Multi-objective spatial tools to inform maritime spatial planning in the Adriatic Sea

Abstract

This research presents a set of multi-objective spatial tools for sea planning and environmental management in the Adriatic Sea Basin. The tools address four objectives: 1) assessment of cumulative impacts from anthropogenic sea uses on environmental components of marine areas; 2) analysis of sea use conflicts; 3) 3-D hydrodynamic modelling of nutrient dispersion (nitrogen and phosphorus) from riverine sources in the Adriatic Sea Basin and 4) marine ecosystem services capacity assessment from seabed habitats based on an ES matrix approach. Geospatial modelling results were illustrated and analysed for three biogeographic subdivisions, Northern-Central-Southern Adriatic Sea. The paper discusses model results for their spatial implications, relevance for sea planning, limitations and concludes with an outlook towards the need for more integrated, multi-functional tools development for sea planning.

A modelling framework for MSP-oriented cumulative effects assessment

“…The Tools4MSP Modelling Framework is a regularly updated open source software suite (Menegon et al, 2016) providing multi-objective toolsets for mar-125 itime spatial planning (Depellegrin et al, 2017). The framework supports the development of spatially explicit results, graphics, tables and multi-dimensional grid dataset that can be utilized for more detailed spatial investigations.…”

“…Currently, the framework implements a Cumulative Effect Assessment (CEA), sea use conflict (SUC) analysis model and a marine ecosystem services (MES) capac-ity model. Tools4MSP can be flexibly deployed to different geospatial contexts ranging from macro-regional Gissi et al, 2017) to local/ regional level assessments (Barbanti et al, 2017). There are two modes of access of the framework: (1) The ADRIPLAN Portal (data.adriplan.eu) provides a user-friendly interface enabling users to run customized scenarios of CEA by 135 choosing the area of analysis, the data layers and the resolution of the model outputs (Menegon et al, 2016).…”

“…Despite the methodological advancements, assessment methodologies still rely on major assumptions leading to potential bias of results (Gissi et al, 2017;Stock and Micheli, 2016): spatial accuracy of input dataset (Ban et al, 2010), assumptions on the additivity of impact, while synergistic and antagonistic effects are neglected (Crain et al, 2008), linear response versus more 20 common non-linear response to pressures (Halpern and Fujita, 2013) are still unsolved bottlenecks within the scientific community dealing with cumulative impact assessment. In addition, another critical aspect is the inconsistency and poor specificity in the principles, definitions and approaches adopted in the CEA applications (Judd et al, 2015;Jones, 2016;Stelzenmüller et al, 2018) 25 leading to large variation of CEA research agendas, creating difficulties in the comparing methods and outcomes and posing barriers to proper interpretation and communication of outputs (Stelzenmüller et al, 2018;Foley et al, 2017;Stock and Micheli, 2016).…”

“…A key aspect of the risk assessment is the identification and understanding of the relationships between the source of a pressure, the pathways by which exposure might occur, and the environmental receptors that could be harmed (source-pressure-pathway-receptor linkages) (Judd et al, 35 2015). Similarly to other sea areas around the globe, also in the Adriatic-Ionian Region (AIR) cumulative impact assessment techniques have been implemented on macro-regional level (Barbanti et al, 2015;Gissi et al, 2017), including regional case studies on high resolved geospatial datasets in Emilia-Romagna region (Barbanti et al, 2017). The CEA in the AIR was performed through the 40 ADRIPLAN Portal (CNR-ISMAR, Tools4MSP group, 2014-2017, an MSPoriented and community-based web-platform for publishing, sharing and processing multidisciplinary geospatial data.…”

“…For each of these 190 first parameters the experts have also associate a confidence value (c) that characterizes the level of reliability of their judgments. For a detailed description of the process we refer to Gissi et al (2017).…”