A Toolkit for Ecosystem Ecologists in the Time of Big Science
- 918 Downloads
Ecosystem ecologists are being challenged to address the increasingly complex problems that comprise Big Science. These problems include multiple levels of biological organization that cross multiple interacting temporal and spatial scales, from individual plants, animals, and microbes to landscapes, continents, and the globe. As technology improves, the availability of data, derived data products, and information to address these complex problems are increasing at finer and coarser scales of resolution, and legacy, dark data are brought to light. Data analytics are improving as big data increase in importance in other fields that are improving access to these data. New data sources (crowdsourcing, social media) and ease of communication and collaboration among ecosystem ecologists and other disciplines are increasingly possible via the internet. It is increasingly important that ecosystem ecologists be able to communicate their findings, and to translate their concepts and findings into concrete bits of information that a general public can understand. Traditional approaches that portray ecosystem sciences as a dichotomy between empirical research and theoretical research will keep the field from fully contributing to the complexity of global change questions, and will keep ecosystem ecologists from taking full advantage of the data and technology available. Building on previous research, we describe a more forward-looking, integrated empirical–theoretical modeling approach that is iterative with learning to take advantage of the elements of Big Science. We suggest that training ecosystem ecologists in this integrated approach will be critical to addressing complex Earth system science questions, now and in the future.
Keywordsmultiple levels of organization interacting spatial and temporal scales technological Advances big data analytics crowdsourcing machine learning
The authors thank Monica Turner and Steve Carpenter for inviting us to contribute to this special issue, and for helpful comments on a previous version. Funding was provided by National Science Foundation awards (DEB-1235828) and (DEB-1440166) to New Mexico State University in support of the Jornada Basin Long Term Ecological Research Program.
- Aceves-Bueno E, Adeleye AS, Bradley D, Brandt WT, Callery P, Feraud M, Garner KL, Gentry R, Huang McCullough I, Pearlman I, Sutherland SA, Wilkinson W, Yang Y, Zink T, Anderson SE, Tague C. 2015. Citizen science as an approach for overcoming insufficient monitoring and inadequate stakeholder buy-in in adaptive management: criteria and evidence. Ecosystems 18:493–506.CrossRefGoogle Scholar
- Box GEP, Draper NR. 1987. Empirical model-building and response surfaces. New York: Wiley.Google Scholar
- Briske DD, Washington-Allen RA, Johnson CR, Lockwood JA, Lockwood DR, Stringham TK, Shugart HH. (2010). Catastrophic thresholds: a synthesis of concepts, perspectives and applications. Ecol Soc 15: 37. [online] URL: http://www.ecologyandsociety.org/vol15/iss3/art37/.
- Groffman PM, Rustad LE, Templer PH, Campbell JL, Christenson LM, Lany NK, Socci AM, Vadeboncoeur MA, Schaberg PG, Wilson GF, Driscoll CT, Fahey TJ, Fisk MC, Goodale CL, Green MB, Hamburg SP, Johnson CE, Mitchell MJ, Morse JL, Pardo LH, Rodenhouse NL. 2012. Long-term integrated studies show complex and surprising effects of climate change in the northern hardwood forest. Bioscience 62:1056–66.CrossRefGoogle Scholar
- Oreskes N, Conway E. 2010. Merchants of doubt: how a handful of scientists obscured the truth on issues from tobacco smoke to global warming. New York: Bloomsbury Press.Google Scholar
- Peters DPC, Havstad KM, Cushing J, Tweedie CE, Fuentes O, Villanueva-Rosales N (2014a). Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Ecosphere 5: art6. 10.1890/ES13-00359.1.