Schluter (1996) describes adaptive radiation as "the diversification of a lineage into species that exploit a variety of different resource types and that differ in the morphological or physiological traits used to exploit those resources". My research focuses on adaptive radiation in the context of limited resources, where frequency-dependence is an important driver of selection (Futuyma & Moreno, 1988; Dieckmann & Doebeli, 1999; Friesen et al., 2004). Adaptive radiation yields a community composed of distinct organism types adapted to specific niches.I study simple communities of digital organisms, the result of adaptive radiation in environments with limited resources. I ask (and address) the questions: How does diversity, driven by resource limitation, affect the frequency with which complex traits arise? What other aspects of the evolutionary pressures in this limited resource environment might account for the increase in frequency with which complex traits arise? Can we predict community stability when it encounters another community, and is our prediction different for communities resulting from adaptive radiation versus those that are artificially assembled?Community diversity is higher in environments with limited resources than in those with unlimited resources. The evolution of an example complex feature (in this case, Boolean EQU) is also more common in limited-resource environments, and shows a strong correlation with diversity over a range of resource inflow rates. I show that populations evolving in intermediate inflow rates explore areas of the fitness landscape in which EQU is common, and that those in unlimited resource environments do not. Another feature of the limited-resource environments is the reduced cost of trading off the execution of building block tasks for higher-complexity tasks. I find strong causal evidence that this reduced cost is a factor in the more common evolution of EQU in limited-resource environments.When two communities meet in competition, the fraction of each community's descendants making up the final post-competition community is strongly consistent across replicates. I find that three community-level factors, ecotypic diversity, community composition, and resource use efficiency can be used to predict this fractional community success, explaining up to 35% of the variation.In summary, I demonstrate the value of digital communities as a tractable experimental system for studying general community properties. They sit at the bridge between ecology and evolutionary biology and evolutionary computation, and offer comprehensible ways to translate ideas across these fields.
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