NDLI: How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

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How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

Content Provider	Springer Nature Link
Author	Duarte, Jorge Rodrigues, Carla Januário, Cristina Martins, Nu Sardanyés, Josep
Copyright Year	2015
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.
Starting Page	341
Ending Page	361
Page Count	21
File Format	PDF
ISSN	00015342
Journal	Acta Biotheoretica
Volume Number	63
Issue Number	4
e-ISSN	15728358
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-05-28
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adaptive dynamics Chaos Coevolution Ecology Predator-prey Predictability Red Queen Philosophy of Biology Evolutionary Biology
Content Type	Text
Resource Type	Article
Subject	Philosophy Applied Mathematics Medicine Biochemistry, Genetics and Molecular Biology Environmental Science Agricultural and Biological Sciences

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