“Merely useful” tools in forest science

A few years ago I enrolled in a workshop from an instructor that spent part of his career in academia. He was an outlier in his department, spending his time focused on developing methods and tools in computer programming. Most of the faculty he worked with were "deep thinkers", spending their time on the theory behind different aspects of computer science.

In short, he was an applied scientist; his colleagues were basic scientists. (For fans of The Big Bang Theory, he was Howard Wolowitz and they were Sheldon Cooper.)?

My workshop instructor told a story of how his work in developing new tools was criticized for being just "merely useful". How was he contributing to the academic literature by designing simple tools?

The underrated scientists are those that develop and deliver new tools that many of us can apply to our everyday work. As a former academic myself, I can attest that some of the most underrated forestry faculty in land grant universities are ones that work in Extension. Extension professionals can distill a lot of research and make it practical to implement, such as creating landowner guides for forest management and diagnostic tools to assess forest health problems. They are some of the most well-known individuals in their state and are the first ones the news media calls on to answer questions about a recent insect outbreak, the impacts of a wildfire, or how the foliage is shaping up to look this fall.

The forestry profession has seen a number of recent advances of “merely useful” tools in the last decade. Remote sensing technologies such as satellites and lidar have provided new ways to quantify the structure and composition of forests. Growth and yield simulation tools have integrated new research and use innovative visualizations to forecast what forests will look like under a different climate and varying forest management regimes in the future.

For those that work with data and code, there are plenty of languages and software to choose from to perform your everyday work. Open-source packages that I use regularly (e.g., rFIA) were developed by colleagues in forest science that have improved the efficiency and productivity of hundreds of students, researchers, and analysts in the forestry community. The widespread use of paired programming tools that use AI (e.g., Github Copilot) can speed up analyses even further and provide an analyst new ways to tackle common problems she is faced with.?????????????

No doubt there is an essential role for deep thinkers in science. We need to tackle large research questions, design long-term experiments, and examine their impacts. Equally important are the scientists that can distill that research for everyone to understand and develop new tools that help use and interpret new data and information.