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My particular emphasis is on the use of time series analysis tools to answer questions such as:

Movement ecology- How can we use wildlife location time series data, combined with habitat and climate information, to detect and disentangle multiscale relationships between behavior, sociality, environment, ecology, and spatiotemporal patterns of population distribution?

Botany- How can we quantify and compare phenology patterns such as mast seeding within and across plant species and communities? What are the consequences of different phenology patterns to the broader community of plant consumers?

Population ecology- What regulates and determines population abundance through time?

Statistical ecology- How can we best use time-frequency domain (e.g. wavelets) and time-domain (e.g. state-space models) in a complimentary manner to probe data variance structure and then model relationships parametrically?

Mathematical and statistical formulations of models that appropriately capture ecological relationships and stochasticity often involve complex likelihood functions and computations. Additionally, ecological data can be somewhat limited by sample size. As a result, my work often involves both simulation studies to evaluate how methods will perform in typically complex and noisy ecological data with limited sample sizes, combined with analysis of empirical data. In this way I believe we can have the most confidence in moving from statistical inference of models to conclusions about biology.