Integration of ecosystem science into radioecology : a consensus perspective

Rhodes, OE; Bréchignac, F; Bradshaw, C; Hinton, TG; Mothersill, C; Arnone, JA; Aubrey, DP; Barnthouse, LW; Beasley, JC; Bonisoli-Alquati, A; Boring, LR; Bryan, AL; Capps, KA; Clément, B; Coleman, A; Condon, C; Coutelot, F; DeVol, T; Dharmarajan, G; Fletcher, D; Flynn, W; Gladfelder, G; Glenn, TC; Hendricks, S; Ishida, K; Jannik, T; Kapustka, L; Kautsky, U; Kennamer, R; Kuhne, W; Lance, S; Laptyev, G; Love, C; Manglass, L; Martinez, N; Mathews, T; McKee, A; McShea, W; Mihok, S; Mills, G; Parrott, B; Powell, B; Pryakhin, E; Rypstra, A; Scott, D; Seaman, J; Seymour, C; Shkvyria, M; Ward, A; White, D; Wood, M; Zimmerman, JK

Abstract

In the Fall of 2016 a workshop was held which brought together over 50 scientists from the ecological and radiological fields to discuss feasibility and challenges of reintegrating ecosystem science into radioecology. There is a growing desire to incorporate attributes of ecosystem science into radiological risk assessment and radioecological research more generally, fueled by recent advances in quantification of emergent ecosystem attributes and the desire to accurately reflect impacts of radiological stressors upon ecosystem function. This paper is a synthesis of the discussions and consensus of the workshop participant's responses to three primary questions, which were: 1) How can ecosystem science support radiological risk assessment? 2) What ecosystem level endpoints potentially could be used for radiological risk assessment? and 3) What inference strategies and associated methods would be most appropriate to assess the effects of radionuclides on ecosystem structure and function? The consensus of the participants was that ecosystem can and should support radiological risk assessment through the incorporation of quantitative metrics that reflect ecosystem functions which are sensitive to radiological contaminants. The participants also agreed that many such endpoints exit or are thought to exit and while many are used in ecological risk assessment currently, additional data need to be collected that link the causal mechanisms of radiological exposure to these endpoints. Finally, the participants agreed that radiological risk assessments must be designed and informed by rigorous statistical frameworks capable of revealing the causal inference tying radiological exposure to the endpoints selected for measurement.

• Journal Article Type: Article
• Acceptance Date: Jun 4, 2020
• Online Publication Date: Jun 9, 2020
• Publication Date: Oct 20, 2020
• Deposit Date: Jun 12, 2020
• Publicly Available Date: Jun 9, 2021
• Journal: Science of the Total Environment
• Print ISSN: 0048-9697
• Publisher: Elsevier
• Volume: 740
• Pages: 140031
• DOI: https://doi.org/10.1016/j.scitotenv.2020.140031
• Publisher URL: https://doi.org/10.1016/j.scitotenv.2020.140031
• Related Public URLs: https://www.journals.elsevier.com/science-of-the-total-environment
• Additional Information: Additional Information : ** Article version: AM ** Embargo end date: 31-12-9999 ** From Elsevier via Jisc Publications Router ** Licence for AM version of this article: This article is under embargo with an end date yet to be finalised. **Journal IDs: issn 00489697 **History: issue date 09-06-2020; accepted 04-06-2020; Funders : Department of Energy Office of Environmental Management; Grant Number: DE-EM0004391