A running theme of my research is exploring biological questions from a top-down statistical perspective. This approach is motivated by the recognition that nature’s complexity often exceeds our ability to fully understand and measure it at scale. To uncover general principles or extrapolate insights to ecologically relevant spatiotemporal scales, it is often necessary to take a step back and examine system-level descriptors from which simple rules may emerge. In practice, this involves investigating stochastic processes and statistical patterns of observables, while formulating biological hypotheses based on purely statistical mechanisms. These mechanisms and patterns are of particular interest because they are pervasive in nature, often unintuitive, and are behind of some of ecology’s most successful theories (e.g., natural selection, portfolio effect). Most importantly, they offer a pathway to general ecological rules, which remain rare and elusive.
I work on a variety of topics that generally revolve around plants and insects using this approach. For instance, I developed a model of herbivory from first principles based on neutral stochastic sampling of herbivores from a regional pool. Comparison of the model to an extensive global data set of herbivory from the HerbVar Network revealed that neutral stochastic processes dominate natural variation in herbivory, but deviations from neutral patterns are detectable and predictable (Pan & Wetzel 2024). That high levels of variation in herbivory exist among leaves and among plants have long been observed. It is important for plant fitness because plant tolerance to herbivory tends to be highly nonlinear, so variable herbivory reduces average plant fitness more than expected.
More recent work involves using methods borrowed from statistical physics and economics to model temporal fluctuations and distribution of traits. I am also exploring the potential to model the diversity and occupancy patterns in phytochemistry by modeling the observation process and underlying stochastic gene expression.