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As an ecologist in the Anthropocene, I feel a responsibility to conduct research that informs our influence on nature. Thus, I am broadly interested in how processes such as climate change, land-cover change, and human activities influence individuals (behavior and morphology), populations (abundance and distribution), and communities (composition and interactions). Examples of research along these themes include my PhD research on behavioral responses to extreme weather and my master’s research on landscape patterns and bird distributions.



The natural world is dynamic. Individual organisms are flames that exploit resources variably through time, and they interact with each other—and the similarly dynamic abiotic environment—in complex ways to create the world we see. Despite this, recent ecological research has  prioritized understanding spatial aspects of ecology over temporal ones—so much so that I often see job ads for “spatial ecologists”, whereas I have never seen a “temporal ecologist” position. I am keen to consider time as a fundamental driver of ecological processes and have recent papers on spatiotemporal dynamics of species’ cooccurrence and circadian activity timing.  



I am a reluctant statistician; math was never my favorite (or even third-favorite) subject. However, I have come to appreciate that ecology is a quantitative discipline and believe there is a need for individuals trained in both ecology and data science to make sense of the data revolution in ecology. In particular, I am keenly interested in data integration, or the process of combining seemingly disparate datasets into a cohesive whole within statistical models. Given my troubled history with math, I prioritize clear and compassionate communication of modeling and take the view that a model is meaningless unless it can be understood and applied by practitioners like graduate students and agency biologists.

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