Each location on the lattice contains several individuals that follow simple rules of reproduction, mutation and migratory dispersal. At each generation, they randomly move within a certain distance, mate with other individuals in their neighborhood and produce offspring, which can also mutate with a small probability. Their diploid genomes comprise two pairs of 1000-base chromosomes assorted independently during mating (Fig. b). Due to an "outbreeding depression" effect, offspring viability is also bound to decrease with the genetic distance between parents.

To explore this pattern of population diversity we plot a frequency histogram of the genetic difference between pairs of randomly chosen individuals, or "mismatch distribution" (Fig. d). While a well-mixed version of the model exhibits a relatively stable and unimodal distribution, a drifting multimodal distribution emerges when the dispersal of individuals is limited to short distances. The long-term persistence of such diverging subpopulations is the essence of biological speciation. ← Less

An alternative but less standardized methodology relies on intensive computer simulation of evolving communities made of simple, explicitly described individuals. Compared to analytical frameworks, two major properties are distinctive of the agent-based computational approach: emergence and adaptation. New properties at higher scales, which were not explicitly included in the model, appear in the system as the individuals interact. Simultaneously, the community can also evolve and adapt to new environmental conditions, therefore broadening the scope of possible experiments and testable hypotheses.

In this work, we focus on an especially puzzling aspect of evolution: diversification. We simulate a virtual community of plants to study the emergence and dynamics of genomic and phenotypic variation during evolution. To model plants, we use rewriting systems, a family of formal methods widely investigated in theoretical computer science and capable of encoding and generating complex structures. In this formal framework, parts of an initial object, generally strings of symbols, are iteratively replaced by other parts, generally longer string segments, following rewrite rules.
  
We observe that our simulated plants evolve increasingly elaborate canopies, which are capable of intercepting ever greater amounts of light. Generated morphologies vary from the simplest one-branch structure of promoter plants to a complex arborization of several hundred thousand branches in highly evolved variants. On the population scale, the heterogeneous spatial structuration of the plant community at each generation depends solely on the evolution of its component plants.

Using this virtual data, the morphologies and the dynamics of diversity production were analyzed by various statistical methods, based on genotypic and phenotypic distance metrics. The results demonstrate that diversity can spontaneously emerge in a community of mutually interacting individuals under the influence of specific environmental conditions. ← Less

In all these cases, there is no particular specified goal: interesting and sometimes unexpected phenomena emerge from the interaction of individuals and their environment. An important insight into the majority of such successful systems is that they involve ecosystemic interactions.

To evaluate our new model and demonstrate its open-endedness, we define a measure of phenotypic diversity on the space of cellular automata. Our results show that HetCA is capable of supporting long-term dynamical behaviour and phenotypic changes, not readily achieved in control homogeneous CA, such as the Game of Life. In particular, we exhibit examples of the strategies discovered by our system, which are characterized by the emergence of competitive behaviour. ← Less