Abstract
Currently most Cumulative Impacts Assessments (CIAs) are risk-based approaches that assess the potential impact of human activities and their pressures on the ecosystem thereby compromising the achievement of policy objectives. While some of these CIAs apply actual data (usually spatial distributions) they often have to rely on categorical scores based on expert judgement if they actually assess impact which is often expressed as a relative measure that is difficult to interpret in absolute terms. Here we present a first step-wise approach to conduct a fully quantitative CIA based on the selection and subsequent application of the best information available. This approach systematically disentangles risk into its exposure and effect components that can be quantified using known ecological information, e.g. spatial distribution of pressures or species, pressure-state relationships and population dynamics models with appropriate parametrisation, resulting in well-defined assessment endpoints that are meaningful and can be easily communicated to the recipients of advice. This approach requires that underlying assumptions and methodological considerations are made explicit and translated into a measure of confidence. This transparency helps to identify the possible data-handling or methodological decisions and shows the resulting improvement through its confidence assessment of the applied information and hence the resulting accuracy of the CIA.To illustrate this approach, we applied it in a North Sea CIA focussing on two sectors, i.e. fisheries and offshore windfarms, and how they impact the ecosystem and its components, i.e. seabirds, seabed habitats and marine mammals through various pressures. The results provide a “proof of concept” for this generic approach as well as rigorous definitions of several of the concepts often used as part of risk-based approaches, e.g. exposure, sensitivity, vulnerability, and how these can be estimated using actual data. As such this widens the scope for increasingly more quantitative CIAs using the best information available.
Highlights
The development and application of cumulative effect assessments (CEAs) and/or cumulative impact assessments (CIAs) is gaining considerable attention in scientific literature (e.g. Halpern and Fujita, 2013; Goodsir et al, 2015; Stelzenmüller et al, 2015; Judd et al, 2015; Korpinen and Andersen, 2016; Willsteed et al, 2017; Stelzenmüller et al, 2018)
The results provide a “proof of concept” for this generic approach as well as rigorous definitions of several of the concepts often used as part of risk-based approaches, e.g. exposure, sensitivity, vulnerability, and how these can be estimated using actual data
In this study we will consider both the likelihood-consequence and exposure-effect approaches and show how these apply to a fully quantitative and systematic approach to calculate cumulative impact consistently across a selection of impact chains. With this exercise we aim to clarify part of the terminology often used as part of CEA/CIA and illustrate potential issues or choices to be made in the process of selecting appropriate data sources to be used for CEA/CIA
Summary
The development and application of cumulative (or combined) effect assessments (CEAs) and/or cumulative impact assessments (CIAs) is gaining considerable attention in scientific literature (e.g. Halpern and Fujita, 2013; Goodsir et al, 2015; Stelzenmüller et al, 2015; Judd et al, 2015; Korpinen and Andersen, 2016; Willsteed et al, 2017; Stelzenmüller et al, 2018). The development and application of cumulative (or combined) effect assessments (CEAs) and/or cumulative impact assessments (CIAs) is gaining considerable attention in scientific literature The terms CEA and CIA are often used interchangeably within the literature (Judd et al, 2015; Korpinen et al, 2019; Lonsdale et al, 2020) but sometimes a distinction is made. Korpinen et al (2021) follow Goodsir et al (2015) to use combined effects when only additive effects are included while cumulative impacts “fundamentally refer to the sum of synergistic, antagonistic and additive effects on the focal environmental aspect”. Following Elliott et al (2020) this effect may be additive, synergistic, antagonistic (compensatory), or masking but in this study we only consider addition.
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