Big data is changing the scientific study of interaction among organisms and their environments, warranting the creation of an entirely new field: macrosystems ecology.
"Ecologists can no longer sample and study just one or even a handful of ecosystems," Patricia Soranno, Michigan State University professor of fisheries and wildlife and macrosystems ecology pioneer, said in a statement. "We also need to study lots of ecosystems and use lots of data to tackle many environmental problems such as climate change, land-use change and invasive species, because such problems exist at a larger scale than many problems from the past."
Soranno and Dave Schimel from the California Institute of Technology's Jet Propulsion Lab define the new field define the new field and provide strategies for ecologists to do this type of research in a special issue of the Ecological Society of America's journal Frontiers in Ecology and the Environment.
"Traditionally, ecologists are trained by studying and taking samples from the field in places like forests, grasslands, wetlands or water and measuring things in the lab," she said. "In the future, at least some ecologists will need to also be trained in advanced computational methods that will allow them to study complex systems using big datasets at this large scale and to help integrate fine and broad-scale studies into a richer understanding of environmental problems."
Ecologists have many decades of accumulated data to which to apply this new perspective, including many small, individual projects from university researchers, government agencies that have been monitoring natural resources for decades, terabytes of data collected from new or existing field sensors and observation networks, as well as millions of high-definition satellite images, according to a press release.
Soranno said data-intensive science is being touted as a new way to do science of any kind, and many researchers think it has great potential for ecology.
"Even ten years ago, it would have been much harder to take this approach," Soranno said. "We didn't have the wonderful intersection that we have today of great tools, volumes of data, sufficient computing power and a better developed understanding of systems at broad scales."
