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The problem of identifying conditions that enable major evolutionary transitions, in which distinct units come together to form a new higher level unit, is a complex and difficult topic spanning many disciplines. Here, we approach this problem from the perspective of the origin of life, which allows us to make the simplifying assumption that the lower-level units are not also evolving. This assumption lets us focus on identifying environmental factors that promote egalitarian major transitions in general and the origin of life specifically. To study this question, we build a simple artificial ecology model. We quantify major-transition-like dynamics using a maximum likelihood approach and a set of null models predicting the behavior of our system under various dynamics. Ultimately, we find that, even in a maximally simple artificial ecology model, we are able to observe evidence of community-level selection and thus the beginnings of a major evolutionary transition. The regions of parameter space that promote community-level selection vary based on species interactions but we observe consistent trends.
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Interaction Strengths Affect Whether Ecological Networks Promote the Initiation of Egalitarian Major Transitions
Identifying conditions that promote egalitarian major transitions, where unlike replicating units unite to form a higher-level unit, is an open problem with far-reaching implications. We propose that egalitarian major transitions can only begin in ecological communities that are conducive to them. To formalize this idea, we introduce the concept of “transition-ability”, which describes the extent to which a community is poised to undergo an egalitarian major transition. We hypothesize that transitionability is a property of ecological interaction networks, which represent the set of pairwise interactions among members of a community. Using a digital artificial ecology that simulates interactions between species based on a static interaction network, we test the transition-ability of interaction networks created by a range of graph-generation techniques, as well as some real-world ecological networks. To measure the extent to which a community is moving towards a major transition, we quantify the increase in community-level fitness relative to individual-level fitness across five different fitness proxies. We find that some network generation protocols produce more transitionable networks than others. In particular, interaction strengths (i.e. edge weights) have a substantial impact on transitionability, despite receiving low attention in the literature.
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- Award ID(s):
- 2218818
- PAR ID:
- 10436374
- Date Published:
- Journal Name:
- ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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