Predictability in the Wild: Forecasting evolution in a graph-embedded, five-species microcosm

While forecasting evolution is feasible in simple contexts, community and spatial structure likely disrupt predictability. Using a stable five-species community described by Castledine et al. (2024), this project evolves replicate metacommunities on programmable graph structures (star, line, lattice) with asymmetric migration as advocated by Abbara et al. (2024). It tests whether adaptive endpoints such as adhesion, dispersal, and cheating resistance are forecastable from single-species priors. The approach combines serial passage with controlled migration matrices and replicate invasion-from-rare assays to quantify coexistence during evolution. Dispersal-focused measurements and trade-offs are integrated, tracking genotype frequencies by species with shallow metagenomics and targeted amplicon sequencing, alongside longitudinal phenotype matrix and dispersal trait measurements. This is novel as no study has explicitly combined forecasting with multi-species stable communities and experimental graph theory under realistic bottlenecks and migration asymmetries. It challenges the norm of high repeatability in short-term evolution by introducing realistic complexity and quantitatively tests graph-theoretic predictions of selection suppression or acceleration at the community scale. The impact includes establishing design rules for extending forecasts to communities and landscapes, critical for microbiome engineering, containment strategies, and eco-evolutionary management.

References:

1. Mutant fate in spatially structured populations on graphs: Connecting models to experiments. Alia Abbara, Lisa Pagani, Celia García-Pareja, Anne-Florence Bitbol (2024). bioRxiv.
2. Characterizing a stable five-species microbial community for use in experimental evolution and ecology. Meaghan Castledine, Joseph Pennycook, Arthur Newbury, L. Lear, Zoltan Erdos, Rai Lewis, Suzanne Kay, Dirk Sanders, David Sünderhauf, A. Buckling, E. Hesse, D. Padfield (2024). Microbiology.