My research focuses on quantitative ecology. This means using advanced mathematical and statistical tools to investigate relationships between living organisms and their environment. Most of the research in the Green Quantitative Ecology lab (QuantEco) is focused on fish and wildlife conservation. The two main themes are:
- Population risk assessment: what biological traits influence the vulnerability of populations to threats like pollution, climate change, and habitat loss? How does uncertainty about basic life history parameters (e.g., survival rates or reproductive rates) "scale up" to population-level effects?
- Measuring human impacts: how do human activities such as agriculture, urbanization, and pollution affect fish and wildlife populations? Can the effects of multiple influences be separated, statistically, using data from large-scale monitoring programs?
Human impacts on Georgia stream communities
Stream organisms respond to natural and human influences at multiple spatial and temporal scales. The abundance and diversity of stream biota are important indicators of overall watershed and stream health. Understanding how different factors influence these organisms across spatial and temporal scales is crucial for conservation of aquatic communities and ecosystems, as well as promoting water quality. Using data from a state-wide stream biomonitoring program, in combination with broad-scale spatial datasets, we are using sophisticated statistical approaches and machine-learning techniques to investigate how natural and human drivers within Georgia watersheds affect the communities that live in the state’s streams.
Population consequences of individual toxic exposure
Many aquatic species are at risk from toxic pollutants released by human activities. Accurate assessment of these risks is necessary to make scientifically sound decisions about the production, regulation, and safe use of chemicals. This project builds on modeling frameworks developed at two biological levels of organization: toxic effects to individuals using the General Unified Threshold of Survival (GUTS) framework, and population modeling using population projection matrices. Integrating these frameworks will allow us to investigate how individual-level effects "scale up" to populations.
The long-term goal of this project is to develop generalized life history-based population dynamics models that can be parameterized for many species, integrate them with toxicokinetic-toxicodynamic models such as GUTS, and use these integrated models to estimate population responses to realistic toxic exposures. By modeling multiple species, and relating the population responses to species-specific life history traits, we hope to identify biological characteristics that predict vulnerability to toxic exposure. The results of this investigation will allow us to predict vulnerability in species for which detailed life history data are not available, and to identify species that might be at risk.
Spatial ecology of small mammals in Indiana
Small mammals such as mice, rats, and voles are an important component of terrestrial ecosystems. As consumers of seeds and vegetation they exert enormous influence over plant communities; they are also an important prey item for numerous predators. Understanding how small mammals interact with plant communities allows for better understanding and management of ecosystems in a restoration context. This project will use hierarchical Bayesian approaches to population estimation and occupancy modeling to investigate associations between small mammal assemblages and habitat characteristics at three bottomland hardwoods restoration sites in northeastern Indiana.
Student involvement in research
My success as a researcher and educator depends on the contributions of students of all levels—including undergraduates—in my research program. This means not only guiding students through the research process in the lab and field, but also keeping them involved through reporting and publication. Training undergraduates in field methods and data analysis has been vital to my research productivity. The quantitative and analytical skills students gain in my lab are essential in the modern job market; the quantitative and programming skills help prepare students for graduate work and for careers in academia, government, non-governmental organizations (NGOs), and industry. If you are interested in joining the QuantEco lab please contact Dr. Green by e-mail to discuss current opportunities.
The Green Quantitative Ecology lab (QuantEco) is currently recruiting for one or two MSIB students (masters) to start in Fall 2021. Students will take on one of the projects above or work with Dr. Green to develop their own project that fits within the QuantEco lab themes. Students interested in small mammals or other terrestrial vertebrates are especially encouraged to apply. Some background in statistics is essential. Experience with programming in R or another language is helpful, but not necessary.