Abstract
Natural populations can contain multiple types of coexisting individuals. How does natural selection maintain such diversity within and across populations? A popular theoretical basis for the maintenance of diversity is cyclic dominance, illustrated by the rockpaperscissor game. However, it appears difficult to find cyclic dominance in nature. Why is this the case? Focusing on continuously produced novel mutations, we theoretically addressed the rareness of cyclic dominance. We developed a model of an evolving population and studied the formation of cyclic dominance. Our results showed that the chance for cyclic dominance to emerge is lower when the newly introduced type is similar to existing types compared to the introduction of an unrelated type. This suggests that cyclic dominance is more likely to evolve through the assembly of unrelated types whereas it rarely evolves within a community of similar types.
Introduction
Natural populations ranging from microbial communities to animal societies consist of many different individuals. Some individuals compete with each other to exploit a shared resource (Hardin, 1960; Connell, 1983), whereas others coexist (Morris et al., 2013). Interactions affect the death or reproduction of individuals and thus shape the composition of populations (Friedman and Gore, 2017). Different types of individuals are distinguishable at the interaction level and they have a complex interaction structure (Farahpour et al., 2018). Because interaction structures themselves can support the coexistence of multiple types, they have been extensively studied in ecology and evolution (Gross et al., 2009; Allesina and Levine, 2011). A particularly exciting type of interaction is cyclic dominance, in which each type dominates another one but is in turn dominated by a separate type, leading to a RockPaperScissors cycle (Maynard Smith, 1982; Hofbauer et al., 1998; Szab and Fth, 2007) as sketched in Figure 1A. None of the types fixates in the population, because each type is dominated by one type while it simultaneously dominates a third type. Thus, it has been argued that this type of interaction can support biodiversity (Reichenbach et al., 2007). Cyclic dominance has therefore attracted substantial attention and it has been extensively studied theoretically (Maynard Smith, 1982; Hofbauer et al., 1998; Frean and Abraham, 2001; Hauert et al., 2002; Reichenbach et al., 2007; Szab and Fth, 2007; Mathiesen et al., 2011; Jiang et al., 2011; Allesina and Levine, 2011; Szolnoki et al., 2014).
A famous example of this type of cyclic dominance in biology is toxin production in Escherichia coli (Kerr et al., 2002; Kirkup and Riley, 2004; Cascales et al., 2007). Toxinproducing (or colicinogenic) E. coli cells can purge cells that are sensitive to the toxin. However such toxin producers are dominated by resistant cells that do not produce the toxin. Once common, resistant cells are again dominated by sensitive cells, which avoid the costs of resistance. This leads to cyclic dominance in a RockPaperScissors manner, as shown in Figure 1B. Another example is the mating strategies of North American sideblotched lizards Uta stansburiana (Sinervo and Lively, 1996). The strategy of males guarding several females dominates the strategy of males guarding only a single female. However, sneaky strategies under which males secretly mate with guarded females can become dominant over the strategy of males guarding several females. Once such a sneaky strategy is common, the strategy of guarding only a single female can be successful again leading to cyclic dominance among the mating types as illustrated in Figure 1C. In addition to E. coli and sideblotched lizards, other examples of cyclic dominance have been described in ecology: Stylopoma spongites (Jackson and Buss, 1975), Drosophila melanogaster (Clark et al., 2000), European lizards Lacerta vivipara (Sinervo et al., 2007), and plant systems (Taylor, 1990; Lankau and Strauss, 2007; Cameron et al., 2009). These types of cyclic dominance arise because of competition, which can happen within and between species at the same trophical level. Mating or sperm competitions are the basis of cyclic dominance within species observed in U. stansburiana and D. melanogaster, whereas common resource competition can happen both between and within species.
However, traditional theoretical work assumes a set of predefined cyclic dominance types without asking how they developed or came together. In ecosystems, the introduction of a new species through migration can lead to such cyclic dominance. In this context, it is often termed intransitive competition (Allesina and Levine, 2011; Soliveres et al., 2015; Gallien et al., 2017). However, immigrating species can also disturb and destroy cyclic dominance. In evolving populations, new types can arise through mutation and recombination. In the same manner, mutation and recombination can lead to the formation of cyclic dominance but can also lead to types that do not fit into such types of dominance and break the cycle. A recent experimental study (Higgins et al., 2017) indicated that in the assembly of microbial ecosystems based on 20 bacterial strains found in a single grain of soil, only 3 of almost 1000 triplets exhibited cyclic dominance. Other soil bacterial species (Wright and Vetsigian, 2016; Friedman et al., 2017) also displayed a lack of cyclic dominance. This rareness is found in both soil bacteria and plant systems (Taylor, 1990). Why is it so difficult for cyclic dominance to emerge by assembly or evolution? In this study, we ask the following question theoretically: How frequent is cyclic dominance in situations in which new types constantly arise, providing an opportunity for new cycles but also breaking old cycles at the same time?
Forming a cyclic dominance from a single type is challenging because as soon as the second type arises, the dominance type will take over the whole population, driving extinction of the other type. Therefore, a third type must arise before the population loses either of the two previous types. Such a precise timing of the arrival of a new type is critical for developing cyclic dominance and it can occur when new types arise at a high frequency, either through high mutation rates, recombination, or immigration (Kotil and Vetsigian, 2018; Tarnita, 2018). This rapid evolution can be achieved through both high mutation rates per capita and large population sizes (Lanfear et al., 2014; Hague and Routman, 2016; Vahdati et al., 2017; Kotil and Vetsigian, 2018; Tarnita, 2018). Thus, we considered a model in which the population naturally evolves to a large population size (Park et al., 2019), which allows the development of cyclic dominances via an evolutionary process.
Once we introduced our model in more detail, we will show that the interaction of ecology and evolution leads to increasing population size. This increases the chances that cyclic dominance arises, but it remains rare. Next, we rationalize this finding: While the lifespans of cyclic and noncyclic dominance triplets are similar, it is more difficult to form cyclic dominance compared to noncyclic dominance. The underlying reason is that similarity between parental and offspring payoffs suppresses the formation of cyclic dominance. Finally, we discuss which genealogical structure can promote or suppress cyclic dominance.
Materials and methods
Interactions between individuals affect their death or birth. A traditional model for describing an interacting population is the generalized LotkaVolterra equation (Ginzburg et al., 1988; Bomze, 1995; Yoshida, 2003). In particular, some studies (Biancalani et al., 2015; Huang et al., 2015; Shtilerman et al., 2015; Barbier et al., 2018; Farahpour et al., 2018) assumed that the interaction determines the likelihood of death from a pairwise competition. These interaction parameters can be written as a form of a matrix, including selfinteraction. However, only a few studies considered novel mutations (Huang et al., 2012; Shtilerman et al., 2015; Farahpour et al., 2018). Drawing new interaction parameters for a new type and extending the interaction matrix, we considered such a novel mutation process. In addition, the size of the interaction matrix can be reduced when types go extinct. We traced an evolving population by dealing with this dynamically changing matrix.
We built the model based on individual reaction rules
where I and J are individuals of types i and j, respectively. We assumed that all types are in the same trophic level, and thus there is only competition and no predation. All types have the same background birth and death rates. Only competition makes a difference (Huang et al., 2015; Farahpour et al., 2018; Park et al., 2019). Because the population always collapses when $\lambda}_{b}\le {\lambda}_{d$, we only focused on $\lambda}_{b}>{\lambda}_{d$. For the sake of simplicity, we only considered wellmixed populations without any other highorder interactions.
Formulating the competition death rate $d}_{ij$ as a function of the payoff $A}_{ij$, we connected evolutionary game theory to the competitive LotkaVolterra type dynamics (Huang et al., 2012; Huang et al., 2015; Park and Traulsen, 2017; Park et al., 2019; Sidhom and Galla, 2020). Note that $A}_{ij$ is the payoff of an individual of type i from the interaction with an individual of type j. Because lower payoffs should increase the probability of death, we used an exponentially decaying function for the competition death rate as follows:
where $\alpha >0$ is the baseline death rate from competition, which ensures that the population remains bounded regardless of the value of the evolving payoffs $A}_{ij$. A larger payoff implies a lower death rate from competition. The overall competition death rate is always positive, such that we remain in the regime of the competitive LotkaVolterra equations.
For a large population size, the abundance $x}_{i$ of type i can be described using the competitive LotkaVolterra equation
where n is the number of types in the population, used as a diversity index, and T is the time. The stability of the population is determined by Equation (3). In parallel, the stability between only two types can be determined by the two associated equations in Equation (3), which are described by four payoff values of the two types.
Once a new mutant type arises during reproduction, new interactions occur. To describe these new interactions, we draw new payoff values from the parental payoff with Gaussian noise
where $\xi$ is a random variable sampled from a Gaussian distribution with zero mean and variance $\sigma}^{2$. This inheritance of payoffs with noise implies that the mutant type $i}^{\mathrm{\prime}$ slightly deviates from the parental type i. Here, we treat selfinteraction $A}_{ii$ and interaction with other types $A}_{ij(\ne i)$ in the same way. Because of new interactions, the population composition changes over time, as shown in Figure 2A. We let the population evolve from a single type with a randomly drawn initial payoff from the normal distribution with mean $\mathrm{ln}(1000)$ and standard deviation 1. As a natural consequence of evolving payoffs, the average population size also evolves. Because different types are fully described by the payoff matrix, we can trace the evolving population by tracking the payoff matrix, as shown in Figure 2B. We do not consider any tradeoff: having higher payoffs does not cost anything. Hence, the evolution tends to increase the payoffs constantly and thus drives the system into a regime where payoff differences become smaller.
To construct a pairwise interaction network, we used the stability between two types. Hence the term interaction refers to the pairwise relationship, considering the stability between two types. There are four possible scenarios for stability (see Appendix 1):
Dominance of type i:
represented by $\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}i\leftarrow \leftarrow \phantom{\rule{negativethinmathspace}{0ex}}\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}j\text{}$ for $A}_{ii}\phantom{\rule{negativethinmathspace}{0ex}}>\phantom{\rule{negativethinmathspace}{0ex}}{A}_{ji$ and $A}_{ij}\phantom{\rule{negativethinmathspace}{0ex}}>\phantom{\rule{negativethinmathspace}{0ex}}{A}_{jj$.
Dominance of type j:
represented by $\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}i\to \to \phantom{\rule{negativethinmathspace}{0ex}}\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}j\text{}$ for $A}_{ii}\phantom{\rule{negativethinmathspace}{0ex}}<\phantom{\rule{negativethinmathspace}{0ex}}{A}_{ji$ and $A}_{ij}\phantom{\rule{negativethinmathspace}{0ex}}<\phantom{\rule{negativethinmathspace}{0ex}}{A}_{jj$.
Bistability:
represented by $\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}i\leftarrow \to \phantom{\rule{negativethinmathspace}{0ex}}\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}j\text{}$ for $A}_{ii}\phantom{\rule{negativethinmathspace}{0ex}}>\phantom{\rule{negativethinmathspace}{0ex}}{A}_{ji$ and $A}_{ij}\phantom{\rule{negativethinmathspace}{0ex}}<\phantom{\rule{negativethinmathspace}{0ex}}{A}_{jj$.
Coexistence:
represented by $\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}i\to \leftarrow \phantom{\rule{negativethinmathspace}{0ex}}\u25ef\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}\phantom{\rule{negativethinmathspace}{0ex}}j\text{}$ for $A}_{ii}\phantom{\rule{negativethinmathspace}{0ex}}<\phantom{\rule{negativethinmathspace}{0ex}}{A}_{ji$ and $A}_{ij}\phantom{\rule{negativethinmathspace}{0ex}}>\phantom{\rule{negativethinmathspace}{0ex}}{A}_{jj$.
Constructing the interaction network, we can examine the formation and the collapse of cyclic dominance, as shown in Figure 2C. If the links are drawn from a random matrix, each stability scenario described above occurs with the same probability. Thus, we find a proportion of 0.50 dominance links and a proportion of 0.25 proportions for bistability and coexistence links, respectively. Because the networks can contain three different link types (dominance, bistability, and coexistence), both cyclic dominance and other types of triplets can be found. However, in the main text, we only focused on cyclic and noncyclic dominance triplets which are composed of only dominance because the proportions of each triplet strongly depend on the proportions of link types. Hence at a given link composition (three dominance links), we investigate how often we can observe cyclic dominance compared to noncyclic one.
Results
Evolution leads to increasing population size
The population dynamics described in Equation (1) appears simple, but its tracing is complicated because of the novel mutations. Due to the emergence of a new mutant and its consequences, the payoff matrix dynamically changes. As large payoffs lower competition death rates, types with higher payoffs are more likely to survive. Therefore, payoffs evolve to larger values, which increases the population size (Park et al., 2019). Since there is no tradeoff on the payoffs, the average payoff increases monotonically. This makes types become more similar, enhancing diversity (Scheffer and van Nes, 2006). However, the population size saturates at a certain level because of the baseline death rate $\alpha$ corresponding to resource limitation and enters a steady state (see Appendix 2).
For small $\alpha$ values (rich environments) in particular, the population size N at the steady state becomes large, containing many different types (see Figure 3A). This evolution toward a large population induces rapid mutation. Once the populations size becomes large, new mutant types are generated faster than in smaller populations given a fixed mutation rate per individual. In this rapid mutation regime, a new mutant can arise before the population equilibrates, thereby establishing a cyclic dominance from a timely emerged mutant (Kotil and Vetsigian, 2018). Thus, cyclic dominance can be established when the populations enters the rapid mutation regime, as shown in Figure 3B. The proportions of triplets were averaged over all surviving realizations. In principle, we can observe a triplet from $t=2$ even though there were fewer than three types on average. Because of the smaller average diversity $\u27e8n\u27e9$, there were large fluctuations in measuring the proportions of triplets in the early regime ($t\lesssim 100$). However, the measurement became more accurate as diversity increased.
Cyclic dominance triplets are rare
The proportions of cyclic and noncyclic dominance triplets increase in the early dynamics and quickly saturate. Whereas population dynamics illustrates the formation of both cyclic and noncyclic dominance, cyclic dominance is much rarer than noncyclic dominance. To quantify this rareness of cyclic dominance, we measured the fraction $\chi$, which is defined by the fraction of cyclic dominance triplets among all dominance triplets (cyclic and noncyclic) (see Figure 3C). In steady state, the fraction yielded $\chi \approx 0.033$, indicating that one cyclic dominance triplet can be found among 30 dominancecomposed triplets. If the pairwise relationships are random (all four possible links appear in the same probability 1/4, called a random network), then the fraction $\chi$ of cyclic dominance should be 0.25 because there are only two configurations of cyclic dominance triplets, whereas six configurations produce noncyclic dominance triplets. Hence cyclic dominance that developed from our population dynamics is much rarer than expected from a random choice of interactions. To elucidate why it is the case, we checked how long each triplet can be sustained in the population and how often they emerge. First, we focused on the lifetime of cyclic and noncyclic dominance and then moved on the formation of each triplet. When it comes to the chance to emerge cyclic and noncyclic dominance, we argued that the genealogy structure shaped by ecoevolutionary dynamics will enhance or suppress the formation of cyclic dominance.
The lifespans of cyclic and noncyclic dominance triplets are similar
The rareness of cyclic dominance triplets may be caused by their shorter lifespan compared with that of noncyclic dominance triplets. Thus, we investigated the lifespan of triplets first to understand the rareness of cyclic dominance. Once triplets arise in populations, we can identify them, and trace how long they persist. Lifespan distributions in the steady state of both cyclic and noncyclic dominance triplets decayed algebraically. We plotted the complementary cumulative distribution functions (CCDFs), clearly revealing a power law decay, as shown in Figure 4A. Surprisingly, there was no difference in the lifespan of both triplets. Both cyclic and noncyclic dominance triplets were destroyed in five mutation events on average. The noncyclic dominance triplet has a higher chance of persisting longer, although the difference is small. In addition, the median is the same for both distributions because almost all probabilities are concentrated on short lifespans. In conclusion, lifespan does not explain why cyclic dominance is rarer than noncyclic dominance. Hence, the lower chance for cyclic dominance to emerge is the reason.
The condition to form cyclic dominance is more strict than that for noncyclic dominance
Why is it more difficult for cyclic dominance to emerge than for noncyclic dominance? One factor is that the conditions needed for an interaction matrix to provide cyclic dominance are more restrictive than those for noncyclic dominance. For a matrix to reveal cyclic dominance, it is necessary that in each of the three columns, the three payoffs $A}_{ij$, $A}_{jj$, and $A}_{kj$ are ordered ($A}_{ij}<{A}_{jj}<{A}_{kj$ or $A}_{ij}>{A}_{jj}>{A}_{kj$). Conversely, the formation of noncyclic dominance requires this condition to be satisfied only in a single column, whereas the other two columns should satisfy a less restrictive condition ($A}_{ij}<{A}_{jj$ and $A}_{kj}<{A}_{jj$ or vice versa). For example, in random payoff matrices where all payoffs are randomly drawn from the standard normal distribution, the fraction of cyclic dominance is $1/13\approx 0.077$ (as shown in Appendix 4), which is smaller than the value of $2/8=0.25$ expected in a random network of directed links. This is because in the matrix approach, unlike a random network, links in a triplet are interdependent; selfinteraction payoffs contribute to the character of several links at once.
Similarity between parental and offspring payoffs suppresses the formation of cyclic dominance
The fraction $\chi \approx 0.077$ in the random matrix is still larger than that obtained from our population dynamics $\chi \approx 0.033$, implying there are other factors suppressing the development of cyclic dominance. A key reason is the correlation between payoffs. In our model, the elements of the payoff matrix are not fully independent because of heredity. Offsprings payoffs are derived from their parents payoffs. We found that this correlation between payoffs plays an important role in suppressing the formation of cyclic dominance. For example, let us imagine two different uncorrelated preexisting types represented by a 2 × 2 random matrix. All elements are drawn from the standard normal distribution. If a third type emerges from a preexisting one by a mutation, the average fraction of cyclic dominance becomes $\chi \approx 0.02$ (the standard normal distribution is used for new payoffs and we average across 5 10^{5} samples), which is lower than that of the assembly of thee uncorrelated types. This is because the payoffs of a new offspring are similar to its parental payoffs: the offspring is likely to have the same relationships with other types in a population as the parental type and any type dominated by the parent will also be dominated by the offspring. The triplets including these offspring and parental types are more likely to form noncyclic dominance than a cyclic one. Hence the correlation between payoffs affects the fractions by which cyclic dominance emerges compared with noncyclic dominance.
To check the effect of the correlation on emerging triplets, we measured the similarity between types as a proxy of the correlation between payoffs. We defined the trait vector $\overrightarrow{T}}_{l$ of the type l using the row capturing with its own payoff and the column of the others payoff against it in the payoff matrix, ${\overrightarrow{T}}_{l}=({A}_{l,i},{A}_{l,j},{A}_{l,k},{A}_{i,l},{A}_{j,l},{A}_{k,l})$, similarly to Farahpour et al., 2018. Because the average payoff increases over time, we shifted all elements in those vectors by a constant to ensure that the average of all values is zero. Then, using normalized vectors after shifting we calculated the scalar product for all pairs of trait vectors as a similarity measure. Larger values indicate that the two types have more similar payoff values. Each triplet has three trait vectors and thus has three similarity measures. Taking the mean and standard deviation of these three similarities, we found that in cyclic dominance the three types tend to be less similar compared to noncyclic dominance, as shown in Figure 4B. The inheritance plays a key role in the emergence of cyclic and noncyclic triplets, giving rise to a correlation between payoffs. Because the payoff correlation is determined by a genealogy structure, in the following we investigated which of these structures have higher or lower chances to promote the emergence of cyclic dominance.
Genealogical structure can promote or suppress cyclic dominance
Between the last common ancestor and the present types, there are intermediary types accumulating mutations between them. Genealogies tell us who is whose parent, tracing back to the common ancestor of the observed types. From the genealogy, we can infer how many mutations were accumulated by each type and the time at which they diverged. If two types have only accumulated a few mutations from the most recent common ancestor, their payoffs are likely to be similar. Hence, the genealogy structure shapes the correlation between payoffs and affects the value of fraction $\chi$. To study the role of genealogies, we first characterized genealogies for three types based on accumulated mutational distances, see Figure 5AB. We found that for a triplet of types, the distribution of payoff elements could be characterized by five parameters of the genealogy, which all measure the number of mutations between types w_{1}, w_{2}, x, y, and z (see Appendix 5).
We analyzed those mutational distances for cyclic and noncyclic triplets found in the simulation at the steadystate. From the analysis, we inferred that the crucial parameter influencing the fraction $\chi$ of cyclic dominance is the fraction $F}_{l$ of mutations accumulated after each type evolves independently from others (the time $t}_{d$ in Figure 5B).
Payoff correlations must be developed before lineages become independent at $t}_{d$, depending on the mutational history (w_{1} and x). With more mutations being accumulated after the types divergence, the correlation between types becomes weaker, increasing the chance of the cyclic dominance to emerge, see Figure 5C. On the other hand, with only few mutations after $t}_{d$ the strong correlation between parental and offsprings payoffs remains, which mostly induces the emergence of noncyclic dominance. This means that the mutations accumulated after all three types diverge are important for decoupling their payoff correlations, increasing the chance to form the cyclic dominance. This finding agrees well with numerically investigated genealogies that gave the minimal and maximal values of the fraction $\chi$, called minimizer and maximizer genealogies, respectively. For minimizer genealogies, the majority of mutations were accumulated before $t}_{d$ (see Appendix 6). Conversely, for maximizer genealogies, the majority of mutations were accumulated after all three lineages diverged from each other.
In addition, numerically calculated minimizer genealogies almost completely suppressed the emergence of cyclic dominance ${\chi}_{\text{min}}<0.001$. For the maximizer, the fraction could be as high as ${\chi}_{\text{max}}=1/6\approx 0.167$. Both the fraction of cyclic dominance arising from the random matrix $\chi \approx 0.077$ and that found in our simulations $\chi \approx 0.033$ fell between the minimal and the maximal values possible with the genealogy structure. The fractions $\chi$ from population dynamics are closer to the minimal value, as shown in Figure 6. Therefore, we can infer that in the genealogies occurred in population dynamics, the similarity of the new types to their parental type prevents the emergence of cyclic dominance.
Discussion
Cyclic dominance is extremely interesting from a conceptual and theoretical perspective and it has thus been analyzed in great detail in mathematical biology (Hofbauer et al., 1998; Hofbauer and Sigmund, 2003; Szab and Fth, 2007). However, the theoretical literature typically refers to only a handful of examples in nature. Moreover, recent experiments have revealed that it is difficult for cyclic dominance to emerge in microbial populations (Wright and Vetsigian, 2016; Friedman et al., 2017; Higgins et al., 2017). Why is the establishment of cyclic dominance so difficult? To address this question, we used an evolutionary process with evolving interactions for the formation of such cyclic dominance instead of following the more conventional approach of using a predefined set of interactions. For example, Kotil and Vetsigian, 2018 observed the formation of cyclic dominance in the fast evolution regime with adaptation, but the involved traits were predefined. However, we argue that it is difficult for cyclic dominance to emerge even in the presence of rapid evolution. In addition, our investigation shows no correlation between diversity and the probability to find cyclic dominance. The diversity observed in our model does not originate from the cyclic dominance structure, but from the generation on a time scale that is fast compared to equilibration of the system. Our results indicate cyclic dominance in general could be a mechanism to support diversity, but it is most probably not essential in a situation where it can emerge, as such situations are characterized by a high diversity in the first place. Even if we rescale the payoffs to prevent going towards neutrality which increases diversity, cyclic dominance remains rare as long as the cyclic dominance is not a main driver for the diversity. This result is robust even when we consider the population frequency weights, see Appendix 7. In our model, the noncyclic dominance can reach a majority of the population, while the cyclic dominance typically remains bounded to a much lower frequency.
We also examined the circumstances under which cyclic dominance can appear more frequently. While the probability of assembling such an interaction structure by chance in a payoff matrix with uncorrelated random entries is small, the probability to evolve such an interaction structure is even smaller. The inheritance of interactions from parent to offspring is a key mechanism shaping the correlations between payoffs and determines the formation of cyclic dominance. From our approach, we found that the introduction of an uncorrelated type is crucial for the formation of cyclic dominance triplets. Because the migration of new species can be interpreted as such an introduction, our results suggest that cyclic dominance might be more frequent on an interspecies basis than on an intraspecies basis. As widespread intransitive competition is found in ecological systems (Soliveres et al., 2015; Gallien et al., 2017), our manuscript nicely supports the basic idea that assembly of unrelated types is more likely to lead to cyclic triplets than evolution, in which emerging types are closely related.
Our approach, which reduces the complexity from continuous values to a categorical classification, may help to bridge the model dynamics and experimental data more easily. Experimental work has provided data regarding both the constituents of a microbial community but also the interactions between them. However for large communities, parameterizing all interactions in the model numerically makes it difficult to identify the fundamental factors shaping the dynamics. Reducing the complexity may permit study of the large scales of experimental data connecting the underlying model dynamics and large datasets.
An important limitation of our work is the assumption of global interactions. In our model, all individuals can interact with each other, ignoring the spatial population structure. A spatial model could localize the interactions and lead to the more frequent formation of cyclic dominance. Such a localization can foster cyclic dominance for a predefined cyclic set (Durrett and Levin, 1997; Durrett and Levin, 1998; Frean and Abraham, 2001; Reichenbach et al., 2007; Szab and Fth, 2007; Mathiesen et al., 2011; Jiang et al., 2011; Mitarai et al., 2012; Szolnoki et al., 2014; Kelsic et al., 2015; Sneppen, 2017). However, before moving into spatial models it appears essential to investigate this issue in the absence of all potentially confounding factors. Such models appear necessary for explaining why cyclic dominance within one species is not found often in nature, and they may open a new direction for the extensive theoretical work on this topic.
Appendix 1
Pairwise relationship based on stability
For large population sizes, the change of abundances of types can be described by deterministic equations. Linear stability analysis reveals which types will persist in equilibrium (Strogatz, 2000). Using this stability, we determine the relationship between two types i and j. In this section, we perform linear stability analysis first and then determine the pairwise relationships based on that stability.
To determine the stability between two types, type 1 and type 2, we focus on the two equations
where $\lambda ={\lambda}_{b}{\lambda}_{d}$. Since the death rates d_{ij} are all positive, the population would always go extinct for $\lambda <0$. Thus, we only consider a positive $\lambda$. There are four fixed points $({x}_{1}^{\ast},{x}_{2}^{\ast})$: One is the extinction of both types $(0,0)$, two are indicating each singletype population $(\lambda /{d}_{11},0)$ and $(0,\lambda /{d}_{22})$, and the fourth one is the coexistence point $\left(\frac{({d}_{22}{d}_{12})\lambda}{{d}_{11}{d}_{22}{d}_{12}{d}_{21}},\frac{({d}_{11}{d}_{21})\lambda}{{d}_{11}{d}_{22}{d}_{12}{d}_{21}}\right)$. To check the stability of each fixed point, we calculate the Jacobian matrix $\mathbf{J}$,
and then calculate the eigenvalues for the fixed point.
The extinction point (0,0) is always unstable, because both eigenvalues are identical to $\lambda >0$. On the other hand, the three other fixed points have one negative eigenvalue $\lambda$, indicating that they are saddle or stable fixed points; If the second eigenvalue is positive, the fixed point becomes a saddle and is unstable. If the second eigenvalue is negative, the fixed point is stable. Hence the fixed point $(\lambda /{d}_{11},0)$ becomes stable when $d}_{21}>{d}_{11$, because the eigenvalue is $({d}_{11}{d}_{21})\lambda /{d}_{11}$. In the same manner, the condition $d}_{12}>{d}_{22$ can be obtained for a stable fixed point $(\lambda /{d}_{11},0)$. Lastly, the coexistence point has an eigenvalue $\frac{({d}_{11}{d}_{21})({d}_{22}{d}_{12})}{{d}_{11}{d}_{22}{d}_{12}{d}_{21}}\lambda$. For meanginful values of abundances, stable coexistence points have to satisfy the conditions ${x}_{1}^{\ast}>0$ and ${x}_{2}^{\ast}>0$. Together with these restrictions, we can find the coexistence fixed point is stable only if both $d}_{11}>{d}_{21$ and $d}_{22}>{d}_{12$ are satisfied.
We classify the pairwise relationship based on these stabilities. Dominance relationships are given if only a singletype fixed point is stable while all other fixed points are unstable. When both singletype fixed points are stable, a bistability relationship is drawn. A coexistence relationship is achieved when only the coexistence 1xed point is stable. We summarized the stabilities of 1xed points at a given condition and named the pairwise relationship in Appendix 1—table 1.
Appendix 2
Population size and diversity at steadystate for various baseline death rates
In this section, we show how the population size N and diversity n evolve in time t with various baseline death rates $\alpha$. Since larger payoffs imply lower death rates, individuals with larger payoffs tend to survive. As a result of evolution, the overall death rates decrease, which enlarges the population size. This can be interpreted that species improve their efficiency to consume a resource. However, if resources are limited, the population size is confined. We implement this resource limit by introducing the baseline death rate $\alpha$. The death rates from the competition cannot be lower than the baseline death rate, and thus the population size saturates at a certain level at the end, $\lambda /\alpha$. For various $\alpha$, we run 5000 independent stochastic simulations and measure the average population size and diversity in time t, see Appendix 2—figure 1A and B. The average runs over the surviving samples at a given time $t=10000$, and we denote the averaged quantity O as $\u27e8O\u27e9$. As we expected, the population sizes evolve to $\lambda /\alpha$, and thus larger $\alpha$ leads to smaller population sizes.
Large population size reduces the time until new types occur in the population, and at the same time it takes longer to equilibrate. Thus, a large population size increases the number of different types in the population via two processes: (i) Even when the most competitive type is expected to fixate in the population, new types which are less fit can constantly emerge due to mutation. (ii) A second mechanism is ecological tunneling (Kotil and Vetsigian, 2018): Even though the population dynamics yields the extinction of certain types, it can be rescued by the emergence of a new type which supports the coexistence of types predicted to be extinct. From those effects, larger populations have higher diversity Appendix 2—figure 1B. We compare the average population size and diversity in the stationary regime as well, see Appendix 2—figure 1C.
We also take a closer look at the average payoff values. Because of selection, the average payoff increases in time, but the increment becomes smaller as the average increases, see Appendix 2—figure 1D. After a transient time, the average payoffs increases logarithmically. It means that the natural selection induced by the payoff difference becomes weaker and is almost neural at the end. Hence, in the long run, the diversity mainly originates from the mutationselection balance.
Appendix 3
Link proportions
From the payoff matrix, we can determine the pairwise relationship between types, constructing a network. We represent the relationship between two types i and j with three different link types, dominance, bistability, and coexistence:
As a basic element of a network, we investigate the frequencies of link types first.
As a reference, we consider a random payoff matrix model wherein all payoffs are randomly drawn from the standard normal distribution. In this case, the probability that one payoff is larger than another is 0.5 because all payoffs are independently sampled from the same distribution. Thus each condition in Appendix 1—table 1 happens with the same probability, 0.25. Thus, the probability to observe a dominance link is 0.5 (since there are two directions), and the others are 0.25. We use these values as a reference to compare the link proportions obtained from population dynamics.
With population dynamics, the behavior of link frequencies differs for different $\alpha$. For small $\alpha$, the ensembleaveraged proportions are stable in time, while with large $\alpha$ values the average proportions fluctuate a lot, see Appendix 3——figure 1A and B. For large $\alpha$, the number of types rarely exceeds one. Hence, the link proportions fluctuate a lot, but the average values are well predicted from a random payoff matrix because the links appear by chance. On the other hand, for small $\alpha$, many links can be formed due to the presence of multiple types. In this case, the average link proportions are stable in the stationary regime, and coexistence links are favored compared to the random matrix case. We also plot the mean of ensembleaveraged link proportions in the stationary regimes $9500\le t\le 10000$ for various $\alpha$ in Appendix 3——figure 1A. As we can see, the link proportions get closer to that of the random matrix as increasing the baseline death rates $\alpha$.
Appendix 4
Triplet proportions
Let us now consider triplets that are constructed by three links. With four kinds of link relationships, in total we can find 64 possible triplets. However, if we take into account symmetries, this reduces to 16 different triplet structure, see Appendix 4—figure 1. We call the number of triplets, which have the same triplet structure degeneracy. For example, with type indices i, j, and k, the cyclic dominance triplet structure can be found in two different triplets: $i\to \to j\to \to k\to \to i$ and $i\leftarrow \leftarrow j\leftarrow \leftarrow k\leftarrow \leftarrow i$. In contrast, there is no degeneracy for the triplet types marked by 1 and 4 in Appendix 4—figure 1A because changing the indices of types does not give any difference. Different triplet structures have different degeneracy because they have different mirror and rotation symmetries, summarized in Appendix 4—figure 1. Since our interest is the triplet structure, we investigate proportions of 16 triplet structures considering those degeneracies.
Again we use a random payoff matrix to attain the reference triplet proportions first. For three types i, j, and k, we can construct a 3 3 random matrix,
All elements are independently drawn from the standard normal distribution $\mathcal{\mathcal{N}}(x0,1)$. Here, $\mathcal{\mathcal{N}}(xm,{\sigma}^{2})$ indicates a Gaussian distribution with mean m and variance $\sigma}^{2$. Any triplet is fully defined by three relationships, and each pairwise game determines one relationship. Mathematically, this corresponds to three $2\times 2$ submatrices of $\mathbf{A}$. However, these matrices are not independent, as it would require $3\times 2\times 2=12$ independent parameters while the payoff matrix has only $3\times 3=9$ entries.
To determine the type of triplet structure, we focus on one type of individual j. For this type j, we can characterize invasion possibilities against the two other types, giving four possible situations,
$i\phantom{\rule{1em}{0ex}}\to j\to \phantom{\rule{1em}{0ex}}k$, when $A}_{ij}<{A}_{jj}<{A}_{kj$
$i\phantom{\rule{1em}{0ex}}\leftarrow j\leftarrow \phantom{\rule{1em}{0ex}}k$, when $A}_{ij}>{A}_{jj}>{A}_{kj$
$i\phantom{\rule{1em}{0ex}}\to j\leftarrow \phantom{\rule{1em}{0ex}}k$, when $A}_{jj}>{A}_{ij$ and $A}_{jj}>{A}_{kj$
$i\phantom{\rule{1em}{0ex}}\leftarrow j\to \phantom{\rule{1em}{0ex}}k$, when $A}_{jj}<{A}_{ij$ and $A}_{jj}<{A}_{kj$
where $i\phantom{\rule{1em}{0ex}}\to j$ means that j is stable with respect to invasion of i from rare (either j dominates i, $i\to \to j$ or they are bistable $i\leftarrow \to j$, hence the notation). Mathematically, this corresponds to three $3\times 1$ columns of $\mathbf{A}$. Since all columns are independent, the probability of finding a certain triplet is the product of three probabilities to find a certain set of stubs for each type.
Thus we calculate the probability to find a certain set of subs for a type. The probability $2\times 2$ that three random variables a, b, and c satisfy the condition $a<b<c$ is given by convolution,
where $\mathrm{\Phi}(x)$ is the probit function, $\mathrm{\Phi}(x)={\int}_{\mathrm{\infty}}^{x}\mathcal{\mathcal{N}}(\theta 0,1)d\theta$. Hence, the probability that a type has in and outstubs is 1/6. On the other hand, the probability $P(c<a\text{and}cb)$ is
In the same way, $P(c>a\phantom{\rule{thinmathspace}{0ex}}\mathrm{a}\mathrm{n}\mathrm{d}\text{}\mathrm{c}\mathrm{b})$ is 1/3. Hence, the probabilities to observe each set of stubs are
$i\phantom{\rule{1em}{0ex}}\to j\to \phantom{\rule{1em}{0ex}}k$ appears with probability 1/6
$i\phantom{\rule{1em}{0ex}}\leftarrow j\leftarrow \phantom{\rule{1em}{0ex}}k$ appears with probability 1/6
$i\phantom{\rule{1em}{0ex}}\to j\leftarrow \phantom{\rule{1em}{0ex}}k$ appears with probability 1/3
$i\phantom{\rule{1em}{0ex}}\leftarrow j\to \phantom{\rule{1em}{0ex}}k$ appears with probability 1/3
With those probabilities and the degeneracies, we can calculate the triplet proportions expected in a random payoff matrix. The results are summarized in Appendix 4—table 1, where we find the cyclic dominance (Triad ID 15) is the rarest one. At the same time, the noncyclic dominance (Triad ID 16) is one of the most abundant triplets. From the proportions we can also obtain the fraction of cyclic dominance among cyclic and noncyclic dominance triplets as $\chi =1/13$, indicating 12 noncyclic dominances can occur while only a single cyclic dominance appears.
Now we move to triplet proportions in population dynamics, see Appendix 4—figure 1B. Since the triplet proportions strongly depend on link proportions, the results for $\alpha =5\cdot {10}^{6}$ and $\alpha =5\cdot {10}^{4}$ are very different. For example, coexistence links are norm for $\alpha =5\cdot {10}^{6}$ and thus the triplet with Triad ID 4 is more abundant than in other cases. Thus, it is hard to directly compare proportions of triplets, which have different link composition. Instead of that, we compare the triplets, which have the same link composition. This comparison allows us to find which structure is more abundant, eliminating the effect of link proportions. There are four such sets of triplets, see Appendix 4—figure 1B. One of the sets consists of cyclic and noncyclic dominance triplets, which are composed of three dominance links. If we look at the fraction $\chi$ of cyclic dominance, we can find that noncyclic dominance is more suppressed in population dynamics than in the random matrix. For the other three sets of triplets, we can also find that the types which have bistability usually dominate another type while the types with coexistence are dominated by others. However, this tendency becomes weaker in population dynamics compared to the random payoff matrix analysis.
Appendix 5
Properties of payoff matrices for three types and a given genealogy
The emergence of a new mutant type l induces a new row and a new column in the payoff matrix. If we denote the parental type of type l as $r(l)$, the new payoffs are written by
where $\xi ({\sigma}^{2})$ are random values sampled from the Gaussian distribution with zero mean and variation $\sigma}^{2$, $\mathcal{\mathcal{N}}(x0,{\sigma}^{2})$. Due to this inheritance, the genealogical structure which tells us who is whose parent type shapes the payoffs. In this section, we show how payoff elements are determined by a given genealogy.
Genealogies can be schematically depicted with time and mutational distance axes, see Appendix 5—figure 1A. For three focal types i, j, and k, a number of possible genealogies exists. We begin with a scenario shown in Appendix 5—figure 1B, where the lineage leading to the type i diverges from the last common ancestor o first and a different lineage leading to the two types j and k diverges from o later. We index the last common ancestor of k and j in their lineage as n. Also, we give an index m for the progenitor of type i at the moment when lineages leading to types j and k diverged.
We begin with a payoff matrix of types i, j, and k and trace it back to the moment of the last common ancestor type o of all three types. All nine payoffs between types i, j, and k are derived from the single payoff value $A}_{oo$.
For a pair of types j and k, all four payoffs $A}_{jj$, $A}_{jk$, $A}_{kj$, and $A}_{kk$ can be traced back to the payoff $A}_{nn$. Let us imagine that y and z mutations happen leading the emergence of type j and k from the type n respectively. If y mutation events occur first before z mutation events, we can write the value of $A}_{jk$ as
Since the random variable $\xi$ follows the normal distribution, we can simply write
Thus, multiple mutation events can be written as a single mutation event with larger variance. For any other order of the mutation events, the final expression is the same even though all intermediate terms will be different.
In the same way, we can proceed with the other three payoffs. Consequently, all four payoffs can be written as
Selfinteractions, $A}_{jj$ and $A}_{kk$, do not change when the other type accumulates a mutation, so their mutational distances from $A}_{nn$ are smaller.
Next, we consider type i which is diverged from the type o but is a different lineage from j and k. Then, the payoff $A}_{ii$ can be traced back to $A}_{mm$ as
where w_{2} is the number of mutations accumulated in the lineage of type i from type m. The payoffs between i and two other types can be traced back to payoffs between their progenitors $A}_{mn$ and $A}_{nm$
Hence, all nine payoffs describing interactions between types i, j, and k can be traced back to four payoffs, $A}_{mm$, $A}_{mn$, $A}_{nm$, and $A}_{nn$, characterizing interactions between m and n. These four payoffs, in turn can be traced back to $A}_{oo$ in the same way,
where x is the number of mutations accumulated from the common ancestor o to the type n, and w_{1} is the number of mutations accumulated from the type o to the type m. In summary, we show how payoffs can be calculated from their common ancestors payoffs, see Appendix 5—figure 2.
The same calculations apply to any other genealogy, for example ones presented in Appendix 5—figure 1CE, if we label the types in a specific way. The labels j and k should indicate the pair of types, which diverged the last, that is, the last common ancestor of j and k is the most recent one among all three pairwise the last common ancestors. By exclusion, the rest one becomes type i. Type n is the last common ancestor of j and k (for genealogy on Appendix 5—figure 1E and F, n is the same as o). Finally, type m is the progenitor of i existed at the moment of divergence of j and k (for genealogy on Appendix 5—figure 1D, m is the same as o).
Appendix 6
Minimizer and maximizer genealogies
In this section, we numerically identify those genealogies which produce the largest or smallest proportions of certain triplets. First of all, we note that the fractions of triplets do not depend on scales of the set of mutational distances $D}_{m}=\{{w}_{1},{w}_{2},x,y,z\$ without population dynamics; rescaling the distances results in the same triplet proportions. Therefore, without loss of generality, we can assume that all five mutational distances sum up to one and are thus located on the simplex ${w}_{1}+{w}_{2}+x+y+z=1$.
We search for extreme genealogies by numerical optimization, implemented by a hill climbing algorithm. The optimization starts from a random set of D_{m} and identifies the local optimum. Since our method can find only local optima, we use multiple initial values to find optimal set. After we identified the mutational distance sets which give local optima, we cluster them using principle component analysis (PCA). We denote the Xth class of genealogies that maximize and minimize the proportion of triplet tri by ${D}^{tri}\text{}X$ and ${D}_{tri}\text{}X$, respectively. For different scenarios, we find different numbers of distinct classes of genealogies.
Maximization of the number of cyclic dominances
First, we ask which genealogies maximize the number of cyclic dominance triplets. Performing PCA analysis, we found five different classes of such genealogies, see Appendix 6—table 1. We denote cyclic dominance triplets as T_{15} and noncyclic dominance ones as T_{16}. Classes ${D}^{15}\text{}1$ and ${D}^{15}\text{}2$ are effectively the same genealogy, where all three types diverge from each other very early and then one of these types accumulates all the mutations (the most isolated type i in ${D}^{15}\text{}1$ or one of the more recently diverged types k in ${D}^{15}\text{}2$, see the end of Appendix 5 for the details of types labelling). Classes ${D}^{15}\text{}3$ and ${D}^{15}\text{}5$ also are also similar each other. All three types diverge from each other as early as possible and then two of them equally share the subsequent mutations (j and k in ${D}^{15}\text{}3$, or i and k in ${D}^{15}\text{}5$). In the class ${D}^{15}\text{}4$, all three types diverge from each other as early as possible. In this case, each of three types have the similar number of accumulated mutations (33%).
Minimization of the number of noncyclic dominances
Next, we ask which genealogies instead minimize the number of noncyclic dominance triplets, with the expectation that they will be similar to the ones above. Performing PCA analysis, we again find five different classes, see Appendix 6—table 2. The obtained genealogies minimizing T_{15} have the same characteristics of mutational distances with the genealogies maximizing T_{16}, see section F. The genealogy class ${D}_{16}\text{}1$ is the same with ${D}^{15}\text{}1$. The class ${D}_{16}\text{}2$ is the same as ${D}^{15}\text{}2$, and ${D}_{16}\text{}3,4,5$ are the same as ${D}^{15}\text{}4$. Classes ${D}_{16}\text{}3$, ${D}_{16}\text{}4$, and ${D}_{16}\text{}5$ differ only in their values of w_{1} and x. Thus these can be considered as a fine structure of a single class. Classes equivalent to ${D}^{15}\text{}3$ and ${D}^{15}\text{}5$ are not found in D_{16}.
Minimization of the number of cyclic dominances
Alternatively, we can also ask for which genealogies it is hardest to obtain cyclic dominances. Performing PCA analysis, we find three different classes, see Appendix 6—table 3. In the class ${D}_{15}\text{}1$, most of mutations are accumulated in the lineage of the type i before types j and k are diverged. The divergence of types j and k tends to be the last mutational event in this genealogy. In the class ${D}_{15}\text{}2$, most mutations are accumulated in the lineage of the common progenitor of types j and k before their divergence. The divergence of types j and k tends to be the last mutational event in this genealogy. In the class ${D}_{15}\text{}3$, mutations are equally shared between the lineage of type i and the common progenitors of types j and k before their divergence. Similar to above two cases, the divergence of types j and k tends to be the last mutational event in this genealogy.
Maximization of the number of noncyclic dominances
Now we ask for which genealogies it is easiest to obtain noncyclic dominances. Performing PCA analysis, we find again three different classes of geneaolgies, see Appendix 6—table 4. Again, the obtained three classes are the same as minimizing cyclic dominances T_{15}: The class ${D}^{16}\text{}1$ is the same as ${D}_{15}\text{}1$, and ${D}^{16}\text{}2$ is the same as ${D}_{15}\text{}2$, and ${D}^{16}\text{}3$ is the same as ${D}_{15}\text{}3$.
Summary for optimization
From all optimization results we find two extreme genealogies for maximizing and minimizing the fraction $\chi$ of cyclic dominances. In the first class, most of mutations occur after all three lineages separate and these mutations are accumulated in a single lineage, see Appendix 6—table 5. We call them maximizer genealogies. These genealogies promote cyclic dominances and suppress noncyclic dominances. In the second class, the most of mutations occur before types j and k diverge. These genealogies suppress cyclic dominances, while promoting noncyclic dominances. We call them maximizer genealogies.
Analytics for maximizer genealogies
We calculate the proportions of cyclic and noncyclic dominance triplets at maximizer genealogies. In that case, the cyclic dominance triplets occur
$i\to j\to k\to i$ with probability $1/96$
$i\to k\to j\to i$ with probability $1/96$
Altogether, this results in $p({T}_{15})=1/48\approx 0.0208$. The noncyclic dominance triplet T_{16} has degeneracy six, and they occur
$j\to k\to i$ with probability $1/96$
$i\to k\to j$ with probability $1/96$
$k\to j\to i$ with probability $1/48$
$k\to i\to j$ with probability $1/48$
$j\to i\to k$ with probability $1/48$
$i\to j\to k$ with probability $1/48$
Altogether, this results in $p({T}_{16})=5/48\approx 0.104$, yielding $\chi =1/6\approx 0.167$. The results agree well with numerical results in Appendix 6—table 5.
Minimal model
Besides genealogies, there is also another way to shape the payoff correlation. Mutational distances are controlled by not only how many steps proceed but also how big the jump is related variance of noise. In a minimal model, we can directly control the closeness of newborn type by using a different variance $\sigma}^{2$ of Gaussian noise. We generate the first mutant type M_{1} from the original type o with the normal distribution, and we use variance $\sigma}_{1}^{2$ when we generate the type M_{1}. Then, we assume that M_{1} is the parental type of type M_{2}, and the variance $\sigma}_{2}^{2$ is used in this case. This procedure is schematically drawn in Appendix 6—figure 1A. Note that only the relative mutational distances are important, and thus we introduce the fraction $F}_{l$ that measures the distances before and after the divergence of the third type. When $F}_{l$ is small, the close type to the resident type arises which is corresponding to the minimizer genealogy. On the other hand, if we use large $F}_{l$, it will reproduce the maximizer by introducing uncorrelated types in the population. The minimum and maximum average fractions $\chi$ are obtained by changing $F}_{l$, yielding almost zero and 0.165 respectively, see Appendix 6—figure 1B. These results agree well with the results of the genealogy approach.
Appendix 7
Correlation between diversity and triplet fraction
We investigated the correlation between diversity and the probability of having cyclic and noncyclic dominance triplets in the system at the steadystate. We measured the Pearson correlation coefficient $r}_{xy$ between two variables x and y defined as
where $\overline{x}$ and $\overline{y}$ indicate the averages of the variables x and y. The index i runs over all samples (in our case, 1944 surviving realizations are used). The correlation coefficient is bound from 1 to 1, representing fully anticorrelated to fullycorrelated variables.
We used three different diversity indices: (1) richness n as the number of different types, (2) Shannon diversity index $H=\sum _{i=1}^{n}{f}_{i}\mathrm{ln}{f}_{i}$, and (3) Simpson diversity index $S={\left(\sum _{i=1}^{n}{f}_{i}^{2}\right)}^{1}$. Note that a population frequency of type i is denoted by $f}_{i$. Correlations were measured between each diversity index and the probabilities to having cyclic or noncyclic dominance triplets, $P}_{cyc$ and $P}_{ncyc$, see Appendix 7—figure 1. All correlation coefficients are summarized in Appendix 7—table 1. The results show weak anticorrelation between diversity and cyclic dominance and stronger anticorrelation between diversity and noncyclic dominance. This tendency does not change even when population frequencies are considered. Finally we measured the correlation coefficient between the fraction of cyclic dominance $\chi$ and diversity, and surprisingly the coefficient showed almost zero. This implies that there is no significant correlation between cyclic dominance and diversity.
Anticorrelation between the fraction of noncyclic dominance $P}_{ncyc$ and diversity could be understood from the population frequencies of noncyclic dominance $f}_{ncyc$. In the steadystate, noncyclic dominance usually occupies the majority of the population, taking around 0.83 of the population on average, see Appendix 7—figure 2AB. It seems that the noncyclic dominance emerges from the majority, and thus their fraction could be high when the winner takes over almost the whole population, leading to low diversity. On the other hand, cyclic dominance usually takes only a minority, showing a peak near ${f}_{cyc}\approx 0$ in Appendix 7—figure 2C.
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Decision letter

Jeff GoreReviewing Editor; Massachusetts Institute of Technology, United States

Patricia J WittkoppSenior Editor; University of Michigan, United States

Jeff GoreReviewer; Massachusetts Institute of Technology, United States

Matthieu BarbierReviewer
In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.
Acceptance summary:
The reviewers and I found your article to be an insightful perspective regarding why nontransitive (e.g. rockpaperscissors) interactions might be rare in natural communities, despite the potential stabilizing effects that nontransitive interactions could have in sustaining diversity. In particular, your approach of analyzing the evolutionary origin of different interactions provides fascinating insight to an important problem.
Decision letter after peer review:
Thank you for submitting your article "Why is cyclic dominance so rare?" for consideration by eLife. Your article has been reviewed by two peer reviewers, including Jeff Gore as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Patricia Wittkopp as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Matthieu Barbier (Reviewer #2).
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
We would like to draw your attention to changes in our revision policy that we have made in response to COVID19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation, but we encourage you to also consider the substantive points raise below.
Summary:
Cyclic dominance has been proposed as a possible stabilizing mechanism for diversity in communities. However, empirical evidence has suggested that cyclic dominance appears to be rare. Why might this be? The authors' explanation for this observation, based on a dynamical ecoevo model and various theoretical arguments, can be summed up as follows:
– When drawing a network of qualitative dominance relations, it may seem that cyclic dominance could happen often (1/4th of possible triplets if relations are drawn at random).
– But when the interactions arise from an underlying matrix of payoffs, the conditions on payoffs that create cyclical dominance are actually quite constraining, happening at random only in 1/13th of triplets of dominance links (which themselves are only 1/10th of triplets, since dominance is not the only outcome: one can also observe coexistence or bistability).
– And when the payoffs themselves arise from a continuous evolutionary process (where new types are introduced with small changes in payoffs from their ancestors), the probability is even lower. The authors' simulations show that it occurs only in 1/30th of dominance triplets.
The authors provide arguments for why this probability is even lower: it is due to correlations between parents and offsprings. They show in their simulation results that cyclical dominance is associated with more difference between types (measured as an overall difference in their payoffs) than noncyclical dominance. They also run a simple genealogy process for a 3type subcommunity (where, instead of each mutation leading to a new type that competes with all the existing types, mutations are now accumulated along genealogical branches as if, for every mutation, the parent type is automatically replaced) to show that cyclical dominance can be favored when many mutations occur after the branching out of the 3 types, thus decorrelating their properties.
Essential revisions:
1) The authors argue the importance of studying cyclic dominance by noting its potential to increase diversity. This paper would, therefore, be strengthened by including an analysis of whether diversity increased in simulations in which cyclic dominance emerged (or at time points during which it was present) as compared to simulations or time points in/at which there was no cyclic dominance. If there is no correlation between the emergence of cyclic dominance and increases in diversity, the authors should address this in the Discussion section. Relatedly, the authors claim in the Discussion "our results indicate cyclic dominance can support diversity over long time scales". We do not believe the first half of this claim (that cyclic dominance can support diversity) is currently supported by their results (it is rather from other literature).
2) For readers to make sense of the triplet analysis it is necessary to know the link frequencies (e.g. between dominance, coexistence, and bistability). This can be brief (no need for a full supplementary figure in main text).
3) The authors argue that it is difficult to evolve cyclic dominance due to mutants being similar to parents. The paper would therefore be strengthened by including an analysis of how the mutation size sigma influences the frequency of cyclic dominance. This is shown for the toy model in Appendix 6, but not for the main model. Many readers would likely appreciate the results of varying sigma within the main model.
4) On a related note, the authors could directly measure and report on the time passed and/or number of mutations accrued since species in cyclic dominance triplets diverged from their last common ancestor and make appropriate comparisons. The current section "Genealogical structure can promote and can suppress cyclic dominance" could be moved to the Appendix if a direct tracing of the genealogy of triplets is added to the paper. (We found the genealogy section to be a bit difficult to parse).
5) The authors could include a brief analysis of how often the species in cyclic dominance triplets gain large population fractions after emerging. This question is relevant because if the three species in a triplet never reach large population fractions then their effects on each other would presumably be small compared to the effects from other members of the population (which would not affect any of the conclusions of this paper but may affect some readers' interpretations of those conclusions).
6) We believe that an outline of the whole argument (a bit like the summary that we give above) should be introduced early in the article, so that the structure is clear: currently, the text reads like a journey where we discover things on the way, but it is not always clear at first why we are doing one thing or another. In particular, something that may not be an issue for theoretical readers, but would be for a more general audience, is that it is not obvious at first which aspects of the model are going to be important or not for the argument, and therefore, whether the results are specific to the model assumptions. In summarizing the results above, we tried to present them in a way that clarifies that a large part of the argument could be made without invoking any specific simulation model. Readers may worry about the interpretation or lack of realism of some assumptions of the dynamics, when they do not really matter for the argument. This is an issue at different points in the paper: for the general ecoevo model, but also for the genealogy "minimodel", where a reader could find it concerning that the process is now different from the main simulation (the text makes it sound as if a type can accumulate mutations without branching out). It would be helpful for the genealogy section to introduce a quick outline of the argument it will make. We also suggest showing the Appendix 5—figure 1A in the main text, maybe as a panel in Figure 5. It would help understand what this minimodel assumes compared to the main simulation model (that all branches except for 3 have disappeared or can be ignored, thus explaining what it means to "accumulate mutations along a branch").
7) Regarding the main simulation model, there is in fact a concern that does not impact the results. A common issue in ecoevo modela is that the evolving traits will keep changing in the same direction, which is generally avoided by imposing some tradeoff.
As the authors note, the average payoff here tends to increase indefinitely, which means that all competition coefficients d_{ij} eventually converge to just α. In ecological terms, we would call that going toward neutral dynamics (where all individuals have the same competition strength, both within and between types, see S.P. Hubbell's 2001 book). This issue is not deadly because, as differences of d_{ij} between types become smaller, all that really happens is that the time it takes for a type to exclude another diverges. But the qualitative nature of the relationships is not changed: there is still dominance or coexistence or bistability, even if by a very small margin. This is simply impractical because one must wait longer and longer for extinctions to actually happen (this is actually theorized in ecology: types could coexist by becoming so similar than one winning over the other takes forever, see Scheffer and van Nes, 2006). So it seems to me that the simulation would have been easier if the payoffs were relative, e.g. constantly setting the average at zero, so that interactions do not converge to neutrality.
In the same spirit:
"For small α values (rich environments) in particular, the population size N at the steady state becomes large, containing many different types " the value of α should change nothing except the biomass scale (population size N ~ M lambda/alpha – incidentally, why introduce parameter M at all? it is never explained). The fact that more types exist for lower α seems like an artefact, perhaps because it takes longer for interactions to tend toward neutrality.
8) Perhaps one could directly show how adding correlations in the matrix diminishes the occurrence of cyclical dominance? This would directly show that nothing else is needed (i.e. that the ecoevo process does nothing more than add these correlations).
9) It is worth noting that there is a large ecological literature about extensions of cyclic dominance to more than triplets, called intransitive competition. Some of that literature has claimed (although we are not necessarily convinced) that intransitive competition is actually common and important. We would suggest reading these articles and figuring out why their claims would be so different – and if that difference is important, then discussing it in the article.
See for instance as starting points: Soliveres et al., 2015; Gallien et al., 2017.
https://doi.org/10.7554/eLife.57857.sa1Author response
Essential revisions:
1) The authors argue the importance of studying cyclic dominance by noting its potential to increase diversity. This paper would, therefore, be strengthened by including an analysis of whether diversity increased in simulations in which cyclic dominance emerged (or at time points during which it was present) as compared to simulations or time points in/at which there was no cyclic dominance. If there is no correlation between the emergence of cyclic dominance and increases in diversity, the authors should address this in the Discussion section. Relatedly, the authors claim in the Discussion "our results indicate cyclic dominance can support diversity over long time scales". We do not believe the first half of this claim (that cyclic dominance can support diversity) is currently supported by their results (it is rather from other literature).
Thank you for this comment. Indeed, we have started out from the concept of diversity as it has often been stated as a crucial consequence of cyclic dominance. However, we have only shown the rareness of cyclic dominance and discussed mechanisms leading to it. As you pointed out, we did not explicitly show that cyclic dominance increases diversity. We briefly mentioned the lifetime of both cyclic and noncyclic dominance triplets but we did not explicitly analyze correlations between triplets with diversity. We have now explored these correlations and added the detailed results in Appendix 7 and argued them in the Discussion. In a nutshell, we found that the fraction of cyclic dominance \chi has almost no correlation with diversity. This means – in contrast to general believe – that diversity is not mainly supported by the emergence of cyclic dominance in our model. However, the long time to equilibration with the removal of all noncompetitive types and a short time to the emergence of new types are main drivers of diversity.
2) For readers to make sense of the triplet analysis it is necessary to know the link frequencies (e.g. between dominance, coexistence, and bistability). This can be brief (no need for a full supplementary figure in main text).
Agreed, we have added an explanation for link frequencies in the main text before the Results section. In addition, we have distinguished the terms “frequency” and “fraction”
preventing confusion. Frequency is only used when the population frequency is considered. In all other cases, we have used “proportion” or “fraction”.
3) The authors argue that it is difficult to evolve cyclic dominance due to mutants being similar to parents. The paper would therefore be strengthened by including an analysis of how the mutation size sigma influences the frequency of cyclic dominance. This is shown for the toy model in Appendix 6, but not for the main model. Many readers would likely appreciate the results of varying sigma within the main model.
In our toy model, the mutational distances, between o and M_{1} and between M_{1} and M_{2}, are controlled by different variances while they are controlled by the number of mutations in the main model. In both cases, the absolute scale does not affect the results but the ratio of mutational distances is important. Hence, changing sigma in the main model affects the overall mutational distance, but the ratio remains unchanged, keeping the fraction of cyclic is the same. We have clarified this by discussing two explicit sigma values in the toy model.
4) On a related note, the authors could directly measure and report on the time passed and/or number of mutations accrued since species in cyclic dominance triplets diverged from their last common ancestor and make appropriate comparisons. The current section "Genealogical structure can promote and can suppress cyclic dominance" could be moved to the Appendix if a direct tracing of the genealogy of triplets is added to the paper. (We found the genealogy section to be a bit difficult to parse).
Thank you. Following this suggestion, we have moved the previous genealogy analysis into the Appendix and instead mentioned the direct measure of genealogical properties found in the simulations. The key variable to find cyclic or noncyclic triplets is the fraction of accumulated mutational distances before and after the payoff correlation appears during diverging process, which agrees well with the toy model result. We have included a new Figure 5 to explain it and have Introduced F_{l} defined by the fraction of mutational distances. Addressing major comment 6, in the new figure we first explained the genealogy. Then we show an example of genealogy for three types and the relationship between the fraction \chi and F__{l}. As many mutations are accumulated after each type evolves independently from others, the chance to emerge the cyclic dominance systematically increases.
5) The authors could include a brief analysis of how often the species in cyclic dominance triplets gain large population fractions after emerging. This question is relevant because if the three species in a triplet never reach large population fractions then their effects on each other would presumably be small compared to the effects from other members of the population (which would not affect any of the conclusions of this paper but may affect some readers' interpretations of those conclusions).
Thank you for this very interesting suggestion. We have measured the population frequencies which belong to cyclic and noncyclic dominance, f_{cyc} and f_{ncyc}. On average, cyclic dominance takes around 33% of the population, while noncyclic dominance takes around 83%. To check how often cyclic dominance can take more than a half population, we have checked the probability distribution function (PDF) of f_{cyc} and f_{ncyc} at the steadystate. Cyclic dominance usually takes very low population frequencies or rarely take the majority, showing a bimodal distribution with two peaks around 0 and 1. The peak near 0 is higher than the other, implying the cyclic dominance usually emerges in small groups. Also, we found that a chance that cyclic dominance takes more than a half of the population is around 30%. On the other hand, the noncyclic dominance usually takes the majority and 85% chance to take over the half population. These results have addressed in a new Appendix 7 and have shortly mentioned it in the Discussion.
6) We believe that an outline of the whole argument (a bit like the summary that we give above) should be introduced early in the article, so that the structure is clear: currently, the text reads like a journey where we discover things on the way, but it is not always clear at first why we are doing one thing or another. In particular, something that may not be an issue for theoretical readers, but would be for a more general audience, is that it is not obvious at first which aspects of the model are going to be important or not for the argument, and therefore, whether the results are specific to the model assumptions. In summarizing the results above, we tried to present them in a way that clarifies that a large part of the argument could be made without invoking any specific simulation model. Readers may worry about the interpretation or lack of realism of some assumptions of the dynamics, when they do not really matter for the argument. This is an issue at different points in the paper: for the general ecoevo model, but also for the genealogy "minimodel", where a reader could find it concerning that the process is now different from the main simulation (the text makes it sound as if a type can accumulate mutations without branching out). It would be helpful for the genealogy section to introduce a quick outline of the argument it will make. We also suggest showing the Appendix 5—figure 1A in the main text, maybe as a panel in Figure 5. It would help understand what this minimodel assumes compared to the main simulation model (that all branches except for 3 have disappeared or can be ignored, thus explaining what it means to "accumulate mutations along a branch").
Thank you for this very helpful suggestion. Based on the summary provided by the reviewers, we have now added an outline after making a point of the rareness of cyclic dominance. Also, taking a suggestion, we have moved Appendix 5—figure 1A to a new Figure 5A in the main text.
7) Regarding the main simulation model, there is in fact a concern that does not impact the results. A common issue in ecoevo model is that the evolving traits will keep changing in the same direction, which is generally avoided by imposing some tradeoff. As the authors note, the average payoff here tends to increase indefinitely, which means that all competition coefficients d_{ij} eventually converge to just α. In ecological terms, we would call that going toward neutral dynamics (where all individuals have the same competition strength, both within and between types, see S.P. Hubbell's 2001 book). This issue is not deadly because, as differences of d_{ij} between types become smaller, all that really happens is that the time it takes for a type to exclude another diverges. But the qualitative nature of the relationships is not changed: there is still dominance or coexistence or bistability, even if by a very small margin. This is simply impractical because one must wait longer and longer for extinctions to actually happen (this is actually theorized in ecology: types could coexist by becoming so similar than one winning over the other takes forever, see Scheffer and van Nes, 2006. So it seems to me that the simulation would have been easier if the payoffs were relative, e.g. constantly setting the average at zero, so that interactions do not converge to neutrality.
We agree with your point. In our model, species or types can coexist because the system is close to the neutral regime. However, our finding (rareness of cyclic dominance) is robust even with rescaling of payoffs. This is because the main driver of the diversity in that case is also not the formation of cyclic dominance. Cyclic dominance cannot be stably sustained, but appears because the system stays in nonequilibrium. Hence, we observe the emergence of cyclic dominance as a chance event only. We have also simulated a model with rescaling of payoff so that the mean payoff becomes zero. In this case the overall population size is controlled by the control parameter M with \alpha=0. Diversity is only observed for large population sizes (large M values), implying that the diversity originated from a large mutation supply. Cyclic dominance does not contribute to diversity, and thus the underlying mechanism found based on the matrix approach also holds. We have cited the paper Scheffer and van Nes, 2006, and have discussed the results with the rescaling method in the Discussion.
In the same spirit:
"For small α values (rich environments) in particular, the population size N at the steady state becomes large, containing many different types" the value of α should change nothing except the biomass scale (population size N ~ M lambda/alpha – incidentally, why introduce parameter M at all? it is never explained). The fact that more types exist for lower α seems like an artefact, perhaps because it takes longer for interactions to tend toward neutrality.
We agree that this deserves a better justification. We originally introduced the two parameters α and M for two different reasons: When the interaction parameters A_{ij} become large, α ensures that the population size remains restricted – and the magnitude α controls the importance of competition in the absence of any game theoretical interactions. On the other hand, M gives us the possibility to control the impact of the overall competition term. However, since we only focused a single M value, we have now dropped M by rescaling α and shifting A_{ij} by _{lnM}. We now have used this alternative notion which hopefully reduces confusion, and thus parameters are recalculated while the results are robust.
8) Perhaps one could directly show how adding correlations in the matrix diminishes the occurrence of cyclical dominance? This would directly show that nothing else is needed (i.e. that the ecoevo process does nothing more than add these correlations).
In our model, the payoff correlations are an emergent property. The payoff matrix changes according to the simple rule with the correlation between parental and offspring types. But we do not impose any genealogies. Because the genealogy is an outcome of the ecoevo process, the whole structure of payoff correlations is also an outcome of the ecoevo process determining the fraction of emerging cyclic and noncyclic dominance. Thus, introducing external correlations seems to come with a risk of introducing artefacts.
9) It is worth noting that there is a large ecological literature about extensions of cyclic dominance to more than triplets, called intransitive competition. Some of that literature has claimed (although we are not necessarily convinced) that intransitive competition is actually common and important. We would suggest reading these articles and figuring out why their claims would be so different – and if that difference is important, then discussing it in the article.
See for instance as starting points: Soliveres et al., 2015; Gallien et al., 2017.
Thank you for these pointers. First of all, we would like to say that the findings in those papers and in our manuscript are not mutually exclusive. In our model, the intransitive links always appear but it does not always connect to the emergence of cyclic triplets due to other link types such as coexistence and bistability. This was also observed in a soil bacterial community (Kotil and Vetsigian, 2018); the structure is hierarchical with some intransitive relationships, which do not lead to the cyclic dominance. Second, we considered nonequilibrium steady state while others have focused on equilibrium states. The cyclic dominance we studied stands out from other forms of intransitive competition since it is virtually unable to evolve from the equilibrium state. This a major difference may be that ecologists typically think about the interaction between different species and evolutionary biologists more in terms of the origin of the interacting partners. Reconciling both aspects, our manuscript supports the basic idea that assembly of unrelated types is more likely to lead to cyclic triplets than evolution, in which emerging types are closely related. We have addressed this literature in the Discussion.
https://doi.org/10.7554/eLife.57857.sa2Article and author information
Author details
Funding
JRG Program at the APCTP through Science and Technology Promotion Fund and Lottery Fund of the Korean Government and Korean Local Government Gyeongsangbukdo Province and Pohang City
 Hye Jin Park
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements
HJP, YP, and AT thank the Max Planck Society for generous funding. HJP was also supported by (1) an appointment to the JRG Program at the APCTP through the Science and Technology Promotion Fund and Lottery Fund of the Korean Government and by (2) the Korean Local Governments  Gyeongsangbukdo Province and Pohang city.
Senior Editor
 Patricia J Wittkopp, University of Michigan, United States
Reviewing Editor
 Jeff Gore, Massachusetts Institute of Technology, United States
Reviewers
 Jeff Gore, Massachusetts Institute of Technology, United States
 Matthieu Barbier
Publication history
 Received: April 14, 2020
 Accepted: August 1, 2020
 Version of Record published: September 4, 2020 (version 1)
Copyright
© 2020, Park et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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