Connecting ecosystem models with evolutionary genetics: Fitness landscapes, persistent networks, and phylodynamic simulation

The evolution of ecological networks depends on the underlying population dynamics, genetics, and interactions of the composite species. In the single species context, theoretical models have simplified the study of adaptation to genotype/phenotype fitness, however complex eco-evolutionary dynamics in multi-species communities have challenged researchers. For example, in microbial and virus-immune response ecosystems, a dynamic fitness landscape determines whether pathogen strains can gain several resistance mutations. This project aims to bridge ecosystem dynamics with evolutionary genetics by analyzing ecological models evolving on fitness landscapes and developing a computational platform for incorporating phylogenetics. The advances can inform vaccines and therapies which prevent pathogen resistance against multiple immune responses or drugs. This research will engage undergraduate and graduate students, providing interdisciplinary training in computational and mathematical biology. Recent work has sought to understand assembly of interacting species in ecosystem models. However, the overwhelming number of species combinations and connecting the models to evolution have challenged researchers, especially in higher dimensional systems. This project aims to address this gap by looking through an evolutionary genetics lens, representing species variants as binary sequences which encode ecological interactions and fitness landscapes. First, persistence and stability of equilibria in models of ecological networks will be analyzed and linked to epistasis (non-additivity) in underlying fitness landscapes, facilitating simplifying rules for ecosystem evolution. Predator-prey and consumer-resource networks pertaining to viral evolution, microbiomes and antibiotic resistance, along with applications to immunotherapy and treatments, will be considered. Next, the PI will construct a flexible computational method for jointly simulating eco-evolutionary trajectories and phylogenetic trees, which can validate the theoretical results, and confront both genetic and population dynamic data. The work will involve interdisciplinary collaboration into how fitness landscapes shape ecological network evolution for HIV-immune dynamics, and microbial resistance to antibiotics and phage infection. Through dynamical systems, stochastic simulation, combinatoric and computational analysis, techniques will be developed for connecting population dynamics and evolutionary genetics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.