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.
- 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!
- 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.
- 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.
- 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!
- 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.
- 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/
- 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.