Abstract
Coevolution between two antagonistic species has been widely studied theoretically for both ecologically- and genetically-driven Red Queen dynamics. A typical outcome of these systems is an oscillatory behavior causing an endless series of one species adaptation and others counter-adaptation. More recently, a mathematical model combining a three-species food chain system with an adaptive dynamics approach revealed genetically driven chaotic Red Queen coevolution. In the present article, we analyze this mathematical model mainly focusing on the impact of species rates of evolution (mutation rates) in the dynamics. Firstly, we analytically proof the boundedness of the trajectories of the chaotic attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. By using symbolic dynamics theory, we quantify the complexity of genetically driven Red Queen chaos computing the topological entropy of existing one-dimensional iterated maps using Markov partitions. Co-dimensional two bifurcation diagrams are also built from the period ordering of the orbits of the maps. Then, we study the predictability of the Red Queen chaos, found in narrow regions of mutation rates. To extend the previous analyses, we also computed the likeliness of finding chaos in a given region of the parameter space varying other model parameters simultaneously. Such analyses allowed us to compute a mean predictability measure for the system in the explored region of the parameter space. We found that genetically driven Red Queen chaos, although being restricted to small regions of the analyzed parameter space, might be highly unpredictable.
Similar content being viewed by others
References
Agrawal AF, Lively CM (2001) Parasites and the evolution of self-fertilization. Evolution 55:869–879
Billingsley P (1965) Ergodic theory and information. Wiley, New York
Blanchard F (2009) Topological chaos: What may this mean? J Diff Eq Appl 15:23–46
Blount ZD, Borland CZ, Lenski RE (2008) Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc Natl Acad Sci USA 105:7899–7906
Carrasco P, de la Iglesia F, Elena SF (2007) Distribution of fitness and virulence effects caused by single-nucleotide substitutions in Tobacco etch virus. J Virol 81:12979–12984
Changpin L, Guanrong C (2004) Estimating the Lyapunov exponents of discrete systems. Chaos 14:343–346
Cooper TF, Rozen DE, Lenski RE (2003) Parallel changes in gene expression after 20,000 generations of evolution in Escherichia coli. Proc Natl Acad Sci USA 100:1072–1077
Crutchfield JP, Packard NH (1982) Symbolic dynamics of one-dimensional maps:entropies. Finite Precis Noise Int J Theor Phys 21:433–466
Day T (2012) Computability, Gödel’s incompleteness theorem, and an inherent limit on the predictability of evolution. J R Soc Interface 9:624–639
Decaestecker E, Gaba S, Raeymaekers JAM, Stoks R, Kerckhoven Van, Ebert D, Meester LD (2007) Host-parasite ’Red Queen’ dynamics archived in pond sediment. Nature 450:870–873
Deng B (2001) Food chain chaos due to junction-fold point. Chaos 11:514–525
Dercole F, Ferriere R, Rinaldi S (2013) Chaotic Red Queen coevolution in three-species food chains. Proc R Soc Lond B 277:2321–2330. doi:10.1098/rspb.2010.0209
Dercole F, Rinaldi S (2008) Analysis of evolutionary processes: the adaptive dynamics approach and its applications. In: Levin Simon A (ed) Princeton series in theoretical and computational biology. Princeton University Press, Princeton
Dercole F, Rinaldi S (2010) Evolutionary dynamics can be chaotic: a first example. Int J Bifurcat Chaos 20:3473
Dieckmann U, Marrow P, Law R (1995) Evolutionary cycling in predator-prey interactions:population dynamics and the Red Queen. J Theor Biol 176:91–92
Dieckmann U, Law R (1996) The dynamical theory of coevolution: a derivation from stochastic ecological processes. J Math Biol 34:579–612
Ebert D (2008) Host-parasite coevolution: insights from the Daphnia-parasite model system. Curr Opin Microbiol 11:290–301
Eckmann J-P, Ruelle D (1985) Ergodic theory of chaos and strange attractors. Rev Mod Phys 57:617–656
Elena SF, Cooper VS, Lenski RE (1996) Punctuated evolution caused by selection of rare beneficial mutations. Science 272:1802–1804
Ellner SP, Turchin P (2005) When can noise induce chaos and why does it matter: a critique. Oikos 111:620–631
Fraedrich K (1987) Estimating weather and climate predictability on attractors. J Atmosph Sci 44:722–728
Gaba S, Ebert D (2009) Time-shift experiments as a tool to study antagonistic coevolution. Trends Ecol Evol 24:226–232
Gandon S (2002) Local adaptation and the geometry of host-parasite coevolution. Ecol Lett 5:246–256
Hamilton WD (1980) Sex versus non-sex versus parasite. Oikos 35:282–290
Hamilton WD, Axelrod A, Tanese R (1990) Sexual reproduction as an adaptation to resist parasites (a review). Proc Natl Acad Sci USA 87:3566–3573
Hastings A, Powell T (1991) Chaos in a three-species food chain. Ecology 72:896–903
Hoffman A (1991) Testing the Red Queen hypothesis. J Evol Biol 4:1–7
Jaenike J (1978) An hypothesis to account for the maintenance of sex in populations. Evol Theory 3:191–194
Katok A, Hasselblatt B (1995) Introduction to the modern theory of dynamical systems. Cambridge University Press, Cambridge. doi:10.1017/CBO9780511809187
Khibnik AI, Kondrashov AS (1997) Three mechanisms of Red Queen dynamics. Proc Roy Soc Lond B 264:1049–1056
King KC, Delph LF, Jokela J, Lively CM (2009) The geographic mosaic of sex and the Red Queen. Curr Biol 19:1438–1441
Kolmogorov AN (1958) New metric invariant of transitive dynamical systems and auto-morphism of Lebesgue spaces. Dokl Akad Nauk SSSR 119:861–864
Lai YC, Liu Z, Billings L (2003) Noise-induced unstable dimension variability and transition to chaos in random dynamical systems. Phys Rev E 67:026210
Letellier C, Aguire LA, Maquet J (2005) Relation between observability and differential embeddings for nonlinear dynamics. Phys Rev E 71:066213. doi:10.1103/PhysRevE.71.066213
Letellier C, Denis F, Aguire LA (2013) What we can learn from a chaotic cancer model. J Theor Biol 322:7–16. doi:10.1016/j.jtbi.2013.01.003
Letellier C, Aguire LA (2002) Investigating nonlinear dynamics from time series: the influence of symmetries and the choice of observables. Chaos 12:549–558. doi:10.1063/1.1487570
Lively CM (1987) Evidence from a New Zealand snail for the maintenance of sex by parasitism. Nature 328:519–521
Lovkovksy AE, Wolf YI, Koonin EV (2011) predictability of evolutionary trajectories in fitness landscapes. PLoS Comp Biol 7:e1002302
Lozovsky ER, Chookajorn T, Brown KM, Imwong M, Shaw PJ, Kamchonwongpaisan S, Neafsey DE, Weinreich DM, Hartl DL (2009) Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proc Natl Acad Sci USA 106:12025–12030
Milnor J, Thurston W (1988) On iterated maps of the interval I and II. Lect. Notes in Math., 1342, Springer, pp 465–563 doi:10.1007/BFb0082847
Misiurewicz M, Szlenk W (1980) Entropy of piecewise monotone mappings. Studia Math 67:45–63
Morran LT, Schmidt OG, Gelarden IA, Parrish RC II, Lively CM (2011) Running with the Red Queen: host-parasite coevolution selects for biparental sex. Science 333:216–218
Morris SC (2009) The predictability of evolution: glimpses into a post-Darwinian world. Naturwissenschaften 96:1313–1337
Morris SC (2010) Evolution: like any other science it is predictable. Philos Trans R Soc B 365:133–145
Parker T, Chua LO (1989) Practical numerical algorithms for chaotic systems. Springer, Berlin
Pesin YB (1976) Lyapunov characteristic exponent and ergodic properties of smooth dynamical systems with an invariant measure. Sov Math Dokl 17:196–199
Rand DA, Wilson HB (1981) Chaotic stochasticity—a ubiquitous source of unpredictability in epidemics. Proc R Soc Lond B 246:179–184
Russell J, Cohn R (2013) Gronwalls inequality. Bookvika publishing, Cimmeria
Salathe M, Kouyos RD, Bonhoeffer S (2008) The state of affairs in the Kingdom of the Red Queen. Trends Ecol Evol 23:439–445
Salverda MLM et al (2011) Initial mutations direct alternative pathways of protein evolution. PLoS Genet 7:e1001321
Sanjuán R, Moya A, Elena SF (2004) The distribution of fitness effects caused by single-nucleotide substitutions in an RNA virus. Proc Natl Acad Sci USA 101:8396–8401
Saxer G, Doebeli M, Travisano M (2010) The repeatability of adaptive radiation during long-term experimental evolution of Escherichia coli in a multiple nutrient environment. PLoS One 5:e14184
Schenk MF, Szendro IG, Krug J, de Visser JAGM (2012) Quantifying the adaptive potential of an antibiotic resistance enzyme. PLoS Genet 8:e1002783
Sinai V (1959) On the concept of entropy for a dynamical system. Dokl Akad Nauk SSSR 124:768–771
Solé RV, Sardanyés J (2013) Red Queen coevolution on fitness landscapes, in Recent Advances in the theory and application of fitness landscapes. In: Richter H, Engelbrecht AP (eds) Emergence, complexity and computation EEC series. Springer, Berlin
Stenseth NC, Maynard Smith J (1984) Coevolution in ecosystems: Red Queen evolution or stasis? Evolution 38:870–880
Thompson JN (1994) The coevolutionary process. Chicago University Press, Chicago
Toprak E et al (2012) Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat Genet 44:101–105
Van Valen L (1973) A new evolutionary law. Evol Theory 1:1–30
Van Valen L (1976) Energy and evolution. Evol Theory 1:179–229
Van Valen L (1980) Evolution as a zero-sum game for energy. Evol Theory 4:129–142
Venturino E (2011) Simple metaecoepidemic models. Bull Math Biol 73:917–950. doi:10.1007/s11538-010-9542-3
Vermeij GJ (1994) The evolutionary interaction among species—selection, escalation and coevolution. Annu Rev Ecol Syst 25:219–236
Weinreich DM, Delaney NF, Depristo MA, Hartl DL (2006) Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312:111–114
Wichman HA, Brown CJ (2010) Experimental evolution of viruses: microviridae as a model system. Philos Trans R Soc Lond B Biol Sci 365:2495–2501
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Duarte, J., Rodrigues, C., Januário, C. et al. How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?. Acta Biotheor 63, 341–361 (2015). https://doi.org/10.1007/s10441-015-9254-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10441-015-9254-z