Ecosystem-Based Integrated Ocean Management: A Framework for Sustainable Ocean Economy Development

• Incentivize behaviours that comply with management measures • Monitor and enforce compliance • Monitor and evaluate outcomes of man- agement actions • Incentivize and facilitate constructive engagement of stakeholders at different stages of the management cycle • Analyse, monitor and evaluate the effective- ness of existing governance processes • Integrate different forms of relevant knowl- edge • Analyse and manage multiple forms of uncertainty • Capture, understand and integrate plural values and epistemologies Technical tools that aredesignedspecifically for EBM are continuously being developed and updated, so online expert communities (such as the EBM Tools Network 19 ) can be a vital resource for practitioners. However, most tools that are used in EB-IOM are, in fact, methods and techniques that have a much broader application, many of which have their own extensive literature and related fields of expertise. This report does not aim to provide a comprehen- sive list or authoritative classification of all possible EB-IOM tools, but instead provides some particu- larly relevant examples, loosely grouped into four types: decision support tools, tools for analysing and modelling conflicts and interactions, tools for governance analysis, and ecosystem services val- uation. ‘Decision support tool’ (DST) is a broad term applied to analytical tools that process and integrate mul- tiple datasets into value layers, future planning scenarios, or models. Such tools can cut through layers of information and generate solutions and insights that would be beyond the capabilities of the human eye and brain, thus helping managers and stakeholders to develop and evaluate planning options. The Nature Conservancy 20 provides an overview of some commonly used DSTs in MSP. An empirical review by Pınarbaşı et al. (2017) found that DSTs are mainly used for planning, despite their potential to also support other stages of the EB-IOM cycle. Janßen et al. (2019) further highlight that their use remains overwhelmingly confined to the research context, which means that DSTs are not yet rou- tinely embedded in real-world processes. Thus, there is untapped potential for DSTs to improve 4.3.2. Decision support tools

planning outcomes across environmental and social spheres (Kockel et al. 2019).

DSTs can be very powerful in some situations, but their use is not a necessary precondition for success. Using them effectively requires time and technical expertise, and the added value they pro- vide is data dependent; if the available input data are sparse and/or unreliable, DST outputs will rep- resent only a limited perspective on complex real- ities (Ardron et al. 2008, Weig & Schultz-Zehden 2019). In very data-poor situations, it can be pref- erable to use expert-based approaches to support decision-making. To add value to real-world planning, DSTs also need to be integrated with the design of the wider IOM process. At a minimum, this means ensuring effective communication at the appropriate level of detail between technical analysts and other process participants. In some cases it may mean adapting entire process elements around a DST, particularly the mechanisms of stakeholder engagement (for example, Adem Esmail & Geneletti 2018, Bonnevie et al. 2019, Estévez & Gelcich 2015, Jumin et al. 2018, Weig & Schultz-Zehden 2019). DSTs can be used by experts to provide stakeholders with visual outputs that spark interest and provide a starting point for discussions and learning processes. Stake- holders can in turn help analysts shape DST input parameters through deliberative processes, though this requires appropriate incentives, support, and training. If it is not possible to provide these to stakeholders, then less technically demanding approaches of integrating stakeholder knowledge and perspectives might be more appropriate (Pope et al. 2019, Portman et al. 2016). In every instance, the potential benefits and draw- backs of different DSTs should be explored and evaluated, so that the most appropriate approaches and tools can be selected for any given situation. The review by Janßen et al. (2019) provides useful guidance, and that by Noble et al. (2019a) exam- ines scientific publications describing GIS-based DSTs that integrate social and ecological spatial data to inform MSP. One commonly used DST is multi-criteria anal- ysis (MCA), which has a range of applications in EB-IOM. Adem Esmail & Geneletti (2018) describe the three stages of MCA as follows: 1) Establish a shared understanding of the structure and context of the problem to be solved, including through defining objec- tives, developing alternative solutions, and

19 See http://www.octogroup.org/EBMTools.html 20 See https://marineplanning.org/tools/software/

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