Bridging Great Divides
Justin Calabrese builds bridges for a living. He connects two communities that are both trying to make sense of the world, that would benefit from the ideas of the other, but that frequently don’t speak the same language.
These two communities are the fields of field ecology and theoretical ecology.
Thousands of ecologists labor in the fields every day, collecting meticulous data on everything from acorn reproduction to whale communication. Similarly, mathematicians and statisticians work to create models to explain natural phenomena. The problem is that the two don’t compare notes often enough, which means that ecologists are often left with theoretical models that don’t fit their data very well.
Calabrese is a quantitative ecologist. That means that he has an inside working knowledge of both communities: he’s worked as a field ecologist and as a theoretician. He melds both sets of skills to help create a completely new, useful, understanding of natural systems. He works with ecologists and conservation biologists to connect their data to theoretical models using custom-designed statistics. Once this bridge is built, the models can be used to help answer important ecological questions, and the data can show where current models are deficient and could be improved.
This is a unique and important skill. Because field ecologists haven’t historically talked much to the theoreticians—or vice versa—the models don’t often reflect biological reality. According to Calabrese, “Theory divorced from data can often go in a silly and useless direction. You need to build the biology into models.”
For example, he’s working with wildlife ecologist Bill McShea on acorns. Most people don’t spend a lot of time worrying about acorn production (called masting), how many acorns there are in the forest at any given time, or why, but the number of acorns in the woods actually drives much of the forest ecosystem. And it’s a system that scientists don’t know much about, despite years of research. Some years there are a ton of acorns, and some years there aren’t many at all, and scientists have yet to come up with a generally accepted explanation for the observed patterns.
“Acorn production isn’t linear,” Calabrese explains, meaning that it’s not simply proportional to factors such as rain or sunny days. “With non-linear systems, you can’t figure out what’s driving the system just by working it out in your head. That’s when you need a model. The human brain is really terrible at dealing with non-linearity; we just don’t have any intuition for that.”
Melding real-life data with theoretical models means that the models may eventually be able to offer reliable predictions for the future of a complex and seemingly inscrutable system. “I try to forge a connection between the theoretical and empirical sides. My strategy is to find a way to connect the data to the models that give you quantitative predictions. When you can do that, you can start to get at which among a set of competing explanations is most consistent with the real system.”
Calabrese’s diverse background means he can work on almost any ecosystem and almost any species. He collaborates with experts on particular systems to modify and enhance existing models to accommodate the important details of the study system. If appropriate models don’t exist, then he builds them himself. This ability and insight means that Calabrese, who just joined SCBI in July 2010, works on a variety of SCBI research projects. In addition to worrying about acorns, he is also working on a study with SCBI scientist Peter Leimgruber to see if they can understand more about large-scale animal migrations based on individual animal interactions and with SCBI scientist Brian Gratwicke to prioritize rescue operations to combat the amphibian population crisis.
“I’m more of a generalist than a lot of folks here. There are a lot of people around here who have very deep expertise on certain biological systems, and who have done field work for a long time,” Calabrese explains. “To me, this is an ideal situation and a key reason why I came to SCBI. It’s fantastic to be able to collaborate with SCBI scientists on a wide range of important conservation problems, and to combine their systems expertise with my analytical expertise.”
