Macrosystems ecology: New scientific field looks at the big picture
Big data is changing the field of ecology. The shift is dramatic enough to warrant the creation of an entirely new field: macrosystems ecology.
“Ecologists can no longer sample and study just one or even a handful of ecosystems,” said Patricia Soranno, Michigan State University professor of fisheries and wildlife and macrosystems ecology pioneer. “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.”
To define the new field and provide strategies for ecologists to do this type of research, Soranno and Dave Schimel from the California Institute of Technology’s Jet Propulsion Lab co-edited a special issue of the Ecological Society of America’s journal Frontiers in Ecology and the Environment.
They worked with many other researchers, funded from the National Science Foundation’s MacroSystems Biology program, who have written nine papers showing the advantages of taking such an approach to solve many environmental problems. 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, Soranno said.
“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. The sources include, 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, just to name a few.
Paired with the near-endless data deluge is easy access to supercomputers. Analysis that once took months or years to complete can now be conducted in hours or days. Ecologists also have access to the latest statistical modeling and geographic information system tools.
“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.”
A significant part of these new approaches involves the integration of biology with other fields, involving scientific, engineering and education areas across NSF, said John Wingfield, NSF assistant director for biological sciences The makeup of newly minted macrosystems ecology research teams should reflect the new demands of data-intensive ecology. Teams should include database managers, data-mining experts, GIS professionals and more.
“An important question we’re facing right now is whether ecologists will be the leaders in solving many of today’s top environmental problems that need a broad-scale approach,” Soranno said. “Seeing the research that has been done to date by macrosystems ecologists already doing this work and reading the papers that make up this issue, the answer is an emphatic ‘yes’,” Soranno said.
- James B Heffernan, Patricia A Soranno, Michael J Angilletta, Lauren B Buckley, Daniel S Gruner, Tim H Keitt, James R Kellner, John S Kominoski, Adrian V Rocha, Jingfeng Xiao, Tamara K Harms, Simon J Goring, Lauren E Koenig, William H McDowell, Heather Powell, Andrew D Richardson, Craig A Stow, Rodrigo Vargas, Kathleen C Weathers. Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment, 2014; 12 (1): 5 DOI: 10.1890/130017
Cite This Page: