A Bayesian approach for assessing the boundary between desirable and undesirable environmental status – An example from a coastal fish indicator in the Baltic Sea

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Laurila-Pant , M , Mäntyniemi , S , Östman , Ö , Olsson , J , Uusitalo , L & Lehikoinen , A 2021 , ' A Bayesian approach for assessing the boundary between desirable and undesirable environmental status – An example from a coastal fish indicator in the Baltic Sea ' , Ecological Indicators , vol. 120 , 106975 . https://doi.org/10.1016/j.ecolind.2020.106975

Title: A Bayesian approach for assessing the boundary between desirable and undesirable environmental status – An example from a coastal fish indicator in the Baltic Sea
Author: Laurila-Pant, Mirka; Mäntyniemi, Samu; Östman, Örjan; Olsson, Jens; Uusitalo, Laura; Lehikoinen, Annukka
Contributor: University of Helsinki, Helsinki Institute of Sustainability Science (HELSUS)
University of Helsinki, Ecosystems and Environment Research Programme
Date: 2021-01
Language: eng
Number of pages: 12
Belongs to series: Ecological Indicators
ISSN: 1470-160X
URI: http://hdl.handle.net/10138/319860
Abstract: Ecological indicator approaches typically compare the prevailing state of an ecosystem component to a reference state reflecting good environmental conditions, i.e. the desirable state. However, defining the reference state is challenging due to a wide range of uncertainties related to natural variability and measurement error in data, as well as ecological understanding. This study propose a novel probabilistic approach combining historical monitoring data and ecological understanding to estimate the uncertainty associated with the boundary value of an ecological indicator between good and poor environmental states. Bayesian inference is used to estimate the epistemic uncertainty about the true state of an indicator variable during an historical reference period. This approach replaces the traditional boundary value with probability distribution, indicating the uncertainty about the boundary between environmental states providing a transparent safety margin associated with the risk of misclassification of the indicator's state. The approach is demonstrated by applying it to a time-series of an ecological status indicator, 'Abundance of coastal key fish species', included in HELCOM's Baltic Sea regional status assessment. We suggest that acknowledgement of the uncertainty behind the final classification leads to more transparent and better-informed decision-making processes.
Subject: 1172 Environmental sciences
Status assessment
Classification uncertainty
MSFD
Perca fluviatilis
Good environmental status
ECOLOGICAL STATUS
ECOSYSTEM SERVICES
CLIMATE-CHANGE
WATER-QUALITY
COUNT DATA
FRAMEWORK
MODEL
UNCERTAINTY
ABUNDANCE
NETWORK
1181 Ecology, evolutionary biology
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