Biodiversity conservation has lagged behind climate research in international urgency, but rising awareness of its critical role in planetary health may be closing that gap.
Jay M. Schoen is a spatial and wildlife ecologist who teaches in Columbia’s Ecology, Evolution and Environmental Biology (E3B) department and the newly launched M.S. in Biodiversity Data Analytics program at Columbia University School of Professional Studies. Drawn to ecology through a love of nature and working with animals, he now works to protect biodiversity and the natural environment through data analysis.
“A lot of colleagues and I have been frustrated that biodiversity has taken a backseat,” Schoen said. “Because the truth is that a changing climate is going to have effects on biodiversity, and if you’re losing biodiversity, that’s going to affect the climate.”
Schoen’s expertise is in geospatial data analysis, remote sensing, and wildlife ecology research. After building a repertoire in data analysis and modeling through his research on carnivore movement ecology—including a study of the movement patterns of jaguars to help model long-range movement events such as dispersal, in which juvenile cats find and establish their own territory—Schoen branched out to fire ecology. He currently works as a geospatial data scientist for the Western Fire & Forest Resilience Collaborative at the Cary Institute of Ecosystem Studies, where he integrates geospatial data into applicable products for fire managers and stakeholders working to address the wildfire crisis in the western U.S.
Read our interview with Schoen about the importance of data analytics skills in biodiversity conservation and how he is working to protect the planet.
What brought you to the field of ecology?
My path has been nontraditional. I took lots of little winding paths to get to where I am now. I grew up in Southern California and went to undergrad there. While studying biology and zoology at U.C. Santa Barbara I started volunteering and eventually working in zoos in Santa Barbara and San Diego. I took a position at the Bronx Zoo in 2012, and that's what brought me out here to New York originally.
All that to say, I always loved working with animals and cared deeply about nature. In 2017, I decided to look into graduate schools, and that brought me to the Columbia E3B master's program in Ecology, Evolution, and Conservation Biology. When I came into the E3B program, I had aspirations of doing fieldwork, and I had some interest in carnivore ecology. With support from Panthera, a leading NGO for wild cat research, local partners and I started a project putting out camera traps in the Mbaracyú Forest Nature Reserve in eastern Paraguay. I also began to get acquainted with programming and coding—things that I hadn’t realized were such a big part of ecology.
Could you introduce your area of expertise, geospatial data analysis? How did you get involved with it and what does your work entail?
Geospatial means you’re looking at something on the earth, spatially. Any data that come from biodiversity are spatial in nature because there’s a coordinate associated with them—we collected the data from this particular point on the map.
I started my Ph.D. in 2019, and when COVID happened in 2020, I had to pivot in a big way to using available datasets and developing novel questions and analyses necessary to earn a Ph.D. That’s when I really jumped into the hardcore data analysis.
I studied carnivore movement ecology. In particular, I wanted to understand how some big cats move from point A to point B. How do they move through complicated areas: agricultural, urban, things like that? To do that kind of analysis, you need to collect (or use available) data—points on a map—and relate them to environmental layers that can be put on the map to build a model. You take basics like elevation, average temperature, and average rainfall and add layers like “distance to the nearest water source,” “distance to the nearest city,” “how far from a forest edge,” and things like that. And you relate those data layers to the points and try to draw conclusions about how the animal is moving through that environment that can help you then predict how it might move through other environments.
How else is biodiversity data analysis applied practically?
My full-time job is centered on fire ecology and studying the impacts of fire on ecosystems in the western U.S. It’s a bit of a pivot because I’m not working in movement ecology but I’m still working with big data layers that are often satellite-derived. We have many goals but right now one major one is to try to understand and map where, if a fire were to burn, it would burn in a way that would benefit the ecosystem. We know that forest fires have been increasingly catastrophic due to factors including climate change and historical fire suppression, but we also know that fire is a natural part of the ecosystem. So how do we incorporate that natural part to return to the more normal fire regimes that will help save people and biodiversity in the long term? The Western Fire and Forest Resilience Collaborative (WFFRC) is a great team of really intelligent and dedicated people working toward this critical goal.
What opportunities might students in the program have in the field?
There’s so much demand in the field right now for people who can do hardcore data analysis. I can confirm myself, having been on the job market after my Ph.D., that one of the most hireable attributes is the ability to do complex data analysis. And in this case, with biodiversity, we all are concerned about the crisis that's going on right now. So if we can learn things specific to managing these datasets on biodiversity, which come in an awfully confusing variety of formats, types, and ways to process them, it’s a unique niche. Plus, because it’s a rather complex niche, the skills translate well to broader data science.
I also think we’re at the precipice of new possibilities in funding for biodiversity conservation. A popular area right now is biodiversity credits (following in the footsteps of the carbon market). The carbon credit system was rolled out and generated a ton of revenue to help programs that are ideally increasing carbon storage, combating the negative effects of climate change, and so on. There were and are a lot of issues with that system, but it is a creative way of working within current economic systems to generate revenue for nature. Doing so for biodiversity specifically comes with incredible challenges (e.g., measuring biodiversity is much more complex and murky than measuring carbon). I’m not sure anyone knows exactly the best route to a successful and equitable system. By learning from the pitfalls of previous efforts with carbon and understanding the strengths and shortfalls of biodiversity data, we may be able to do better if biodiversity markets really take off. We need to be really careful, and we need the best people with the best skills. We need to be able to equip people to provide the right kinds of analyses and data products to make the best decisions possible.
What do you hope students will go on to accomplish?
Climate and biodiversity are so intertwined. A lot of colleagues and I have been frustrated that biodiversity has taken a backseat in the public eye and even the funding pipeline, because the truth is that a changing climate is going to have effects on biodiversity, and if you’re losing biodiversity, that’s going to affect the climate.
We are trying to make sure that people see them as complex and interwoven. I think if we can do that, if a program like this can bring in people and equip them with the tools to then go out and make this message known, we can really increase awareness. Looking at my own newsfeed, I feel like I’ve seen more about the biodiversity crisis and its relationship to climate change. And so it gives me a little bit of hope. I hope that this program will equip more people to multiply the effects of that.
About the Program
The Master of Science in Biodiversity Data Analytics program at Columbia University equips a new generation of leaders with the data literacy, analytical tools, and interdisciplinary expertise to design evidence-based solutions that benefit both people and nature.
Designed for both working professionals and early-career changemakers, this online program allows students to learn from anywhere while gaining the skills to collect, analyze, and translate biodiversity data into meaningful action. Coursework prepares students to take on nature data applications across industries, from ESG finance to urban planning to environmental consulting, and culminates in a hands-on capstone with industry partners that provides practical experience, valuable networks, and the tools to make an immediate impact.
The priority application deadline for the M.S. in Biodiversity Data Analytics program is February 15, with a final deadline of June 1. Learn more about the program here.
