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 14 

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.

The EFI2024 Conference provided the second 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. Poster or oral presentations were anonymously reviewed by three volunteer reviewers with no conflicts of interest with the presenters. In addition to being recognized for their outstanding work, award winners get to select any item 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!

• Gabrielle Koerich (University of Canterbury) won for her oral presentation, “Modelling bryophyte distributions in Antarctica: unveiling the influence of water availability, sampling bias, and spatially complex dynamics” and
• Nima Farchadi (San Diego State University) won for his poster “Integrating diverse data for robust species distribution models in a dynamic ocean.”

See Gabrielle and Nima’s presentation details below.

Modelling bryophyte distributions in Antarctica: unveiling the influence of water availability, sampling bias, and spatially complex dynamics

Gabrielle Koerich¹ , Hao Ran Lai¹ , Grant Duffy² , Eva B. Nielsen¹ , Jonathan D. Tonkin^1
1University of Canterbury, Christchurch, New Zealand. ²University of Otago, Dunedin, New Zealand

Abstract: In Antarctica, observations of biodiversity are often incomplete and commonly restricted to presence-only data, as sampling in the continent is difficult. Thus, understanding and forecasting the range dynamics of key taxa, such as bryophytes, requires a modelling framework capable of dealing with such challenges. Here, we developed log-Gaussian Cox process models of bryophytes’ across the entire Antarctic continent to (1) assess whether broad-scale bryophytes distributions are driven by water availability, as widely hypothesized; (2) map and forecast their distributions and identify under sampled regions; and (3) determine if there’s a spatial dependency between “patches” of bryophytes, which may be related to their limited dispersal in Antarctica. Results show that the main driver of bryophytes distributions in Antarctica is indeed related to areas where water tends to accumulate (97.5% CI:-0.886;-0.797). Maximum temperature was the second most important predictor (97.5% CI: 0.511; 0.681), signalling the importance of elevated temperatures for bryophytes’ restricted metabolism in this extreme environment. The covariate related to human activity showed a high level of sampling bias, and by accounting for this covariate in predictions, we detected habitat suitability for bryophytes in two under-sampled mountain ranges. Finally, the inclusion of a Gaussian random field to account for spatial autocorrelation increased model performance (pseudo R2 and mapped mean estimated density), indicating a spatial dependency between the presence of mosses. Our study demonstrates that a spatially structured modelling framework can provide robust results and allow for valuable forecasts of biodiversity change in data-poor regions.

Integrating diverse data for robust species distribution models in a dynamic ocean

Nima Farchadi¹ , Camrin Braun² , Martin Arostegui² , Rebecca Lewison^1
1San Diego State University, San Diego, USA. ²Woods Hole Oceanographic Institution, Woods Hole, USA

Abstract: Species distribution models (SDMs) are an important tool for marine conservation and management. Despite the burgeoning use of SDMs, limited guidance is available on how to leverage distinct data types to build robust models. Here we assess whether an integrative model framework improves performance over traditional data pooling or ensemble approaches when synthesizing multiple data types. We trained traditional, correlative SDMs and integrative SDMs (iSDMs) with three distinct data types that represent the distribution of a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the North Atlantic. Weevaluated data pooling and ensembling approaches in a correlative SDM framework and compared performance to a model-based data integration approach designed to explicitly account for data-specific biases while retaining the strengths of each dataset. We found that while each integration approach can result in robust models, there was variation in predictive accuracy among data types, with all models predicting fishery-dependent data more accurately than fishery-independent data. Differences in performance were primarily attributed to each model’s ability to explain the spatiotemporal dynamics of the training data, with iSDMs including spatiotemporal terms to have the most accurate and ecological realistic estimates. Our findings reveal trade-offs in the current techniques for integrating data in SDMs between accurately estimating species distributions, generating ecologically realistic predictions, and practical feasibility. With increasing access to growing and diverse data sources, comparing integration approaches can provide valuable guidance for practitioners navigating diverse data types in SDM development and will help users better understand model biases and estimate error.