August 24, 2026

The EFI2026 Conference provided the fourth opportunity for EFI to give out the EFI Futures Outstanding Student Presentation Award and each year the number of student presenters continues to increase!

This award is given to promote, recognize, and reward an outstanding student presenter and provides valuable feedback to student presenters on their research and presentation skills.  Awards were given to students who gave both Posters and Oral Presentations. Each poster or oral presentation were anonymously reviewed by two to three volunteer reviewers with no conflicts of interest with the presenters. In addition to being recognized for their outstanding work, award winners received an item of their choice from the EFI shop. We thank all the students who presented and the volunteers who reviewed the presentations!

Congratulations to this year’s Outstanding Presentation Award recipients!

Oral Presentation Award Winner:
Guillermo Gómez Peña (Doñana Biological Station (EBD-CSIC))
“Forecasting dryland resilience via resource-driven dung beetle facilitation”

Poster Presentation Award Winner:
Hannah O’Grady (University of Notre Dame)
“What can long-term data show us about uncertainties in forest models?”

See Guillermo and Hannah’s abstract below.

“Forecasting dryland resilience via resource-driven dung beetle facilitation“

Guillermo Gómez Peña_¹, Walt Jubber², Sonja Huber_¹, Sanne Evers_¹, Maria Paniw_¹

_¹Doñana Biological Station (EBD-CSIC), ²University of the Witwatersrand

Dryland ecosystems are highly vulnerable to global warming and increasingly prone to desertification, yet their resilience often hinges on complex biotic interactions. At the center of these interactions are dung beetles, acting as potential ecosystem engineers. Local experiments have demonstrated their powerful ability to buffer warming impacts—significantly reducing warming-induced shrub growth loss and mortality, while also reversing density-dependent plant competition. Crucially, this plant-facilitation mechanism is strictly dependent on the presence of ungulates, which provide the essential resource (dung) that fuels beetle populations. Forecasting how this multi-trophic cascade (ungulate-beetle-shrub) will perform under higher risks of extreme weather events remains a critical challenge for landscape management. Here, we present a predictive framework that integrates this complex ecological cascade with Bayesian modeling to forecast ecosystem resilience in two dryland systems. To power these forecasts, we synthesize a multi-layered dataset integrating biotic interactions alongside key abiotic drivers: spatial distribution and abundance monitoring of ungulates (resource availability), constant-effort pitfall trapping of dung beetles (population dynamics monitoring), and field experiments of shrub growth under dung-beetle activity. Crucially, our experiments show that dung beetles completely reverse density-dependent growth suppression, shifting a 9.6% competitive penalty into a 3.1% net facilitation, and strongly buffer plant mortality, sustaining high survival rates between 88.5% and 91.8%. By integrating this robust empirical data into our Bayesian models, we can effectively scale individual-level processes to landscape-level dynamics.

“What can long-term data show us about uncertainties in forest models?”

Hannah O’Grady, Jason McLachlan

University of Notre Dame, Notre Dame, IN, USA

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 40 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.

June 2, 2025

The EFI2025 Conference provided the third opportunity for EFI to give out the EFI Futures Outstanding Student Presentation Award. This award is given to promote, recognize, and reward an outstanding student presenter and provides valuable feedback to student presenters on their research and presentation skills.  Awards were given to students who gave both Posters and Oral Presentations. Each poster or oral presentation were anonymously reviewed by two to three volunteer reviewers with no conflicts of interest with the presenters. In addition to being recognized for their outstanding work, award winners received an item of their choice from the EFI shop. We thank all the students who presented and the volunteers who reviewed the presentations!

Congratulations to this year’s Outstanding Presentation Award recipients!

Oral Presentation Award Winner:
Charlotte Malmborg (Boston University)
“Towards Forecasting Recovery After Disturbance: A Case Study and Potential Directions for Forest Management”

Poster Presentation Award Winner:
Parul Vijay Patil’s (Virginia Tech)
“Gaussian Process Forecasting of Tick Population Dynamics”

See Charlotte and Parul’s abstracts below.

Towards Forecasting Recovery After Disturbance: A Case Study and Potential Directions for Forest Management 

Charlotte Malmborg¹, Michael Dietze¹, Audrey Barker-Plotkin² 

¹Boston University, Boston, Massachusetts, USA. ²Harvard Forest, Petersham, Massachusetts, USA  

Disturbance events are ubiquitous in all ecosystems, playing key roles in nutrient cycling, maintaining biodiversity, and driving community composition over long timescales. As a result, disturbance events receive ample attention from ecologists across sectors, from research to management and policy. While many efforts focus on how disturbance events arise and predicting impacts disturbances will have, there is decidedly less attention on forecasting the recovery process following disturbance, including predicting how ecosystems reorganize, whether an ecosystem’s state will reset or change, and which recovery trajectory will be established. In forests, where trees are the dominant community members, the reorganization phase just after disturbance can influence composition and function for centuries and thus has an outsized impact on recovery outcomes. Being able to forecast which recovery trajectory a system will experience, and which factors determine recovery rates, is vital for understanding how ecosystems will respond to more frequent and more severe disturbance events occurring under more variable climate regimes. In this presentation I will discuss how disparate recovery trajectories arise following an invasive pest outbreak, focusing on the differences between sites experiencing tree mortality and sites where trees re-leaf following defoliation. I will show results from a proof-of-concept model that predicts recovery rates as a response to disturbance magnitudes, environmental conditions, and mortality, and introduce the concept of “assisted recovery”, a way that forecasting recovery trajectories can intersect with forest management. 

Gaussian Process Forecasting of Tick Population Dynamics 

Parul Vijay Patil, Leah R. Johnson, Robert B. Gramacy 

Virginia Tech, Blacksburg, USA 

The Ecological Forecasting Initiative Research Coordination Network (EFI-RCN) is currently hosting a NEON Forecasting Challenge in which the aim is to forecast ecological patterns across five themes. Here we focus on forecasting within the Tick Population theme. The goal of this theme is to be able to forecast the abundance of Amblyomma americanum, more commonly known as the lone-star tick. This tick is native to eastern parts of the United States and is a vector of several diseases. Since incidence of tick-borne diseases is assumed to be correlated 42 to tick populations, it is important to predict tick abundance to develop strategies to control and prevent the spread of these diseases. We sought to build a forecasting model that is able to predict abundance and that can handle the often sparse, and irregularly sampled observations. Although tick populations are known to vary with temperature and relative humidity, it is challenging to predict weather accurately, which would be necessary to build weather-driven abundance models. Instead, we use a flexible, nonparametric Gaussian Process (GP) model which attempts to learn seasonal patterns of tick abundance across different sites over the past decade. We are able to use the fitted GP to make forecasts into the future with uncertainty quantification. We benchmark our GP forecasts out-of-sample against simple linear time series models that include temperature and other seasonal covariates and observe that the GP outperforms the linear models overcoming issues such as sparse training data which are unequally spaced in time.

The ESA Statistical Ecology section presented the 2024 Ecological Forecasting Outstanding Publication Award to Cerres Barros and co-authors, Yong Luo, Alex Chubaty, Ian Eddy, Tatiane Micheletti, Céline Boisvenue, David Andison, Steven Cumming, & Eliot McIntire, for their 2023 Methods in Ecology and Evolution paper:

“Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation.”

The award committee was strongly impressed by the way this paper clearly organized and presented cyberinfrastructure principles that are an increasingly important part of doing ecology well. This paper helps to translate software engineering concepts into clear take home messages that are accessible to ecologists (e.g., predictions depend on workflows, not just models) and draws attention to the need for Interdisciplinary teamwork in successful ecological forecasting. The committee was further impressed by this team’s commitment to developing tools that are accessible, reusable, and multi-purpose, and that address the challenges of operationalizing ecological forecasts.

In addition to an award plaque and prize ($200), this year’s recipient will also receive a £120 credit from Routledge books.

Nominate Papers for the 2025 Award

Individuals wishing to nominate papers published in the past  3 years for the 2025 award are encouraged to do so by the March 1, 2025 deadline. Additional information can be found at https://www.esa.org/stats/awards/ecological-forecasting-outstanding-publication-award/

Full List of Award Winners & Citations

2024 – Cerres Barros (University of British Columbia)
Barros, C., Luo, Y., Chubaty, A. M., Eddy, I. M. S., Micheletti, T., Boisvenue, C., Andison, D. W., Cumming, S. G., & McIntire, E. J. B. (2023). Empowering ecological modellers with a PERFICT workflow: Seamlessly linking data, parameterisation, prediction, validation and visualisation. Methods in Ecology and Evolution, 14, 173–188. https://doi.org/10.1111/2041-210X.14034

2023 – Jacob Zwart (USGS)
Zwart, J.A., Oliver, S.K., Watkins, W.D., Sadler, J.M., Appling, A.P., Corson-Dosch, H.R., Jia, X., Kumar, V., and Read, J.S. 2023. “Near-Term Forecasts of Stream Temperature Using Deep Learning and Data Assimilation in Support of Management Decisions.” JAWRA Journal of the American Water Resources Association 59 (2): 317–37. https://doi.org/10.1111/1752-1688.13093.

2022 – Kelly Heilman (University of Arizona)
Heilman, K.A., Dietze, M.C., Arizpe, A.A., Aragon, J., Gray, A., Shaw, J.D., Finley, A.O., Klesse, S., DeRose, R.J., & Evans, M.E.K. (2022). Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty. Global Change Biology 28(7):2442-2460 doi.org/10.1111/gcb.16038

2021 – Sarah Saunders (National Audubon Society)
Saunders, S.P., F.J. Cuthbert, and E.F. Zipkin. “Evaluating Population Viability and Efficacy of Conservation Management Using Integrated Population Models.” Journal of Applied Ecology 55, no. 3 (2018): 1380–92. https://doi.org/10.1111/1365-2664.13080.

2020 –  Paige Howell (USGS)
Howell, P.E., B.R. Hossack, E. Muths, B.H. Sigafus, A. Chenevert‐Steffler, and R.B. Chandler. “A Statistical Forecasting Approach to Metapopulation Viability Analysis.” Ecological Applications 30, no. 2 (2020): e02038. https://doi.org/10.1002/eap.2038.

2019 – Maria Paniw (CREAF, Ecological and Forestry Applications Research Centre)
Paniw, M., N. Maag, G. Cozzi, T. Clutton-Brock, and A. Ozgul. “Life History Responses of Meerkats to Seasonal Changes in Extreme Environments.” Science 363, no. 6427 (February 8, 2019): 631–35. https://doi.org/10.1126/science.aau5905.

2018 – Quinn Thomas (Virginia Tech)
Thomas, R.Q., E.B. Brooks, A.L. Jersild, E.J. Ward, R.H. Wynne, T.J. Albaugh, H. Dinon-Aldridge, et al. “Leveraging 35 Years of Pinus Taeda Research in the Southeastern US to Constrain Forest Carbon Cycle Predictions: Regional Data Assimilation Using Ecosystem Experiments.” Biogeosciences 14, no. 14 (2017): 3525–47. https://doi.org/10.5194/bg-14-3525-2017.