Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A novel approach to assessing natural resource injury with Bayesian networks
https://orcid.org/0000-0002-1041-5301
https://orcid.org/0000-0001-6739-6036
https://orcid.org/0000-0002-3667-7481
https://orcid.org/0000-0002-4912-5766
https://orcid.org/0000-0002-4237-2158
AbstractQuantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream‐dwelling bird species. We compared the BN‐generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field‐based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562–573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Key Points When quantifying the effects of contaminants on natural resources, it is difficult to separate direct and indirect impacts of hazardous substance(s) from the influence of other confounding factors. We present a proof‐of‐concept example of how Bayesian networks (BNs) may be helpful in assessing natural resource injury within the Natural Resource Damage Assessment and Restoration (NRDAR) program in the United States. Bayesian networks can be useful in describing relationships, identifying key environmental factors contributing to potential injury outcomes, and helping advise decisions. The main strengths of using a BN approach to injury determination are twofold: first is the formal inclusion of expert knowledge along with use of empirical data, and second, BNs show the implications of uncertainty in outputs.
A novel approach to assessing natural resource injury with Bayesian networks
https://orcid.org/0000-0002-1041-5301
https://orcid.org/0000-0001-6739-6036
https://orcid.org/0000-0002-3667-7481
https://orcid.org/0000-0002-4912-5766
https://orcid.org/0000-0002-4237-2158
AbstractQuantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream‐dwelling bird species. We compared the BN‐generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field‐based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562–573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Key Points When quantifying the effects of contaminants on natural resources, it is difficult to separate direct and indirect impacts of hazardous substance(s) from the influence of other confounding factors. We present a proof‐of‐concept example of how Bayesian networks (BNs) may be helpful in assessing natural resource injury within the Natural Resource Damage Assessment and Restoration (NRDAR) program in the United States. Bayesian networks can be useful in describing relationships, identifying key environmental factors contributing to potential injury outcomes, and helping advise decisions. The main strengths of using a BN approach to injury determination are twofold: first is the formal inclusion of expert knowledge along with use of empirical data, and second, BNs show the implications of uncertainty in outputs.
A novel approach to assessing natural resource injury with Bayesian networks
https://orcid.org/0000-0002-1041-5301
https://orcid.org/0000-0001-6739-6036
https://orcid.org/0000-0002-3667-7481
https://orcid.org/0000-0002-4912-5766
https://orcid.org/0000-0002-4237-2158
AbstractQuantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream‐dwelling bird species. We compared the BN‐generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field‐based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562–573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Key Points When quantifying the effects of contaminants on natural resources, it is difficult to separate direct and indirect impacts of hazardous substance(s) from the influence of other confounding factors. We present a proof‐of‐concept example of how Bayesian networks (BNs) may be helpful in assessing natural resource injury within the Natural Resource Damage Assessment and Restoration (NRDAR) program in the United States. Bayesian networks can be useful in describing relationships, identifying key environmental factors contributing to potential injury outcomes, and helping advise decisions. The main strengths of using a BN approach to injury determination are twofold: first is the formal inclusion of expert knowledge along with use of empirical data, and second, BNs show the implications of uncertainty in outputs.
A novel approach to assessing natural resource injury with Bayesian networks
Integr Envir Assess & Manag
Rowland, Freya E. (Autor:in) / Kotalik, Christopher J. (Autor:in) / Marcot, Bruce G. (Autor:in) / Hinck, Jo Ellen (Autor:in) / Walters, David M. (Autor:in)
Integrated Environmental Assessment and Management ; 20 ; 562-573
01.03.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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