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!

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.