Abstract
Classical theory shows that large communities are destabilized by random interactions among species pairs, creating an upper bound on ecosystem diversity. However, species interactions often occur in highorder combinations, whereby the interaction between two species is modulated by one or more other species. Here, by simulating the dynamics of communities with random interactions, we find that the classical relationship between diversity and stability is inverted for highorder interactions. More specifically, while a community becomes more sensitive to pairwise interactions as its number of species increases, its sensitivity to threeway interactions remains unchanged, and its sensitivity to fourway interactions actually decreases. Therefore, while pairwise interactions lead to sensitivity to the addition of species, fourway interactions lead to sensitivity to species removal, and their combination creates both a lower and an upper bound on the number of species. These findings highlight the importance of highorder species interactions in determining the diversity of natural ecosystems.
Introduction
A major challenge of theoretical ecology is explaining the stable coexistence of multispecies communities^{1,2,3,4,5}. While species interactions are important for maintaining natural diversity^{1,6,7,8,9,10,11,12}, their random nature destabilizes large communities. Even a community that satisfies the exclusion principle^{13}, or is otherwise inherently stabilized^{6}, can have its stability jeopardized by random interactions among species^{11}. This challenge was embodied in the seminal work by May^{14}, showing that the number of species that can stably coexist in randomly interacting communities is inversely proportional to the strength of their effective pairwise interactions^{15}. In ecological communities with strictly random pairwise interactions, this result predicts an upper threshold on diversity, as observed in random pairwise interaction models^{16,17}. Constrained properties of interactions, such as trophic level^{18,19}, intervality, broad degree distributions^{20}, positive and negative reciprocity^{21,22,23}, distribution skewness^{24,25} and connectance between differentially selfregulating species^{26} have been suggested as ways to explain the coexistence of large communities. Less is known about the feasibility, and possibly even the necessity, of diversity within the strict context of unconstrained random interactions.
Species can exhibit interactions that inherently involve multiple species^{27}. While modelling in ecology often assumes species communities with pairwise interactions (Fig. 1a), species can also interact in higherorder combinations, that is, the interactions between two species can be modulated by other species^{27,28,29,30}. For instance, while a microbial species can produce an antibiotic that inhibits the growth of a competing species, this pairwise inhibitory effect can be attenuated by a third species that produces an enzyme that degrades the antibiotic^{31,32,33}. This third species thus modifies the interaction between the antibioticproducing and sensitive species, without having a direct effect on any of them in isolation (Fig. 1b). The expression or activity of the antibioticdegrading enzyme may in turn be inhibited by compounds produced by a fourth species^{34}, thereby generating a fourway interaction (Fig. 1c). Another general class of highorder interactions may arise when species exhibit adaptive behaviour^{30,35}, such as a predator switching its prey when a more preferred prey becomes available^{36}, or a prey that reacts to the presence of a predator by decreasing its foraging activity directed at a third species^{37}. Such behavioural effects can quickly lead to interactions at even higher order; for example, the third species may subsequently increase its activity against a fourth species, generating an effect that is inherently fourway^{28,38}. While the paradigm of pairwise interactions can capture the densitymediated effect of a predator on a species devoured by its prey that arises from the predator’s impact on the prey’s density, it misses the often larger traitmediated effect^{28,37,39} of the predator on this third species through its impact on the prey’s behaviour.
While the importance of highorder interactions has been recognized, their general impact on critical community size has not been studied. One class of threeway interactions, whereby one species attenuates the negative interactions between two others, can stabilize wellmixed communities in specific network configurations^{31}. Threeway interactions have further been shown to promote diversity in patchy environments by increasing sensitivity to initial conditions^{40}. While increasing the order of interactions weakens their destabilizing effect^{41} and can increase the variance of species abundances at stationary states^{42}, it is unclear how highorder interactions affect the critical threshold on community size^{28,43}, and whether the classical result that random interactions impose a cap on community diversity still applies.
Here, we combine simulations and theoretical analysis of random communities to examine the relation between diversity and stability in ecosystems with both pairwise and higherorder interactions. We find that highorder interactions impose a lower bound on the number of species, in addition to the upper bound imposed by pairwise interactions. Altogether, interactions of different orders therefore define an optimally stable range of diversities.
Results
Modelling communities with pairwise and highorder interactions
We use a replicator dynamics model^{16,23,44,45} to simulate communities with interactions of different orders^{41,42,46}. Our model assumes N species with abundances x_{i}, and whose percapita growth rates are determined by the sum of pairwise interactions A_{ij}x_{j} (where A_{ij} determines the effect of species j on species i), of threeway interactions B_{ijk}x_{j}x_{k} (where species j and k have a joint effect on species i) and so on. This model therefore represents the Taylor expansion of a general system that includes traitmediated effects as well as pairwise interactions:
The model assumes random interactions perturbing an inherently stable community. The stability is provided by the first term (−x_{i}), which reflects the assumption that species are selflimiting in high concentrations. The pairwise interactions matrix is given by , where is a random matrix whose elements are drawn from a Gaussian distribution with mean 0 and variance 1, and α determines the strength (variance) of the pairwise interactions. The threeway interactions are set by the tensor , where is a random threedimensional (N × N × N) tensor whose elements are drawn from a Gaussian distribution with mean 0 and variance 1, and β represents the strength of threeway interactions; and similarly with fourway interactions whose strength scales with a parameter γ. As the total biomass is often determined by external resources, such as nutrients, energy and space, we keep the sum of all species abundances constant by subtracting from the individual growth rate of each species the average growth rate of the species in the community.
Highorder interactions set a lower bound on diversity
As expected from the analysis by May^{14} and subsequent results^{16,17}, without highorder interactions we find that coexistence is lost when the pairwise interaction strength α is increased beyond a critical threshold that decreases with the number of species. We ran simulations with strictly pairwise interactions (α>0 with β=0, γ=0) for varying interaction strengths α and different numbers of species N. When the pairwise interaction strength α is small the simulations reach a steady state where all species coexist (Supplementary Fig. 1a), but when α increases simulations start exhibiting species extinctions (Supplementary Fig. 1b). Therefore, for each number of species N, we can define a critical threshold α_{c} as the strength of pairwise interactions at which community feasibility (positive abundance for all species at equilibrium^{24}) is lost for a given fraction of the simulations (5%, Supplementary Fig. 1c,d). When the number of species N is increased, the critical threshold α_{c} decreases as 1/N, in accordance with the result by May^{14} (Fig. 2a). Thus, as communities become more diverse they are more sensitive to random pairwise interactions (Fig. 2b).
Strikingly, this classic relation between diversity and stability is inverted when highorder interactions are considered. We repeated the analysis above with strictly threeway interactions (only β>0) and strictly fourway interactions (only γ>0). Similar to the case of pairwise interactions, when the interaction strengths β or γ are increased beyond a certain level, β_{c} or γ_{c}, feasibility is lost and extinctions abound. However, in contrast to pairwise interactions, the critical thresholds for highorder interactions do not decrease with the number of species. The critical threshold of threeway interactions β_{c} remains unchanged, while the critical threshold of fourway interactions γ_{c} increases in proportion to the number of species (Fig. 2a). These fourway interaction communities can therefore withstand stronger interactions the more species they have. For a fixed strength of fourway interactions, these communities are stabilized, rather than destabilized, as the number species increases (Fig. 2c; increased stability with diversity can also appear in reciprocity models^{21}). Thus, while pairwise interactions put a higher bound on diversity, it is a lower bound that is created by highorder interactions. Provided that the total abundance is constant (does not change with the number of species), these results are robust to various changes in the underlying model: they hold for different distributions of the random coefficients (Supplementary Fig. 2), for connectance values smaller than 1 (Supplementary Fig. 3) and when highorder diagonal terms are set to zero (C_{ijk}≠0 only when i≠j≠k≠i; Supplementary Fig. 4). Similar scalings of the critical strengths of the different interaction orders with the number of species also occur when the dynamic equations are replaced by a Lotka–Volterra model (Supplementary Fig. 5).
Diversity scales differently with different interaction orders
To better understand the relation between the diversity of a community and its sensitivity to interactions of different orders, we examine a modified pairwise interactions matrix that captures the highorder interactions around a fixed point. Considering an approximation for how the interactions of different orders contribute to the effective pairwise interactions considered in the analysis by May^{14} (namely, the Jacobian), we derive a scaling relationship for the dependence of the feasible range of N on the different orders of interactions (see the ‘Methods’ section, Supplementary Fig. 6):
Intuitively, this condition tests whether the s.d. of the sum of all random interactions affecting a species near a fixed point is smaller than the selfstabilizing term (assuming fixed total abundance, see the ‘Methods’ section).
This theoretical analysis explains the different scaling of each of the interaction orders with N in accordance with our simulation results (Fig. 2a). When highorder interactions are turned off (only α>0), the critical strength of pairwise interactions α_{c} decreases as 1/N. The critical threshold of exclusively threeway interactions β_{c} does not depend on the number of species, while that of strictly fourway interactions γ_{c} is proportional to the number of species. Thus, interactions of any given order destabilize communities, and the effects of different orders are additive, but they scale differently with diversity. Small differences between these theoretical slopes and the numerical slopes of Fig. 2a might result from the small species numbers of the simulations, for which the analytic analysis holds only approximately.
Mixed interactions define range of allowed diversities
Since communities with a small number of species are sensitive to highorder interactions, while communities with a large number of species become sensitive to pairwise interactions, we asked whether the combination of pairwise and highorder interactions might dictate an intermediate range of species numbers that optimizes feasibility. To answer this question, we ran simulations with both pairwise and fourway interactions for three different community sizes N (for simplicity we set β=0; the effect of β>0 is discussed below). For each value of N, we scanned α and γ and identified the domain where communities are feasible (Fig. 3a). Since the critical value of α decreases with N, while the critical value of γ increases, there are regions where an intermediatesized community would be feasible, while either smaller or larger communities would not (area within the N=8 domain but not within the N=5 or the N=18 domain). To understand how the feasibility of specific communities depends on the number of species, we ran simulations where we gradually add species with random interactions to communities with given values of interaction strengths α and γ: pairwisedominated, fourway dominated and mixed. When pairwise interactions are dominant, feasibility decreases when N is increased beyond a threshold (Fig. 3b). In contrast, when fourway interactions are dominant, the community becomes sensitive to the removal of species (Fig. 3d). In the mixed case, when pairwise and highorder interactions are combined, feasibility is maximized for an intermediate number of species; both the removal and the addition of species destabilize the community (Fig. 3c).
The combination of pairwise and highorder interactions thus defines a range of stable diversities. Assuming that a given natural ecosystem can be characterized by its average strengths of interactions of different orders (α, β, γ…), which are characteristics of its species and the environment, the upper and lower bounds on its diversity N can be derived as the roots of equation 2 (see the ‘Methods’ section; Supplementary Fig. 7). As the total strength of interactions α+β+γ increases, these upper and lower bounds grow closer and the range of allowed diversities narrows around a defined number of species N* (Fig. 4 for β=0; using β=γ gives qualitatively similar results, Supplementary Fig. 8). This value of optimally stable diversity is determined by the relative strengths of the fourway and pairwise interactions (, see the ‘Methods’ section). Highorder dominated and strong interactions can thus require communities to maintain a large number of species to remain feasible.
Discussion
The combination of pairwise and highorder interaction strengths determines both upper and lower bounds on the diversity of multispecies communities. While very little is known about the abundance and strength of highorder interactions in nature, these results bring a new perspective to understand much empirical evidence showing that communities are sensitive not only to the addition of species but also to the opposite perturbation, namely the removal of species^{47,48,49,50}. Considering the perplexing effects of highorder interactions may allow a better understating of why high diversity is often a necessity rather than an option for natural ecosystems.
Methods
Numerical simulations of community dynamics
To find the probability that a random community made up of N species with specified strengths of pairwise and highorder interactions is feasible, we numerically integrate equation 1 with different randomizations of the interaction matrices. For a given N, we generate a set of random pairwise, three and fourway interaction matrices , and then for a given choice of α, β, γ we substitute into equation 1: , and . We then follow the species abundances as they change from a uniform initial value (x_{i}=1/N; in the stable regime, the equations should not be sensitive to initial conditions^{45}) using the MATLAB ode45 solver with default integration properties. Simulations were ended when they reach a persistent state (steadystate or bounded oscillations). We repeat this process R times (∼300) with different sets of random pairwise, three and fourway interaction matrices.
We concluded that a community had reached a persistent state by demanding that the entropy (−Σ_{i}x_{i} log(x_{i})) remain constant or bounded in its fluctuations. We evolve the equations for a fixed number of time units (Δt=10), and after each of those intervals, we calculate the ratio between the minimal value taken by the species distribution entropy in two different periods of time: the last tenth and the last threetenths of the simulation time length. If the ratio between those two values, as well as the similar ratio between the entropy maxima in those two periods, falls within a range of of 1, we concluded the simulation. If this condition was not met after a long time (10^{4} time units) we concluded the simulation as well, and to account for the possibility that a species would have become extinct after this time threshold, we counted those simulations as unfeasible in the calculation of the lower errorbars.
We defined a community as feasible if all the species existed at the end of the simulation, with an abundance above a defined threshold (set as 10^{−5}/N). The probability that a community would be feasible was then defined as the fraction of stable communities out of the R random communities simulated. To find the critical strength of interactions for a given number of species, we used a fixed collection of R sets of pairwise, three and fourway interaction matrices and increased the interaction parameters until 5% of the communities exhibited extinctions. In Fig. 2 (as well as Supplementary Figs 2–5) this was done by increasing the strength of interactions of a given order, while the rest are kept at 0. In Fig. 3a this was done along radial lines in log (α), log (γ) space: α=0.001·1.2^{r·cos θ}, γ=0.05·1.3^{r·sin θ}, such that for each value of θ, we increased r until stability was lost.
Deriving stability criterion by considering effective pairwise interactions
The Jacobian of the system at a given point is:
Where, is the Kronecker delta. At a fixed point, the first term vanishes. We may also neglect the terms in the last line which are derivatives of the normalization, as we will justify below. Assuming stability of all species coexisting, the value of species abundances at this fixed point must scale as x_{i}≈1/N (because the total abundance is fixed to 1). Equation 1 can therefore be approximated as pairwise dynamics:
with
the fixed point will be stable if the eigenvalues of the Jacobian all have negative real parts. Within our assumption x_{i}≈1/N, we may focus on the eigenvalues of A^{eff}, which differs from the Jacobian by a constant factor N that does not affect the signs of the eigenvalues (as previously reported^{15}, the actual Jacobian in a steadystate deviates somewhat from this simple analysis due to deviation of the fixedpoint x_{i} from 1/N; Supplementary Fig. 9).
The entries of the random component of A^{eff} are sums of independent identically distributed Gaussians with 0 mean, and the variance of such a sum is additive. For simplicity, we assume that only elements with j>k>l are nonzero (such as in Supplementary Fig. 4), so the threeway interactions term averages over N1 such elements and its variance is therefore ∼N times smaller, and similarly with the higherorder terms. The variance of the random component of A^{eff} is thus given by:
by Girko’s circular law, as N→∞, the eigenvalues of a random matrix M of order N × N will form a disk of radius around the origin on the complex plane. In a large enough community, and taking into account the stabilizing term, the eigenvalues of A^{eff} will therefore be approximately distributed in a disc around (−1, 0) (Supplementary Fig. 6), so that the community will be stable with high probability if the radius of the disc is smaller than 1, or:
If we remove the assumption that only elements with j>k>l are nonzero, the highorder interaction strengths in the last equation are multiplied by constant factors, but the scaling is unaffected.
This analysis does not depend on the specific distribution of the matrix elements^{51}, as long as their variance is fixed, and our simulations indeed show it holds for different distributions (Supplementary Fig. 2). Similar scaling results can be obtained relying on Gershgorin’s circle theorem (see ref. 52 for the case of pairwise interactions; generalization to highorder case follows from the same reasoning applied above). The normalization term should not have a significant impact on the instantaneous stability analysis, since by the same reasoning, its contribution to the variance of the Jacobian would be N times smaller than the interaction term, as it is itself an average of N interaction terms. However, this analysis does depend on the sum of species abundances being constant (and x_{i} thereby scaling as 1/N); a different scaling of critical interaction strengths will appear when the total abundance scales with the number of species^{41,42,46}.
Note that local stability analysis models exclusively pairwise interactions, since the equation is linearized around a point and the highorder interactions are therefore embedded in the coefficients of the effective pairwise interactions obtained in the linearization. Our analysis therefore extends the result by May^{14} in the sense that it suggests how the interaction strength in the Jacobian May analyses should scale with the number of species with respect to the strengths of interactions of different orders.
Deriving stability criterion by analysis of interaction variance
The same scaling of diversity with interactions of different orders can also be obtained by demanding that the expected variance of the random interactions be weaker than the selfstabilizing term. The nspecies interaction term is given by:
with an ndimensional interaction tensor whose variance is 1, and α^{(n)} the strength of the nspecies interactions. Since the value of species abundances near a fixed point with all species coexisting scales as x_{i}≈1/N, and from the additivity of the variance of independent random variables, the standard deviation of such a term scales as . Comparing this value with the stabilizing term −x_{i}≈1/N, we get the condition α^{(n)}<N^{n−3}, in agreement with our result (α<1/N for pairwise interactions, β<1 for threespecies interactions and so forth). The additivity of the variance allows us to extend this reasoning to obtain equation 2 when interactions of different orders are combined.
The optimal community size N*
Restricting interactions up to the fourth order, communities are stable if they satisfy the equation:
demanding an equality and solving for N gives the lower and upper bounds for number of species:
these two bounds narrow around a defined optimal number of species when the discriminant vanishes: , which gives .
Code availability
MATLAB code for the replicator and Lotka–Volterra models is available from the corresponding author on request.
Data availability
The simulation results are available from the corresponding author on request.
Additional information
How to cite this article: Bairey, E. et al. Highorder species interactions shape ecosystem diversity. Nat. Commun. 7:12285 doi: 10.1038/ncomms12285 (2016).
References
 1.
Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).
 2.
Shoresh, N., Hegreness, M. & Kishony, R. Evolution exacerbates the paradox of the plankton. Proc. Natl Acad. Sci. USA 105, 12365–12369 (2008).
 3.
Rosenzweig, M. L. Species Diversity in Space and Time Cambridge University Press (1995).
 4.
Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).
 5.
Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).
 6.
Simon, A. L. Community equilibria and stability, and an extension of the competitive exclusion principle. Am. Nat. 104, 413–423 (1970).
 7.
Armstrong, R. A. & McGehee, R. Competitive exclusion. Am. Nat. 115, 151–170 (1980).
 8.
McCann, K. S. The diversitystability debate. Nature 405, 228–233 (2000).
 9.
Allesina, S. & Tang, S. Stability criteria for complex ecosystems. Nature 483, 205–208 (2012).
 10.
Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180–183 (2000).
 11.
Gross, T., Rudolf, L., Levin, S. A. & Dieckmann, U. Generalized models reveal stabilizing factors in food webs. Science 325, 747–750 (2009).
 12.
Johnson, S., DominguezGarcia, V., Donetti, L. & Munoz, M. A. Trophic coherence determines foodweb stability. Proc. Natl Acad. Sci. USA 111, 17923–17928 (2014).
 13.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
 14.
May, R. M. Will a large complex system be stable? Nature 238, 413–414 (1972).
 15.
Allesina, S. & Tang, S. The stability–complexity relationship at age 40: a random matrix perspective. Popul. Ecol. 57, 63–75 (2015).
 16.
Diederich, S. & Opper, M. Replicators with random interactions: a solvable model. Phys. Rev. A 39, 4333–4336 (1989).
 17.
Sinha, S. & Sinha, S. Evidence of universality for the MayWigner stability theorem for random networks with local dynamics. Phys. Rev. E 71, 2–5 (2005).
 18.
James, A. et al. Constructing random matrices to represent real ecosystems. Am. Nat. 185, 680–692 (2015).
 19.
Haerter, J. O., Mitarai, N. & Sneppen, K. Phage and bacteria support mutual diversity in a narrowing staircase of coexistence. ISME J. 8, 1–10 (2014).
 20.
Allesina, S. et al. Predicting the stability of large structured food webs. Nat. Commun. 6, 7842 (2015).
 21.
Mougi, A. & Kondoh, M. Diversity of interaction types and ecological community stability. Science 337, 349–351 (2012).
 22.
Tang, S., Pawar, S. & Allesina, S. Correlation between interaction strengths drives stability in large ecological networks. Ecol. Lett. 17, 1094–1100 (2014).
 23.
Chawanya, T. & Tokita, K. Largedimensional replicator equations with antisymmetric random interactions. J. Phys. Soc. Jpn 71, 429–431 (2002).
 24.
Emmerson, M. & Yearsley, J. M. Weak interactions, omnivory and emergent foodweb properties. Proc. Biol. Sci. 271, 397–405 (2004).
 25.
BanasekRichter, C. et al. Complexity in quantitative food webs. Ecology 90, 1470–1477 (2009).
 26.
Haydon, D. T. Maximally stable model ecosystems can be highly connected. Ecology 81, 2631–2636 (2000).
 27.
Wootton, J. T. Indirect effects in complex ecosystems: recent progress and future challenges. J. Sea Res. 48, 157–172 (2002).
 28.
Werner, E. E. & Peacor, S. D. A review of traitmediated indirect interactions in ecological communities. Ecology 84, 1083–1100 (2003).
 29.
Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: why ecological interactions vary through space and time. Oikos 124, 243–251 (2015).
 30.
Wootton, J. T. The nature and consequences of indirect effects in ecological communities. Annu. Rev. Ecol. Syst. 25, 443–466 (1994).
 31.
Kelsic, E. D., Zhao, J., Vetsigian, K. & Kishony, R. Counteraction of antibiotic production and degradation stabilizes microbial communities. Nature 521, 516–519 (2015).
 32.
Perlin, M. H. et al. Protection of Salmonella by ampicillinresistant Escherichia coli in the presence of otherwise lethal drug concentrations. Proc. Biol. Sci. 276, 3759–3768 (2009).
 33.
Abrudan, M. I. et al. Socially mediated induction and suppression of antibiosis during bacterial coexistence. Proc. Natl Acad. Sci. USA 112, 11054–11059 (2015).
 34.
Reading, C. & Cole, M. Clavulanic acid: a betalactamaseinhibiting betalactam from Streptomyces clavuligerus. Antimicrob. Agents Chemother. 11, 852–857 (1977).
 35.
Schmitz, O. J., Krivan, V. & Ovadia, O. Trophic cascades: the primacy of trait‐mediated indirect interactions. Ecol. Lett. 7, 153–163 (2004).
 36.
KoenAlonso, M. in From energetics to ecosystems: the dynamics and structure of ecological systems 1–36Springer (2007).
 37.
Beckerman, A. P., Uriarte, M. & Schmitz, O. J. Experimental evidence for a behaviormediated trophic cascade in a terrestrial food chain. Proc. Natl Acad. Sci. USA 94, 10735–10738 (1997).
 38.
Abrams, P. A. Predators that benefit prey and prey that harm predators: unusual effects of interacting foraging adaptation. Am. Nat. 140, 573–600 (1992).
 39.
Holt, R. & Barfield, M. in TraitMediated Indirect Interactions: Ecological and Evolutionary Perspectives (eds Ohgushi, T., Schmitz, O. & Holt, R. D.) 89–106 (Cambridge Univ. Press, 2012).
 40.
Wilson, D. S. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73, 1984–2000 (1992).
 41.
de Oliveira, V. M. & Fontanari, J. Random replicators with highorder interactions. Phys. Rev. Lett. 85, 4984–4987 (2000).
 42.
Yoshino, Y., Galla, T. & Tokita, K. Rank abundance relations in evolutionary dynamics of random replicators. Phys. Rev. E 78, 1–11 (2008).
 43.
Walsh, M. R. The evolutionary consequences of indirect effects. Trends. Ecol. Evol. 28, 23–29 (2013).
 44.
Josef Hofbauer, K. S. Evolutionary games and population dynamics. Proc. Biol. Sci. 273, 67–85 (1998).
 45.
Opper, M. & Diederich, S. Replicator dynamics. Comput. Phys. Commun. 121, 141–144 (1999).
 46.
Galla, T. Random replicators with asymmetric couplings. J. Stat. Mech. Theory Exp. 39, 3853–3869 (2005).
 47.
Raup, D. M. Biological extinction in earth history. Science 231, 1528–1533 (1986).
 48.
O’Gorman, E. J. & Emmerson, M. C. Perturbations to trophic interactions and the stability of complex food webs. Proc. Natl Acad. Sci. USA 106, 13393–13398 (2009).
 49.
Kareiva, P. M., Levin, S. A. & Paine, R. T. The Importance of Species: Perspectives on Expendability and Triage Princeton University Press (2003).
 50.
Stachowicz, J. J. Species diversity and invasion resistance in a marine ecosystem. Science 286, 1577–1579 (1999).
 51.
Tao, T., Vu, V. & Krishnapur, M. Random matrices: universality of ESDs and the circular law. Ann. Probab. 38, 2023–2065 (2010).
 52.
Haydon, D. Pivotal assumptions determining the relationship between stability and complexity: an analytical synthesis of the stabilitycomplexity debate. Am. Nat. 144, 14 (1994).
Acknowledgements
We thank Ariel Amir, Michael Baym, Guy Bunin, Michael Elowitz, Renan Gross, Simon A. Levin and Joshua Weitz for important feedback and discussions. We would also like to thank Stefano Allesina and additional anonymous reviewers for their constructive input. E.B. acknowledges the support of the Technion Rothschild Scholars Program for Excellence. E.D.K. acknowledges government support by the Department of Defense, Office of Naval Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. R.K. acknowledges the support of European Research Council Seventh Framework Programme ERC Grant 281891, National Institutes of Health Grant R01GM081617, and Israeli Centers of Research Excellence ICORE Program ISF Grant No. 152/11.
Author information
Author notes
 Eric D. Kelsic
Present address: Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.
Affiliations
Department of Physics, Technion—Israel Institute of Technology, Haifa 3200003, Israel
 Eyal Bairey
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
 Eric D. Kelsic
 & Roy Kishony
Department of Biology and Department of Computer Science, Technion—Israel Institute of Technology, Haifa 3200003, Israel
 Roy Kishony
Authors
Search for Eyal Bairey in:
Search for Eric D. Kelsic in:
Search for Roy Kishony in:
Contributions
E.B., E.D.K. and R.K. designed the study; E.B. did the simulations and analytical analysis; E.B., E.D.K. and R.K. analysed the data and wrote the paper.
Competing interests
The authors declare no competing financial interests.
Corresponding author
Correspondence to Roy Kishony.
Supplementary information
PDF files
 1.
Supplementary Information
Supplementary Figures 19
 2.
Peer Review file
Rights and permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
About this article
Further reading

Ecological networks reveal resilience of agroecosystems to changes in farming management
Nature Ecology & Evolution (2019)

Multiple stable states in microbial communities explained by the stable marriage problem
The ISME Journal (2018)

Growth tradeoffs produce complex microbial communities on a single limiting resource
Nature Communications (2018)

Beyond pairwise mechanisms of species coexistence in complex communities
Nature (2017)

Mapping the ecological networks of microbial communities
Nature Communications (2017)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.