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Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved). ==Overview== In nature, species do not evolve in isolation but in large networks of interacting species. One of the main goals in evolutionary ecology is to disentangle the evolutionary mechanisms that shape and are shaped by patterns of interaction between species. A particularly important question concerns how coevolution, the reciprocal evolutionary change in local populations of interacting species driven by natural selection, is shaped by the architecture of food webs, plant-animal mutualistic networks, and host-parasite communities. The concept of diffuse coevolution, where adaptation is in response to a suite of biotic interactions,〔Janzen, D. H. (1990). When is it coevolution? ''Evolution'' 34: 611-612.〕 was the first step towards a framework unifying relevant theories in community ecology and coevolution. Understanding how individual interactions within networks influence coevolution, and conversely how coevolution influences the overall structure of networks, requires an appreciation for how pair-wise interactions change due to their broader community contexts as well as how this community context shapes selective pressures.〔Fox, L. R. (1988). Diffuse coevolution within complex communities. ''Ecology'' 69: 906-907.〕〔Thompson, J. N. (1994). The coevolutionary process. University of Chicago Press.〕 Accordingly, research is now focusing on how reciprocal selection influences and is embedded within the structure of multispecies interactive webs, not only on particular species in isolation.〔 Coevolution in a community context can be addressed theoretically via mathematical modeling and simulation, by looking at ancient footprints of evolutionary history via ecological patterns that persist and are observable today, and by performing laboratory experiments with microorganisms.〔Bohannan BJM, Lenski RE (2000) Linking genetic change to community evolution: insights from studies of bacteria and bacteriophage. Ecology Letters 3: 362-377.〕 In spite of the long time scales involved and the substantial effort that is necessary to isolate and quantify samples, the latter approach of testing biological evolution in the lab has been successful over the last two decades. However, studying the evolution of interspecific interactions, which involves dealing with more complex webs of multiple interacting species, has proven to be a much more difficult challenge. A meta-analysis of host-phage interaction networks, carried out by Weitz and his team, found a striking statistical structure to the patterns of infection and resistance across a wide variety of environments and methods from which the hosts and phage were obtained. However, the ecological mechanisms and evolutionary processes responsible have yet to be unraveled. Digital ecological networks enable the direct, comprehensive, and real time observation of evolving ecological interactions between antagonistic and/or mutualistic digital organisms that are difficult to study in nature. Research using self-replicating computer programs can help us understand how coevolution shapes the emergence and diversification of coevolving species interaction networks and, in turn, how changes in the overall structure of the web (e.g., through extinction of taxa or the introduction of invasive species) affect the evolution of a given species. Studying the evolution of species interaction networks in these artificial evolving systems also contributes to the development of the field, while overcoming limitations evolutionary biologists may face. For example, laboratory studies have shown that historical contingency can enable or impede the outcome of the interactions between bacteria and phage, depending on the order in which mutations occur: the phage often, but not always, evolve the ability to infect a novel host. Therefore, in order to obtain statistical power for predicting such outcomes of the coevolutionary process, experiments require a high level of replication. This stochastic nature of the evolutionary process was exemplified by Stephen Jay Gould's inquiry ("''What would happen if the tape of the history of life were rewound and replayed?''"〔Gould SJ (1990) Wonderful life: the Burgess Shale and the nature of history. W. W. Norton.〕) Because of their ease in scalability and replication, evolving digital ecological networks open the door to experiments that incorporate this approach of ''replaying the tape of life''. Such experiments allow researchers to quantify the role of historical contingency and repeatability in network evolution, enabling predictions about the architecture and dynamics of large networks of interacting species. The inclusion of ecological interactions in digital systems enables new research avenues: investigations using self-replicating computer programs complement laboratory efforts by broadening the breadth of viable experiments focused on the emergence and diversification of coevolving interactions in complex communities. This cross-disciplinary research program provides fertile grounds for new collaborations between computer scientists and evolutionary biologists. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Evolving digital ecological networks」の詳細全文を読む スポンサード リンク
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