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Evolving Digital Ecological Networks

Overview of attention for article published in PLoS Computational Biology, March 2013
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Title
Evolving Digital Ecological Networks
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002928
Pubmed ID
Authors

Miguel A. Fortuna, Luis Zaman, Aaron P. Wagner, Charles Ofria

Abstract

"It is hard to realize that the living world as we know it is just one among many possibilities" [1]. 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).

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
Germany 3 2%
France 2 1%
Portugal 2 1%
United States 2 1%
Japan 2 1%
Tanzania, United Republic of 1 <1%
Norway 1 <1%
Israel 1 <1%
Other 6 4%
Unknown 119 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 31%
Researcher 31 22%
Student > Master 15 11%
Student > Bachelor 11 8%
Professor 8 6%
Other 20 14%
Unknown 13 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 70 49%
Computer Science 18 13%
Environmental Science 14 10%
Social Sciences 5 4%
Business, Management and Accounting 3 2%
Other 14 10%
Unknown 18 13%