August 27, 2026

This third installment of the ongoing “Early Career Forecaster Spotlight” series spotlights Ogonna Eli at Northern Arizona University.

The Early Career Forecaster Spotlight series is run by EFI’s Student and Early Career Association (EFISECA) and highlights early career forecasters to learn about their journey and work within ecological forecasting and to share knowledge and tips for those interested or currently working in the ecological forecasting field. If you would like to nominate an early career forecaster to be interviewed, please do so using this form.

Here is the link to all the posts in the “Early Career Forecaster Spotlight” series.

1. How did you get introduced to ecological forecasting?

Through my research. In my dissertation, I explore the spatial dynamics of mountain pine beetle (MPB) outbreaks and how drought and other environmental conditions impact forest mortality from MPB. In 2025, I attended a workshop hosted by the VectorByte team on vector abundance and time-dependent data analysis at the University of Notre Dame, where I was introduced to EFI. 

2. What are your current research or academic interests and how do they relate to ecological forecasting?

My current research sits at the intersection of environmental data science, remote sensing, and management decision-making. I focus on identifying environmental conditions, such as drought, for example, that make MPB outbreaks likely, and on determining the spatial extent an outbreak reaches once those conditions are present. In short, I develop spatiotemporal ecological forecasting models that identify conditions conducive to MPB outbreaks and forecast the resulting progression of forest mortality.

3. What motivates you in ecological forecasting? If applicable, are there any stakeholders or end-users you work with?

My research is central to my profession and to the scientific career I am building. I am passionate about my research – every aspect of it: from the fieldwork and lab work to the computational side as well. I value continuous learning, so I value the opportunity to regularly attend talks and workshops related to my research. Above all, I am motivated by translating scientific research into practical solutions that can help to understand environmental challenges and contribute to management decisions, particularly in forestry: how we protect forests from drought and MPB, and how we expect forests to behave under a given set of conditions.

4. What are the key lessons you have learned in your ecological forecasting work?

In my work, I have encountered errors that could have been caught during the data exploration stage but were missed. So the key lesson I carry with me is to be careful during the data exploration stage and not to confuse it with hypothesis generation. Careful data exploration saves me a ton of errors later in the process.

5. What is the biggest challenge you have faced in ecological forecasting and how did you overcome it?

The biggest challenge I had when I started working with ecological data was with data exploration. I overcame that by being intentional with addressing the challenge. I took related coursework and relied on the guidance published in: Alain F. Zuur, Elena N. Ieno, and Chris S. Elphick, “A protocol for data exploration to avoid common statistical problems” (https://doi.org/10.1111/j.2041-210X.2009.00001.x). The paper includes a protocol for data exploration in R, and now each time I start a new analysis, I first go through the steps. The paper also included guidelines on addressing problems encountered during the data exploration process.

6.  What do you want to share with folks interested in becoming involved with ecological forecasting (e.g., resources, advice, etc.)?

Graduate-level coursework provided me with a solid background. I also worked with “Data Analysis and Visualisation in R for Ecologists” materials. It is a good resource for getting started with R in general. I also found the book “Practical Time Series Forecasting With R – A Hands-on Guide” by Galit Shmueli and Julia Polak helpful. 

7. Optional fun question! Do you have a special or favorite place from your research, travels, or education? 

Definitely the Arizona Sky Islands! They are truly remarkable ecological laboratories, where extraordinary mosaics of ecosystems and biodiversity are shaped by steep gradients in elevation and microclimate across isolated mountain peaks rising from the Arizona desert. When I went to the Sky Island mountains in the summer of 2025, I started in the desert where the daytime temperature may reach 120° Fahrenheit, and I ended up on a mountaintop where the temperature is perhaps 75° Fahrenheit, and you feel comfortable wearing a jacket. I was amazed by their rich diversity of ecosystems within a 1-hour drive. So it’s home to many diverse and unusual plants.

June 26, 2026

In this second installment of the ongoing “Early Career Forecaster Spotlight” series, the spotlight is on Rachel Torres, who just started a new position as a Data Instruction and Outreach Librarian at the University of California, Berkeley.

This Early Career Forecaster Spotlight series is run by EFI’s Student and Early Career Association (EFISECA) and highlights early career forecasters to learn about their journey and work within ecological forecasting and to share knowledge and tips for those interested or currently working in the ecological forecasting field. If you would like to nominate an early career forecaster to be interviewed, please do so using this form.

Here is the link to the “Early Career Forecaster Spotlight” series, which will be populated with this and future posts.

1. How did you get introduced to ecological forecasting?

I was introduced to ecological forecasting through joining a project as a postdoc aimed at supporting education in environmental data science for Native American and other underrepresented students. Ecological forecasting is one application within the larger field of environmental data science that allows students to learn about and get involved in the research process. I have really enjoyed learning about all the different areas of ecological forecasting through meetings with the Education and DEI EFI working groups!

2. What are your current research or academic interests and how do they relate to ecological forecasting?

I am interested in making data science more accessible and inclusive for students who have been historically underrepresented. Ecological forecasting is a pathway to do this because it brings place-based and relatable environmental data to students, and involves all steps within the data science process from downloading, wrangling and tidying, to visualizing. I have worked with undergraduate student projects where they have selected NEON sites and forecasted daily to annual phenology patterns, terrestrial carbon fluxes, and aquatic nutrients. 

3. What motivates you in ecological forecasting? If applicable, are there any stakeholders or end-users you work with?

As an educator, I am motivated by the students I work with! I have found that guiding students through the research process and allowing them to explore their own interests and ask their own questions has motivated me to learn more about areas that I wouldn’t necessarily consider on my own. 

4. What are the key lessons you have learned in your ecological forecasting work?

Working with students who are new to coding and data science can take patience for everyone involved, but bringing it back to the “why” of the data – “why does this matter? Why ecological forecasting?” – can often motivate students and lead to interesting discussions and questions! 

5. What is the biggest challenge you have faced in ecological forecasting and how did you overcome it?

For undergraduate students who are new to data science, the biggest challenge is usually going from the raw downloaded data to a data set they can actually work with, analyze, and create plots. I think it’s important to involve students in this step, but I learned over time that it is a lot quicker for me to help. I make sure to share code and the steps taken so that students are still learning about the process. 

6.  What do you want to share with folks interested in becoming involved with ecological forecasting (e.g., resources, advice, etc.)?

If you are looking for resources to teach beginners to forecasting or coding, EFI Educational Resources is a great place to start, also check out the NEON QUBES hub for resources that include NEON sites. There are many resources available online, but also don’t be afraid to reach out to people to ask questions!

I will also highlight that if you are mentoring a student in research, mentorship is a skill to be developed just like any other data science skill. A good place to start for mentorship resources is the Center for the Improvement of Mentored Experiences in Research: https://cimerproject.org/

7. Optional fun question! Do you have a special or favorite place from your research, travels, or education? 

I did my graduate research in Santa Barbara, California, where I modeled terrestrial carbon and water fluxes of urban trees under different climate scenarios. This was not quite forecasting, but I spent a lot of time thinking about parameter uncertainty for vegetation in land surface models and how that relates to real life. Now every time I see a Eucalyptus, Coast Live Oak, or California Sycamore, I am reminded of model uncertainty! 

Me (left) and undergraduate student researcher Crystal (right), presenting her poster on ecological forecasting of spring phenology in the Great Smoky Mountains National Park.

May 18, 2026

In this first installment of an ongoing “Early Career Forecaster Spotlight” series, we put the spotlight on Abigail Lewis, who is currently a postdoctoral fellow with the Smithsonian Environmental Research Center.

This Early Career Forecaster Spotlight series is run by EFI’s Student and Early Career Association (EFISECA) and highlights early career forecasters to learn about their journey and work within ecological forecasting and to share knowledge and tips for those interested or currently working in the ecological forecasting field. If you would like to nominate an early career forecaster to be interviewed, please do so using this form.

Here is the link to the “Early Career Forecaster Spotlight” series, which will be populated with this and future posts.

1. How did you get introduced to ecological forecasting?

I did my Ph.D. with Cayelan Carey at Virginia Tech and was introduced to ecological forecasting through her lab. Cayelan co-taught a graduate seminar on ecological forecasting with Quinn Thomas in the spring of 2020 (what a time) that was a really helpful introduction to the field. 

My first involvement in EFI was during the all-hands meeting in 2020. I remember volunteering to take notes for my breakout group and then immediately regretting it because I didn’t know how to spell the forecasting acronyms and jargon people were throwing around. But after that meeting I kept looking around and wondering what would be needed to forecast the things around me. Could I predict where an ant would move next? Could I predict what birds I would hear on my walk? Are some things inherently unpredictable? Eventually, I decided I needed help answering these questions, so I joined the EFI Theory Working Group, and I have been closely involved with that group ever since.

2. What are your current research or academic interests and how do they relate to ecological forecasting?

My research is at the intersection of aquatic science and ecological forecasting. Currently, I am a postdoctoral Climate Change Fellow at the Smithsonian Environmental Research Center (SERC), where I am working to develop forecasts of methane emission from coastal wetlands. 

One question that has been especially interesting to me recently is whether global change may alter the predictability of ecological dynamics. Researchers at SERC have been running two intensive whole-ecosystem experiments in the marsh, where they experimentally raise temperatures up to 6 ºC above ambient. Using data from these experiments, we are seeing that methane emissions are not only higher but also more variable and less predictable under warmer conditions. 

Next up, I will be starting a faculty position in the Odum School of Ecology at the University of Georgia in August 2027. In that position, I plan to continue building a research program focused on understanding and forecasting the effects of global change on aquatic ecosystem function.

 

3. What motivates you in ecological forecasting? If applicable, are there any stakeholders or end-users you work with?

One of the main end users I am currently working with is the California Air Resources Board (CARB). CARB is building a comprehensive greenhouse gas budget across the state, and I am helping to set up the capacity for process-based forecasts of methane emission from coastal wetlands as part of that effort. This project involves collaboration with several other EFI members, including the pecan team and Jim Holmquist, one of my supervisors at SERC. 

4. What are the key lessons you have learned in your ecological forecasting work?

One key lesson has been that ecological forecasting shifts the research process in subtle ways that are beneficial even outside of a forecasting context. To be able to generate near-term forecasts at SERC, I needed to set up workflows for real-time data processing. Now, using those workflows, we are able to visualize the data coming in every day and immediately assess if there are any maintenance needs or if something surprising is happening that we might want to monitor with additional sampling. By looking at the data and seeing when forecasts are more or less accurate, we have started to find new and interesting patterns that help us better understand the fundamental ecosystem ecology happening out in the marsh. 

5. What is the biggest challenge you have faced in ecological forecasting and how did you overcome it?

There are lots of incremental challenges in ecological forecasting. Developing real-time workflows for driver and observation data can be difficult, and keeping those data pipelines working requires attention. I have found that I need to budget a little bit of time every day to monitor and maintain data pipelines and visualizations, especially as these workflows are getting used by a growing community of researchers across SERC. Throughout all of this, I have been fortunate to work on top of lots of pre-existing cyberinfrastructure from the EFI-NEON forecasting challenge, Virginia Reservoirs LTREB, and the Technology in Ecology lab at SERC. 

Another challenge is the learning curve of vocabulary and methods in ecological forecasting. Both EFISECA and the EFI Education working group have been helpful for me—these groups do a lot of work to help compile resources and provide support for new forecasters. 

6.  What do you want to share with folks interested in becoming involved with ecological forecasting (e.g., resources, advice, etc.)?

Do it! 

I have really enjoyed being involved with EFI working groups. They are always open to new members—all you need to do is hop on the monthly zoom call for whatever group interests you. EFISECA is an especially great home for early career forecasters, and early career forecasters are also encouraged to join the other thematic working groups. 

EFI members have created and compiled many resources for getting started with forecasting, and any summary I try to do here would be woefully incomplete. The EFI website is a great place to start exploring those resources.

When all else fails, the best advice is usually to reach out to Jody Peters 🙂 Jody is unbelievably great about connecting folks with resources, researchers, working groups, etc. related to ecological forecasting. 

7. Optional fun question! Do you have a special or favorite place from your research, travels, or education? 

Most recently, I just got back from a short backpacking trip on Assateague Island National Seashore. It was beautiful! Lots of cool birds; I woke up to ~20 egrets, a family of green-winged teal, and a greater yellowlegs hanging out in the wetland by our campsite (see photo).