Digital Evolution for Ecology Research: A Review
In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition t...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:3424ebe7228b46e58616cd57f4db34302021-12-01T14:08:21ZDigital Evolution for Ecology Research: A Review2296-701X10.3389/fevo.2021.750779https://doaj.org/article/3424ebe7228b46e58616cd57f4db34302021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fevo.2021.750779/fullhttps://doaj.org/toc/2296-701XIn digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.Emily DolsonEmily DolsonEmily DolsonCharles OfriaCharles OfriaCharles OfriaFrontiers Media S.A.articledigital evolutioneco-evolutionary dynamicscompetitionpredationmulti-trophic communitiescrossfeedingEvolutionQH359-425EcologyQH540-549.5ENFrontiers in Ecology and Evolution, Vol 9 (2021) |
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digital evolution eco-evolutionary dynamics competition predation multi-trophic communities crossfeeding Evolution QH359-425 Ecology QH540-549.5 |
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digital evolution eco-evolutionary dynamics competition predation multi-trophic communities crossfeeding Evolution QH359-425 Ecology QH540-549.5 Emily Dolson Emily Dolson Emily Dolson Charles Ofria Charles Ofria Charles Ofria Digital Evolution for Ecology Research: A Review |
description |
In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research. |
format |
article |
author |
Emily Dolson Emily Dolson Emily Dolson Charles Ofria Charles Ofria Charles Ofria |
author_facet |
Emily Dolson Emily Dolson Emily Dolson Charles Ofria Charles Ofria Charles Ofria |
author_sort |
Emily Dolson |
title |
Digital Evolution for Ecology Research: A Review |
title_short |
Digital Evolution for Ecology Research: A Review |
title_full |
Digital Evolution for Ecology Research: A Review |
title_fullStr |
Digital Evolution for Ecology Research: A Review |
title_full_unstemmed |
Digital Evolution for Ecology Research: A Review |
title_sort |
digital evolution for ecology research: a review |
publisher |
Frontiers Media S.A. |
publishDate |
2021 |
url |
https://doaj.org/article/3424ebe7228b46e58616cd57f4db3430 |
work_keys_str_mv |
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1718405049316540416 |