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Spatial structure is hypothesized to be an important factor in the origin of life, wherein encapsulated chemical reaction networks came together to form systems capable adaptive complexification via Darwinian evolution. In this work, we use a computational model to investigate how different patterns of environmental connectivity influence the emergence of adaptive processes in simulated systems of self-amplifying networks of interacting chemical reactions (autocatalytic cycles, “ACs”). Specifically, we measured the propensity for adaptive dynamics to emerge in communities with nine distinct patterns of inter-AC interactions, across ten different patterns of environmental connectivity. We found that the pattern of connectivity can dramatically influence the emergence of adaptive processes; however, the effect of any particular spatial pattern varied across systems of ACs. Relative to a well-mixed (fully connected) environment, each spatial structure that we investigated amplified adaptive processes for at least one system of ACs and suppressed adaptive processes for at least one other system. Our findings suggest that there may be no single environment that universally promotes the emergence of adaptive processes in a system of interacting components (e.g., ACs). Instead, the ideal environment for amplifying (or suppressing) adaptive dynamics will depend on the particularities of the system. 

Prior research on evolutionary mechanisms during the origin of life has mainly assumed the existence of populations of discrete entities with information encoded in genetic polymers. Recent theoretical advances in autocatalytic chemical ecology establish a broader evolutionary framework that allows for adaptive complexification prior to the emergence of bounded individuals or genetic encoding. This framework establishes the formal equivalence of cells, ecosystems and certain localized chemical reaction systems as autocatalytic chemical ecosystems (ACEs): food-driven (open) systems that can grow due to the action of autocatalytic cycles (ACs). When ACEs are organized in meta-ecosystems, whether they be populations of cells or sets of chemically similar environmental patches, evolution, defined as change in AC frequency over time, can occur. In cases where ACs are enriched because they enhance ACE persistence or dispersal ability, evolution is adaptive and can build complexity. In particular, adaptive evolution can explain the emergence of self-bounded units (e.g. protocells) and genetic inheritance mechanisms. Recognizing the continuity between ecological and evolutionary change through the lens of autocatalytic chemical ecology suggests that the origin of life should be seen as a general and predictable outcome of driven chemical ecosystems rather than a phenomenon requiring specific, rare conditions. 

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. 

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.