To answer foundational and contemporary questions at the intersection of ecology and evolution, my research uses model plant communities to conduct high-throughput and rigorous experiments in the lab, greenhouse, and using natural field communities.

High-throughput lab experimental coevolution

In the lab, I use the (co-)evolution of competing duckweed species and their microbial symbionts to experimentally test theories on how population, community, and evolutionary dynamics unfold over space and time in real-time. The rapid generation time of duckweeds (2-7 days) are highly amenable to experimental evolution, and gnotobiotic techniques allow the experimental control and manipulation of host-associated microbial communities. My collaborators and I are currently integrating our robot-operated, imaging system with machine-learning approaches to tracking plant growth and phenotypes across space and over time.

Large-scale mesocosm experiments

I also conduct large-scale, mesocosm experiments in the greenhouse to conduct common gardens and experimental evolution work involving plant communities at the scale of hundreds of thousands of individuals. I merge these mesocosm experiments with molecular work (population genetic, genomic, and epigenetic approaches) to provide extensive experiments that link molecular mechanisms and processes with ecological dynamics unfolding across multiple scales.

Natural field experiments

Experiments conducted in the lab and greenhouse are most powerful when connected to ecological and evolutionary processes and patterns observed in natural plant communities in the field. I use a variety of approaches (e.g., transplants, exclusions, experimental evolution) in the field to connect the natural history and ecology of various plant systems to large-scale eco-evolutionary processes shaping species distributions and coexistence.

Meta-analyses and scientific replicability

Meta-analyses in ecology and evolution can be a powerful tool for synthesizing overarching temporal (e.g., Usui et al. 2017 J. Anim. Ecol.) and spatial (e.g., Bontrager et al. 2021 Evol.) trends and elucidating their eco-evolutionary predictors. I have also used meta-analyses (and new meta-analytic methods) to explore scientific replicability: In an interdisciplinary collaboration spanning the fields of evolutionary biology (I-DEEL lab, University of New South Wales; Senior Lab, University of Sydney) and biomedical science (CAMARADES team, University of Edinburgh), we explored the idea that, counter to long-held belief, experimental standardization may actually lead to reduced replicability through generating idiosyncratic effects. We argue the importance of embracing variability in effect-sizes, and suggest that experimental methodologies that generate variability in effect sizes should be incorporated in a systematic manner (Usui et al. 2020 PloS Biology). I am exploring this idea further in ecology and evolution, and would love to chat with any potential collaborators!