Research in the lab is largely driven by theory and curiosity. That is, theory makes a prediction about how a natural system might operate, which leads to experimentation or empirical tests using observational data. Different systems are more suited for testing different theory. As a result, we do not deal with one specific system, but work with laboratory microcosms, observational data, and manipulative experimentation in natural populations. We are always open to a new system if it allows a demonstration or test of ecological theory.
Temporal variability, synchrony, and extinction are fundamental population processes that are of interest to the lab. Further, spatial and environmental processes that determine the distribution of species and their abundance are also a central focus. As such, we pull from population theory, niche theory, coexistence theory, and the mathematical theory of stochastic population processes to understand the relative roles of the environment, geography, and the existing community on population processes.
But species do not exist only as populations, but are subject to interactions with many other species. Considering competitive interactions within the community of species, one thing my research has focused on is the relative importance of species interactions on determining where a species can be within their climatic niche tolerances, as well as the corresponding geographic area. Detecting and quantifying species interactions has been a long-standing interest of mine, as has predicting community composition of both free-living communities and parasite communities.
The lab is involved with the LIFEPLAN initiative (https://www.helsinki.fi/en/projects/lifeplan), a distributed observational program to quantify biodiversity around the globe. This will putatively generate both estimates of current biodiversity, inform models of biodiversity change, and produce openly available community data with which to test existing ecological theory.
A variety of work in the lab is focused on epidemiology and disease ecology. For example, we have examined epidemic progression on social networks, and the incorporation of age, stage, and social structure into epidemic modeling approaches. Recently, Bret Elderd and Tad Dallas received an NSF rapid grant to examine epidemiological complexity on parameter estimation and forecasting of infectious disease, with clear links to the COVID-19 pandemic. Host-parasite networks have also been a large part of research in the lab, examining their structure, predictability, and scaling relationships over space and time.
Macroecology is an area where there are a lot of conceptual hypotheses based on empirical data about how things appear to work. An extension of much of the theory from population and community ecology, macroecological laws have become an interest of mine. Gauging support for/against existing ideas, developing simulation models to test some core macroecological ideas, and quantifying the predictability of macroecological relationships are three general themes of my work in this realm.
I build tools for accessing and analyzing ecological data. I also maintain an open lab notebook, and have been interviewed by Georgia public radio about Open Access publishing (John Drake and I are around the 15 minute mark). A majority of the code and data used in my publications is freely available and hosted on Figshare