We work on pure and applied problems in evolutionary ecology and natural resource management.
Nonlinear Dynamics, Statistical Ecology, and Multi-species Management
We develop tools for ecosystem management that are robust to ‘structural uncertainty.’ What does that mean? Well, for most ecosystems, it isn’t feasible to write down the complete list of species present, let alone precisely specify all of the equations that govern how those species change through time. As a consequence, modeling ecosystems always involves ‘making simplifying assumptions.’ For theoretical studies this is just fine: we make assumptions and see where they lead. But for practical problems with real-life consequences this seems like a recipe for trouble.
So, instead of filtering observations through assumptions, we try to let the observations of the ecosystem- and how it has responded to previous management actions- indicate where the system is going next. We use this information to help develop sustainable policies for conservation and management. Doing so involves developing new approaches to modeling complex systems, as well as making use of recent developments in other fields like nonlinear dynamics, physics, and machine learning.
These tools often outperform more traditional approaches to ecological modeling (paper, paper). We have applied them to predicting recruitment in harvested fish populations (paper), estimating state-dependent species interactions (paper), and understanding (a)synchrony in marine metapopulations.
Current research areas include Bayesian nonlinear forecasting, spatio-temporal delay embedding, and approximate dynamic programming.
This work focuses on understanding and predicting short-term changes to phenotypes in response to changes in environmental drivers, predator abundance, and harvesting. We combine lab experiments, field studies, meta-analyses, and theory to address these topics. Past projects include fisheries induced evolution (paper, paper), latitudinal gradients in ectotherm lifespans (paper), transgenerational thermal plasticity (paper), and the evolution of mortality trajectories (paper).
- April 2019 Drs. Munch and Rogers received a grant from NOAA’s High Performance Computing Initiative for research entitled “A dynamical systems / machine learning hybrid for predicting and mitigating ecological extremes.”
- August 2019 Bethany Johnson received a NMFS Sea Grant Population Dynamics Fellowship for research entitled “Assessment and management of short-lived species with empirical dynamic programming.” Way to go, Bethany!