Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due to Functional Trait Uncertainty

The Science

This study simulates vegetation recruitment, growth, competition, and mortality at an Alaskan tundra site under historical and future climates using a dynamic vegetation model. Researchers found multiple plant strategies can lead to similar composition and biomass as seen in the field. However, these strategies produce different trajectories under future climate, with uncertainties twice as large as climate-induced changes. The uncertainties are due to unknown cold tolerance of each plant type, recruitment rate, and how big and tall the canopy can grow at the same stem size. Better quantification of these traits will likely improve model predictions.

The Impact

Rapid warming in the Arctic is expected to change the types of plants that grow there and their ability to store carbon, but predicting these changes accurately is still difficult. This research highlights the importance and uncertainty of vegetation demographic dynamics. Vegetation demographic’s interaction with climate change ultimately shapes Arctic vegetation change. Models will likely better predict such change by considering vegetation demography and incorporating more measurements of critical traits. These findings will contribute to future studies that integrate models and data at large scales, as well as efforts to compare different models focused on Arctic ecosystems.

Summary

This study uses a dynamic vegetation model ELM-FATES (E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator) to explore how plant traits affect Arctic vegetation biomass, composition, and productivity in response to climate change. The model reproduces the observed biomass and composition of the three plant functional types (PFTs) co-existing in the tundra study site.

Researchers identified key traits—such as photosynthetic capacity, carbon allocation, allometry, and phenology—that significantly influence model estimates under historical climate conditions. Notably, various trait configurations can yield similar biomass and composition results. These observations provide a baseline for understanding Arctic vegetation dynamics.

The model predicts that, on average, biomass and net primary productivity will increase with warming and increased carbon dioxide levels. However, different trait configurations lead to varying future projections, with trait-related uncertainties being twice as large as the change caused by climate change. The uncertainty arises from different emerging PFT compositions under novel climate regimes, primarily explained by traits controlling cold-induced mortality, recruitment, and allometry.

To improve predictions of Arctic ecosystem composition and productivity, better estimates of these key traits are essential. Better predictions will also benefit from improved model representations and observations of plant-nutrient interaction, plant dieback mechanisms, and acclimation of Arctic ecosystems.

Principal Investigator

Colleen Iverson
Oak Ridge National Laboratory
[email protected]

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
[email protected]

Funding

This material is based upon work supported by the Next-Generation Ecosystem Experiments Arctic project, which is supported by the Biological and Environmental Research program in the U.S. Department of Energy’s Office of Science.

References