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

defining criteria against which to evaluate each solution. 2) Analyse the potential solutions against the criteria, including weighting and/or aggre- gating criteria and carrying out sensitivity analysis. 3) Ranking or clustering of solutions depend- ing on their performance against the crite- ria and taking a decision on the preferred option. There are many approaches to MCA (Noble et al. 2019a). In the context of IOM, GIS-based MCA techniques are very common and have been used to: • Analyse visual impacts of offshore wind energy developments in order to support siting decisions (Depellegrin et al. 2014) • Analyse the global space potentially suitable for aquaculture developments (Dapueto et al. 2015, Gentry et al. 2017) • Select sites suitable for offshore renewable/ multi-use platforms combining renewables and aquaculture (Gimpel et al. 2015, Zanut- tigh et al. 2016) • Support artificial reef site selection (Barber et al. 2009, Mousavi et al. 2015) • Zone activities in MPAs (Portman et al. 2016, Villa et al. 2002), including in combination with stakeholder input, either post-hoc (Habtemariam & Fang 2016) or eliciting input from stakeholders into the weighting of factors within the MCA (Martínez-López et al. 2019, Portman et al. 2016) • Inform ecosystem-based fisheries manage- ment (Rossetto et al. 2015) • Incorporate ecosystem service evaluations into site suitability analyses (Portman et al. 2016) Adem Esmail & Geneletti (2018) provide relevant and useful guidance on engaging stakeholders in MCA, and Estévez & Gelcich (2015) propose an outline of a participative multi-criteria decision analysis (MCDA) that is in itself a variation of the adaptive management cycle presented in section 4.2. Research is now focusing on integrating spa- tial MCA with social network analysis used to com- prehend and understand stakeholder conflicts and dynamics of interaction (Noble et al. 2019b). Another type of DST that has a lot of relevance in EB-IOM is spatial optimisation software, a well- known example being Marxan 21 , a suite of free software tools developed at the University of Queensland, Australia (Ardron et al. 2008, Ball & Possingham 2000, Ball et al. 2009, Watts et al. 2009, Watts et al. 2017a, Watts et al. 2017b). Marxan

has been used to support the planning and evalu- ation of efficient, coherent and representative MPA networks (Airamé et al. 2013, Klein et al. 2009, Ruiz- Frau et al. 2015, Schill et al. 2015), explore trade- offs in ocean multi-use planning (Yates et al. 2015) and support the efficient zoning of marine uses in order to meet multiple sectoral objectives (Agostini et al. 2010, Jumin et al. 2018, Mazor et al. 2014). Marxan uses an algorithm that finds multiple effi- cient solutions to spatial optimization problems based on systematic conservation planning princi- ples (see section 3.3.2). It was initially developed to help design spatial reserve network configurations that protect multiple conservation features at a minimum cost (in money or other value parame- ters; for example, see Carwardine et al. 2008, Car- wardine et al. 2010). Its current version (Marxan with Zones) is a mul- ti-objective spatial planning tool that identifies opti- mal spatial configurations for multiple zones, each of which protect different values. For example, if simultaneously planning MPAs, fishery zones, and recreational areas, the analyst can set targets for specified amounts of conservation features to be protected in MPAs, specified amounts of high- value fishing grounds to be represented in a sep- arate fishery zone, and specified amounts of high- value recreation areas in the recreation zone. By allowing different relative weightings of targets within and between zones, Marxan with Zones can help explore trade-offs where no ‘perfect’ solutions exist that meet 100% of the targets for each zone. 4.3.3. Tools for analysing and modelling conflicts and interactions User-environment interactions are at the core of EB-IOM and are primarily characterized and assessed through EIAs and SEAs (section 4.2.2). In addition, EB-IOM must also address user-user interactions, including those generated by MPAs restricting or displacing activities. There are dif- ferent approaches and frameworks that can help ocean managers understand and analyse user- user interactions. Klinger et al. (2018) differentiate between neutrally compatible, positive, and neg- ative (conflicting) interactions, with five basic cat- egories: • Competition (mutually negative impacts) • Antagonism (impacts are neutral in one direction and negative in the other) • Amensalism (impacts are neutral in both directions) • Commensalism (impacts are neutral in one direction and positive in the other) • Mutualism (mutually positive impacts)

21 See http://marxan.org/.

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