Addressing cumulative effects, maritime conflicts and ecosystem services threats through MSP-oriented geospatial webtools

Abstract To solve conservation and planning challenges in the marine environment, researchers are increasingly developing geospatial tools to address impacts of anthropogenic activities on marine biodiversity. The paper presents a comprehensive set of built-in geospatial webtools to support Maritime Spatial Planning (MSP) and environmental management objectives implemented into the Tools4MSP interoperable GeoPlatform. The webtools include cumulative effects assessment (CEA), maritime use conflict (MUC) analysis, MSFD pressure-driven CEA and a CEA-based marine ecosystem service threat analysis (MES-Threat). The tools are tested for the Northern Adriatic (NA) Sea, one of the most industrialized sea areas of Europe using a case study driven modelling strategy. Overall results show that coastal areas within 0–9 nm in the Gulf of Trieste, Grado-Marano and Venice lagoon and Po Delta outlet are subjected to intense cumulative effects and high sea use conflicts mainly from port activities, fishery, coastal and maritime tourism and maritime shipping. Linking MES into CEA provided novel information on locally threatened high MES supporting and provisioning habitats such Cymodocea beds and infralittoral fine sands, threats to cultural MES are most pronounced in coastal areas. Results are discussed for their geospatial relevance for regional planning, resource management and their applicability within MSP and environmental assessment.


Introduction
Current conservation and planning challenges of the marine environments require flexible tools that ensure to different types of user the access, management, sharing, processing and visualization of a multitude of spatial and non-spatial dataset. Ideally, these datasets are stored within platforms capable to organize a multitude of data and convey them into easily and quickly accessible graphical user interfaces (GUI). The use of Maritime Spatial Planning (MSP, Directive 2014/89/EU) as practical process to achieve environmental, social and economic objectives and minimize conflicts (Hansen et al., 2017) in European seas has posed novel demands to amount, quality and sources of data. Despite the ongoing governance process, considerable work has been done by the scientific community for the development of Spatial Data Infrastructure (SDI; Fowler et al., 2010) in support of a knowledge-based implementation of national and regional plans. In recent years the application of cumulative effects assessment and sea use conflict analysis have emerged as common analytical tool to support decision-makers in the development of spatial plans and in support of the ecosystem-based management of marine resources. This is also reflected in an emerging number of decision support tools enabling user to perform cumulative effects assessment in various contexts. An extended review of decision support systems performed by Krueger and  Step 0 -Webtool selection; Step 1 -Case study area selection; Step 2 -Study area selection & Dataset configuration and Step 3 -Geospatial and statistical outputs.

2.2.
Step 0: Webtools selection This step allows user to select a comprehensive set of webtools available in the Geoplatform (Fig. 2) namely a Cumulative Effects Assessment (CEA), Maritime Use Conflict (MUC) analysis and Marine Ecosystem Services Threat analysis (MES-Threat). In Fig. 2 (right) the buttons to prompt user to the webtool model run. In the following section a detailed description of theoretical and methodological background of the webtools is provided.

Cumulative Effects Assessment (CEA)
The Tools4MSP Geoplatform implements a Cumulative Effects Assessment (CEA) for the analysis of cumulative effects generated by anthropogenic activities on marine environmental components. Its implementation is based on archetypical CEA implementations proposed in various geographical scales (Halpern et al., 2008;Andersen et al., 2013). In detail, we define CEA as a systematic procedure for identifying and evaluating the significance of effects from multiple pressures and/or activities on single or multiple receptors (Judd et al., 2015). The CEA incorporates two major improvements, such as the modulation of propagation of pressures through a distance model M(Ui, Pj, Ek) based on 2D Gaussian spatial convolution and the distinction of sensitivity scores (sj,k) into sensitivity values combined with use-specific relative pressure weight (wi,j,k). The CEA algorithm implemented in the Geoplatform is described in Eq.1. The algorithm takes into account an additive effects combination, meaning that cumulative effects correspond to the sum of individual effects on an environmental component (CEAA-ACEE, 2016), and considers a linear response of the environmental component to the pressure. The CEA score on a single grid cell is calculated as follows: (1) where eff is the effect of pressure P over the environmental component E, whereas, U = i-th human use P = j-th pressures derived from the MSFD (/EC, 2008) E = k-th environmental components eff(Pj, Ek) = effect exerted by the pressure Pj over the k-th environmental component, Ek. s(Pj, Ek) = sensitivity of the environmental component Ek to the j-th pressure Pj wi,j,k = use-specific relative pressure weight d(Ek) = Intensity or presence/absence of the k-th environmental component on the cell (x, y), which is 1 for fixed E (seabed habitats), and varies from 0 to 1 for mobile special features (turtles, marine mammals and seabirds). i (Ui, M(Ui, Pj, Ek)) = distance model propagating j-th pressure caused by i-th activity over the k-th environmental component M (Ui, Pj, Ek,) = 2D gaussian kernel function used for convolution considers buffer distances at 1 km, 5 km, 10 km, 20 km and 50 km ' (tick) = effect rescaling operator (from 0 to 1) Further details on proposed CEA algorithm can be obtained from Menegon et al. (2018a).

Maritime Use Conflict (MUC) Analysis
Maritime Use Conflict (MUC) analysis is based on a methodology developed within the COEXIST Project -Interaction in European coastal waters: A roadmap to sustainable integration of aquaculture and fisheries (COEXIST, 2013). In particular the methodology presented by Gramolini et al. (2010) enables the identification of current/potential human uses and assesses their interaction in terms of conflicts. The algorithm implemented for the MUC score on a single grid is presented in Eq. 2: (2) where, cij = potential conflict score between Ui and Uj p(Ui) = presence (1) or absence (0) of the i-th human use in the cell p(Uj) = presence (1) or absence (0) of the j-th human use in the cell The potential conflict score (cij) between two uses Ui and Uj can vary from 0 (no conflict) to 6 (very high conflict score) and was calculated following the COEXIST methodology: i) application of an expert judgment approach to characterize each human uses through four attributes (vertical scale, spatial domain, temporal domain and mobility); ii) automatic assessment of the potential score for each use combination based on uses characterization and COEXIST rules application; iii) supervised expert based adjustment of the cij coefficients to take into account legal and practical constraints between uses (see Appendix 1 for human uses characterization and COEXIST rules). Appendix 2 presents the potential conflict score matrix representing the cij coefficients applied for each use combination. For further details on the methodology applied in the study area we refer to Barbanti et al. (2015) and Depellegrin et al. (2017).

Threat analysis to Marine ecosystem services (MES-Threat)
The MES-Threat assessment builds on existing theoretical and practical approaches for the integrated analysis and mapping of stressor/pressure effects on MES supply units within the Great Lakes Restoration Initiative (Allan et al., 2013), North Sea (Hooper et al., 2017 or the ODEMM linkage framework (Options for Delivering Ecosystem-Based Marine Management; White et al., 2013). We define as MES-Threat the risk of MES reduction, partial or permanent loss of provision or impairment of use due to single or multiple anthropogenic effects targeting the MES providing ecological components (Worm et al., 2006;Maron et al., 2017). The tool incorporates an expert based MES capacity scoring and mapping procedure implemented for the Adriatic Sea's EUNIS habitats (see Appendix 3) with CEA modelling capabilities of the Geoplatform. The MES-Threat algorithm is presented in Eq. 3: ( 3) where, CEA = cumulative effects assessment model as described in Eq. 1 capk = marine ecosystem services supply capacity (0-2, see Appendix 3) p(Ek) = Presence/absence of the k-th EUNIS habitat on the cell (x, y) 2.2.
Step 1: Case study selection After selecting the webtool to be applied, the system prompts the user to a pre-selected case study list. At the current stage three different geospatial domains are available, namely Mediterranean Seabasin level, macro-regional level for the Adriatic Sea and regional level for Emilia-Romagna Region (Fig.  3). Each case study represents a pre-configured set of webtool-specific data, with consistent spatial coverage of human uses and environmental components and incorporating all other necessary parameters for the model run. For the tools application in the case study a pre-configured grid resolution of 500 m x 500 m was applied.

2.3.
Step 2: Case study setup 2.3.1 Study area definition After selecting the case study, the Geoplatform prompts the user to the case study setup using an interactive web mapping application with a polygon selector tool (Fig. 4a). The Northern Adriatic (NA) Sea was selected as area of analysis based on the biogeographic boundaries defined by Bianchi (2004). The NA biogeographic region covers about 22,500 km 2 and is delimited by the Conero Regional Park to the southern tip of Istrian Peninsula (Bianchi, 2004). The NA is relatively shallow, with depth not exceeding 50 m (Turk and Odorico, 2009). From an administrative point of view, the NA embraces three countries and five coastal regions, including Italy (Emilia-Romagna, Veneto and Friuli-Venezia-Giulia Region), Slovenia (Coastal Karst Region) and Croatia (Istria Region). The NA is an extremely complex environment as it combines intensive anthropogenic activities (e.g. maritime transport, commercial fishery, aquaculture, coastal and maritime tourism), with sensitive coastal and marine ecosystems (e.g. essential fish habitats, nursery and spawning grounds of species of high commercial interest, seabirds and hotspots of Species of Community Interest such as Caretta caretta turtles and marine mammals, mainly Tursiops truncatus).

Dataset configuration
After the selection of the study area, the user can select the configuration of the geospatial dataset to be modelled ( Fig. 4b-d). The Geoplatform incorporates a stocktake of over 65 MSP relevant geospatial layers for viewing, querying and download ( Table 1). The user can select the human uses, the environmental components and the MSFD pressures to be included in the case study development. The dataset configuration is a key element for a case study development strategy as it allows to customize model outputs. The most updated version of the dataset can be freely download at Menegon et al. 2018b (https://doi.org/10.5281/zenodo.1173764 In total 13 layers of human uses were available in the Tools4MSP Geoplatform. The dataset is used by all three webtools (CEA, MUC and MES-Threat). Sources of the dataset are multiple and include: EU wide datasets (e.g. EMODnet Data Portals, European Atlas of the Seas, EEA map services), project portals specific datasets (e.g. the SHAPE Adriatic Atlas, COCONET WebGIS), data made available by research institutions (e.g. HCMR -Hellenic Centre for Marine Research; CNR-ISMAR -Italian National Research Council -Institute of Marine Sciences) and from national (e.g. OTE S.A. -Hellenic Telecommunication Organization; MIPAAF -Italian Ministry of Agriculture, Food and Forests) and regional authorities (Veneto and Emilia-Romagna). The dataset of environmental components is used for the CEA and MES-Threat analysis and is based on 20 layers. Marine habitat layers include 15 distinct habitats, which were derived from EUNIS classification based EUSeaMap dataset (Populus, 2017). Layers for marine mammals, Loggerhead turtles and Giant Devil Ray densities were obtained from UNEP-MAP-RAC/SPA (Fortuna et al., 2015) and are based on a weighted presence/absence (wP/A) in terms of individuals per 20 km x 20 km. The nursery areas of 33 valuable commercial fishery species, including European pilchard (Sardina pilchardus), common sole (Solea solea), Norway lobster (Nephrops norvegicus), red mullet (Mullus barbatus) were obtained from the MEDISEH MAREA (Mediterranean sensitive habitats; www.mareaproject.net/medviewer) Project. These layers are available as dummy indicator of presence/absence (P/A).
The CEA model implements 15 MSFD pressures out of 18 provided by the MSFD (EC, 2008). In the description of Table 2, pressures are grouped into three pressure themes, according to MSFD amended version (EC, 2017, Annex 4, Table 2): biological (2 pressures), physical (5 pressures) and a mixed substances-litter-energy (8 pressures) theme. The three pressures related to significant changes in salinity regime, introduction of radio-nuclides and introduction of microbial pathogens were omitted from the pressure dataset due to lack of reference and expert judgement. The marine ecosystem services component necessary for the MES-Threat analysis is based on a MES capacity matrix (Appendix 3) rescaled for the NA Sea according to an initial assessment by Depellegrin et al. (2017). The MES capacity matrix adopts a qualitative indicator of the potential ES supply of the habitat ranging from 0 (neglectable capacity) to 2 (high capacity) adopted from a methodology proposed by Galparsoro et al. (2014) and Salomidi et al. (2012). In the NA the matrix implements 12 MES (x-axes) and 15 EUNIS habitats (y-axes) grouped into four MES categories (provisioning, regulating, cultural and supporting).

Case study strategy development
In order to provide meaningful assessment for environmental management and planning in the NA Sea, the webtools were applied by operating seven distinct dataset configurations resulting into three webtool base runs (CEA/MUC/MES-Threat), three CEA/MUC sector-specific runs (maritime traffic, commercial fishery and coastal and maritime tourism), three MSFD pressure themes driven CEA runs (biological, physical and substance-litter-energy pressures) and four MES-Threat model runs, one for each MES category (provisioning, regulating, cultural and supporting). In Table 2 a summary of the dataset requirements and setup for each model run is presented.

Step 3: Geospatial and statistical outputs
The Tools4MSP Geoplatform provides to the user a full range of geospatial and statistical results that can be used for further deepening of the analysis. In Fig. 5 a GUI example of the multiple outputs are provided including exploration of geospatial and statistical results (Fig.5 a and d), the view layer functionality to share and download modelling results (geotiff format) with the user community (Fig.5 b) and the complete metadata functionality to compile metadata information on the modelling results ( Fig.5 c). In particular the downloaded results can be used for further investigation or re-analysis using dedicated GIS software. In the following Section geospatial visualizations were presented using Quantum-GIS (QGIS Development Team, 2018) and statistical results were presented with Python numeric and scientific libraries including Numpy, Scipy, Pandas, Matplotlib and Seaborn (van der Walt, 2011; McKinney, 2010).

Cumulative Effects Assessment
In Fig. 6 a-d results from CEA case study development are presented. The CEA base run show that highly impacted sea areas are located mainly in Italian coastal areas such as the Gulf of Trieste and along a coastal segment in front of the Po river outlet (Fig. 6a). On overall the NA Sea reaches a mean CEA score of 3.06. The maximum CEA score of 8.3 is located in proximity of the port of Trieste, in the North-Eastern NA.
The sectorial CEA application for maritime transport is presented in Fig. 6b. CEA scores reach a maximum score of 2.73. Areas of highest CEA score are located mainly offshore, and correspond to high density shipping lanes (up to 400-500 vessels per year) connecting the main Adriatic ports (e.g. Venice, Trieste and Koper) to the Mediterranean Sea potentially affecting valuable hotspots of marine mammals (mainly T. truncatus) and loggerhead turtles (C. caretta). The sectorial CEA application for coastal and maritime tourism is presented in Fig. 6c. Areas of highest CEA score are located in the Gulf of Trieste (score 1.5) and Venice Lagoon and the Malamocco outlet. The Gulf of Trieste is particularly densely populated with 26 marinas on the Italian coastal areas and 3 on the Slovenian coastal areas. To notice is that on overall the the Italian coastal regions of Friuli-Venezia-Giulia and Veneto have higher CEA scores distributed along the entire coast, compared to Slovenian and Croatian coastal segments. The sectorial CEA score from the commercial fishery is represented in Fig. 6d. The maximum CEA score is 4.9 in proximity of Riccione (Emilia-Romagna Region). Other sea areas of high CEA score (3.8) are located in front of the Po Delta inlet. Both areas are subjected to intense fishery activities along Italian coasts, especially trawling (e.g. bottom otter trawl, pair pelagic trawl), which greatly affects both biological resources and seafloor integrity. Areas of lower intensity CEA scores can be attributed to the 3 nm boundary, where trawling activities are forbidden (EC Regulation 1967). Results for the MSFD pressure-specific CEA case study runs are presented in Fig. 7. Geospatial results for the biological theme (Fig. 7a) show that highest CEA score (1.2 -1.5) have a patchy distribution, mostly corresponding with commercial fishing activities in offshore areas, in front of the Venice Lagoon, Po Delta outlet, Rimini Port (Emilia-Romagna Region), and in offshore areas in front of the coastal settlements of Rovinj (Istria Region) (CEA score = 1.4). The environmental components with highest sensitivity to biological pressures refer to commercial fishes nursery habitats, marine mammals and turtles. The geospatial distribution of the physical pressure theme (Fig. 7b) shows a more homogenous distribution. In particular the offshore areas (about 3-6 nautical miles) in front of the Italian coastal Regions of Veneto, Emilia-Romagna and Marche Region are areas of high CEA scores (2.5 -2.8) in front of the Po Delta outlet and between Port of Rimini and Pesaro, which clearly relate these pressures to intense trawling activities. The environmental components most affected by the physical pressures refer to infralittoral and circalittoral sand a mud habitats, Cymodocea beds and infralittoral rock and other hard substrata. The geospatial results for the substances-litter-energy pressure theme (Fig. 7c) has the highest relative score (3.97) among all three pressure themes. High CEA score are concentrated in small area in front of the Po Delta outlet. Other high CEA score areas are located in offshore areas in proximity of hotspots of Species of Community Interest (C. caretta and T. truncatus).  coastal segment between the Croatian coastal settlements of Novigrad and Pula (MUC score between 5 and 8).

Maritime Use Conflict analysis
The analysis of sector-specific MUC runs are presented in Fig. 8b-d: Results for maritime transport (Fig. 8b) show that areas of highest MUC (>8; 580 km 2 ) are located in front of main port areas in the NA Sea (Trieste, Koper) and northern Po River Delta, Port of Ravenna and Ancona. Highest MUC score (> 8; 612 km 2 ) for coastal and maritime tourism (Fig. 8c) show a similar pattern to the maritime transport. The MUC run for commercial fishery (Fig. 8d) evidences clear patterns of conflict between the different types of fishing and with maritime transport. Areas of highest MUC scores (>15; 179 km 2 ) are located in front of Chioggia and Venice lagoon , followed by port of Trieste and a narrow offshore area between 3 and 4 nm in front of Veneto, Emilia-Romagna and Marche region (MUC score 10-15; 983 km 2 ) and a widespread offshore area along the main maritime traffic corridors (MUC score 5-10; 6254 km 2 ).

Comparison of CEA/MUC outputs
In Fig. 9a the CEA (by MSFD pressure themes) and MUC contribution as function of distance from coast in nautical miles (nm) is illustrated. In terms of pressure-specific CEA, the cumulative effects coming from input of substances, litter and other forms of energies contributes to 41.1% to the total CEA score, followed by the cumulative effects from physical pressures with 40.9% and the cumulative effects from biological pressures with 18%. Within the 0-3 nm the the CEA Substanceslitter-energies contributes to 60.9% to the total CEA, followed by physical pressures with 29.3% and biological pressures with 9.8%. Beyond the 12 nm, the CEA from physical pressures contributes to 41.7%, followed by CEA from Substances-litter-energies (38.3%) and from biological (20%) pressures.
The MUC analysis evidences that 50% of the conflict relies within the 9 nm. Peak of conflict is located within the 3-9 nm with contribution of 21% of the total MUC score. On the contrary, about the 70% of the CEA score is almost uniformly distributed between the 3 and 24 nm. Highest CEA score is located between 15-18 nm. Sector-specific CEA/MUC as a function of distance (nm) are presented in Fig. 9b. The overall contribution of maritime traffic, commercial fishery and coastal maritime tourism represents the 95% ot the total CEA score and the 74% of the MUC score. Commercial fishery has the highest contribution to CEA (55.4%) and MUC (44%) overall score, followed by maritime transport (CEA = 31.3% and MUC = 20.2%) and coastal and maritime tourism (CEA = 5% and MUC = 10.2%). To notice is that 94 % of CEA score derived from coastal and maritime tourism is concentrated within 0-9 nm, similarly 99% of MUC score contribution comes from this segment.

MES threats analysis
In Fig. 10a results from MES-Threat base run are presented. The areas of highest threat (MES-Threat score >25; 165 km 2 ) are located in front of Grado-Marano Lagoon coastal area referring and smaller patchy areas in proximity of the Venice Lagoon. Threatened habitats refer to Cymodocea beds (A5.531), habitats providing a multitude of MES (e.g. providing nursery, biodiversity, food provisioning, nutrient cycling, water quality; Appendix 3) associated with areas of high cumulative pressures. CEA applied on provisioning MES (Fig. 10b) shows potential threats (MES-Threat score > 10) located in the Gulf Trieste for coastal habitats responsible for food provisioning capacity, such as circalittoral sandy (A5.35), circalittoral fine mud (A5.36) and circalittoral muddy sand (A5.26). CEA applied on regulating MES (Fig. 10c) has a more patchy distribution of high threat areas (MES-Threat score > 3) localized in the Gulf of Trieste, coastal areas of Grado-Marano and the segment from Venice Lagoon to Po Delta. CEA applied on cultural MES (Fig. 10d) shows high threats in nearshore areas along Friuli-Venezia-Giulia Region (MES-Threat score >10), Veneto Region (MES-Threat score > 4) and southern Emilia-Romagna and Marche Region (MES-Threat score > 2). To notice is that in the Eastern NA more extended threat areas occur, but with lower threat score (MES-Threat score > 2). Threats are particular relevant for infralittoral habitats and Cymodocea beds. Threat analysis for supporting MES (Fig. 10e) responsible for sustaining biodiversity and nursery provision show a threat distribution compared to the MES-Threat base run, with highest threat scores (>12) in proximity of Grado-Marano, Venice lagoon and Conero Promontory. In Fig. 11 the MES-Threat score contribution in percentage as function of distance (in nm) from coastline were presented. On overall results show that highest threat scores from MES-Threat are located within 3-6 nm (18.7 % of total contribution), where about 50% of the contribution is due threats to provisioning MES, 14% to regulating MES, 4% cultural MES and 33 % to supporting MES. To notice is that threat areas for MES cultural capacity are entirely located within the 0-9 nm from coastline with a 65% contribution within 0-3 nm, 28 % within 3-6 nm and 7% within the 6-9 nm. Fig. 11. Percent contribution of MES-Threat base run (divided into four MES categories) to total MES-Threat score according to distance intervals of 3 nautical miles (nm) from coastline.

Overall results
The presented webtools embedded into the Tools4MSP Geoplatform in combination with a clearly defined case study modelling strategy exemplified how meaningful geospatial and statistical results can be obtained in support of planning and environmental management considerations. The geospatial webtools in support of planning and environmental management provide set of advantages as largely discussed in literature (Atkinson and Canter, 2011;González Del Campo, 2017;Palomino et al., 2017) such as transparency, objectivity and replicability of model outputs, all considered fundamental within a pragmatic planning process. Particularly relevant are the dynamic functionalities coupled to the Spatial Data Infrastructure (SDI) enabling data pre-processing (normalization and aggregation, rescaling, filtering), access to existing and novel datasets as they become available and republish of spatial outputs for utilization within user communities and possibilities for model re-run. The case study developed for the Northern Adriatic demonstrated the potentialities of the Tools4MSP Geoplatform to be further developed towards an operational Decision Support System for a multitude of marine and coastal environmental management and MSP-oriented planning tasks. In addition to the presented modelling capabilities, the Geoplatform aggregates a multitude of geospatial dataset and formats into already normalized datasets and therefore enhances its usability by reducing time and manpower for extensive data preparation. The tool can be flexibly applied to different spatial scales (from seabasin to regional level) by defining the study area and depending on the availability and quality of dataset, also grid resolution can be customized. The case study for the NA Sea was performed on a resolution 500 m x 500 m grid. A higher resolution of analysis can be considered in combination with local datasets. In the NA, this is particularly required for inland waters such as Venice Lagoon and Grado-Marano Lagoon and the Gulf of Trieste, where anthropogenic activities are particularly intense (Gallmetzer et al., 2017;Malačič et al., 2008;Munaretto and Huitema, 2012). The geospatial results outline the complexity of anthropogenic impacts and interactions in the study area. CEA and MUC outputs highlight the need for thorough planning measures in order to deal with the intense conflicts occurring in the area, especially in the western segment of study area along Italian nearshore areas. The CEA analysis presented in Fig. 6a-d provides an overview of the spatial distribution of the cumulative and the sectorial effect scores in the study area. The CEA score exerted by commercial fishery has the most evident effects across the study area ( Fig. 6d and 9b). The Northern Adriatic Sea is one of the most intensively fished area in Europe, where most of the harvested stocks are overexploited, especially in the western sector, due to intense non-selective fishing activities from Italian fleets exerted on fish habitats (Colloca et al., 2013;Russo et al., 2015, Bastardie et al., 2017. In nearshore areas (about 3 nm) lower CEA scores refer to areas where towed gears are banned or unsuitable, usually in favour of small scale fisheries (mainly set gears and longlines). Maritime traffic effects are located mainly offshore (Fig 6b), in the central-eastern portion of the study area, where the north-south Adriatic traffic route connects to the Mediterranean. Those areas intersect with biodiversity hotspots of valuable marine species, such as loggerhead turtles and marine mammals (Fortuna et al., 2015). The cumulative effects from coastal and maritime tourism sector generates main impact phenomena in proximity of coastal areas ( Fig. 6c and 9b), where the necessary infrastructure and facility occur (Papageorgiou, 2016) and where the majority of vulnerable ecosystem are present. In the NA, summer recreational resorts from Friuli-Venezia-Giulia, Veneto Region and Emilia-Romagna, belong to the top 20 most popular tourist destinations on EU level (Eurostat, 2017). The MSFD-driven CEA representation (Fig. 7) shows that the anthropogenic pressures within 0 to 3 nm derive from the MSFD pressure theme substances-litter-energies exerted mainly by land-based activities (Fig. 7c, 9a). The higher scores are linked to riverine discharge from Po and Adige rivers, that are the biggest contributors of freshwater, nutrients and pollutants of the Adriatic Sea (Chiogna et al., 2016;Simonini et al., 2004), commercial traffic in proximity of ports (Venice, Chioggia, Ravenna), coastal tourism and leisure boating. In comparison, the MSFD pressure themes concerning biological and physical pressures close to the coast have lower effects intensity (Fig. 7a,b and 9a) and mainly derive from artisanal fisheries and maritime transport. High CEA scores are evidenced for valuable habitats such as Cymodocea beds and nursery areas. A major reason for lower CEA score by physical pressures are related to limitations of towed gears ban within the 3nm off the coast, with the significant exception of those close to port and marinas or subjected to intense artisanal fisheries with bottom impacting tools. Beyond 3 nm limitations, physical pressures became the most intensive ( Fig.  7b and 9a), due to the strong contribution of bottom trawling fisheries on the CEA score, determining high physical (e.g. abrasion) pressures on essential fish and seabed habitats (Pranovi et al., 2000). Moreover, trawling fisheries are also highly responsible for biological pressures (e.g. extraction of species; Eigaard et al., 2016) and, together with maritime traffic, releases of marine litter and substances, potentially affecting seabed habitats and the populations of marine turtles and mammals. The application of the MUC and its setup for sectoral analysis of conflicts allowed to identify main areas of conflict to compare results among the most relevant sea uses in the Northern Adriatic Sea (Fig. 8). According to Fig. 9b over 60% of the total MUC score is concentrated within the 12 nm boundary, mainly caused by intense interactions between coastal tourism, maritime transport, fisheries and other activities (e.g. aquaculture), especially close to ports (e.g. Trieste, Koper, Venice and Ravenna) and marinas, while in offshore areas spatial conflicts occur between traffic routes and trawling fishery grounds (Fig. 8). The distribution of conflicts evidences the high demand for sea space in proximity of coastal areas in a relatively small sea space. Soft uses (e.g. fishery and coastal tourism) and hard uses (e.g. aquaculture, Oil & Gas exploitation or maritime transport) need to trade- off especially in coastal areas that function as hub aggregating infrastructure and facilities necessary to support maritime economic activities.
The MES-Threat analysis demonstrated how CEA based modelling capabilities can be linked with MES mapping based on the MES supply capacity of EUNIS habitats. Spatial results provided novel insights on the distribution of threats to MES and therefore to the risk of reduction, loss or impairment of the MES provisioning capacity of a particular habitat or combination of habitats. From a planning perspective, the produced results can be considered as highly integrative to the CEA and MUC outputs, as they incorporate societal values into the analysis (Maron et al., 2017), can more efficiently delineate protection priorities (Werner et al., 2014) and support the design of restoration plans (Allan et al., 2013) for coastal areas or habitats (e.g. Cymodocea beds) subjected to highest threat from anthropogenic stressors (Fig. 10a). Similar to CEA/MUC results, the analysis showed that threats to MES are highest within the 0-6 nm, a critical area for MES provision (Fig. 11), but also for intensity, variability of pressures and conflict areas (Fig. 9). Particularly affected categories are provisioning and supporting MES, which are responsible for the provision of fundamental goods and services sustaining various components of coastal economies such as commercial fishery, aquaculture and tourism.
The use of a case study development strategy demonstrated a high degree of customization of the webtools by the user and a flexible adaptation to different MSP stages: First, the webtool can facilitate data gathering through a community-based approach and interoperable access to EU-level and international SDIs, such as EmodNet, EEA, SeaDATANet or International Hydrographic Organization (IHO). Current datasets can be flexibly visualized and recombined through interoperable view services (i.e WMS, TMS, ArcGIS REST service) in order to create shareable online maps. Second, the webtools can be used for the identification of specific planning constraints and current conditions of the sea space in terms of multiple and single pressure on environmental components and existing conflict among uses. Third, the model outputs can be used to evaluate different management actions or define alternative scenarios through the comparison of two case studies and understand variations of cumulative effects, consequences for sea use conflict and threats to ecosystem service supply capacity.

Webtools support to EIA and SEA
MSP, as an area-based management framework can represent plans, programs or policies that require environmental impact assessment (EIA) and strategic environmental assessment (EIA). Common aim for MSP, EIA and SEA is to promote sustainable development through the integration of environmental considerations into the planning process and reduce spatial negatives (IUCN, 2013).
In the context of the EIA (2011/92/EC) and SEA (2001/41/EC) the presented webtools can deliver a promising support to address several requirements of both Directives: (1) The presented Geoplatform facilitates the access and usage of geospatial datasets that can be relevant to EIA and SEA (Vanderhaegen and Muro, 2005). (2) Although on different spatial and implementation scales the CEA/MUC can be flexibly deployed on sectoral level ( Fig. 6 and 8b-d) and MSFD pressure specific (Fig. 7) local project as required within EIA [Article 5(1), ANNEX IV], while as requested within SEA [Article 3(5) ANNEX II], the tool can flexibly address the cumulative nature of effects on a broader scale from regional to national and also transnational level.
(3) The implemented CEA isotropic distance model (Eq.1) is capable to modulate propagation of pressures and can further complement the analysis of spatial influence of the proposed project on local, regional, national or transboundary level. Although methods for ES-inclusive SEA are still lacking (Slootweg and van Beukering 2008;Söderman et al., 2012), the presented marine ES-inclusive threat analysis approach based on EUNIS habitats MES supply scores can be a valid complement to SEA. In particular the ESdriven concept can be flexibly incorporated along a SEA activities and evidence sea areas of socioecological importance, possible impacts of present and future plans on key ES and identify solutions and restoration measures to reduce anthropogenic effects (Geneletti, 2011).

Webtools support to MSP
The relevance of geospatial tools to support MSP implementation has been evidenced by many academic, planning and decision-making communities around Europe (MSP Platform, 2018;JPI Ocean, 2017;WESTMED, 2017). In particular the tools for cumulative effects and associated processes (e.g. strategic environmental assessments) have found vivid development in the last decade, with application in various planning and spatial contexts (Andersen et al., 2013;Depellegrin et al., 2017;Stelzenmüller et al., 2013). At this stage the presented tool need to be seen as a test toolset, with particular limitations related to data availability and model robustness. Depending on the modelling approach for uses, pressures and the environmental components, its over-simplifications do not allow to take decisions with high socio-economic relevance. Moreover, the connections with provisions from other policy instruments (e.g. MSFD, WFD, CFP, H&BD) are relevant and are only partially explored, while maritime plans have to carefully consider the coordination and compliance with all relevant policies. Within a typical MSP methodology (Ehler and Douvere, 2009;Barbanti et al., 2015), these tools can be used both in the analysis phase, defining and analysing existing conditions, and in the planning phase, supporting the development of measures and scenarios and the evaluation of their effectiveness. Finally, the presented webtools are part of a wider ecosystem of analytical techniques supporting MSP. In fact CEA, MUC and MES-Threat can be combined with each other ensuring a multi-functional approach or can play a complementary role in support of tool functionalities, such as Displace for use specific investigations (Bastardie et al., 2017), Marxan with Zones for scenario development (Ban et al., 2013) or Seasketch (2018) for incorporating participatory stakeholder engagement into spatial modelling.

Datasets
Although the presented webtools benefit from a multitude of geospatial datasets (in total 65) composed by human uses, environmental components and pressures further extension can be considered. In particular, integration of novel datasets can be used for scenario analysis of emerging sectors of the marine economy in the Adriatic Sea, such as potential offshore wind energy farms off the coastal settlement of Rimini (Emilia-Romagna Region; Schweizer et al., 2016) or in front of the coastal settlement of Pula (Istria Region; Hadžić et al., 2014), extensions of the ports of Ravenna (RER, 2015), Trieste (TMT, 2016) and Koper (Port of Koper, 2015) or increasing aquaculture development to meet fish food demand (Piante and Ody, 2015) should be incorporated and analysed for its environmental impacts and the creation of potential sea use conflicts. Moreover, our case study shows high variability in CEA/MUC scores between western and eastern coastal areas, this is related to higher number and intensities of human activities along the Italian coasts compared to Slovenian and Croatian ones, but also due to a high heterogeneity in human activities (especially Oil and Gas extraction, aquaculture and shipping) and the number of datasets available from different countries. Concerning the environmental components, higher resolved geospatial datasets on habitats, benthic communities and species (Certain et al., 2015;Marcotte et al., 2015) should be integrated considering their potential sensitivity towards specific anthropogenic pressures (Eno et al., 2013) and with proper classification schemes. While the current dataset incorporates a multitude of endogenic pressures, generated within the system and that can be managed (Elliott, 2011), there is the need to incorporate as well exogenic pressures such as climate change in order to align the methodology to other CEA assessments around the globe (Halpern et al., 2015;Clarke Murray et al., 2015). This would support the analysis of climate change scenarios and its influence on coastal areas, marine ecosystems and interactions with human activities (Pinarbasi et al., 2017). In fact, ecological and geomorphological conditions of the Northern Adriatic Sea make it particularly sensitive to changing hydrological and oceanographic conditions (Bosnjakovic and Haber, 2015), inducing harmful algal blooms (Barale et al., 2008), red tides (Socal et al., 2011) or different hydrodynamic impacts (e.g. inundations, storm surges or coastal erosion).

Limitations
The tested tools are not free of limitations. Difficulties in the parametrization of the model induced the implementation of an additive and linear model, while ongoing research in cumulative assessment demonstrate the need of integration of mitigative and antagonistic effects in the pressureenvironmental components interaction. There is spatio-temporal inhomogeneity among dynamic environmental components (seabirds, mammals and turtle datasets) with coarser resolution datasets compared to EUNIS marine habitats (100 m x 100 m) and differing nominal scales (e.g. individuals/km 2 versus presence-absence indicators).
Although an extensive sensitivity and uncertainty analysis has been performed for the Adriatic-Ionian Sea , an implementation of uncertainty in the webtools is currently absent. Uncertainty analysis is an important component within CEA, as it supports realistic knowledge aggregation optimised methodological procedures which are baseline for MSP (Judd et al., 2015). In particular an uncertainty analysis as part of model-based decision support, should be an integral part of the webtool results to better identify data gaps (Meyer, 2012), support effective risk assessment (Stelzenmüller et al., 2015), take into account the precautionary principle into planning considerations and communicate the uncertainty within a participative dialogue (Bijlsma et al., 2011). A limitation of the presented MUC is the absence of representation of synergies in the sea space, as the MUC model only considers spatial conflicts focused only on use-use conflicts, without considering multiple interaction (within three or more sea uses) or dynamic interaction from highly mobile sea uses (e.g. maritime traffic, commercial fishery, coastal and maritime tourism). We consider the MES-Threat analysis a first methodological approach for integration of the socioecological dimension into cumulative effects assessment. Major challenges remain identifying the suitable spatial extent and resolution for quantifying MES and models for taking into account their space-time variability. Further research is needed to aggregate other environmental components into MES datasets (e.g. marine mammals and turtles), develop pressure specific MES sensitivity charts (Hooper et al., 2017), deepen the link of biodiversity attributes with ecosystem services supply (Harrison et al., 2014) and provide theoretical and methodological integrations of MES into environmental and socio-economic impact assessment also for specific pollution risks (Depellegrin and Blažauskas, 2013;Song et al., 2017) and emerging new uses, such as renewable energy (Papathanasopoulou et al., 2015).

Conclusions
The Tools4MSP Geoplatform provides a novel system in support of planning and environmental management, incorporating within a single geoplatform three operational webtools: the cumulative effects assessment, maritime use conflict analysis and marine ecosystem services threat analysis. This is an added value for decision-makers and planners that seek for a rapid exploratory mapping of human-environment interactions. Modelling outputs were guided by a structured case study development strategy allowing overall analysis and context specific investigations, such as by marine sector, by MSFD pressure themes or by marine ecosystem services categories. The community-based Geoplatform demonstrated to be highly versatile instrument for the spatialization and geostatistical evaluation of MSP relevant knowledge applicable in several stages of an MSP process and potentially also supporting EIA and SEA. In particular the Geoplatform can ensure notable support for transparent analysis that can engage a multitude of user communities into decision-making, ensure replicability of the modelling process and iterative data assimilation, as it becomes available. The tool can be used by a broad spectrum of stakeholders, including decision-makers, planners, academics, research institutions and the general public.
Appendix 1. Human use classification and rules for spatial conflicts according to COEXIST applied in the maritime use conflict (MUC) webtool methodology (Gramolini, 2010). Human uses can be classified according to five traits: vertical, spatial (horizontal), temporal scale, mobility, and location.
Rules for spatial conflicts: rule system to define conflict score for each pair of human uses.
• Rule 1: if vertical domain of activity 1 is different from vertical domain of activity 2 and no one of them interests the whole water column then conflict score is equal to 0; • Rule 2: If both activities are "mobile" then conflict score is equal to the minimum of temporal domain plus the minimum of spatial domain.
• Rule 3: if Rule1 and Rule2 cannot be applied then the conflict score is equal to the maximum value of temporal domain plus the maximum value of spatial domain.

Appendix 3. Marine ecosystem services capacity matrix
The matrix is based on EUNIS Habitats extracted for the Northern Adriatic Sea (adopted from Depellegrin et al., 2017). The score ranges from 2 (high capacity) to 0 (no or neglectable capacity). In total 15 EUNIS habitats were extracted for the Northern Adriatic Sea.