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Advances in the aquatic sciences
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Emergent technologies and analytical approaches for understanding the effects of multiple stressors in aquatic environments

A. A. Chariton A B , M. Sun A B , J. Gibson C , J. A. Webb D , K. M. Y. Leung E , C. W. Hickey F and G. C. Hose G H
+ Author Affiliations
- Author Affiliations

A Molecular Ecology and Toxicology, CSIRO Oceans and Atmosphere, Locked Bag 2007, Kirrawee, NSW 2232, Australia.

B Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia.

C Biodiversity Institute of Ontario and Department of Integrative Biology, University of Guelph, Guelph, ON, N1G 2W1, Canada.

D Department of Infrastructure Engineering, The University of Melbourne, Vic. 3010, Australia.

E The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region, China.

F National Institute of Water and Atmospheric Research (NIWA), PO Box 11115, Hamilton 3251, New Zealand.

G Department of Biological Sciences, Macquarie University, NSW 2109, Australia.

H Corresponding author. Email: grant.hose@mq.edu.au

Marine and Freshwater Research 67(4) 414-428 https://doi.org/10.1071/MF15190
Submitted: 14 May 2015  Accepted: 6 August 2015   Published: 21 October 2015

Abstract

In order to assess how emerging science and new 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, Sydney, Australia. This workshop aimed to explore the potential offered by new approaches to characterise stressor regimes, to explore stressor-response relationships among biota, to design better early-warning systems and to develop smart tools to support sustainable management of human activities, through more efficient regulation. In this paper we highlight the key issues regarding biological coverage, the complexity of multiply stressed environments, and our inability to predict the biological effects under such scenarios. To address these challenges, we provide an extension of the current Environmental Risk Assessment framework. Underpinning this extension is the harnessing of environmental-genomic data, which has the capacity to provide a broader view of diversity, and to express the ramifications of multiple stressors across multiple levels of biological organisation. We continue to consider how these and other emerging data sources may be combined and analysed using new statistical approaches for disentangling the effects of multiple stressors.

Additional keywords: big data, causality, environmental genomics, predictive models.


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