Quantitative Ecologist

I work at the intersection of landscape ecology, biodiversity science, and remote sensing, where I use spatial and trait-based data to understand how ecological systems persist, shift, and recover in the face of disturbance. My work is grounded in the idea that ecological structure—whether in a canopy’s vertical complexity or a beetle’s elytra width—contains clues about ecosystem processes and resilience.

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I’m especially drawn to forest ecosystems as model systems for exploring these ideas. Forests store disturbance legacies in their structure and composition, and by using tools like LiDAR, drone imagery, and satellite time series, I can extract and model those legacies across space and time. I integrate these structural signals with ecological theory, particularly size-abundance scaling and trait diversity frameworks, to ask how ecosystems self-organize and respond to stress.

 

Equally important to me is the role of biodiversity—not just the number of species, but the distribution of traits within a community. Much of my current work focuses on measuring biodiversity more meaningfully using trait data, often in novel ways. This includes developing computer vision pipelines to extract morphological traits from thousands of invertebrate specimens and combining those data with environmental gradients and disturbance history to test hypotheses about community assembly and ecosystem function.

 

Underlying all of this is a strong commitment to open science. I believe that big ecological questions demand transparent, scalable, and reproducible methods. Whether I’m segmenting tree crowns from LiDAR, synthesizing trait data from NEON, or building tools for phenotyping museum collections, I aim to contribute resources that other scientists can use, adapt, and build upon.

 

Ultimately, my research asks how structure and diversity shape the long-term resilience of ecosystems, and how we can use data and theory together to support their future.