NSF-SNSF: Biodiversity-inspired lake modeling linking processes from cells to ecosystems

Phytoplankton are a broad group of microscopic organisms that use light and nutrients to grow in open water and form the base of the food web in lakes and other aquatic ecosystems. The amount and composition (quantity and quality) of phytoplankton in a lake determines the food resources for fish and other human-harvested organisms as well as the clarity or “greenness” of water. These emergent properties directly impact lake ecosystem function, and the economic and other benefits people derive from lakes. The fundamental variables that determine phytoplankton growth in lakes (e.g., temperature, light, and the availability of fertilizing nutrients) are all changing. Understanding how these concurrent changes affect lake ecosystems requires integrated modeling of the impacts on phytoplankton physiology, biodiversity, and function. This project will use laboratory experiments and field observations to generate newly parameterized lake models grounded in mechanistic understanding of phytoplankton growth strategies. The scientific knowledge, data and open-source computational tools generated by this project will launch the use of biodiversity-inspired lake models to explore the responses of planktonic diversity to rapid and multivariate change. Through art exhibits and a pilot citizen science activity, the project aims to improve public understanding and appreciation for the unseen, microscopic diversity of lakes. Lake ecosystem models currently contain equations detailing how a limited number of phytoplankton species (typically two or three representing different phytoplankton functional groups) respond to gradients in temperature and resources, but such oversimplified models cannot predict how the biodiversity or the composition of these communities will respond to multiple environmental changes. The central goal of this international collaborative proposal is to upgrade mechanistic lake models to incorporate greater planktonic biodiversity in order to predict community composition responses to concurrent changes in temperature and resource availability and improve predictions of emergent ecosystem properties. The project aims to empirically explore how population growth parameters and stoichiometry are related to cellular physiology, metabolism, gene expression and macromolecular allocation. The team will update widely used, open source lake models and use sensitivity analyses to determine which parameters need to be constrained with empirical data to generate accurate predictions of phytoplankton diversity and key ecosystem functions. Updated models will be assessed with isolated strains of the same phytoplankton species from multiple Swiss lakes. The project will train graduate students and post-doctoral scholars and support better understanding of importance of local trait adaptation to accurate lake model predictions of phytoplankton composition and abundance. This collaborative US-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.