Phylotrack: C++ and Python libraries for in silico phylogenetic tracking

In silico evolution instantiates the processes of heredity, variation, and differential reproductive success (the three "ingredients" for evolution by natural selection) within digital populations of computational agents. Consequently, these populations undergo evolution, and can be used as virtual model systems for studying evolutionary dynamics. This experimental paradigm -- used across biological modeling, artificial life, and evolutionary computation -- complements research done using in vitro and in vivo systems by enabling experiments that would be impossible in the lab or field. One key benefit is complete, exact observability. For example, it is possible to perfectly record all parent-child relationships across simulation history, yielding complete phylogenies (ancestry trees). This information reveals when traits were gained or lost, and also facilitates inference of underlying evolutionary dynamics. The Phylotrack project provides libraries for tracking and analyzing phylogenies in in silico evolution. The project is composed of 1) Phylotracklib: a header-only C++ library, developed under the umbrella of the Empirical project, and 2) Phylotrackpy: a Python wrapper around Phylotracklib, created with Pybind11. Both components supply a public-facing API to attach phylogenetic tracking to digital evolution systems, as well as a stand-alone interface for measuring a variety of popular phylogenetic topology metrics. Underlying design and C++ implementation prioritizes efficiency, allowing for fast generational turnover for agent populations numbering in the tens of thousands. Several explicit features (e.g., phylogeny pruning and abstraction, etc.) are provided for reducing the memory footprint of phylogenetic information.

Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile toolkit to study large-scale evolving populations.