Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems
K. A. Dafforn A B I , E. L. Johnston A B , A. Ferguson C , C.L. Humphrey D , W. Monk E , S. J. Nichols F , S. L. Simpson G , M. G. Tulbure A and D. J. Baird H
+ Author Affiliations
- Author Affiliations
A Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
B Sydney Institute of Marine Sciences, Mosman, NSW 2088 Australia.
C Office of Environment and Heritage, PO Box A290, Sydney South, NSW 1232, Australia.
D Environmental Research Institute of the Supervising Scientist, PO Box 461, Darwin, NT 0801, Australia.
E Canadian Rivers Institute, Faculty of Forestry and Environmental Management, University of New Brunswick, PO Box 4400, Fredericton, NB, E3B 5A3, Canada.
F Institute for Applied Ecology and MDBfutures Collaborative Research Network, University of Canberra, Canberra, ACT 2601, Australia.
G CSIRO Land and Water, Centre for Environmental Contaminants Research, Locked Bag 2007, Kirrawee, NSW 2232, Australia.
H Environment Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, PO Box 4400, Fredericton, NB, E3B 5A3, Canada.
I Corresponding author. Email k.dafforn@unsw.edu.au
Marine and Freshwater Research 67(4) 393-413 https://doi.org/10.1071/MF15108
Submitted: 12 March 2015 Accepted: 20 July 2015 Published: 22 October 2015
Journal Compilation © CSIRO Publishing 2016 Open Access CC BY-NC-ND
Abstract
Aquatic ecosystems are under threat from multiple stressors, which vary in distribution and intensity across temporal and spatial scales. Monitoring and assessment of these ecosystems have historically focussed on collection of physical and chemical information and increasingly include associated observations on biological condition. However, ecosystem assessment is often lacking because the scale and quality of biological observations frequently fail to match those available from physical and chemical measurements. The advent of high-performance computing, coupled with new earth observation platforms, has accelerated the adoption of molecular and remote sensing tools in ecosystem assessment. To assess how emerging science and tools can be applied to study multiple stressors on a large (ecosystem) scale and to facilitate greater integration of approaches among different scientific disciplines, a workshop was held on 10–12 September 2014 at the Sydney Institute of Marine Sciences, Australia. Here we introduce a conceptual framework for assessing multiple stressors across ecosystems using emerging sources of big data and critique a range of available big-data types that could support models for multiple stressors. We define big data as any set or series of data, which is either so large or complex, it becomes difficult to analyse using traditional data analysis methods.
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