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Macrosystems ecology: New scientific field looks at the big picture
(February 3, 2014) — 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.”….. “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…. > full story


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



Pioneering a new field: Microsystems’ Ecology –short YouTube Video

Published on Feb 3, 2014

Big data is changing the field of ecology. The shift is dramatic enough to warrant the creation of an entirely new field: macrosystems ecology. Patricia Soranno, MSU professor of …



Frontiers in Ecology and the Environment:  Special Issue – Microsystems Ecology: An Emerging Perspective


Guest Editorial



Macrosystems ecology: big data, big ecology

Patricia A Soranno, David S Schimel
Citation . Full Text . PDF (167 KB) 

Ecologists are increasingly confronted by questions that, in one way or another, involve analysis or prediction across vast geographic areas or time periods. There is little doubt that many of the problems facing environmental systems have broad-scale components. These problems range from understanding the spatial distributions of invasive species to discerning how the local ecology of forests interacts with regional fire patterns to influence continental fluxes of carbon. Although ecologists have been successful at answering research questions and developing theory at fine scales, they are now rapidly adding new techniques to their toolkit that facilitate the study of broad-scaled regional processes, and interactions with fine-scaled and global processes. This is where “macrosystems ecology” (MSE) fits in.

The papers in this Special Issue were prepared by participants in the US National Science Foundation’s MacroSystems Biology program. A common theme throughout most of these articles is a seemingly simple but challenging topic – data! Specifically, it’s the data required to study large, complicated, and highly variable objects typical of macrosystems research. The amount of data involved in MSE research is far beyond that which a single research lab can collect and process. What then are the options available to ecologists for conducting data-intensive research if they clearly cannot collect, process, or analyze it all on their own? At least some ecologists will have to develop the concepts and methodology for studying ecological systems at broad scales; revitalize the culture in which they work to be even more collaborative, open, and interdisciplinary than it already is; and embrace the era of “big data“.

To date, ecologists have used any of four strategies for acquiring ecological big data: (1) Collate existing small but information-rich datasets to create spatially, temporally, and thematically extensive datasets. This strategy is extremely difficult, is unexpectedly expensive, and can result in datasets with geographic or temporal gaps. (2) Compile data from remote-sensing platforms that are spatially and often temporally extensive. This approach is limited by the fact that the variable(s) measured must be drawn from a narrow set of features that can be observed remotely, and which are frequently proxies for the actual quantity of interest. (3) Link spatially distributed sensor-based observatories or experiments that use common methods. Such efforts often require complex and expensive instrumentation, and can also have geographic or temporal gaps. (4) Launch “big science” programs that span continental scales, use standardized methods, and are designed from the outset to address broad-scaled ecological research questions. These strategies can be costly; require management, computing, and systems engineering skills unfamiliar to most ecologists; and may be subject to spatiotemporal gaps.

So, which strategy should ecologists focus their efforts on to boldly venture into data-intensive research? The answer is all of them. The various approaches for collecting big data have different strengths and weaknesses, and data-intensive science of ecological systems (ie “big ecology”) will best progress when all strategies are harnessed to their full potential. Some scientists have dismissed big science approaches in ecology because the International Biology Program (IBP) of the 1960s and 1970s is today frequently portrayed as a failure. However, its legacy may be due for a reassessment: the IBP provided lasting foundational science and datasets used to this day. It can also provide valuable lessons, both positive and negative. Ecologists have made progress over the past 40 years in developing and applying novel methods to address problems across a wide range of scales.

The experiences of the emerging MSE community, some of which are discussed in this Special Issue, demonstrate that ecology needs to integrate the single-investigator model of science with a collaborative, open, and interdisciplinary one. Between the extremes of big science and single-investigator science is a wide range of research conducted by groups of varying size, as small or large teams, working groups, networks, and networks of networks. This Special Issue highlights the growing emphasis on collaboration and a culture shaped by focused, broad-scale scientific questions. Although individual investigator-driven research is still the dominant mode of ecological research, the current successes of MSE research suggest that it is only one of several different possible approaches.

To understand and solve many of today’s problems, ecologists need “big data” and “big ecology”. This Special Issue of Frontiers provides a wealth of new perspectives on this necessity. Hampton et al. (Front Ecol Environ 2013; 11[3]: 156–62) asked whether the leaders of big ecology will even be ecologists: this issue suggests the answer is an emphatic “yes”.





Macrosystems ecology: understanding ecological patterns and processes at continental scales

James B Heffernan, Patricia A Soranno, Michael J Angilletta Jr, 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
Abstract . Full Text . PDF (3466 KB) . Supplemental Material 




Approaches to advance scientific understanding of macrosystems ecology

Ofir Levy, Becky A Ball, Ben Bond-Lamberty, Kendra S Cheruvelil, Andrew O Finley, Noah R Lottig, Surangi W Punyasena, Jingfeng Xiao, Jizhong Zhou, Lauren B Buckley, Christopher T Filstrup, Tim H Keitt, James R Kellner, Alan K Knapp, Andrew D Richardson, David Tcheng, Michael Toomey, Rodrigo Vargas, James W Voordeckers, Tyler Wagner, John W Williams
Abstract . Full Text . PDF (2065 KB) . Supplemental Material 




Completing the data life cycle: using information management in macrosystems ecology research

Janine Rüegg, Corinna Gries, Ben Bond-Lamberty, Gabriel J Bowen, Benjamin S Felzer, Nancy E McIntyre, Patricia A Soranno, Kristin L Vanderbilt, Kathleen C Weathers
Abstract . Full Text . PDF (1082 KB) . Supplemental Material 




Creating and maintaining high-performing collaborative research teams: the importance of diversity and interpersonal skills

Kendra S Cheruvelil, Patricia A Soranno, Kathleen C Weathers, Paul C Hanson, Simon J Goring, Christopher T Filstrup, Emily K Read
Abstract . Full Text . PDF (1888 KB) . Supplemental Material 




Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success

Simon J Goring, Kathleen C Weathers, Walter K Dodds, Patricia A Soranno, Lynn C Sweet, Kendra S Cheruvelil, John S Kominoski, Janine Rüegg, Alexandra M Thorn, Ryan M Utz
Abstract . Full Text . PDF (1343 KB) . Supplemental Material 




Riverine macrosystems ecology: sensitivity, resistance, and resilience of whole river basins with human alterations

Kevin E McCluney, N LeRoy Poff, Margaret A Palmer, James H Thorp, Geoffrey C Poole, Bradley S Williams, Michael R Williams, Jill S Baron
Abstract . Full Text . PDF (2449 KB) . Supplemental Material 




Climate forcing of wetland landscape connectivity in the Great Plains

Nancy E McIntyre, Christopher K Wright, Sharmistha Swain, Katharine Hayhoe, Ganming Liu, Frank W Schwartz, Geoffrey M Henebry
Abstract . Full Text . PDF (1638 KB) . Supplemental Material 




Cross-scale interactions: quantifying multi-scaled cause–effect relationships in macrosystems

Patricia A Soranno, Kendra S Cheruvelil, Edward G Bissell, Mary T Bremigan, John A Downing, Carol E Fergus, Christopher T Filstrup, Emily N Henry, Noah R Lottig, Emily H Stanley, Craig A Stow, Pang-Ning Tan, Tyler Wagner, Katherine E Webster
Abstract . Full Text . PDF (2883 KB) . Supplemental Material 




Ecological homogenization of urban USA

Peter M Groffman, Jeannine Cavender-Bares, Neil D Bettez, J Morgan Grove, Sharon J Hall, James B Heffernan, Sarah E Hobbie, Kelli L Larson, Jennifer L Morse, Christopher Neill, Kristen Nelson, Jarlath O’Neil-Dunne, Laura Ogden, Diane E Pataki, Colin Polsky, Rinku Roy Chowdhury, Meredith K Steele
Abstract . Full Text . PDF (1666 KB) . Supplemental Material 

Life Lines



What is one datum worth?

Adrian Burton
Citation . Full Text . PDF (221 KB) 



Macrosystems Ecology: The More We Know The Less We Know.

Posted on February 4, 2014
Post by Simon Goring, Postdoc at the University of Wisconsin-Madison. This post originally appeared at downwithtime.

One of the answers was simply that as ecologists we often recognize the depth of knowledge of our peers and as such, are unlikely (or are unwilling) to comment in an area that we have little expertise. ….This speaks to a broader issue though, and one that is addressed in the latest issue of Frontiers in Ecology and the Environment.  The challenges of global change require us to come out of our disciplinary shells and to address challenges with a new approach, defined here as Macrosystems Ecology.  At large spatial and temporal scales – the kinds of scales at which we experience life – ecosystems cease being disciplinary.  Jim Heffernan and Pat Soranno, in the lead paper (Heffernan et al., 2014) detail three ecological systems that can’t be understood without cross-scale synthesis using multi-disciplinary teams.

….Interdisciplinary research is not something that many of us have trained for as ecologists (or biogeographers, or paleoecologists, or physical geographers. . . but that’s another post).  It is a complex, inter-personal interaction that requires understanding of the cultural norms within other disciplines.  Cheruvelil et al. (2014) do a great job of describing how to achieve and maintain high-functioning teams in large interdisciplinary projects, and Kendra also discusses this further in a post on her own academic blog.

Figure 2. From Goring et al., (2014). Interdisciplinary research requires effort in a number of different areas, and these efforts are not recognized under traditional reward structures.

The interactions (red arrows) that are rewarded among individuals, institutions, and funding agencies. The traditional reward system applied to disciplinary-based research (a) is well supported and most of the depicted interactions are valued and rewarded. In contrast, the traditional reward system applied to interdisciplinary-based collaborative research (b) shows that while certain interactions are favored and rewarded (red arrows, similar to those in [a]), there are many interactions that are undervalued (gray arrows). The size and shape of symbols within the collaboration in (b) represent career stage and type of discipline, respectively. Undervaluing collaboration provides weak support for individuals engaged in this kind of research (ie fewer red arrows), even as outlets for interdisciplinary research dissemination increase. By expanding evaluation criteria for interdisciplinary research (Table 2), a more complex set of interactions is supported. These expanded measures of success should support the investment in time and effort required for effective interdisciplinary collaboration.

In Goring et al. (2014) we discuss a peculiar issue that is posed by interdisciplinary research.  ….. As we move towards greater interdisciplinarity we begin to recognize that simply superimposing the traditional rewards structure onto interdisciplinary projects (Figure 2) leaves a lot to be desired.  This is particularly critical for early-career researchers.  We are asking these researchers (people like me) to collaborate broadly with researchers around the globe, to tackle complex issues in global change ecology, but, when it comes time to assess their research productivity we don’t account for the added burden that interdisciplinary research can require of a researcher.….In Goring et al. (2014) we propose a broader set of metrics against which to evaluate members of large interdisciplinary teams (or small teams, there’s no reason to be picky).  This list of new metrics (here) includes traditional metrics (numbers of papers, size of grants), but expands the value of co-authorship, recognizing that only one person is first in the authorship list, even if people make critical contributions; provides support for non-disciplinary outputs, like policy reports, dataset generation, non-disciplinary research products (white papers, books) and the creation of tools and teaching materials; and adds value to qualitative contributions, such as facilitation roles, helping people communicate or interact across disciplinary divides…..

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