Collaborative research: improved understanding of the effects of natural variability on tree growth across the united states

The National Science Foundation Division of Environmental Biology awarded the University of Montana $349,289 on September 1, 2026, under the Biological Sciences program (CFDA 47.074) to improve understanding of how natural variability in temperature and water availability affects tree growth across the United States.

The project develops mechanistic predictors of tree growth responses to meteorological fluctuations on daily-to-seasonal timescales, scaling findings across forest types to continental U.S. scope. Research uses highly-sensitive automated point dendrometers installed at multiple sites across the National Ecological Observatory Network (NEON) to track above- and below-ground tree growth dynamics at hourly-to-daily resolution, enabling fine-scale understanding of how individual trees respond to temperature and moisture changes. The project generates daily-to-seasonal growth datasets accessible to the research community and integrates education and career development through hands-on learning experiences for undergraduate and graduate students, K–12 teachers, and students. Results support forest management decision-making to maintain forest health, sustain rural economies, and ensure long-term resource sustainability.

Performance occurs in Missoula, Montana, with a period of performance from September 1, 2026, through August 31, 2031. The award is classified as a Project Grant under NSF's basic research portfolio.

Abstract

Forest productivity is extremely sensitive to changes in naturally variable conditions such as water availability and temperature. Yet we still lack reliable ways to predict how tree growth will respond to varying meteorological conditions, especially on timescales less than a year. This project develops a better understanding of how variability in environmental conditions affects tree growth to improve dynamic quantification of forest productivity in the united states. These new insights and improved tools help decision makers form practical, science based decisions that keep forests healthy, support rural economies, and ensure long term resource sustainability. The study supports education and career development by giving undergraduate students, graduate students, and k?12 teachers and students hands on learning experiences that explore how tree growth at daily to seasonal scales is shaped by weather variability. The daily to seasonal (i.e., intra-annual) above and below-ground tree growth data generated by this project are also accessible to those who want a clearer understanding of how forests grow and change over time. The project aims to identify the mechanistic predictors of tree growth on very fine temporal scales, while simultaneously scaling across forest types to the spatial scale of the continental U.S. Through a robust methodology. The project builds on preliminary work showing how fine-scale (daily to monthly) growth responses of individual trees respond to temperature and moisture fluctuations. Highly-sensitive, automated point dendrometers are installed at many sites across the U.S. Using the national ecological observatory network (neon). These dendrometers track both above- and below-ground tree growth dynamics at time scales of hours to days, enabling mechanistic understanding of short term growth changes. The study also uses satellite remote sensing data to further test how observations from both optical and thermal remote sensing are linked with the intra-annual dynamics of tree growth. The project builds a model framework through testing both statistical methods and an established artificial intelligence (AI) approach. This project advances NSF?s priorities in artificial intelligence and biotechnology. 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.