My research focuses on developing and applying robust and efficient nonparametric statistical procedures for nonlinear modeling of climate change effects on groundfish communities in the northern Pacific Ocean. Working with non- and semi-parametric models to explore the effects of climate changes on populations has highlighted the inadequacy of current methods to appropriately calculate effect size measures for predictors in high-dimensional model structures. As model structures become complex in order to accommodate more ecological interactions, accurately measuring the size of such effects becomes equally convoluted. I am therefore interested in creating robust effect size measures suitable for such model structures to expand the usability of these models to scientists who require appropriate inference to develop ecological theory and make management decisions.

Studies of multiple paternity in mammals and other animal species generally report proportion of multiple paternity among litters, mean litter or clutch sizes, and mean number of sires per litter or clutch. I collaborate with a population ecologist and a statistician to estimate a null model for multiple paternity across a variety of animals using Bayesian regression models. We show how these variables can be used to produce an estimate of the probability of reproductive success for a male that has mated with a female. This estimate of male success is more closely aligned to the intensity of sexual selection and is more informative about the mating system that alternative measures, like the proportion of litters with multiple paternity or the number of sires. The probability of success for a mated male can be measured both theoretically and empirically, and gives an estimate of a male’s “confidence of paternity” upon mating.

While causality is a highly explored area of research in biostatistics and epidemiology, it has not been explored extensively for ecological data. Causal analysis has the potential to establish direct causal links and uncover indirect casual links in large ecosystem networks, however many methods used to establish causality require separability, which is not possible to establish in nonlinear systems common in nature. Causal inference has the potential to be exceedingly informative in identifying causes behind population fluctuations linked to climate change and planning interventions to reduce the impacts of climate change on community dynamics. I work to expose and ameliorate weaknesses in causal analysis techniques for ecological data and test the performance of such methods in detecting multiple causal influences in dynamic, nonlinear systems. I also apply such methods to well-studied ecological systems using intuitive model frameworks to encourage wider examination, modification and utilization of causal analysis techniques for ecological data.