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.

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.

The surface metabolism theory posits that adaptive evolution initiated when autocatalytic chemical systems became spatially localized on mineral surfaces. We searched for such surface-limited metabolisms (SLiMes) using a chemical ecosystem selection paradigm. This involves creating a prebiotic microcosm containing mineral grains and a “soup,” rich in food and potential sources of chemical energy, and then serially transferring a subset of the grains to a new microcosm containing fresh soup and new grains. This repeated dilution should enrich for chemical systems that can self-propagate more rapidly than the rate of serial dilution, and such enrichment should be detectable based on changes in microcosm chemistry over the course of multiple transfers. We deployed chemical ecosystem selection on several different soups and minerals and identified a combination that appears to be conducive to the enrichment of a SLiMe. In these conditions, chemical changes were observed over the first 12–18 transfers, most notably a loss of both orthophosphate and organics (as detected by optical density) from the solution. This loss from the solution correlated with the appearance of fractal structures on the surface of the grains. The putative SLiMes show clear evidence of self-propagation ability and manifest basic ecological dynamics. Ongoing work is evaluating the systems’ evolutionary capacity.