My research aims to build quantitative, predictive understanding of ecological communities. It falls into three broad categories: mathematical theory, field-based natural history, and predictive models of communities. I try to integrate these fields in my work, with the goal of developing tools that are quantitatively and logically rigorous, but are sufficiently informed by biology to remain realistic, and simple enough to be parameterized for real-world systems.
Ecological communities include a lot of moving parts, with the direction and magnitude of interactions between individual species or species and environments varying greatly across space and time. This makes it difficult to analyze them using traditional tools. My interest in theoretical ecology is driven by a need for mathematical, statistical, and algorithmic tools that can be used to analyze and understand such complex systems. I also spend a lot of time working with and redesigning existing tools that I find especially useful or interesting. I am currently working on a number of projects that fall into this category:
1. Testing for causal interactions in complex systems:
To build predictive models, it's important to first have a good understanding of how components of a system influence on another. In 2012, the Sugihara lab at the Scripts Institute at University of California, San Diego, published a numerical method for testing for causal relationships in complex systems where experimental data is lacking. In collaboration with them, I have built an R package multispatialCCM that extends their method to work for short time series that are replicated spatially (i.e. plot replication). This is useful for many ecological systems, where plot-based replication is more common than long time series, and experiments can be difficult to perform. Our results are published in a 2015 paper in the journal Ecology: "Spatial ’convergent cross mapping’ to detect causal relationships from short time-series".
2. Analyzing the consequences of spatial interactions:
In many ecological systems, community dynamics are strongly driven by three major spatial processes: dispersal limitation, negative density dependence, and environmental filtering. However, these processes can interact at many different spatial scales, making it difficult to determine the relative influences of each. Working with Matteo Detto, Helene Muller-Landau, and other researchers at STRI, we published a set of tools for analyzing spatial patterns that help separate the relative influences of these processes at different spatial scales. Our results are available in a 2018 paper in the Journal of Ecology: "Functional traits of tropical trees and lianas explain spatial structure across multiple scales".
Field-based natural history
An enormous advantage of natural science is that the real world provides a definitively "correct" set of outcomes against which to test the predictions of the models that we dream up. Experiments and field observations help inspire simple abstractions of complex mechanisms, and help me make sure that the theory I work on remains relevant to ecological problems. In the past, the majority of my taxonomic and field experience has been with ant communities in North America and the Caribbean. Now, I am still involved in several field projects:
1. Prairie plant community change in old field succession:
Succession is one of the first major ecological question that sough to explain how communities of species, rather than individual species, influenced one another an their environments. Old field succession is particularly relevant today, as it describes the recovery of natural communities following disturbances such as farming or intensive grazing. Working at the Cedar Creek LTER (predominantly in experiment 14 and experiment 54), I am expanding measurements from a "chronoseries" characterizing a century of succession. These include various plant community and physical properties, including new measurements of changes in soil moisture and seed dispersal characteristics.
2. Ant community responses to plant diversity and experimental warming:
While I was an undergraduate, I worked as a technician on an experiment testing ant community responses to global warming at the Harvest Forest. At Cedar Creek, I've added a similar component to the BAC biodiversity and warming experiment at Cedar Creek. This is the only existing experiment that tests insect responses to both changes in plant biodiversity and warming (that I know of - if I'm wrong, please email me!), and includes one of the first checklists for ants in central Minnesota.
Predictive models of communities
For me, the most exciting piece of ecological research is to discover simple mechanisms that explain why the world works the way it does. The ultimate test of a theoretical model, therefore, is whether it can make quantitative predictions that match empirical observations in the real world. While this doesn't necessarily mean that the model is "right", it is at least a good indication that the model can be a useful predictive tool. Based on my dissertation work in prairie succession, I am developing two predictive models of community change:
1. Community-wide tests of resource competition:
In order to test theoretical mechanisms that influence community dynamics, we first need simple, tractable models that are able to make accurate predictions of community dynamics based on simple, widely reproducible field measurements. Recently, working with David Tilman and Clarence Lehman, we developed a resource competition model that uses measurements in experimental monocultures to predict the relative abundance of prairie plants in multi-species plots in the Big Biodiversity experiment at Cedar Creek. The model is thus meant both as an empirical test of resource competition theories, as well as a predictive model that can be used in ecosystem engineering. The results are published in a 2018 issue of Ecology Letters: "Identifying Mechanisms that Structure Ecological Communities by Snapping Model Parameters to Empirically-Observed Tradeoffs".
2. Synthesis of multiple predictive models of community composition and coexistence (sMultiMod)
What are the major mechanisms determining coexistence and community composition in ecological communities, and how do these differ across systems and sites? Though many mechanisms are hypothesized to explain coexistence in ecological communities, there have been comparatively few tests to determine which mechanisms matter the most within any given site or system. In my current position, in collaboration with Stan Harpole and Helmut Hillebrand at iDiv, we are working to synthesize understanding from observational studies and experiments in grasslands and aquatic systems to quantify the relative importance of potential coexistence mechanisms. We focus on these systems because of the wide availability of existing theory and data. For more details, see here.
We are working to address the following questions: (1) What are the primary mechanisms that are currently hypothesized to be important for determining community composition in grassland and aquatic systems?; (2) How much support is there for each of these hypothesized mechanisms across several case studies from well-studied experimental systems?; (3) How can we develop predictive models that identify when and why mechanisms’ influences change across systems and sites?
To address these questions, we are working to: (1) Produce novel reviews of coexistence mechanisms spanning multiple systems and sites; (2) Determine the importance of major mechanisms within specific systems and sites; (3) Synthesize results to develop novel predictive models.
Page last updated: 23 Feb. 2018