My research aims to develop mathematical models and statistical analysis methods to better understand complex ecological systems.
Currently a major focus is development of a general statistical computing framework for algorithms such as Markov chain Monte Carlo, sequential Monte Carlo, and the like. These are among the most flexible methods that have enabled estimation of many kinds of models from many kinds of data across many scientific fields. We are involved in improving statistical methods for model estimation, selection and assessment, applying such methods to data analysis problems, and improving the algorithms involved. Examples of ecological applications include estimating models for population time-series data, capture-recapture and occupancy data, animal resource use and movement data, tree growth and mortality data, cohort development data, and many more.
My ecological research has centered on population dynamics: how and why do populations change through time? Answers to these questions draw together physiology, behavior, evolution, life history theory, species interactions, and other fields. Understanding population dynamics is central both to basic ecological questions about the abundance and distribution of species and to applied questions about herbivore outbreaks, invasive species dynamics, extinction risks, and sustainable harvests. This intersection of topics and applications makes population dynamics a fountain of interesting research questions. My recent theoretical work has focused on the importance of individual heterogeneity in stage-structured population dynamics and life history evolution.
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