccessful. Simulations are run until all demes have been taken over by mutants.

The fat-tailed jump kernels we use are of the form J(r) = μr−(1+μ), with μ > 0 to ensure that the kernel is normalizable. The exponent μ characterizes the “heaviness” of the tail of the distribution. We have chosen power-law kernels because they span a dramatic range of outcomes that connect the limiting cases of well-mixed and wavelike growth upon varying a single parameter. The growth dynamics of more general fat-tailed kernels in the stochastic regime of interest (i.e. driven by rare long jumps) are largely determined by the power-law falloff of the tail, and details of the dispersal kernel at shorter length scales are less consequential. Therefore, our qualitative results should extend to kernels sharing the same power law behaviour of the tail, provided the typical clones are large enough so that rare jumps picked from the tail of the distribution become relevant. The underlying analysis leading to the results is even more general, and can be applied to any jump kernel that leads to faster-than-linear growth in the extent of an individual clone with time.

The output of a simulation at a given set of L, μ and values is the final configuration of mutants, which can be grouped into distinct clones of the same allelic identity. Note that we have ignored the post-sweep mixing of alleles which are now relatively neutral to each other due to migration; this is justified by the separation of time scales between fast fixation and slow neutral migration [11]. In addition, although we restrict ourselves to weak mutation and migration at the deme level, the population-level mutation and migration rates Nu, Nm are typically large which allows for soft sweeps with strong migration effects.

While our theoretical results are valid for all dimensions, computational limitations prevented us from running extensive simulations in dimensions higher than one. Therefore, we primarily report simulations of linear habitats (d = 1) in the main text. Preliminary results from planar simulations (d = 2) are reported in S1 Appendix, Section B and are consistent with our theoretical arguments, although quantitative comparisons are limited by finite-size effects.

Jump-driven growth and the core-halo structure of mutant clones

Some typical outcomes of the simulation model are shown in Fig 1 for both two-dimensional (2D) and one-dimensional (1D) ranges. To emphasize variations in the spatial patterns for the same average clone size, simulations were chosen in which the final state has exactly ten unique alleles; this required varying the rescaled mutation rate as μ was increased. This feature, which is tied to the slower growth of individual clones apparent in the space-time plots of Fig 1(b), is explored in depth in Section Characteristic scales via mutation-expansion balance.

Fig 1. Spatial soft sweep patterns with the same number of distinct alleles vary strongly with jump kernel.

(a) Final states of 2D simulations on a lattice of size 512 × 512, for a range of values of the kernel exponent μ. Each pixel corresponds to a deme, and is coloured according to the identity of the allele occupying that deme; demes belonging to the same mutant clone share the same colour. Simulations were chosen to have ten unique alleles in the final state; colours reflect the temporal order of the originating mutations as labeled in the lower right panel. Rescaled mutation rates are 3 × 10−6, 10−6, 10−6, and 10−7 for kernel exponents 0.5, 1.5, 2.5, and 3.5 respectively. The solid and dotted circles in the second panel indicate the extent of the core and the halo respectively for the light green clone, as quantified by the measures req and rmax respectively (see text for definitions). The subrange highlighted by a dashed box contains six distinct alleles for μ = 1.5 but only one allele for μ = 3.5. (b) Full time-evolution of 1D simulations with L = 16384 for three kernel exponents, chosen so that the final state has ten unique alleles. Each vertical slice displays the lattice state at a particular time (measured in generations), starting from an empty lattice (white) and continuing until all sites are filled and the sweep is completed. The rescaled mutation rates are 3 × 10−5, 7 × 10−6, and 7 × 10−7 respectively from left to right. In the last panel, the colours are labelled according to the order of appearance of the originating mutation; the same order is shared among all panels in the figure.

In both 2D and 1D, the spatial soft sweep patterns of Fig 1 display systematic differences as the kernel exponent is varied. Clones are increasingly fragmented as the kernel exponent is reduced; i.e. as long-range dispersal becomes more prominent. At the highest value of μ in each dimension, the range is divided into compact, essentially contiguous domains each of which shares a unique mutational origin. As the kernel exponent μ is reduced, the contiguous structure of clones is lost as they break up into disconnected clusters of demes. For most clones, however, a compact region can still be identified in the range which is dominated by that clone (i.e. the particular allele reaches a high occupancy that is roughly uniform within the region but begins to fall with distance outside it) and in turn contains a significant fraction of the clone. We call this region the core of the clone. The remainder of the clone is distributed among many satellite clusters which produce local regions of high occupancy for a particular clone. The satellites become increasingly sparse and smaller in size as we move away from the core. For the broadest kernels (μ = 0.5 in 2D and μ = 0.7 in 1D), most clones also include isolated demes which do not form a cluster but are embedded within cores and satellite clusters of a different allele. We term the collection of satellites and isolated demes the halo region surrounding the core of the clone. The circles in the second panel of Fig 1(a) illustrate the extent of core and halo, quantified via distance measures which we introduce later on for a particular clone (the fifth clone entering the population, colored light green). The spatial extent of the clone including the halo can be many times the extent of the core alone, and increases relative to the core extent as μ is reduced. (We will use “extent” to refer to linear dimensions, and “mass” or “size” to refer to the number of demes).

The space-time evolution displayed in Fig 1(b) for linear simulations reveals the role of jump-driven growth in producing the observed spatial structures. At μ = 2.5, the growth of clones appears nearly deterministic, with fronts separating mutant from wildtype advancing outwards from the originating mutations at near-constant velocity. These fronts are arrested when they encounter advancing fronts of other clones, leaving behind a tessellation of the range into contiguous clones. By contrast, at the lower values μ = 1.3 and 0.7, the stochastic nature of jump-driven growth becomes apparent. Clones advance through long-distance dispersal events, which seed satellite clusters that may merge with each other before the sweep is complete. For all except the smallest clones, the originating mutation is surrounded by a region which is dominated by that particular allele—these form the core regions defined above. Satellites are seeded by stochastic jumps that extend over regions which either were occupied by a different allele already, or get filled in by a different allele before the satellite has a chance to merge with the core. For μ = 1.3, haloes extend only a short distance out from the core, whereas at μ = 0.7 the haloes often extend over a distance many times the core extent.

The increased fragmentation of clones with broader dispersal kernels has a marked impact on local diversity in sub-regions of the range. Haloes belonging to different alleles overlap to produce regions of high diversity, as exemplified by the dashed box in Fig 1(a) for μ = 1.5, which contains demes belonging to six of the 10 unique alleles despite being a small fraction of the total range area. By contrast, the same region contains only one allele at μ = 3.5 for which clones form contiguous domains. Other effects of broadening the dispersal kernel are also visible in Fig 1: the spread in clone sizes becomes larger, and individual clones take many more generations to attain a given size.

To build a quantitative understanding of these variations, we begin by noting that at early times in Fig 1(b), each clone grow largely unencumbered by other clones. We can therefore gain insight from existing results on the jump-driven growth of a solitary advantageous clone expanding into a wildtype background [17]. The key features are summarized here and illustrated for the blue clone in Fig 2. Consider a clone that grows from a mutation that originated at time t = 0 at the origin. At times longer than a short transient, the clone fills most sites out to some distance from the origin. In line with the terminology established above, we call this region of high occupancy the core of the growing clone. Its typical extent over time (i.e. the average radius of a core that has grown for time t) is quantified by a function (t) which itself depends on the dispersal kernel (a precise definition is given at the end of this section). As sites in the core get filled, they send out offspring through long-range dispersal events drawn from the specified kernel, which then grow into independent satellite clusters. As a result, at any time t there are also demes outside the core which are occupied by the mutant. However, the occupancy of sites outside the core decays as r−(d+μ) with distance r from the originating mutation [17], fast enough that the total mass of the clone at time t is proportional to d(t).

Fig 2. Marginal dynamics of jump-driven soft sweeps.

When long-range dispersal is significant, clones of different allelic identity (distinguished by colors) grow out of their originating mutations (stars) by accumulating satellite clusters (translucent cones). If only a single mutation were present, the extent (t) of the high-occupancy core (consisting of satellite clusters which have merged with the cluster growing out of the originating mutation) would, on average, follow a faster-than-linear growth rule, depicted schematically by the border of the opaque regions. For a wide range of kernels in the vicinity of μ = d, the growth rule arises from a hierarchy of length and time scales related by doublings in time: the satellite that merges with the core at time t would typically originate from a key jump out of the core at time t/2, that extended over a length of order (t) (solid arrows out of blue region). Looking forward in time, a clone whose core has grown for time t without being obstructed will have likely seeded satellites out to a distance of order (2t). In the presence of recurrent mutations, these satellites may be obstructed from merging with the core due to intervening cores and satellites of different mutational origin (green and red regions). For very broad dispersal kernels, the halo also includes rarer jumps out of the core (dashed arrow) that land in regions that are being taken over by other alleles, but establish themselves in stochastic gaps in those regions. The disconnected satellite clusters and isolated demes comprise the halo region of the mutant clone.

As sketched in Fig 2, the core grows through mergers of satellite clusters that grew out of rare but consequential “key jumps” out of the core at earlier times (solid arrows in Fig 2). [17] identified qualitative differences in the behaviour of key jumps and the resulting functional forms of (t) as the kernel exponent is varied. When μ > d + 1, the extent of typical key jumps remains constant over time, which implies that they must originate and land within a fixed distance from the boundary of the high-occupancy region at all times. As a result, clones advance via a constant-speed front similar to the case of wavelike growth; i.e. (t) ∝ t. Furthermore, the separation between the core and satellites is insignificant at long times, giving rise to essentially contiguous clones. By contrast, for μ < d + 1, growth is increasingly driven by jumps that originated in the interior of the core at earlier times, and key jumps become longer with time. The resulting growth of (t) is faster-than-linear with time. The value μ = d is an important marginal case which separates two distinct types of long-time asymptotic behaviour for (t): power-law growth for d < μ < d + 1 and stretched-exponential growth for 0 < μ < d (see the second column of Table 1 for the asymptotic growth forms in all regimes). As μ → 0, spatial structure becomes increasingly irrelevant and the growth dynamics approaches the exponential growth of a well-mixed population.

Table 1. Core growth asymptotics and characteristic core sizes.

The table catalogues the asymptotic behaviour of (t) (from [17]) along with the expected scaling of the characteristic clone size χas, omitting distance and time scales for and t respectively. W is the Lambert W-function, Bμ ≈ 2d log(2)/(μd)2, and η = log[2d/(d + μ)]/log 2. The subscript in χas indicates that the asymptotic (t) was used as opposed to the more accurate functions listed in S1 Appendix, Section A. Note that for values of μ near d, the asymptotic growth forms are of limited value since the time befor the asymptotics is reached becomes very large. In this situation, χ must be computed using the more accurate scaling forms, see S1 Appendix, Section A for more details.

These features of solitary-clone growth can be directly connected to the spatial patterns in Fig 1 when recurrent mutations are allowed. The tessellation of the range into contiguous domains for the highest values of μ is exactly as expected from the wavelike growth situation when μ > d + 1. When μ < d + 1, by contrast, each clone consists of a growing core and well-separated satellite clusters at any time. Unlike the solitary-mutant case, satellites belonging to a particular clone are no longer guaranteed to merge with the core or with each other at later times: due to allelic exclusion, mergers are obstructed by cores and satellites with a different allelic identity, as shown schematically in Fig 2. The final pattern of frozen-in satellite clusters comprises the previously identified halo structure around each core when μ < d + 1.

Notation and definitions: Before we proceed, we summarize the various quantities in our analysis, and the conventions used in representing them. (A complete list of variables and definitions is provided in Table 2). One set of physical quantities, represented as Latin symbols without a time argument, measures properties of individual clones after the soft sweep has been completed; i.e. quantities measured from the final simulation outputs such as those displayed in Fig 1. (These quantities could also, in principle, be measurable from a real spatial population that has recently experienced a sweep). Of these, quantities that have dimensions of length are the mass-equivalent clone radius req and the clone extent rmax (defined in Table 2). The solid and dotted circles in Fig 1(a) illustrate these quantities for a specimen clone. The final clone mass is designated by the symbol X. Ensemble averages of these quantities for a given set of model parameter values, obtained by averaging first over all clones within a single simulation and then across many independent simulations, are denoted by 〈…〉.

Table 2. Glossary of length and size scales.

Table summarizes the various characteristic lengths (denoted by Greek letters) and measured quantities (masses in capital Roman letters and lengths in small Roman letters). The characteristic time t* is implicitly defined in Eq 1. Except where explicitly noted, the definitions are valid in all dimensions.

Our analysis connects these properties of the final, static soft sweep pattern to the dynamic growth behaviour of a solitary clone under the same dispersal kernel, in the absence of interference from other clones. For a given dispersal kernel, the typical growth behaviour is captured by the core growth function (t) which we introduced previously. A precise definition of (t) requires making a choice about how to identify the core region. In contrast to the case of wavelike growth, there is no sharp advancing front which separates the high-occupancy region of a growing clone from its surroundings; the average radial occupancy profile at time t (defined as the probability that a deme at distance r from its point of origin is occupied by the clone) is close to one out to some distance from the origin, beyond which it crosses over to a profile that decays as a power law with increasing distance. One possibility, proposed in [11], is to define (t) as the distance at which the average occupancy profile falls below some low threshold probability ε. Here, we make a different choice motivated by the property, proved in [17], that the total mass of the clone (which we call M(t)) is proportional to d(t). We define (t) as the expected mass-equivalent radius of the clone at time t: (t) ≡ E[(M(t)/ωd)1/d], where ωd is the volume of the d-sphere of radius 1 (ω1 = 2, ω2 = π). For a particular solitary-clone growth simulation, M(t) is straightforward to measure since the clone mass is readily accessible. For a particular value of μ, (t) is then estimated using an ensemble average over many independent solitary-clone simulations (see S1 Appendix, Section A for details). Our choice of (t) is proportional to (t) defined using an occupancy threshold, provided ε is small enough. We expect that using other definitions of (t) which scale proportionately with the core region will not significantly change our results, at most shifting the magnitude of reported quantities by constant factors of order unity as long as we are sufficiently far from the well-mixed limit μ → 0.

Finally, the interplay between the expansion of individual clones and the introduction of new mutations is used to derive various time-independent characteristic lengths, which are represented as Greek symbols. These length scales depend on the dispersal kernel via the functional form of (t), and the rescaled mutation rate . Precise definitions of the characteristic length scales are provided in Table 2 and in the forthcoming sections.

Marginal dynamics and the relative sizes of core and halo.

We can quantify the expected spatial extent of entire clones (including haloes) relative to cores by considering the dynamics in the vicinity of the marginal value μ = d. Although the long-time asymptotic dynamics are qualitatively different above and below this value (power-law in t for d < μ < d + 1, and stretched-exponential for 0 < μ < d), the approach to the asymptotic behaviour is extremely slow for values of μ close to d, with the intermediate-time evolution controlled by the marginal dynamics at μ = d. As a result, the marginal dynamics is important for a wide range of values of μ at biologically-relevant time scales [17].

In the marginal regime, the scaling behaviour of key jumps follows a particularly simple pattern, illustrated schematically in Fig 2: satellite clusters which merge with the core at time t are seeded by key jumps that typically happened around time t/2 and covered a distance of order (t) ≫ (t/2). Therefore, a core that has grown up to some extent of order (t) has likely already seeded satellites out to a distance of order (2t), some of which will have reached an appreciable size as illustrated in Fig 2. If the core has grown to some linear size l, we then expect satellites that have reached a significant size to extend to a distance l′ ≡ (2−1(l)), which may be considered to be a lower bound on the expected extent of the halo. When isolated demes embedded within cores and satellites belonging to other clones are included, the full spatial extent of the clone is even larger, because there remains a finite probability of rare jumps from the core out to distances farther than l′ (dotted arrow in Fig 2).

The above estimate for l′, when approximated using the long-time asymptotic growth rules for different jump kernels, reveals qualitatively different scaling behaviours for the clone extent on either side of the critical point μ = d. For power-law growth, (t) ∼ t1/(μd), we find l′/l ∼ 21/(μd); i.e. the ratio of halo size to core size is a constant that grows as μd but is independent of the size of the clone. By contrast, in the stretched-exponential regime, with (t) ∼ exp(Btη) where B and η depend on μ, we find ; i.e. the ratio of halo to core depends on the core size as well as on the kernel exponent. Since η = log[2d/(d + μ)]/log 2 > 1, the halo becomes increasingly prominent as μ → 0. These scaling estimates break down as μ approaches d—for instance, the ratio l′/l diverges in the power-law growth regime—mirroring the limited utility of the approximate asymptotic forms for (t) near μ = d. Instead, we must use the more accurate forms for (t) (S1 Appendix, Section A) to evaluate l and l′. However, upon using these forms with fitted magnitude and time scales, the qualitative picture is largely unchanged: the ratio l′/l becomes very weakly dependent on the core size as μd, but the dependence is much stronger when μ drops below d. For instance, at μ = 1.2 in 1D, the predicted ratio l′/l merely doubles from 4 to 8 as l spans four orders of magnitude from l = 10 to l = 105; by contrast, at μ = 0.8 the ratio changes by an order of magnitude (from roughly 5 to 70) over the same range of core sizes.

In summary, the growth dynamics for solitary clones at different kernel exponents predicts the following structure for large mutant clones: contiguous, compact clones for μ > d + 1; a high-occupancy core with a halo of well-developed satellite clusters that extends out to a size-independent (but kernel-dependent) multiple of the core radius for d < μ < d + 1; and a sparse halo which is significantly larger in extent than the core and becomes more prominent with increasing clone size for μ < d. We now assume that these conclusions, and the associated scaling relations for the linear extent of the core and halo, also apply to the spatial structure of mutant clones that have grown during soft sweeps and have been frozen in due to interference with clones of differing mutational origin. This key assumption is tested in the following section.

Occupancy profiles.

To verify the structural features outlined above, we measured average occupancy profiles of distinct clones in the final states of 1D soft sweep simulations (Fig 3). Occupancy profiles from clones of different sizes are combined by scaling the distance coordinate of each profile by the mass-equivalent radius req, derived from the total mass X of that clone via req ≡ (X/ωd)1/d, and performing an ensemble average as described in Table 2. The choice of distance scale is motivated by our definition of in terms of the clone mass, and justified by the observation that averaged occupancy profiles for a given kernel with vastly different average clone sizes collapse onto a single curve when the distance coordinate is rescaled by the size, consistent with the core radius being proportional to req, see S1 Fig.

Fig 3. Occupancy profiles of clones show the reduced prominence of the core with increased long-range dispersal.

(a) Ensemble-averaged occupancy profiles of mutant clones in 1D, with L = 106 and . The occupancy profile of a particular clone is defined as the probability ρ(r) that a deme at distance r from its point of origin is occupied by that clone. Colours signify different dispersal kernels, with exponents μ = {0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2, 3} in order of increasing occupancy at the origin. Curves are obtained by averaging individual occupancy profiles from all clones with total mass X > 100 to obtain a range-averaged occupancy profile 〈ρ〉 for each of 100 independent simulations for each dispersal kernel; these were then averaged to obtain the ensemble-averaged profile . Inset illustrates the origin of the variation in occupancy for r/req < 2 in the wavelike growth regime: the mutational origin need not be positioned at the centre of mass of the contiguous domain, giving rise to a single-clone occupancy profile ρ(r) = {1, 0 < rr1; 1/2, r1 < rr2; 0, r > r2}. (b) Same data as in (a) on logarithmic scales. The dashed lines show the power-law dependence 〈ρ〉 ∝ r−(μ+d). (c) Core occupancy, defined as the fraction of the ensemble-averaged occupancy that lies within 0 < r/req < 2, as a function of kernel exponent μ.

Ensemble-averaged occupancy profiles for different jump kernels are shown in Fig 3(a) with . We observe that when μ > d + 1, the averaged occupancy is negligible for r/req > 2, and the curve has a point of symmetry at (r/req, 〈ρ〉) = (1, 1/2), such that 〈ρ〉(r/req) = 1 − 〈ρ〉(2 − r/req) for 1 < r/req < 2. This form is consistent with the entire clone being contained in a single domain, which grows to different lengths on either side of the originating mutation, as illustrated in the inset.

The predicted breakup of clones due to long-range dispersal is reflected in the overall broadening of occupancy profiles as the kernel exponent μ is reduced below the critical value d + 1. In this range, an appreciable portion of the clone lies outside the maximal distance from the origin (r/req = 2) that could be measured for a contiguous domain in a linear habitat. (This upper limit would correspond to a clone that was obstructed by an occupied deme adjacent to its mutational origin on one side, and attained its final mass by expanding only in the other direction). The dropoff in occupancy becomes increasingly steep at low values of r/req as μ is reduced, but more gradual at larger distances, consistent with a narrowing high-occupancy region balanced by a halo of increasing prominence. At large distances, the falloff in occupancy is consistent with the power-law behaviour expected from the solitary-clone growth dynamics, 〈ρ〉 ∝ r−(μ+d) [dashed lines in Fig 3(b)], which supports our assumption that the final structure of a mutant clone in a spatial soft sweep is similar to that of a solitary clone expanding without interference.

To quantify the relative prominence of the halo to the core across all growth regimes, we define the core occupancy as the fraction of the total occupancy contained within the maximal range of distances that could be measured for contiguous domains, 0 < r/req < 2. We find that the core occupancy is close to 100% for μ > d + 1 (= 2 in 1D), consistent with the contiguous clones and insignificant haloes expected for wavelike growth [Fig 3(c)]. For broader kernels, the core occupancy falls with μ, reflecting the increasing prominence of the halo as μ approaches zero. However, the core still contains an appreciable fraction of the total occupancy for all values of μ that we have simulated. This observation further supports our approach of connecting the geometric extent of cores to the total mass of their corresponding clones, as we do in the following section.

Characteristic scales via mutation-expansion balance

So far, we have focused on the spatial structure of individual clones within a soft sweep, and have shown that many aspects of this structure can be understood from the theory of growth of a solitary clone under the same dispersal kernel. To address questions of global and local allelic diversity, however, we need to explicitly consider the concurrent growth of multiple clones. We now show how the balance between jump-driven growth and the dynamics of introduction of new mutations sets the typical size and spatial extent of clones.

Size of a “typical” clone.

In an infinitely large range, a solitary clone could grow without bound, but in the presence of recurrent mutations, the growth of any one clone is obstructed by other clones. Balancing mutation and growth gives rise to a characteristic time scale t*, and associated characteristic linear extent χ, for mutant clones in multi-origin spatial sweeps [11]. These scales determine whether a sweep will be “hard” or “soft” within a finite range of given size.

When clones grow as compact, connected domains, growth is interrupted when the advancing sharp boundary of the clone encounters a different allele. However, for clones growing via long-range dispersal events, a sharp boundary no longer exists, and small obstacles can be traversed by jumps. The picture of jump-driven growth that we have developed suggests that haloes belonging to different clones can freely overlap, whereas core regions cannot. Therefore, new mutations arising within the halo region of a growing clone do not significantly impede its growth. Instead, the crucial factor restricting the growth of a clone is when its high-occupancy core encounters a different clone, as depicted schematically in Fig 2. Since (t) defines precisely the time-evolution of the core extent of a solitary clone, we define t* as the time interval for which exactly one mutation is expected to occur in the space-time region swept out by the growing core: (1) The corresponding characteristic extent, χ(t*), matches the length scale introduced in [11] to characterize spatial soft sweeps.

Rough estimates for t* and χ can be obtained by using the long-time asymptotic forms for (t) in the different growth regimes, see Table 1. These estimates highlight the vastly different functional dependences of the characteristic scales on the rescaled mutation rate as the kernel exponent is varied. For quantitative tests, we use scaling forms for (t) derived in [17], which are much more accurate at short times when μ is close to d. All theoretical forms for (t) include unspecified multiplicative factors A and B for the length and time variables, which are fixed by fitting the functional forms to the growth of isolated clones starting from a solitary seed, see S1 Appendix, Section A for details.

The scales χ and t* provide the appropriate rescaling of space and time to compare two sweeps with different mutation rates but the same growth rule (t), and therefore capture the dependence of many soft sweep features on the mutation rate. Most significantly, they set the expected number of independent mutational origins in a range of a given size. When both sides of Eq 1 are multiplied by the total number of demes Ld, it equates to a condition for the range to be completely filled by mutations accumulated at a rate over a time t* without interference, each of which grows to the characteristic size (t*). The expected number of mutational origins in the range therefore scales as . If , or equivalently Lχ, it is unlikely that many independent mutations will arise: the sweep is likely to be hard. By contrast, if the range is large compared to the characteristic length χ, the number of independent origins grows in proportion to the range area. Consequently, the total number of demes in the range divided by the expected number of origins converges to a well-defined value as L increases, which we call the expected clone mass Xave. For a given dispersal kernel, the mutation-rate dependence of Xave is captured by the variation of χ with , with a factor of proportionality that dependes on the details of the growth dynamics.

To test whether the characteristic length scale quantifies the number of mutational origins in a range, we compare the ensemble-averaged clone mass measured in simulations, 〈X〉, to computed using the theoretical forms for (t). Results for 1D simulations are shown in Fig 4. (Definitions of measured quantities and averages are provided in Table 2). For clarity, the expected scaling with mutation rate in the wavelike spreading limit, , is divided out. We find that the average clone sizes for different system sizes coincide at , consistent with 〈X〉 being an estimate of an underlying expected clone mass, Xave, that is determined by the mutation-expansion balance and is independent of system size. For each value of μ, the computed quantitatively captures the dependence of 〈X〉 on over many orders of magnitude, up to a constant factor (the factor depends on model details and is treated as a fitting parameter, but it turns out to not vary significantly with μ). These results confirm that the mutation-expansion balance captured in Eq 1, first identified in [11], remains relevant for characterizing the compact core regions of clones in when long-range dispersal is prominent.

Fig 4. Average clone mass is set by mutation-expansion balance.

The ensemble-averaged final mass of mutant clones 〈X〉 as measured from 1D simulations as a function of rescaled mutation rate, scaled by the expected dependence () for wavelike growth of clones. Results from different system sizes (symbols) are presented for each dispersal kernel quantified by μ (colours); the values are (from top to bottom) μ = {0.2, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2, 3}. Each point represents an average over 20 or more independent simulations. Error bars denote measured standard deviation across repetitions. Dashed lines show the theoretical predictions for for each dispersal kernel (see S1 Appendix, Section A for details), multiplied by a μ-dependent magnitude factor whose value is 1.5 for 1 ≤ μ < 2, 1.6 for μ > 2, and 1.65 for all other values.

Characteristic extent of clones.

The analysis of the spatial structure of individual clones (Section Jump-driven growth and the core-halo structure of mutant clones) showed that the extent of clones (i.e. the portion of the range over which demes belonging to the clone can be found) can be many times larger than the expected extent for a compact clone, especially for broad dispersal kernels. Therefore, the average clone size may not be representative of the spatial extent of typical clones over the range, pointing to the potential relevance of additional length scales when characterizing jump-driven spatial soft sweeps. To quantify this effect, we measure the extent, rmax, of a clone in our 1D simulations as half the largest distance between any pair of individuals belonging to that clone. The disparity between the true extent of clones and the extent expected from the average clone mass can be evaluated by comparing the ensemble-averaged extent 〈rmax〉 to the average mass-equivalent radius 〈req〉. If all clones were perfectly contiguous and compact, we would expect 〈rmax〉 = 〈req〉.

Fig 5 shows the ratio of average clone extent to average mass-equivalent radius for different dispersal kernels and mutation rates. The ratio is unity when μ > d + 1, which is the expected regime of compact clones. For broader kernels, we find that the average spatial extent is larger than the mass-equivalent radius, consistent with our expectation from jump-driven growth. Two separate behaviours can be identified in this regime. In the range d < μ < d + 1, the ratio 〈rmax〉/〈req〉 is independent of the rescaled mutation rate. By contrast, for μd, the ratio of lengths shows a mutation-rate dependence, and grows dramatically in magnitude. At the smallest values of μ, the measured extent of the largest clones becomes limited by the system size (the maximum measurable extent under periodic boundary conditions is L/2). This finite-size effect artificially suppresses the ratio 〈rmax〉/〈req〉, as is apparent upon comparing the measurements at L = 106 and L = 107. (Note that the measurement of 〈req〉 does not suffer from finite-size effects, which was verified in Fig 4, since the core extent remains much smaller than the system size for these parameters).

Fig 5. The spatial extent of clones can be much larger than the expectation derived from the average mass.

Ensemble-averaged spatial extent of clones 〈rmax〉, where rmax is defined as half the distance between leftmost and rightmost demes belonging to a particular clone. Values are normalized by the ensemble-averaged mass-equivalent radius 〈req〉 in 1D simulations. Each point is an average over values from 20 independent simulations. Dotted lines connect simulation data points. Dashed lines show the theoretical expectations ζ/χ (with ) and ψas/χas in the ranges μ < 1 and 1 < μ < 2 respectively, multiplied by model-dependent numerical factors. For μ < 1, is evaluated as described in S1 Appendix, Section A; for 1 < μ < 2, the ratio ψas/χas is independent of . The fall in the measured ratio at low values of for μ < 1 is due to finite-size effects, as seen by comparing the two system sizes: the measured values of rmax in this range are below the values that would be measured in an infinitely large system.

To explain these features, we return to the core-halo picture of clone structures. We had previously identified two contributions to the halo region of a clone (Fig 2). One contribution comes from satellite clusters which are established with high probability during the growth process, but are obstructed from merging with the core by clusters belonging to other clones. In addition, the heavy-tailed jump kernel could populate demes arbitrarily far from the growing core. These would form isolated demes or small clusters embedded within other clones, without any related clusters in the neighbourhood. These two mechanisms lead to different characteristic length scales which we now analyze in detail.

We first quantify the extent of the region in which satellite clusters are established. We had argued that, in the vicinity of the critical value μ = d, a clone whose core has grown to some size (t) will have likely established satellites of a significant size out to a distance (2t). Having derived a characteristic time scale t* (defined in Eq 1) for the growth of a typical clone restricted by mutation-expansion balance, a rough estimate for the extent of its halo is provided by the quantity (2) which quantifies the extent of established satellites of the typical clone. Although this estimate ignores interference among haloes of different clones, we note that the expected number of new mutational origins encountered by each satellite cluster is less than one since the satellites are smaller than (t*), which limits the amount of interference. An alternative argument, which balances the rate of key jumps out of the core with the expected rate of mutations arising in the target region of the jumps and therefore incorporates some interference effects, produces a similar estimate for the typical extent of the satellite cluster region (see S1 Appendix, Section C).

Since the existence of satellite clusters is closely tied to the growth process of the core, we expect the typical halo extent to be at least of order ψ. In this part of the halo, the distribution of satellite clusters of the same identity as the core is relatively dense: in the absence of interference with other clones, the maximum separation between satellite clusters would be of order (t*) = χ. However, the halo also includes contributions from rare long-distance dispersal events which land well outside the dense region of satellite clusters. Due to the heavy tail of the dispersal kernel, the growing core could send out offspring to arbitrarily large distances, which are not restricted by the length scale ψ. If these rare jumps land on unoccupied demes, they would establish isolated demes or small clusters. Unlike the satellite clusters, these isolated clusters would be very sparsely distributed, being separated from their relatives by distances much larger than χ. However, they would still count as part of the discontiguous halo of their parent core. In particular, the extremal measure rmax is sensitive to isolated offspring even if they do not belong to satellite clusters of significant size.

The outer limit of jumps made by the core during the sweep can be estimated using prior scaling arguments. Since our time units are set by the migration rate, the net number of jumps out of a typical clone which grows over a time t* is roughly ωdt*d(t*), which equates to by the definition of t*. The fraction of these jumps which end up beyond a distance l is . A crude estimate for the outer limit ζ of these rare events is obtained by requiring the net number of jumps from the core to distances ζ or greater to be equal to 1: (3) Unlike the extent of the satellite cluster region, ζ does not depend explicitly on the core growth function (t). However, the scaling for ζ does not account for the fact that many of the long-distance rare jumps would fail to establish because they land on the high-occupancy cores of competing clones, which fill up the range over the same time scale t*. Therefore, ζ is likely to be relevant when the sparse halo regions become dominant over the cores, i.e. for μ < d.

To test whether ψ or ζ determines the average clone extent in the different growth regimes, we also need to fix an overall magnitude factor, which is not predicted by the scaling arguments. The most general behaviour would be for these magnitude factors to themselves depend on μ. However, our measurements of the clone mass (Fig 4) showed that the magnitude factors only vary slightly over the range of values of μ. Therefore, we evaluate the effectiveness of the theoretical length scales by testing whether they reproduce the simulation data up to a μ-independent magnitude factor, which we treat as a fit parameter.

The asymptotic growth forms for (t) from Table 1 can be used to obtain the qualitative behaviour of the characteristic satellite cluster extent; we term the resulting estimate ψas. (We expect asymptotic estimates to become inaccurate as μ approaches d). In the region of power-law growth, d < μ < d + 1, we find that ψas and ζ both have the same mutation-rate dependence for a given dispersal kernel, . However, the ratios of the length scales to the characteristic core size χas show different behaviour as μ is varied: ψas/χas = 21/(μd) has no remaining dependence on χas or (t), whereas depends on the magnitude parameter Aμ which characterized the solitary-clone growth. The measured ratio of average clone extent to average mass-equivalent radius agrees well with the theoretical prediction for ψas/χas for μ ≥ 1.4 (red dashed line in Fig 5; the overall magnitude factor was chosen to match the value at μ = 2). By contrast, the prediction ζ/χ (cyan dashed line) deviates by factors of order one from the measured ratio due to the residual dependence on Aμ, although it agrees with the overall trend. As expected, the asymptotic estimates become increasingly inaccurate as μ → 1 which reflects the breakdown of the asymptotic growth form.

At the marginal value μ = d, the asymptotic form predicts for , where cd is a numeric constant of order one. In 1D, the very weak additional dependence on is not sufficient to distinguish this form from the alternative length scale for μ = 1. However, the differences in ψ and ζ become significant in the region of stretched-exponential growth, μ < d. As μ approaches zero, the strong divergence in Eq 3 for rescaled mutation rates below one induces ζ to grow much faster than ψas. For our 1D simulations, the ratio ψ/χ only varies by about an order of magnitude, regardless of whether the asymptotic forms or the more accurate scaling forms from S1 Appendix, Section A are used. Therefore, the satellite cluster region cannot account for the dramatic increase in average clone extent observed at low values of μ in Fig 5. By contrast, ζ grows rapidly over many orders of magnitude over the same range. Upon fitting an overall magnitude factor, the ratio ζ/χμ successfully captures the variation in 〈rmax〉/〈req〉 for the largest rescaled mutation rate (cyan dashed line in Fig 5).

To summarize, we have identified two characteristic scales, ψ and ζ (defined in Eqs 2 and 3 respectively), that could set the average halo extent in our spatial soft sweeps. Differences between ψ and ζ are small when dμ < d + 1, but become significant for μ < d. Comparisons with the measurements of clone extent in simulations (Fig 5) support the hypothesis that ζ sets the halo extent for the highly sparse clones that arise when μ < d, while ψ sets the halo extent for the more compact but still discontiguous clones when μd < d + 1.

Clone size distributions vary with dispersal kernel and influence global sampling statistics

Unlike our simulations, studies of real populations do not have access to complete allelic information over the entire range. Instead, the allelic identity of a small number of individuals is sampled from the population. The likelihood of detecting a soft sweep in such a random sample is determined not only by the total number of distinct clones in the range, but also by their size distribution: if the range contains many clones, but all but one are at extremely small frequency (defined as the fraction of demes in the range that belong to that clone), the sweep is likely to appear “hard” in a small random sample which would with high probability contain only the majority allele. Long-range dispersal can therefore influence soft sweep detection not only by setting the average clone size, but also by modifying the distribution of clone sizes around the average. Having already established that the dispersal kernel has a significant effect on the average clone size (Fig 4), we now analyze its effects on the clone size distribution and the consequences for soft sweep detection.

Clone size distributions were quantified by computing the allele frequency spectrum f(x), defined such that f(x)δx is the expected number of alleles which have attained frequencies between x and x + δx in the population [18]. The allele frequency spectrum is related to the average probability distribution of clone sizes, but has a different normalization which allows sampling statistics to be expressed as integrals involving f(x) (we will exploit this fact in Section Global sampling statistics below). Analytical results for f(x) can be derived for the deterministic wavelike growth limit μd + 1 in 1D by mapping the spatial soft sweep on to a grain growth model [19], and for the panmictic limit μ → 0 in any dimension via a different mapping to an urn model [20]. The resulting functions, termed fw and f for the two limits respectively, provide bounds on the expected frequency spectra at intermediate μ. Details of the mappings and complete forms for the functions fw and f are provided in S1 Appendix, Section D.

Fig 6(a) shows allele frequency spectra computed from the outcomes of 1D soft sweep simulations for system size L = 107 and mutation rate . We find that the frequency spectra vary strongly with the dispersal kernel, and approach the exact forms f and fw for small and large μ respectively. Generically, spectra become broader as the kernel exponent is reduced: as μ → 0, more high-frequency clones are observed. Although this broadening is partly explained by the increase in the average clone size due to accelerated expansion, which would lead to more high-frequency alleles, there are also systematic changes in the overall shapes of the distribution as the dispersal kernel is varied. Upon reducing the rescaled mutation rate to [Fig 6(b)], all frequency spectra broaden due to the increase in the average clone size, but the variations in shapes of the f(x) curves with μ remain consistent across the two mutation rates. These observations suggest that spatial soft sweep patterns with similar numbers of distinct alleles in a range might nevertheless have vastly different clone size distributions due to different dispersal kernels, with implications for sampling statistics.

Fig 6. Variation of allele frequency spectra with mutation rate and dispersal kernel.

(a) Allele frequency spectra f(x) estimated from 1D simulations with L = 107 and . Each curve is the average of frequencies measured from 20 independent simulations. Curves are coloured by dispersal kernel according to the legend in (c). The uptick for the lowest bin is an artifact of the logarithmic bin sizes together with the hard lower cutoff in allele frequency at x = 1/L. Black dotted and dash-dotted lines show the analytical distributions for wavelike spreading and panmictic limits respectively. (b) Same as in (a) except with . (c) Frequency spectra from (a) and (b), rescaled to remove the expected variation due to changes in average clone size. Inset, dependence of the cut-off frequency xc on the exponent p when frequency spectra are approximated by a power law f(x) ∝ xp for xxc and f(x) = 0 for x > xc. Dots show the cutoff estimated numerically as the value for which the second derivative of the rescaled curves first drops below −4, plotted against the observed exponents p ≈ {−0.9, −0.72, −0.47, 0, 1} for the small-frequency behaviour.

To uncover variations due to long-range dispersal beyond changes in the average clone size, we rescaled the frequency spectra by the expected dependence on Xave, which we have already established as being set by the mutation-expansion balance via the characteristic size χ. To establish the form of this rescaling, we assume that for a given dispersal kernel, soft sweep patterns at different mutation rates are self-similar when distances are rescaled by the characteristic length χ. Under this assumption, the probability distribution of clone sizes in an infinitely large range is a function only of the rescaled clone mass sX/Xave; i.e. the probability of finding a clone between s and s + δs is Pμ(s)δs, where the density function Pμ depends only on the dispersal kernel and not on the rescaled mutation rate.

For finite ranges of extent L much larger than χ, we can now express the average allele frequency spectrum in terms of Pμ. The expected number of unique alleles in the range is Ld/Xave. Within these alleles, the probability of finding an allele in the frequency range (x, x + δx) is Pμ(Ldx/Xave) × Ldδx/Xave. Therefore, the expected number of alleles with frequencies between x and x + δx is Upon comparing this expression the definition of the allele frequency spectrum for the finite range, we arrive at (4)

Eq 4 implies that for a given dispersal kernel, the dependence of the allele frequency spectrum on mutation rate and range size is completely captured by the ratio Ld/Xave. In particular, when f(x) is multiplied by (Xave/Ld)2 and the frequency by Ld/Xave, frequency spectra for different values of ought to collapse onto a single curve for each μ. Fig 6(c) shows that upon such a rescaling (with 〈X〉 used as a simulation-derived estimate of Xave), curves for the same value of μ from panels (a) and (b) largely coincide, confirming that most of the dependence of the frequency spectrum on mutation rate is captured by the variation of the single length scale χ and, through it, the expected clone mass Xave. Note that we can use the fact that Xaveχd with a kernel-independent prefactor to rewrite Eq 4 as f(x) = (L/χ)2dGμ(Ldx/χd), where Gμ is independent of , which explicitly shows the role of χ in scaling the allele frequency spectrum.

The arguments leading to Eq 4 relied on the assumption that only one characteristic scale exists for the soft sweep patterns. For our class of kernels, this assumption is only exact in the regime of power-law growth, for which the halo extent scale ψ is proportional to χ. In the stretched-exponential and marginal growth cases, by contrast, ψ acts as an independent length scale from χ with its own mutation-rate dependence. In S1 Appendix, Section E, we show that the consequent corrections to Eq 4 are weak (logarithmic in mutation rate and system size) and are strongest when μ approaches 0, validating the effectiveness of the proposed rescaling over all regimes away from the well-mixed limit.

The scaled frequency spectra show that broader dispersal kernels favour broader allele frequency spectra even after accounting for changes in the average clone size. At μ = 4, the steep decline in the frequency spectrum occurs near the frequency expected of an average clone, x ≈ 〈X〉/L. As μ is reduced, the falloff occurs at higher frequencies; at μ = 0.4, for instance, clones with frequencies an order of magnitude higher than the average clone are still likely. Qualitatively, this trend is a result of the increased nonlinearity of the growth functions (t) for broader dispersal. If we assume no interference among distinct clones until the time t*, the size of an allele which arrives at time ti is proportional to ℓd(t* − ti). For a given spread of arrival times of mutations, the spread of final clone sizes is significantly enhanced by nonlinearity in (t). Therefore, the increased departure from linear growth in (t) as μ → 0 gives rise to broader clone size distributions. Deterministic approximations to the clone size distributions expected for the asymptotic (t) forms in 1D, described in S1 Appendix, Section E, support this heuristic picture.

Although we do not have analytical expressions for the frequency spectra at intermediate μ, the measured curves and deterministic calculations suggest a simple approximate form for the allele frequency spectra: extend the power-law behaviour observed at intermediate frequencies [straight parts of the curves in Fig 6(a)–6(b)] from x = 0 up to a cutoff frequency corresponding to the location of the sharp dropoff in f(x). Quantitatively, we consider an ansatz for the frequency spectra with two parameters: (5) i.e. a power-law behaviour characterized by exponent p, up to some maximal frequency xc, with the constant of proportionality determined by the normalization. The values p and xc are determined from the numerical data, but are also consistent with theoretical arguments (S1 Appendix, Section E). The small-x behaviour of the two limiting spectra, f(x) ∼ x−1 and fw(x) ∼ x as x → 0, imply that p is restricted to vary from −1 to 1 as μ increases from zero. Despite its simplicity, this approximation can be used to quantify the relationships among various features of the clone size distributions as we show in S1 Appendix, Section F. For instance, the power-law ansatz predicts a relation between the average clone size and the cutoff frequency, Lxc/Xave = (p + 1)/(p + 2), which matches the trends observed in the rescaled frequency spectra, see inset to Fig 6(c).

Global sampling statistics.

The utility of f(x) in the context of soft sweep detection is made apparent by noting that the probability Phard(j) of finding only one unique allele in a sample of size j ≥ 2 drawn randomly from the population (i.e. detecting a hard sweep) is [18] (6) The probability of observing a soft sweep in a sample of size j is simply Psoft = 1 − Phard(j). (Although Psoft might be more relevant to soft sweeps, we deal with Phard(j) in the following sections because it is more straightforward to compute and manipulate mathematically). Since xf(x) does not diverge as x → 0 for all observed frequency spectra, the integral in Eq 6 is dominated by contributions from the high-frequency region of f(x) and is therefore highly sensitive to the breadth of the frequency spectrum. Using the power-law ansatz for the frequency spectrum, Eq 5, gives for large j (see S1 Appendix, Section F): the dominant behaviour is an exponential decay with sample size, with the decay scale set by the high-frequency cutoff xc. At a given rescaled mutation rate, this cutoff falls by many orders of magnitude as μ is increased, as we saw in Fig 6(a)–6(b). As a consequence, the probability of finding a monoallelic sample also falls dramatically with increasing μ, see Fig 7(a). Analytical calculations of Phard using f(x) and fw(x) in the μ → 0 and μd + 1 limits (dashed and dash-dotted lines) provide bounds on the variation (see S1 Appendix, Section G for explicit forms of Phard(j) in these limits).

Fig 7. Probability of observing only one allele in a finite sample.

(a) The probability Phard(j) of observing a hard (i.e. monoallelic) sweep in a sample of size j chosen at random from the range, computed from simulated clone size distributions for different dispersal kernels (colours, labeled) with L = 106 and in 1D. Three analytical forms are shown as dotted lines (from top to bottom): the Ewens’ sampling result for the panmictic case, the approximate form derived using a hard-cutoff ansatz for the allele frequency spectrum for μ = 1 (S1 Appendix, Eq (A12)), and Phard calculated from the exact f(x) for the wavelike spreading limit (S1 Appendix, Section G). (b) The same quantity computed across a range of rescaled mutation rates (symbols), and scaled by the expectation for a range with all clones having the same size and hence the same frequency Xave/L.

We have seen that increased long-range dispersal broadens the frequency spectrum both by increasing the average clone size, and by enhancing the spread of clone sizes around the average. In the hypothetical case of all clones having the same size Xave, a monoallelic sample of size j is obtained by having the last j − 1 samples drawn from the same clone as the first sample, which occurs with probability (Xave/Ld)j−1 ∝ (χ/L)d(j−1). To distinguish the effect of the shape of f(x) from that of the average size of clones, we scale Phard(j) in 1D simulations by (〈X〉/L)j−1 for a range of rescaled mutation rates, see Fig 7(b). (As before, we use 〈X〉 for a simulation-derived estimate of Xave). If the sampling statistics were determined primarily by the average clone size (which in turn is set by χ) and the effect of variations in the shape of f(x) were insignificant, we would expect the rescaled Phard(j) for different kernels to all collapse on the same curve. Instead, we find that the sampling statistics vary significantly with μ even when accounting for differences in average clone size. Whereas the rescaling captures a significant amount of the variation in Phard(j) within each value of μ (with a residual that differs for the different regimes of (t), and is due to the relevance of the additional length scales outside the power-law growth region), the rescaled curves vary widely among the different dispersal kernels.

Fig 7(b) quantifies the influence of long-range dispersal on soft sweep detection beyond merely setting the average size of clones: if mutation rates are adjusted so that the characteristic length scales χ and hence the average clone sizes are comparable for different dispersal kernels, soft sweeps continue to be less likely to be detected for broader kernels (smaller μ). This happens because the range has a larger contribution from high-frequency clones with x > (χ/L)d for broader dispersal kernels, making monoallelic sampling more likely. In summary, not only does broadening the dispersal kernel make sweeps harder, it also makes their detection less likely. Since a wide range of possible outcomes separates the two limits of panmictic (μ → 0) and wavelike spreading (μd + 1), predictions based on these extremes might perform poorly in making inferences from sampling statistics in populations with intermediate long-range dispersal.

Local sampling protocols are highly sensitive to the core-halo structure

Population genomic studies are often limited not only in the number of independent samples available, but in their geographic distribution as well. Samples tend to be clustered in regions chosen for a variety of reasons such as anthropological or ecological significance, or practical limitations. The analysis of the last section would apply to comparing samples across different regions, provided that these are relatively well spread out in the range. Here we focus on the variation within local samples from a subrange of the entire population. As illustrated by the wide variation in local diversity within the highlighted subranges (dashed boxes) in Fig 1(a), inferences based on local sampling can be significantly different from inferences based on global information, and may be very sensitive to modes of long-range dispersal.

Long-range dispersal enhances local diversity. When clones extend over a much wider spatial range than required by their mass (Fig 5), local subranges contain alleles whose origins lie far away from the subrange, and are consequently more diverse than expected from the diversity of the range as a whole. To quantitatively illustrate this effect, we compute sampling statistics for different dispersal kernels and subrange sizes from 1D simulations with a global range size much larger than the characteristic length scale χ (Fig 8). (Subrange size, denoted by Ls, and extent are equivalent in our 1D simulations). We observe that the smaller clones expected at higher values of μ favour the detection of soft sweeps globally (Fig 8a), but the diversity is less detectable in samples from subranges that are smaller than the characteristic size shared by the compact domains at μ = 4. By contrast, samples from smaller subranges continue to show signatures of soft sweeps for broader dispersal kernels (Fig 8b and 8c).

Fig 8. Dispersal promotes soft sweep detection in small subranges.

(a)–(c) Probability of observing a hard sweep in j samples randomly chosen from contiguous subranges of different sizes Ls in simulated 1D ranges of size L = 106, with rescaled mutation rate . At the same mutation rate, broader dispersal kernels lead to a larger average clone size (〈X〉 ≈ 980, 1.6 × 104, 4 × 104 for μ = {4, 1, 0.6} respectively), which reduces the total number of alleles and favours hard sweep signatures when the sampling is done over the entire range [L = Ls, (a)]. However, when Ls is reduced [(b)–(c)], the detection of soft sweeps become increasingly likely for the broader dispersal kernels; the broken-up structure of clones compensates for their smaller overall number. For small enough subranges, the order of values of Phard(j) with increasing μ is inverted compared to the values at Ls = L.

To compare the sensitivity of soft sweep detection to subrange size across different dispersal kernels and mutation rates, we focus on the probability of detecting the same allele in a pair of individuals randomly sampled from a subrange, Phard,s(2) (also called the species homoallelicity of the subrange). This probability is high only when the subrange is mostly occupied by the core of a single clone; it is low if the subrange contains cores belonging to different clones, or a combination of cores and haloes. Therefore, we expect χ (or equivalently the average mass-equivalent radius 〈req〉, which we may use as a simulation-derived estimate for χ in 1D) to also be the relevant scale to compare Ls values across different situations. Fig 9(a) shows the dependence of Phard,s(2) on Ls/〈req〉 for different dispersal kernels and mutation rates in the χL limit. As with the global sampling probabilities reported in Fig 7(b), we find that the rescaling of subrange size with 〈req〉 captures much of the variation among different mutation rates (symbols) for a given dispersal kernel. In contrast with the global sampling statistics, however, hard sweep detection probabilities are suppressed (or equivalently, soft sweeps are easier to detect for the same rescaled subrange size) as the jump kernel is broadened. At high values of μ in the wavelike expansion limit, the shape of the curves is well-approximated by the null expectation for an idealized clone size distribution where all clones are perfectly contiguous segments of equal size Xave. As μ falls below d + 1, the prevalence of overlapping haloes increases local diversity at the scale of satellite clusters, much smaller than the typical clone size would dictate. The effect is especially strong in the marginal and stretched-exponential growth regimes (μd), which was associated with the halo dominating over the core (Figs 3 and 5).

Fig 9. Different measures of diversity within a subrange are sensitive to different characteristic scales.

(a) Probability Phard,s(2) of observing a single allele in a pair drawn from a subrange of size Ls for different dispersal kernels (colours, labeled) and mutation rates [symbols, see legend in panel (b)], for 1D simulations with L = 106, as a function of the ratio Ls/〈req〉. In all cases, the population range was chosen to be many times larger than the characteristic size χ and harbours many distinct alleles. The dashed line is the prediction if all clones are of the same size Xave, in which case geometry dictates that Phard,s(2) = {1 − x/3, x < 1; 1/x − 1/(3x2), x ≥ 1}. The inset shows data for μ = {0.6, 1.0, 3.0} on log axes. (b) Number of distinct alleles nc,s observed in a subrange of size Ls, shown as a function of the ratio Ls/〈rmax〉. Values are scaled by 〈rmax〉/〈req〉, the expected number of clones in the area occupied by the average halo. The solid line corresponds to nc,sreq〉/〈rmax〉 = Ls/(2〈rmax〉), or equivalently nc,s = Ls/(2〈req〉).

A different measure of subrange diversity is the total number of distinct alleles present in a subrange on average, which we call nc,s. Unlike the subrange homoallelicity, which was dominated by the most prevalent clone in the subrange, this measure gives equal weight to all clones, and is sensitive to haloes that overlap with the subrange. The expected number of distinct cores in the subrange is Ls/(2〈req〉); in the absence of haloes, we would expect nc,s to be equal to this value. However, haloes of clones whose cores are outside the subrange would cause nc,s to exceed the number of cores in the subrange. This enhancement in diversity due to encroaching haloes would be expected to occur only when the subrange is smaller than the average clone extent including the halo, i.e., when Ls < 2〈rmax〉. When the subrange is larger than the typical halo extent, the cores of clones whose haloes contribute to nc,s are also expected to lie within the subrange, and are accounted for in Ls/(2〈req〉). This expectation is confirmed in Fig 9(b). When the subrange size is rescaled by the extent of the clone including the halo, the average number of distinct alleles in the subrange follows nc,s = Ls/(2〈req〉) (solid line) in all cases, provided Ls/〈rmax〉 > 2. For smaller subrange sizes, nc,s lies above this estimate, reflecting the enhancement of local diversity due to encroaching haloes.


Adaptation in a spatially extended population often uses different alleles in different geographic regions, even if the selection pressure is homogeneous across the entire range. The probability of such convergent adaptation [21] and the patterns of spatial soft sweeps that result depend on two factors: the potential for the population to recruit adaptive variants from either new mutations or from the standing genetic variation, and the mode of dispersal. Previous work has focused on the two extremes of dispersal phenomena: panmictic populations without spatial structure [35] or wavelike spreading due to local diffusion of organisms [11, 21]. However, gene flow in many natural populations does not conform strictly to either limit. Many species experience some long-distance dispersal either through active transport or through passive hitchhiking on wind, water, or migrating animals including humans [1214]. The dynamics of adaptation of populations with a large range can be strongly influenced by long-distance dispersal even when dispersal events are rare [22].

We have described spatial patterns of convergent adaptation for a general dispersal model, with jump rates taken from a kernel that falls off as a power-law with distance. Although the underlying analysis is applicable to more general dispersal kernels, our specific choice of kernel allows us to span a wide range of outcomes using a single parameter. We have shown that long-range dispersal tends to break up mutant clones into a core region dominated by the clone, surrounded by a disconnected halo of satellite clusters and isolated demes which mingle with other alleles. A key result of our analysis is that although the total mass of a clone is well-captured by the extent of the core region, the sparse halo can extend out to distances that are significantly larger than the core, sometimes by orders of magnitude. Therefore, understanding clone masses alone provides incomplete information about spatial soft sweep patterns, and can vastly underestimate the true extent of mutant clones.

By analyzing the balance between the jump-driven expansion of solitary clones and the introduction of new mutations, we have identified three characteristic length scales that quantify the spatial relationships between core and halo: the characteristic core extent χ, which sets the average clone mass; the radial extent ψ within which well-developed satellite clusters are expected; and the outer limit ζ within which both satellite clusters and isolated demes are typically found. As the kernel exponent μ is varied, these length scales demarcate three regimes with qualitatively different core-halo relationships: compact cores with insignificant haloes, similar to the case of wavelike growth, for μ > d + 1; a dominant high-occupancy core surrounded by a halo of well-developed satellite clusters which extend to a size-independent multiple of the core radius (ζψχ) when d < μ < d + 1; and a halo including a significant number of isolated demes in addition to satellite clusters, which may extend over a region orders of magnitude larger than the core (ζψχ) when μ < d.

We have also studied the signatures left behind by these patterns on population samples that are taken either from a local region, or globally from the entire range. Under which conditions, and for which types of samples, can we expect to observe a soft sweep? We have found that when ranges with similar overall diversity (as judged by the number of distinct clones in the entire range) are compared, broadening the dispersal kernel has opposing effects on soft sweep detection at global and local scales: soft sweeps become harder to detect in a global random sample, but easier to detect in samples from smaller subranges.

Besides having consequences for detecting and interpreting evidence for spatial soft sweeps, the breakup of mutant clones by long-range dispersal also impacts future evolution after the soft sweep has completed. Our analysis describes the spatial patterns arising in the regime of strong selection, where the large advantage of beneficial mutants over the wildtype dominates the evolutionary dynamics. Once the entire population has adapted to the driving selection pressure, smaller fitness differences among the distinct alleles will become significant, and modify the spatial patterns on longer time scales. Selection is most sensitive to these fitness differences at the boundaries separating demes belonging to different clones. For the same global diversity, the total length of these boundaries is strongly influenced by the connectivity of clones, and grows significantly as the kernel exponent is reduced, thereby modifying the post-sweep evolution of the population. The post-sweep evolution could also favour well-developed satellite clusters over isolated demes of one allele within a region dominated by another: isolated demes are likely to be taken over by their surrounding allele through local diffusion of individuals. Therefore, the characteristic length ψ may prove to be a relevant spatial scale for the post-sweep evolution, even in the regime μ < d where ζ sets the extent of the halo in the sweep patterns.

Although a quantitative evaluation of our model using real-world genomic data is beyond the scope of this work, some qualitative features of long-range dispersal can be identified in previous studies of spatial soft sweeps. The evolution of resistance to widely-adopted drugs in the malarial parasite Plasmodium falciparium is a well-studied example of a soft sweep arising in response to a broadly applied selective pressure. While multiple mutant haplotypes conferring resistance to pyrimethamine-based drugs have been observed across Africa and South-east Asia, the number of distinct haplotypes is smaller than would have been expected if resistance-granting mutations were confined to their area of origin [23]; this feature has been linked to long-distance migration of parasites through their human hosts, which allowed individual haplotypes to quickly spread across disconnected parts of the globe [24]. Within the same soft sweep, high levels of spatial mixing of distinct resistant lineages was also observed in some sub-regions [25]. These observations are consistent with the contrasting effects of long-range dispersal we have quantified in our model: at a given rescaled mutation rate, dispersal reduces diversity globally, but increases the mixing of alleles locally. Advances in sequencing technology have driven rapid improvements in the spatiotemporal resolution of drug-resistance evolution studies [26], making them a promising candidate for quantitative analysis of the spatial soft sweep patterns we have described.

Many interesting questions remain to be explored. Our simulation studies in d = 2 could be significantly expanded. We have also focused on the limit in which the average clone size is many times smaller than the entire range. It would also be interesting to study the statistics of soft sweeps when the extent of the range is comparable to the characteristic length scale χ, making a soft sweep an event of low but significant probability which may vary significantly with the dispersal kernel.

The applicability of our results to continuous populations without an imposed deme structure is an open problem. In our model, the deme structure is used to impose a local population density and allows us to separate the local dynamics of fixation from the large-scale behaviour driven by rare but consequential jumps. However, the theoretical picture of growth via the merger of satellite outbreaks with an expanding core does not rely on the deme structure. Therefore, we expect aspects of our results to also hold in continuous populations under certain parameter regimes. However, explicitly translating the parameters and defining the correct continuum limit of deme-based models is known to be challenging [27], and presents an interesting avenue for future work. Our simulations could also be modified to exploit advances in computational modeling of continuum populations [28].

The model can also be extended to include additional mechanisms involved in parallel adaptation. Besides recurring mutations, standing genetic variation (SGV) in the population is a important source of diversity for soft sweeps [3]. Long-range dispersal could impact both the spatial distribution of SGV before selection begins to act, and the spreading of alleles from distinct variational origins during the sweep [21]; both situations can be explored through extensions of our model. In the latter case, we expect the distinct regimes of core-halo patterns for different jump kernels to persist, but with the characteristic core size set by the initial distribution of variational origins rather than mutation-expansion balance.

The necessity of including heterogeneity motivates a natural set of extensions of the model. When soft sweeps arise due to mutations at different loci producing similar phenotypic effects, some variation in fitness among the distinct variants is inevitable. In panmictic models, fitness variations do not significantly affect the probability of observing a soft sweep, provided that the variations are small relative to the absolute fitness advantage of mutants over the wildtype [5]. Since spatial structure restricts competition to the geographic neighbourhood of a clone, we expect the effect of fitness variation to be even weaker than for panmictic populations, and our results should be robust to a small amount of variation in fitness effects. However, when fitness variations among mutations are large enough to be significant, the impact of the variations could depend on the dispersal kernel, and show qualitatively different behaviours in the distinct regimes of power-law and stretched-exponential growth. Similarly, spatial heterogeneities in the selection pressures could lead to so-called “patchy” landscapes which lead to certain mutations being highly beneficial in some patches but neutral or even deleterious in others [29]. Convergent adaptation on patchy landscapes is likely to be significantly impacted by long-range dispersal which would allow mutations to spread efficiently to geographically separated patches.

Finally, the assumptions of strong selection and weak mutation/migration allowed us to ignore the dynamics of introduction of beneficial mutations within a deme. Relaxing these assumptions would lead us to a more general model with an additional time scale characterizing the local well-mixed dynamics at the deme level. The interplay between this time scale and the time scales governing the large-scale dynamics driven by long-range dispersal could lead to new patterns of genetic variation during convergent adaptation.

Materials and methods

Simulation methods

Simulations were written in the C++ programming language, and utilized the standard Mersenne Twister engine to generate pseudorandom numbers. A simulation of linear size L in d dimensions is begun by initializing an array of integers of size Ld. Each array position corresponds to a single deme, and the associated integer value stores the allelic type. The array is initialized with all demes bearing the value 0 signifying the wildtype (WT).

As described in the text, the simulations only need to incorporate the two types of events which could potentially change the identity of a deme: a mutation of a WT deme, or an attempted migration from a mutant deme. To accomplish this, each deme is assigned a weight of if WT, and 1 if a mutant deme. At each discrete simulation step, a deme is picked at random with probability proportional to its weight. If the deme chosen is WT, it is assigned a unique integer that was not previously present in the array. If the deme chosen contains a mutant allele, a jump is attempted. The jump distance r is obtained by drawing a random number X evenly distributed between 0 and 1, and computing the variable r = X−1/μ; this produces a variable with normalized probability density function P(r) = μr−(1+μ) for kernel exponent μ. The distance is then multiplied with a random d-dimensional unit vector (simply ±1 in d = 1, and evenly distributed on the unit circle in d = 2). Each vector component is rounded to the nearest integer to obtain a jump vector on the lattice. The target position for the migration attempt is obtained by adding this jump vector to the source position, and wrapping the result into the range of size Ld assuming periodic boundary conditions.

If the target deme is WT, its value is updated with the allelic identity of the source; otherwise the migration attempt is unsuccessful. If the simulation step ends in a mutation or a successful migration, the probability weights associated with the demes are updated and the next step is executed. The simulation continues until all Ld array positions contain nonzero integers signifying the completion of the sweep. The final array of Ld integers constitutes the simulation output.

A single simulation took between a few minutes and 24h of CPU time depending on the parameter values. Simulation results were processed using scripts written in the Python programming language. All reported results were obtained by averaging over 20-100 independent simulations for each set of parameters, depending on system size.

Supporting information

S1 Appendix. Supporting analysis and simulations.

Detailed descriptions of analytical results and approximations used in the text, verified by additional simulation results. Includes analysis of preliminary simulation results for planar (2D) ranges.


S1 Code. Computer code used for simulations.

Code written in the C++ programming language. Instructions for compilation and execution are provided in the associated README file.


S1 Data. Raw data for Figs 39.

Text files containing tables of numerical data for all graphs in the manuscript, organized by Figure number and parameter set. Detailed descriptions of file layouts are provided in associated README files.


S1 Fig. Occupancy profiles for different mutation rates collapse when the radial coordinate is rescaled by clone size.

Averaged occupancy profiles 〈ρ〉(r/req) measured from the final states of 1D simulations with L = 106. Panels correspond to different dispersal kernels quantified by μ = 0.4 (a), μ = 1 (b), and μ = 1.6 (c). Colors indicate different rescaled mutation rates. Each curve is itself an average over clones of different sizes, and the average clone sizes vary by orders of magnitude among the different values of . Despite this variation, the profiles for a given dispersal kernel collapse onto a single curve, confirming the validity of the rescaling of the distance variable r with the mass-equivalent clone radius req. The smallest and largest average clone sizes (at and respectively) are (130, 5.8 × 104) for μ = 0.4; (84, 1.6 × 104) for μ = 1.0; and (56, 4100) for μ = 1.6.



The authors thank Graham Coop and the reviewers for valuable feedback during the review process. JP thanks Diana Fusco and Benjamin H. Good for insightful discussions. This research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California, Berkeley (supported by the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information Officer); and resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.


  1. 1. Innan H, Kim Y. Pattern of polymorphism after strong artificial selection in a domestication event. Proceedings of the National Academy of Sciences. 2004;101(29):10667–10672.
  2. 2. Przeworski M, Coop G, Wall JD. The Signature of Positive Selection on Standing Genetic Variation. Evolution. 2005;59(11):2312. pmid:16396172
  3. 3. Hermisson J, Pennings PS. Soft Sweeps: Molecular Population Genetics of Adaptation From Standing Genetic Variation. Genetics. 2005;169(4):2335–2352. pmid:15716498
  4. 4. Pennings PS, Hermisson J. Soft sweeps III: The signature of positive selection from recurrent mutation. PLoS Genetics. 2006;2(12):1998–2012.
  5. 5. Pennings PS, Hermisson J. Soft Sweeps II—Molecular Population Genetics of Adaptation from Recurrent Mutation or Migration. Molecular Biology and Evolution. 2006;23(5):1076–1084. pmid:16520336
  6. 6. Messer PW, Petrov DA. Population genomics of rapid adaptation by soft selective sweeps. Trends in Ecology and Evolution. 2013;28(11):659–669. pmid:24075201
  7. 7. Hermisson J, Pennings PS. Soft sweeps and beyond: understanding the patterns and probabilities of selection footprints under rapid adaptation. Methods in Ecology and Evolution. 2017;8(6):700–716.
  8. 8. Kwiatkowski DP. How Malaria Has Affected the Human Genome and What Human Genetics Can Teach Us about Malaria. The American Journal of Human Genetics. 2005;77(2):171–192. pmid:16001361
  9. 9. Tishkoff SA, Reed FA, Ranciaro A, Voight BF, Babbitt CC, Silverman JS, et al. Convergent adaptation of human lactase persistence in Africa and Europe. Nature Genetics. 2007;39(1):31–40. pmid:17159977
  10. 10. Jones BL, Raga TO, Liebert A, Zmarz P, Bekele E, Danielsen ET, et al. Diversity of lactase persistence alleles in ethiopia: Signature of a soft selective sweep. American Journal of Human Genetics. 2013;93(3):538–544. pmid:23993196
  11. 11. Ralph P, Coop G. Parallel adaptation: One or many waves of advance of an advantageous allele? Genetics. 2010;186(2):647–668. pmid:20660645
  12. 12. Kot M, Lewis MA, van den Driessche P. Dispersal Data and the Spread of Invading Organisms. Ecology. 1996;77(7):2027–2042.
  13. 13. Clobert J, Baguette M, Benton TG, Bullock JM. Dispersal Ecology and Evolution. OUP Oxford; 2012. Available from:
  14. 14. Bullock JM, Mallada González L, Tamme R, Götzenberger L, White SM, Pärtel M, et al. A synthesis of empirical plant dispersal kernels. Journal of Ecology. 2017;105(1):6–19.
  15. 15. Mollison D. The rate of spatial propagation of simple epidemics. In: Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, Volume 3: Probability Theory. The Regents of the University of California; 1972.
  16. 16. Lewis MA, Pacala S. Modeling and analysis of stochastic invasion processes. Journal of Mathematical Biology. 2000;41(5):387–429. pmid:11151706
  17. 17. Hallatschek O, Fisher DS. Acceleration of evolutionary spread by long-range dispersal. Proceedings of the National Academy of Sciences. 2014;111(46):E4911–E4919.
  18. 18. Ewens WJ. The sampling theory of selectively neutral alleles. Theoretical Population Biology. 1972;3(1):87–112. pmid:4667078
  19. 19. Axe JD, Yamada Y. Scaling relations for grain autocorrelation functions during nucleation and growth. Physical Review B. 1986;34(3):1599–1606.
  20. 20. Hoppe FM. Polya-like urns and the Ewens’ sampling formula. Journal of Mathematical Biology. 1984;20(1):91–94.
  21. 21. Ralph PL, Coop G. The Role of Standing Variation in Geographic Convergent Adaptation. The American Naturalist. 2015;186(S1):S5–S23. pmid:26656217
  22. 22. Nathan R. Long-distance dispersal of plants. Science. 2006;313(5788):786–788. pmid:16902126
  23. 23. Roper C, Pearce R, Bredenkamp B, Gumede J, Drakeley C, Mosha F, et al. Antifolate antimalarial resistance in southeast Africa: A population-based analysis. Lancet. 2003;361(9364):1174–1181. pmid:12686039
  24. 24. Roper C, Pearce R, Nair S, Sharp B, Nosten F, Anderson T. Intercontinental spread of pyrimethamine-resistant malaria. Science. 2004;305(5687):1124. pmid:15326348
  25. 25. Pearce RJ, Pota H, Evehe MSB, Bâ EH, Mombo-Ngoma G, Malisa AL, et al. Multiple Origins and Regional Dispersal of Resistant dhps in African Plasmodium falciparum Malaria. PLoS Medicine. 2009;6(4):e1000055. pmid:19365539
  26. 26. Okell LC, Griffin JT, Roper C. Mapping sulphadoxine-pyrimethamine-resistant Plasmodium falciparum malaria in infected humans and in parasite populations in Africa. Scientific Reports. 2017;7(1):1–15.
  27. 27. Barton NH, Etheridge AM, Véber A. Modelling evolution in a spatial continuum. Journal of Statistical Mechanics: Theory and Experiment. 2013;2013(01):P01002.
  28. 28. Haller BC, Messer PW. SLiM 3: Forward genetic simulations beyond the Wright-Fisher model. bioRxiv. 2018.
  29. 29. Ralph PL, Coop G. Convergent Evolution During Local Adaptation to Patchy Landscapes. PLOS Genetics. 2015;11(11):e1005630. pmid:26571125