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From the same journal

Integrated presentation of ecological risk from multiple stressors

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Integrated presentation of ecological risk from multiple stressors. / Goussen, Benoit Regis Marc; Price, Oliver R.; Rendal, Cecilie; Ashauer, Roman.

In: Scientific Reports, Vol. 6, 36004, 26.10.2016.

Research output: Contribution to journalArticlepeer-review

Harvard

Goussen, BRM, Price, OR, Rendal, C & Ashauer, R 2016, 'Integrated presentation of ecological risk from multiple stressors', Scientific Reports, vol. 6, 36004. https://doi.org/10.1038/srep36004

APA

Goussen, B. R. M., Price, O. R., Rendal, C., & Ashauer, R. (2016). Integrated presentation of ecological risk from multiple stressors. Scientific Reports, 6, [36004]. https://doi.org/10.1038/srep36004

Vancouver

Goussen BRM, Price OR, Rendal C, Ashauer R. Integrated presentation of ecological risk from multiple stressors. Scientific Reports. 2016 Oct 26;6. 36004. https://doi.org/10.1038/srep36004

Author

Goussen, Benoit Regis Marc ; Price, Oliver R. ; Rendal, Cecilie ; Ashauer, Roman. / Integrated presentation of ecological risk from multiple stressors. In: Scientific Reports. 2016 ; Vol. 6.

Bibtex - Download

@article{6e669b3f956945d499db75ccb360c57c,
title = "Integrated presentation of ecological risk from multiple stressors",
abstract = "Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.",
keywords = "Communication and replication, Ecological modelling, Environmental impact, Population Dynamics",
author = "Goussen, {Benoit Regis Marc} and Price, {Oliver R.} and Cecilie Rendal and Roman Ashauer",
note = "{\textcopyright} 2016, The Authors. ",
year = "2016",
month = oct,
day = "26",
doi = "10.1038/srep36004",
language = "English",
volume = "6",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Springer Nature ",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Integrated presentation of ecological risk from multiple stressors

AU - Goussen, Benoit Regis Marc

AU - Price, Oliver R.

AU - Rendal, Cecilie

AU - Ashauer, Roman

N1 - © 2016, The Authors.

PY - 2016/10/26

Y1 - 2016/10/26

N2 - Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

AB - Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

KW - Communication and replication

KW - Ecological modelling

KW - Environmental impact

KW - Population Dynamics

U2 - 10.1038/srep36004

DO - 10.1038/srep36004

M3 - Article

VL - 6

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 36004

ER -