When ecologists study how species interact in their natural environments, one of the most important things to understand is the structure of food webs, the relationships between predator and prey, or who eats what. As you can imagine, these webs can get pretty complex in the real world as the number of species in a given ecosystem multiplies. On the Computation Institute’s Scale Out blog, Rob Mitchum writes about a new study by Anna Eklöf and Stefano Allesina from the University of Chicago Department of Ecology and Evolution that crunched the numbers on the minimum number of dimensions and animal traits needed to accurately describe an ecosystem:
Dimensionality gives a flavor of the complexity of a food network. Imagine a pond with 10 fish species of various sizes. If body size is the only trait that determines the food network and each fish just eats the fish that are smaller than itself, then the network is one-dimensional. If the fish are unevenly distributed and the big fish only eat the smaller fish in their part of the pond, then the network is two-dimensional. If blue fish only eat red fish, a third dimension must be added to fully describe the network, and so on.
When all was said and done that number of dimensions was surprisingly low, which may save field researchers time and effort when studying ecosystems. This reminds us of the study by evolutionary biology PhD student Jonathan Mitchell we wrote about in November. He used software tools to model ancient food webs and predict how certain dinosaur species went extinct, and validated those predictions against the fossil record. Both are cases of new meets old, where researchers combined modern computational tools with some good old fashioned field work.