A little blurb about me
I tend to see myself as a quantitative ecologist (about 70%) and a statistical ecologist (about 30%).
As Ruth King mentioned in her talk at the BioSS Annual Meeting 2021, “Quantitative Ecology” is a more ecology-driven thought process that focuses on applying advanced mathematical and statistical methods to ecology.
In contrast, “Statistical Ecology” focuses more on developing new methodologies for analyzing ecological data than on applying them.
When I think of Quantitative Ecology, I always think of two distinct sub-fields: theoretical (mathematical) and statistical applications.
Fortunately, I was exposed to both worlds through two wonderful PhD advisors, Allison Shaw, a theoretical ecologist, and John Fieberg, a statistical ecologist, who showed me what it means to be a quantitative ecologist.
During my PhD, I learned how different these two perspectives are, and how each helps me develop and apply methods in movement ecology.
In theoretical ecology, you build your own world with mathematical formulas to ask under what conditions we would see the ecological phenomena we observe in nature.
With Allison, I got to play with ordinary differential equations to understand how partial migration emerges in parasite-migratory host systems.
We also allowed for different host defense mechanisms, each with associated trade-offs: here, we incorporated the reproductive costs of becoming tolerant and/or migratory to reduce the costs of infection (Kim & Shaw. 2021; Journal of Animal Ecology).
Allison, the lab members, and friends (myself included) also put together a guideline of how each ecologist has their own persona for building worlds with mathematical formulas, showing that developing a theoretical model is not as complex or scary as it seems (Shaw et al. 2025; Proc B).
In statistical ecology, you develop and apply statistical methods to understand associations between factors that may influence ecological processes.
With John, I got to learn habitat selection models (Step Selection Functions (SSFs), in particular!) and how to parameterize familiarity (or memory) by including spatiotemporal covariates or cognitive maps within the SSF framework (Kim et al. 2024; Movement Ecology).
I also worked on applying different formulations of hidden Markov models to movement data to infer disease progression in free-roaming animals, in collaboration with Théo Michelot and Smithsonian researchers, Jared Stabach and Katherine Mertes (Kim et al. 2026; Journal of Applied Ecology).
Besides quantitative ecology, which parts of movement ecology am I interested in?
I would say identifying the role of different internal states in animal movement, such as disease, memory, and energetics (paper coming soon!).
This interest came from conversations with one of my postdoc advisors, Paul Moorcroft, during my first postdoc.
Interestingly, Paul, Rhemi (a graduate student in the lab), and I are all interested in the effects of particular internal states that drive animal movement: Paul is into memory, I am into disease, and Rhemi is into energetics.
This made me realize that, after all, I am interested in developing and applying methods in movement ecology to understand how different internal states influence animal movement.
So, going back to what I wanted to say... I love solving problems that require a quantitative solution. I love understanding the effects of internal states that drive animal movement.
I love this quote from W. Edwards Deming, which my PhD advisor John put on his lab website: “In God we trust; all others must bring data.”
Okay, I’ve talked too much! So what are my research interests? See below.
