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I like to joke that I study imaginary fish. People often remember that or, even better, ask what it means. Then, I get to tell them that I study theoretical ecology; I use mathematical tools to investigate how organisms interact with each other and with their environment. I am studying in the MIT Joint Program with the Woods Hole Oceanographic Institution, so my organisms of interest tend to be marine, but the only places they swim are in my computer, in equations, and, always, in my heart.

Right now, I am interested in how metacommunities — imagine groups of coral reef communities that can share larvae, for example — are impacted by fishing. I am especially intrigued by how species interactions change the way the ecological community responds to fishing. Species interactions include predation, or the symbiosis of anemones and clownfish, or parasitism; they might seem obviously important to study, but we don’t know much about how these underlying processes influence human interactions with these systems. Biological systems and their component organisms can do many complicated things — from evolving, to having babies, to expressing phenotypes dependent on their environment, to reacting to their environment. And yet, many of our models for fisheries are for only one species.

Unfortunately, those complicating factors like species interactions and community response to fishing are really hard to measure. That’s what attracted me to biology from engineering in the first place — it’s weird, it’s complicated, it’s fascinating. Some corals undergo reverse metamorphosis — that’s the equivalent of a butterfly turning back into a caterpillar. Some organisms feed by spitting out huge mucus nets, catching things, and reeling them back in. Microbes can work together to make food. I find it mind-bogglingly, awesomely interesting. All this complication is compounded by the fact that the ocean is even harder to study than land; as a John Shepherd quote I have hanging in my office summarizes, “Counting fish is like counting trees except you can’t see them and they move.”

Figuring out how strongly fish interact and whether or not that is what influences how they respond to fishing is almost impossible, which is where modeling comes in. We can test all sorts of simple rules and scenarios — extreme scenarios that are physically impossible or “realistic” scenarios that might seem closer to what we think happens — and see how the system behaves. We can use these results to help tease apart the otherwise hopelessly complicated mess that is how and why nature does what it does.

So I swoop in, cloaked in my superhero cape, ready to rescue the situation with math. Right now, I have a biological model — which dictates how the fish colonize new habitats and interact with each other — coupled with an economic model which describes how fishing will proceed. My biological model is a patch occupancy model, which means that I use ordinary differential equations to track which species are present in an individual patch of habitat; how the populations change is how I incorporate the ecologically relevant characteristics of the system. The economic model prescribes how much each fish is worth and allows me to ask questions about the profit arising from the system. Then, I can dive into the heart of the matter and figure out how the ecological properties, like diversity, influence profit.

One thing I found surprising is that under some types of community organization, such as when strong competition is present and colonization rates are low, there isn’t a huge tradeoff; you make more money when there are more fish present so the habitat will be optimally managed to yield more diversity. In other circumstances, such as when the colonization rates are high and fish engage in a mutual relationship, there is a stark trade-off. I am excited to bring a spatial element into my model, which will help us understand how the arrangement of communities and fishing patterns in space influences both the community of fish and fishermen.