McLachlan Lab

Congratulations to graduate student, Hannah O’Grady, who won the “EFI Futures Outstanding Student Presentation Award” for her poster presentation on “What can long-term data show us about uncertainties in forest models?” at the EFI2026 Conference in Toronto in August!

Title: What can long-term data show us about uncertainties in forest models?

Authors: Hannah O’Grady¹, Jason McLachlan¹, Michael Dietze², Yinghao Sun^2
1University of Notre Dame, ²Boston University

Abstract: One of the most important priorities for improving forecasts of forests is understanding what processes dominate uncertainty in forecasts. One way to incorporate data and process uncertainty into predictions is to iteratively test forest models against data from real forest stands. This entails generating an ensemble of model runs across parameter values and input data and iteratively stopping those model runs, checking how well the ensemble of model runs agrees with observed data, adjusting the ensemble to be consistent with observed data, and restarting the ensemble. Each of these steps presents a non-trivial technical and ecological challenge that is tailored to the specific model that is being run and the site that is being modeled. For instance, generating a reasonable ensemble of model runs for data assimilation to act on requires detailed parameterization of all present species as well as the disturbance history of the site and updating the model state requires understanding how each state variable is updated and interconnected in the model’s internal logic. We have set up a data assimilation framework to run the forest model LPJ-Guess with observed above ground biomass from Harvard Forest in Massachusetts, USA. We found that generating a reasonable ensemble requires incorporating a detailed understanding of the disturbance history of a site and successful adjustments to above ground biomass in the system require adjusting not only the biomass of individuals but also the density of individuals within each cohort.

Keywords: Data Assimilation, Forest Modelling

Jason, Jody, and Hannah attended the EFI2026 Conference in Toronto on August 4-7.

Jason led the working group on “Data Visualization and Storytelling,” which helped participants learn about leveraging recent advances in data visualization technologies and design methodologies, including using comics to share research and teach data science.

Hannah won the 2026 “EFI Futures Outstanding Student Presentation Award” for her poster on “What can long-term data show us about uncertainties in forest models?”

We also were able to connect with Alfred P. Sloan Grant collaborators, Rachel Torres (University of California, Berkeley), Antoinette Abeyta (University of New Mexico, Gallup), and meet for the first time, collaborator, Rebekah Fields (Salish Kootenai College), while enjoying a great meal at Jolly Ethiopian Bar.

Congratulations to undergraduate student, Finley Shinnick, who was selected to be an oral presenter and received an award for best presentation for the 2026 Colleges of Science and Engineering Joint Annual Meeting (COSE-JAM). Finley presented her work with Hannah and Jason on “Improving Model Reconstructions of Vegetation Signal.”

Abstract: Information on how ecosystems responded to climate events in the past informs predictions of how present ecosystems may react to future climate changes. Pollen grains preserved in lake sediment at measurable depths capture this historical vegetation response across thousands of years. The Spatio-Temporal Empirical Prediction from Pollen in Sediments (STEPPS) model integrates hundreds of these pollen records using a Bayesian statistical framework to reconstruct vegetation across large regions with time steps of centuries. STEPPS is useful at capturing long-term ecosystem boundary shifts in response to climate events, including in the Upper Midwest, but predicting future vegetation change requires knowing how short-term events, like droughts and wild fires, contribute to long-term trends. In this project, we begin updating the STEPPS model framework to reconstruct local vegetation at decadal intervals using finer resolution pollen data. We first corrected the STEPPS coding framework to be compatible with modern software and capable of handling a flexible number of lake sites. As an initial step toward reconstructing vegetation using our finer-resolved lake sites, we ran STEPPS within single-grid cells. We found that vegetation trends using reduced, finer resolution lakes remain consistent following STEPPS reconstruction. This suggests that modeling finer temporal resolution using fewer, better sampled lakes captures the same vegetation process modeled by the full STEPPS model. This is a promising first step towards incorporating the finer-resolution vegetation process into the larger STEPPS model and improving our understanding of natural disturbance’s contributions to long-term ecosystem change. 

October 31, 2025

Two of McLachlan lab undergraduate researchers presented posters at this year’s University of Notre Dame Fall Undergraduate Research Fair, on October 30, 2025!

Finley Shinnick presented a poster on how different pollen sampling designs impact how clearly a signal of vegetation change can be seen in that pollen.

Lily Church presented a poster about indigenous fire stewardship in the midwestern US.

Congratulations to both students and we look forward to more presentations in the future!

ESA’s Announcement of the Award Winners

George Mercer Award

Megan L. Vahsen, Michael J. Blum, J. Patrick Megonigal, Scott J. Emrich, James R. Holmquist, Brady Stiller, Kathe E. O. Todd-Brown and Jason S. McLachlan

ESA presents the 2024 George Mercer Award to the authors of “Rapid plant trait evolution can alter coastal wetland resilience to sea level rise,” published in Science Jan. 26, 2023.

The George Mercer Award was established in 1948 and is given annually for an outstanding ecological research paper published within the past two years with an early-career lead author.

This study calls attention to the significant, yet often overlooked, role of rapid evolution in shaping how ecosystems respond to global change. Examining a dominant coastal marsh sedge, the research reveals how genetically-based variation in a plant’s traits—specifically traits affecting root growth—can evolve rapidly and influence a marsh’s resilience to sea level rise.

The research team used a unique approach, growing “resurrected” plants from decades-old seeds recovered from marsh soils and employing an ecosystem modeling approach to demonstrate that genetic variation observed among the plants and rapid evolution can affect the ecosystem’s ability to store carbon and build soil elevation. In turn, these functions directly impact the wetland’s capacity to keep pace with rising sea levels, a key factor in its long-term persistence.

Bridging quantitative genetics and ecosystem modeling, this study highlights the need to consider evolutionary processes in ecological forecasting. The paper’s innovative use of historical seed samples, genetic methods and simulation models advance our understanding of the interplay between evolutionary and ecosystem-level responses to anthropogenic change.