We live in a rapidly changing environment, yet scientists’ understanding of the ecological consequences of wholesale changes in climate and land use is in its infancy. So too is the incorporation of this knowledge into environmental management and policy, which is so critical because both climate and land use strongly affect ecosystems and the services that they provide to society. The main goal of this research is to develop tools to measure and understand how climate and land use by themselves and as interacting factors affect lake ecosystems across scales of time and space (cross-scale interactions), even as these factors are themselves, changing. A cross-scale interaction occurs when a factor at one scale, such as agricultural land use around a lake, interacts with a factor at another scale, such as the climate of the region the lake is located within. Such interactions can lead to situations where lakes in different climatic zones respond differently to agricultural land use in their watersheds, all else being equal.Without an understanding of such interactions, it is challenging to develop and apply models that are effective in different regions. Unfortunately, to date, very few cross-scale interactions have been measured so that they can be incorporated into models relevant to ecosystems and policy. This project will identify and measure the most important cross-scale interactions that control lake nutrients and water quality.The research will be guided by a landscape limnology conceptual framework. Although the study focuses on lake nutrients, the models, tools, and knowledge will be useable to study cross-scale interactions in other important ecosystems. This collaborative team from three universities will collect an unprecedented dataset on lakes, nutrients, and watersheds, including over 5,000 lake ecosystems in 11 U.S. states spanning up to 30 years. Several new and innovative statistical modeling approaches will be used to tackle these important problems. For example, Bayesian hierarchical modeling (a robust statistical method for learning and modeling complex relationships in data) will be used to detect and model cross-scale interactions and to communicate these complex dynamics to other researchers and policy-makers.
Managing ecosystems as society intensifies changes in them requires new approaches, models, information, and skill sets. Identifying the conditions or the environments prone to cross-scale interactions is needed to forecast, manage, and repair damages from environmental change at local to regional scales. This project will help to develop these much-needed strategies and will change the way people view and conduct research on large-scale, living systems because of the project’s geographical scope, its foundation in a reliable conceptual framework, and its use of innovative statistical and numerical methods. The results will provide insight into these important ecosystems and problems beyond the lakes under study. This collaborative project across three institutions will also train a new generation of biologists who will know how to tackle broad-scaled research and policy problems. In addition, because the researchers will use commonly-measured lake water quality variables that are used to set water resources policy, results will directly inform the state and federal agencies responsible for lake and water management. Inclusion of several undergraduate, graduate, and post-doctoral researchers, along with state and federal agency partners will ensure the project’s success and improve the world’s capacity to manage our changing ecosystems.
PI: Patricia A. Soranno, MSU
Co-PIs: Kendra Spence Cheruvelil (MSU), Emily H. Stanley (UW), John A. Downing (ISU), Pang-Ning Tan (MSU), Noah R. Lottig (UW)
Senior Personnel: Tyler Wagner (PSU), Craig A. Stow (NOAA-GLERL), Katherine E. Webster (Queens Univ.), Mary T. Bremigan (MSU)