- Podcast and press release about a recent article (Data-intensive ecological research is catalyzed by open science and team science. BioScience) by Kendra Cheruvelil and Patricia Soranno.
- *** Post-doctoral Position Available ****
The members of the Data Intensive Landscape Limnology Lab at Michigan State University study inland lakes and their landscapes by the thousands. The lab, created in 2016, is co-directed by Drs. Patricia Soranno and Kendra Spence Cheruvelil, Professors in the Fisheries and Wildlife Department.
Our lab includes a highly collaborative group of researchers working together to conduct research in landscape limnology and macrosystems ecology, with special focus on developing the critical underlying principles of these important subdisciplines of ecology, and applying those principles to the management and conservation of freshwater resources.
Landscape limnology is the spatially-explicit study of lakes, streams, and wetlands as they interact with freshwater, terrestrial, and human landscapes to determine the effects of pattern on ecosystem processes across temporal and spatial scales (Soranno et al. 2010).
Macrosystems ecology is the study of ecological phenomena (biological, geophysical, human) at regional to continental scales (Heffernan et al. 2014; Fei et al. 2016; Rose et al. 2017).
We conduct research at multiple spatial and temporal scales, including the macroscale, as well as a variety of lake physical, chemical, and biological characteristics. To conduct this research, we have had to incorporate approaches not only from data-intensive science, but also open science and team science. To do so, it has been essential for us to collaborate with other scholars from other sub-disciplines and disciplines including: stream and wetland scientists to study the integrated freshwater landscape; computer scientists and statisticians to develop and test novel methods to study ecosystems at these macroscales; and, philosophers and historians of science and psychologists to study the cultures and practices of data-intensive science.