Do you enjoy social media and creating content? Would you like to help EFI expand its reach, strengthen its online presence, and connect with a broader audience? The EFI Steering Committee is looking for volunteers for a new EFI Communications Committee.
The goal of the EFI Communications Committee is to advance EFI’s strategic outreach, community engagement, and scientific visibility by amplifying community achievements, translating ecological forecasting research into accessible stories and updates, and fostering multi-directional communication across working groups, regional chapters, allied societies, and the public.
In launching a Communications Committee, the EFI Steering Committee is explicitly seeking to build a creative team whose collective talents and interests span multiple modes of communication, including but not limited to written, graphical or visual, audio, and video production.
For more details, see the draft Charge for the Communications Committee here. Communications Committee members will have an opportunity to provide input before the Steering Committee ratifies the Charge.
Emailinfo@ecoforecast.org if you are interested in participating or leading the EFI Communications Committee.
It seems like this might be a question for a forecaster. But is it?
Where I live, on the shores of the northern North Atlantic, fishing for lobsters is a multi-generational pastime that supports the livelihoods and cultures of hundreds of coastal villages. Despite the small scale of individual operations, the fishery, taken in total, has often been the most valuable in North America. Over the many generations, there have been ups and downs, and with so many uncertainties looming, it’s natural to ask about the future.
Here’s where you might think it’s a good time to pick up the yellow pages and dial your local forecaster. At least, this was my line of thinking when I started doing ocean forecasting in the early aughts. There’s lots of interesting data and some practical questions about the future… it’s just what ecological forecasting is meant for.
Yet in the world of lobstering, forecasts are typically met with dismissal and even derision. For example, following a forecast in 2000, one reporter quoted a lobsterman saying, “Lobstermen have concrete numbers that they see out there and the scientists have mathematical numbers on a computer that are soft like Jell-O.“
Oof.
Just like lobstering, the practice of making scientific forecasts for the lobster fishery is also a multi-generational pastime. The mathematics of fisheries forecasting was set out more than a century ago, and the scientific perspective of using it as an iterative hypothesis-testing loop is probably just as old. The fisheries scientist Arthur Simpson put it this way in the 1950s: “predicting what is going to happen and checking it against the actual events is to the fishery biologist one form of the universal research tool of formulating a hypothesis and putting it to the test.” In this tradition, scientists have been making lobster forecasts for generations.
Yet year after year, decade after decade, lobster forecasts have met an unforgiving audience. Quotes like the Jell-O one are not just amusing; they’re common. If you want to entertain yourself with a decades-long history of this type of response to forecasts, stay tuned. Meanwhile, my question is: why? What are we getting wrong?
To figure out the disconnect, I found myself collaborating with (among others) the historical ecologist Dr. Loren McClenachan, who was helping to run the Oceans Past Initiative (OPI). Beginning around 2005, OPI aims to “uncover the history of marine ecosystems and human-ocean relationships”. Loren, myself, and a few other collaborators took a deep dive into the history of lobster forecasting. Using a news corpus (see Figure 1 for an example), policy documents, landings and climate data, and a review of scientific forecasting methods, we tried to understand the tension between forecasts and their intended audience.
If you want to find out what we discovered, and of course read more anti-forecast quotes, you can (shameless plug), read the paper here.
Figure 1: A short 1987 newspaper article from the historical corpus analyzed.
The reason I’m sharing this is not about lobster forecasting in particular, though. It has to do with the importance of understanding the historical context of the forecasts we’re focusing on. Loren looped me into OPI, and I attended their annual meeting. It was an amazing week. I found myself adventuring down histories of clam gardens, diaries, parasites, and whale communication. I now find myself collecting Victorian era cookbooks (à la Figure 2) and poring over them with friends on Saturday nights because the recipes tell me which fish were abundant in centuries past. I also made countless connections in my mind between the ocean’s past and its future.
One thing I try to say often is that forecasting isn’t exactly a scientific or mathematical exercise. It’s a social act. One dimension of that social act is its historical context. No matter how fancy the next generation of algorithms gets, forecasters will not be helpful if we miss this dimension.
Figure 2: Old cookbooks containing hidden information on past fisheries.
I recommend to any aspiring forecaster: try to get to an OPI meeting at some point. Or, if your area of ecological forecasting has nothing to do with the ocean, find a similar meeting with opportunities to connect with historians in your realm.
When it comes to knowing the ocean’s future, a historian is just as valuable as a forecaster. Or, as the old saying goes, those who predict the future without understanding the past are doomed to be humorously mocked by lobstermen.
Record NR, Evanilla J, Goode AG, Groner M, McClenachan L. 2026. Forecasting in hindsight for the American lobster fishery. FACETS. 11:1-0. https://doi.org/10.1139/facets-2025-0113
Simpson AC. 1956. The pelagic phase. Ibid. 207-250.
Authors: Rachel Torres (University of California, Berkeley) and Antoinette Abeyta (University of New Mexico, Gallup) developed and led the workshop; Jody Peters (University of Notre Dame) provided editorial assistance
Our workshop, “Slow Data: Building intentionality and accessibility to environmental data science and forecasting” came together in Toronto to start a discussion about environmental data science education and share examples of slow data science. These activities were developed to build foundational data science skills through hand drawn data visualizations in journal prompts. We are following up with this blog post to share examples and resources for anyone interested, whether or not you were able to attend the workshop.
Slow data refers to activities that engage learners in slowing down, observing, and visualizing, within the context of environmental science and ecological forecasting. The idea of developing slow data material for the classroom setting was inspired by our experiences working with different student populations who had one thing in common – a lack of reliable internet access. Looking to engage learners with data literacy offline, activities were developed based on the work of Georgia Lupi and Stefanie Posavec, who created the Dear Data project to share hand-written postcards with data about their personal lives. The materials we developed were geared toward rethinking activities related to environmental sciences, ecology, and geologic data.
The workshop began with a brief introduction to the concept of ‘slow data.’ The main hands-on activity was for everyone to create Data Portraits that were turned into buttons (Figure 1). The portraits were a visual representation of seven questions about participants’ work, involvement with EFI, and travel to the conference. It was a fun way for participants to think about their own experience and participation in EFI, and it also led to a discussion about how something like this could be used to engage students in the classroom.
Figure 1. Workshop participants created buttons of their ‘Data Portraits’, physical representations of data about us. The colors and symbols represent responses to questions such as “How far did you travel to be here?” and “Are you in an EFI working group or chapter?”
Antoinette also shared examples of ‘Data Journals’ created by students in her Introduction to Environmental Science class. One example shared below on phenology visualizations (Figure 2; top) has the potential to scale up to help students think about working with big data or in conjunction with ecological forecasts. In this activity, the goal was for students to interpret time series of GCC (green chromatic coordinates) captured by phenocams, to describe and compare seasonal patterns. This presented a challenge for students new to ecological data as understanding how to connect images to values of GCC and interpreting the chart was not intuitive. To overcome this challenge, students selected snapshots in time for each month of a given year (Figure 2; top) and filled in a relative amount of greenness, and then interpreted patterns and related them back to the time series plot (Figure 2; bottom).
Figure 2. Student GCC visualization of monthly snapshots (top) and GCC timeseries for the same location screenshotted from the PhenoCam website (bottom).
Along with the workshop, we included a Color-a-Pixel activity (Figure 3) as part of the poster session and throughout the week, inviting all conference attendees to participate in creating a slow data raster image based on satellite imagery. Put together, the pixels at our workshop, all colored in, will reveal a raster image of the Great Lakes.
This was our first time trying this activity, and we learned a few things for future reference:
Print larger, and if possible, as one poster-sized sheet of paper, so that the numbers are easier to see. We understand the pixels were quite small!
Label the crayons with the number on the pixel. Although there was a legend that connected the number to specific crayon colors (with creative Crayola names like ‘Tropical Rain Forest’, ‘Asparagus’, and ‘Timberwolf’), it would save some time to be able to identify the number on the crayon directly.
Set up and announce the activity early in the conference and promote it multiple times throughout the conference to encourage people to take time for a coloring break
We hope these activities sparked interesting discussions and provided opportunities for brainstorming for your own teaching and research.
Figure 3. Process of Color-a-pixel activity in the poster session of the conference.
Here is a list of resources and references that inspired our workshop:
Raffaghelli, J. E., Ferrarelli, M., & Rodríguez, N. L. (2025). Slowness as postdigital positionality in the era of generative AI: A conversation. Postdigital Science and Education, 7(4), 1224-1249. https://doi.org/10.1007/s42438-025-00554-z
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.
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.
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.
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.
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.
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.
What do you want to share with folks interested in becoming involved with ecological forecasting (e.g., resources, advice, etc.)?
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.
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 Peña1, Walt Jubber2, Sonja Huber1, Sanne Evers1, Maria Paniw1
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.
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.
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.
