CHAMPAIGN, Ill. — One of ecology’s greatest challenges is to explain what sustains—or undermines—biodiversity within ecosystems. A new study published in Science introduces a model that uses tree census data and genomic information from multiple species to forecast future shifts in species abundance within forests.

The research was led by James O’Dwyer, plant biology professor at the University of Illinois Urbana-Champaign, alongside Andy Jones of Oregon State University and James Lutz of Utah State University.

Why Predicting Species Abundance Matters

O’Dwyer has long studied the drivers of ecological change, focusing on models that anticipate long-term forest dynamics.

“This work is crucial because changes in abundance or loss of a species from a forest can have cascading effects on other species,” O’Dwyer said. He explained that forests with fewer tree species are more vulnerable to pests and disease, making it vital to identify which species are most at risk.

“Species diversity is lower in forests of the western United States than in other parts of the U.S., but most species have unique roles in the forest,” said Lutz, who has led an annual census of the Wind River Forest Dynamics Plot in southern Washington since 2010. “Losing one species, when there are few to begin with, could result in a less productive forest and potentially one that doesn’t support as many small plants or animals.”

Challenges in Forecasting Forest Change

Predicting how species populations will fluctuate is complex.

“In a forest, there are constantly varying environmental conditions, as well as different tree neighborhoods, with species competing for resources like sunlight and water,” Lutz noted. “Neighboring trees influence each other while living and after death, as snags and wood, all amidst variation in rain and soil conditions.”

Collecting enough long-term data is an intensive process. Global projects such as the Smithsonian Forest Global Earth Observatory (ForestGEO), which compiles decades of data from 78 sites worldwide, have made these analyses possible. The Wind River plot is part of this network.

Building on Previous Modeling Efforts

O’Dwyer’s earlier work laid the foundation for this new model. In 2023, he and graduate student Kenneth Jops developed an approach in Nature that examined whether species could coexist over time based on their life history traits—growth, reproduction, and mortality patterns.

“The upshot of that study is that we identified certain combinations of life histories across plant communities that act to maintain diversity over longer timescales, while other combinations would lead to lower diversity,” O’Dwyer said.

Later research in Panama extended these ideas to multispecies systems, revealing that effective population size is a strong predictor of short-term fluctuations.

Integrating Genomic Data for Better Predictions

For the new study, Jones and colleagues introduced genomic data into the model. They sampled genes from about 100 individuals of eight tree species that make up 90% of the stems and biomass at the Wind River plot.

“Effective population size is a fundamental concept in evolutionary biology, first described almost 100 years ago. Although the true nature of the factors that ultimately determine effective population size is complex, it is perhaps easiest to think of it as the number of individuals that contribute offspring, and therefore their genes, to the next generation,” Jones explained. “The effective population size is typically lower — sometimes much lower — than the number of trees of a species that we can count in a forest. This is because some individual trees leave more offspring than others, which is how populations evolve. When this occurs, we find an increase in nonrandom associations between genes.”

“That balance between random and nonrandom associations in the genome is closely related to effective population size,” O’Dwyer added. “Those life history traits are in the background, shaping that genomic data. I would say the genome is like a hidden recording device of the history of that species in that forest.”

Results of the Model

The team combined genomic insights with tree census data collected in 2011 from all trees larger than 1 centimeter in diameter. The model successfully predicted changes in species abundance for 2016 and 2021.

“The predictions were highly correlated with the observed fluctuations in abundance,” O’Dwyer said. “That’s very exciting.”

“My sense is that the population genomic variation that we’re looking at is an underused resource,” he continued. “It’s carrying a lot of information about the history of that species.”

Looking Ahead

The researchers plan to refine the model further and test its applicability in forests with less comprehensive datasets.

“If we can further distill the relationship between genomic variation, census data and ecological dynamics, this could allow us to build predictive models, with consequences for conservation and management across a broad range of ecosystems,” O’Dwyer said.

The work was supported by the National Science Foundation and the Simons Foundation.

Summary of Key Points

• Researchers developed a new model combining genomic data and tree censuses to forecast species abundance in forests.
• Forest biodiversity is critical for resilience, but western U.S. forests are particularly vulnerable due to lower diversity.
• Genomic data provide insights into effective population size, revealing hidden dynamics of species survival.
• The model accurately predicted forest changes over a decade at the Wind River plot in Washington.
• The approach could inform conservation and ecosystem management across diverse forest systems worldwide.

Original Publication
Authors: James P. O’Dwyer, James A. Lutz, Tyler Schappe, Dana Alegre, Andrew N. Black, Niklaus J. Grünwald and F. Andrew Jones.
Journal: Science
DOI: 10.1126/science.adu6396
Method of Research: Data/statistical analysis
Subject of Research: Not applicable
Article Title: Genomic demography predicts community dynamics in a temperate montane forest
Article Publication Date: 18-Sep-2025
COI Statement: The authors declare no competing interests.

Original Source: https://news.illinois.edu/wp-admin/post.php?post=35601&action=edit