Elsevier

Journal of Environmental Management

Volume 226, 15 November 2018, Pages 340-346
Journal of Environmental Management

Research article
The science-policy interface of risk-based freshwater and marine management systems: From concepts to practical tools

https://doi.org/10.1016/j.jenvman.2018.08.053Get rights and content

Highlights

  • Sector-specific management measures generate different levels of residual pressures.

  • Model integrates effectiveness of management measures to predict residual pressure.

  • Residual pressures are a key in understanding cumulative environmental effects.

  • Model integrates spatial and temporal pathways of ecosystem effects.

  • Model can be used to predict residual pressures from a variety of sector activities.

Abstract

Maintaining the current state of ecosystem services from freshwater and marine ecosystems around the world is at risk. Cumulative effects of multiple human pressures on ecosystem components and functions are indicative of residual pressures that “fall through” the cracks of current industry sector management practices. Without an understanding of the level of residual pressures generated by these measures, we are unlikely to reconcile the root causes of ecosystem effects to improve these management practices to reduce their residual pressures. In this paper, we present a new modelling framework that combines a qualitative and quantitative assessments of the effectiveness of the measures used in the daily operations of industry sectors to predict their residual pressure that is delivered to the ecosystem. The predicted residual pressure can subsequently be used as an input variable for ecosystem models. We combine the Bow-tie analysis of the measures with a Bayesian belief network to quantify the effectiveness of the measures and predict the residual pressures.

Introduction

The interface between science and policy eventually needs to operationalize ecosystem-based approaches to management (Gavaris, 2009; Murawski, 2007; Cormier et al., 2017) so as to carry into effect policy objectives. Formalizing and defining the science-policy interface within a management system or even among management systems is a key challenge in achieving sustainable development while maintaining the current state of ecosystems (Creed et al., 2016; Gluckman, 2016). Human activities and their demands for ecosystem services generate pressures that can cause physical changes, chemical interferences as well as biological and ecological disturbances within marine and freshwater ecosystems (Halpern et al., 2008; Allan et al., 2013). Cumulative effects assessment has been the hallmark approach to unravel the complex pressure-effect relationships and inform mitigation strategies to reduce them (Ban et al., 2010; Andersen et al., 2015; Jones, 2016; Stelzenmüller et al., 2018). However, mitigation strategies are most often focused on reducing the effects (Mangano and Sarà, 2017) and seldom consider or integrate an assessment of the effectiveness of the management measures implemented to reduce the pressures at their sources (Katsanevakis et al., 2011; ICES, 2014; Elliott et al., 2017).

One strategy for identifying how pressures are managed is by analysing the management system of policies, processes, and procedures that are implemented to reduce the pressures (ISO, 2009). Performance of such a system is a measure of the degree to which policy objectives are being achieved (ISO, 2005) such as the effectiveness of current mitigation strategies in reducing environmental effects (Batista et al., 2015). In addition to compliance and external factors (Girling, 2013; Green, 2015), performance relies significantly on the effectiveness of so called operational controls (e.g. procedures, tasks, maintenance, repairs) that are implemented in the daily operations on the ground (Anthony and Dearden, 1980). In this operational context, effectiveness is the extent to which controls can produce their expected result or outcome. Lack of performance could be attributed to either the effectiveness of the controls or the legislation and policies that are intended to regulate the phenomenon in question (Cormier et al., 2017). For example, best management practices that are meant to reduce sediment input to watercourses are designed to operate effectively within certain boundary conditions (Cooke et al., 2015). Thus, despite proper installation and maintenance, residual amounts of sediment still reach the watercourse. Based on the effectiveness of a given control design, we are of the view that the collective residual materials, substances or wastes released to the environment can represent significant pressures to sensitive aquatic ecosystems. This implies that controls implemented as regulatory requirements or best management practices are inadvertently contributing to cumulative environmental effects despite the requirements and objectives stipulated in legislation and policy (Sardà et al., 2014; Jones, 2016; Cormier et al., 2017).

Outside the influence of natural or climate driven processes, we call ‘residual pressures’ the pressures that are generated by the residual materials, substances or wastes as a result of the level of effectiveness of the controls that are implemented in the daily operations of industry sectors. Without the capability of estimating the level of the residual pressures, we are unlikely to reconcile the root causes of disturbances to ecosystems with the management practices for addressing those disturbances and ultimately, the performance of their management systems in achieving environmental objectives. We use the Bow-tie analysis (IEC/ISO, 2009) and a Bayesian Belief Network (Badreddine and Amor, 2013) as an approach to predict the residual pressure. This approach provides a predicted residual pressure that would serve as an input variable to ecosystem models. In this paper, we tested this approach in two distinct case studies being 1) nutrient loading in the Great Lakes, and 2) sea-floor integrity of the North Sea. Based on these two case studies, we identify knowledge and data gaps and reflect on lessons learned from implementing such an approach.

Section snippets

Materials and methods

We use the Bow-tie analysis to develop a qualitative model of the controls implemented to reduce a pressure generated from the activities of multiple sectors. We then use a Bayesian belief network model (Marcot et al., 2006) to predict the residual pressure based on the integration of the effectiveness of each control, the implementation compliance of the controls and external factors that could undermine the effectiveness of the controls. Here, we are using the predicted residual pressure as

Results

We find that the Bow-tie and Bayesian belief network combined models can be used to analyse the effectiveness of different management systems of prevention controls regardless of the ecosystem setting. The case studies demonstrate that the models can be used for either freshwater or marine ecosystems because the analysis assesses the effectiveness of the prevention controls in reducing the pressures generated by the activities and not the ecosystem effects. As mentioned above, the predicted

Matching the scales of the sources of the pressure and ecosystem effects

The Bow-tie/Bayesian belief network models must capture the inherent spatial and temporal properties of the pressure-effect pathways. There may be significant separation between where and when the initial pressure load is occurring, the prevention controls are being implemented, the total residual pressure load is being released and the resulting ecosystem effects. Each side of the Bow-tie demarks two spatial and temporal pathways of risk. The left side of the Bow-tie represents the boundary of

Conclusion

This paper puts a spotlight on key challenges of working at the science-policy interface, particularly when considering the operational context of ecosystem-based approaches to the management of the daily activities of the sectors operating in a given area. Our modelling approach shifts the focus from the assessment of ecosystem effects, to an assessment of the effectiveness of the prevention controls in reducing the pressures and ultimately their effects. Regardless of the results of any

Acknowledgments

We would like to thank the Working Group on Marine Planning and Coastal Zone Management of the International Council for the Exploration of the Sea (ICES) for facilitating this research. We would also like to thank the Rijkswaterstaat, The Netherlands, the Thünen Institute of Sea Fisheries, Germany and Western University, Canada for hosting the workshops that led to this work. Finally, we would like to thank all of the participants that contributed their expertise and insight in the development

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