Collaborative research: ultra-data: the metabolic regimes of lake ecosystems

The National Science Foundation Division of Environmental Biology awarded Grand Valley State University $485,620 on August 1, 2026, to synthesize lake ecosystem metabolism data and build infrastructure for global lake research under the Biological Sciences program (CFDA 47.074).

The funded work assembles the Global Lake Metabolism Repository (GLAMR), a public data repository integrating daily metabolism estimates and associated metadata from at least 300 globally distributed lakes, spanning 1,500 lake-years and 400,000 lake-days. The project synthesizes existing data from multiple studies, including long-term observations from at least six LTER sites and seven NEON sites, to characterize metabolic regimes—the annual patterns of ecosystem metabolism (primary production and respiration balance)—across lake ecosystems worldwide. The research team will analyze these data using machine learning, time series clustering, spatial statistics, and other tools to test a conceptual model of lake metabolic regimes, evaluating drivers, seasonal decoupling, temperature effects, and spatial clustering of metabolism patterns. The work supports workforce development in STEM with cross-training in biology and data science.

Place of performance is Muskegon, Michigan. The period of performance runs from August 1, 2026, through July 31, 2030.

Abstract

A primary function of ecosystems is to convert solar energy into organic matter (primary production) that fuels the maintenance (RESPIRATION) and growth of organisms up the food web. Ecosystem metabolism is the balance of primary production and RESPIRATION at the ecosystem-scale. For terrestrial ecosystems, the metabolic regime (annual pattern of ecosystem metabolism) is largely predictable by climate and biogeography. In lakes, however, ecosystem metabolism is highly dynamic because the physical structure of lake ecosystems (STRATIFICATION) and other driving variables are highly variable on sub-daily to seasonal timescales. This project seeks to characterize the metabolic regimes of lake ecosystems and to explain how and why they vary through time and around the world. By synthesizing data and comparing across multiple model formulations, the research team will assess drivers, seasonal decoupling, temperature effects, and spatial clustering of metabolic regimes. This project will also build transformative infrastructure for lake research and education, catalyze broad use of that infrastructure, and help to develop a globally competitive stem workforce with cross-training in biology and data science. This data synthesis project will assemble the first large-scale public repository of lake ecosystem metabolism data ? The global lake metabolism repository (GLAMR) ? Which will include daily metabolism estimates and associated data and metadata from at least 300 globally distributed lakes, 1,500 lake-years, and 400,000 lake-days. These data will be analyzed to evaluate a conceptual model of lake metabolic regimes. Hypotheses derived from the conceptual model will be tested using machine learning, time series clustering, spatial statistics, and other tools, by combining and synthesizing data from many studies (including long-term data from at least six lter sites and seven neon sites in four neon domains), the project aims to build a rich and robust understanding of the metabolic regimes of lake ecosystems, and new and durable global capacity for lake ecosystem research. 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.- subawards are not planned for this award.