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Ocean and Coastal Management 251 (2024) 107051
Available online 12 March 2024
0964-5691/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Site selection within the maritime spatial planning: Insights from use-cases
on aquaculture, offshore wind energy and aggregates extraction
Andrej Abramic
a
,
*
, Alejandro Garcia Mendoza
a
, Victor Cordero-Penin
a
, Maria Magalh˜
aes
b
,
Yaiza Fern´
andez-Palacios
a
, Carlos Andrade
c
, Helena Calado
d
, Sachi Kaushik
a
,
Gilberto Carreira
b
,
c
, Natacha Nogueira
e
, Deborah Shinoda
f
,
g
, Ricardo Haroun
a
a
Biodiversity & Conservation Research Group, ECOAQUA, Univ. Las Palmas de Gran Canaria, Scientic & Technological Marine Park, Crta. Taliarte s/n, 35214, Telde,
Spain
b
Regional Secretariat for the Sea and Fisheries, Regional Directorate for Sea Affairs, Rua Consul Dabney, Apartado 9, 9900-041 Horta, Açores, Portugal
c
MARE – Marine and Environmental Sciences Centre / ARNET - Aquatic Research Network, Agˆ
encia Regional para o Desenvolvimento da Investigaç˜
ao Tecnologia e
Inovaç˜
ao (ARDITI), Ed. Madeira Tecnopolo, 9020 - 105, Funchal, Madeira, Portugal
d
School of Sciences and Technology (FCT) University of Azores, Mare Research Center, Rua da M˜
ae de Deus, 9500 Ponta Delgada, Portugal
e
Regional Directorate of the Sea (DRM), Regional Secretariat for the Sea and Fisheries, Autonomous Region of Madeira, Funchal, Portugal
f
Faculty of Sciences and Technology, University of the Azores, M˜
ae de Deus St., Ponta Delgada, Portugal
g
Gaspar Frutuoso Foundation, Ponta Delgada, Portugal
ARTICLE INFO
Keywords:
Maritime Spatial Planning
Aquaculture
Offshore wind energy
Ecosystem-based approach
Suitability zoning framework
ABSTRACT
Maritime Spatial Planning (MSP) has received increasing attention from policy-makers around the world as an
ecosystem-based approach to the waters under the jurisdiction of coastal states, with the aim of enhancing socio-
economic development while promoting environmental protection and conservation. However, this planning
process requires abundant and diverse types of data and information that are not easily operationalised in a
spatially efcient manner for MSP. Aiming to overcome this barrier, the present study proposes a suitability
zoning methodology based on an ad hoc developed decision support system (i.e. INDIMAR) capable of inte-
grating the required spatial data collected and structured around a proposed suitability framework organised
around ve key components: environmental sensitivity, marine conservation, natural oceanographic potential,
land-sea interactions, and operational maritime uses and activities. This suitability zoning framework and de-
cision support system was tested for individual maritime activities in different Atlantic outermost regions,
conguring different use cases: aquaculture in the Canary Islands, offshore wind farms in the Madeira archi-
pelago and aggregate extraction in the Azores. The proposed methodology has resulted in a exible model that
identies the most suitable sites for the sustainable development of maritime activities, taking into account the
natural potential and compatibility with nature conservation, while mitigating potential environmental impacts
and minimising conicts with other coastal and maritime activities. However, it’s important to note that the
results of this study are strongly inuenced by the availability and quality of data, identifying the main gaps in
each region that are recommended to be lled in view of the formal processes of MSP. In essence, this study
underlines the broad applicability of the proposed methodology and framework, which can be adapted and
implemented in other regions after due consideration of several aspects such as: data availability, contextual
differences, legal and governance frameworks, institutional capacity and spatial interactions. By taking these
aspects into account, the resulting decision support system has the potential to provide valuable insights, thereby
increasing the effectiveness of MSP efforts.
1. Introduction
Over the past two decades, maritime spatial planning (MSP)
processes have grown globally as an emerging practice to manage ma-
rine resources more sustainably (Ansong et al., 2017a,b). MSP processes
aim to allocate maritime activities both in space and time, using
* Corresponding author. ECOAQUA, Univ. Las Palmas de Gran Canaria, Scientic & Technological Marine Park, Crta. Taliarte s/n, 35214, Telde, Spain.
E-mail address: abramic@vik-ing.eu (A. Abramic).
Contents lists available at ScienceDirect
Ocean and Coastal Management
journal homepage: www.elsevier.com/locate/ocecoaman
https://doi.org/10.1016/j.ocecoaman.2024.107051
Received 7 September 2023; Received in revised form 2 February 2024; Accepted 4 February 2024
Ocean and Coastal Management 251 (2024) 107051
2
strategic planned zoning as a mechanism to allow the oceans to sus-
tainably produce the goods and services on which the blue economy
depends, while maintaining and protecting the structure and func-
tioning of marine ecosystems (Borja et al., 2013). By strategically allo-
cating maritime activities in both space and time, MSP aims to achieve
planned zoning as a mechanism of policing/power used to protect
human health and safety by limiting private uses. At the same time, it
seeks to regulate common areas for a range of purposes beyond human
health and safety (Agardy, 2010; Ritchie, 2011).
The marine environment meets human needs through a variety of
maritime uses and activities. While new maritime activities are
emerging, the expansion of existing activities continues to intensify
competition for marine space (Christie et al., 2014). Thus, both marine
abiotic space and associated habitats and biological counterparts are
becoming increasingly scarce and threatened natural resources
(IOC/UNESCO et al., 2011). Thus, the warning conditions described by
Hardin (1968) about the ‘tragedy of the commons’ could be met in the
case of the ocean - an unlimited number of users, unrestricted by any
limits on their access to the space. Hardin (1968) also argues that one
solution to avoid these warning conditions is to manage the relevant
resources through a governance system. In this sense, MSP processes
seem relevant as a public policy that aims to promote the prudent and
rational use of marine areas under national jurisdiction (Calado et al.,
2019).
Currently, around 50% of coastal states have some form of MSP
initiative underway (Ehler, 2021), with the majority of these efforts led
by European countries (Chalastani et al., 2021). In particular, the
combined marine Exclusive Economic Zones (EEZs) of these countries
are the largest in the world. Indeed, the European Union (EU) has been
identied as a major MSP hub, where tools, initiatives, discussions and
innovations are nancially supported and promoted by European gov-
ernments (UNESCO-IOC/European Commission, 2021). The European
MSP Directive (Directive 2014/89/EU of the, 2014) created a govern-
mental framework for the implementation of MSP processes in the
member states, which committed to adopt their respective maritime
spatial plans by March 2021 (Friess and Gr´
emaud-Colombier, 2019;
Ehler, 2021). The EU MSP legal framework explicitly includes envi-
ronmental objectives. Many authors see the MSP process as a tool to
support and implement the objectives of European environmental
legislation on the sea, in particular the Marine Strategy Framework
Directive 2008/89/EC (MSFD) (Haapasaari et al., 2022; Alison et al.,
2015; Maccarrone et al., 2015). The development of MSP provides an
ongoing opportunity to apply and update an ecosystem-based approach
and to achieve and maintain Good Environmental Status (GES), which is
the primary objective for European seas under the MSFD.
Existing international MSP process guides provide a structured step-
by-step approach (Ehler and Douvere, 2009), through main phases
(Fraz˜
ao Santos et al., 2019) or by relevant general themes
(UNESCO-IOC/European Commission, 2021), providing clear guidance
to promote practical policy- and governance-oriented MSP initiatives.
A bibliometric assessment of progress in MSP showed that this is a
rapidly growing eld of research, dominated by qualitative approaches,
which calls for progress in the development of quantitative and/or
modelling methods (Chalastani et al., 2021). The use of data for
evidence-based decision making has been highlighted as a prerequisite
for effective MSP (Zuercher et al., 2022). This development leads to the
formulation of geographic zoning methodologies, which divide the
marine area into zones based on geographic features or characteristics
that can be contrasted and associated with potential marine uses. In an
effort to increase the efciency of the developed methods and to assist
planners in identifying the sustainable allocation of maritime activities,
the MSP community started to develop interactive systems for analysing
problems and evaluating spatial and non-spatial data, applying tech-
niques for spatial and geostatistical analysis, commonly referred to as
decision support tools (DSTs) (Sprague and Carlson 1982; Depellegrin
et al., 2021).
These DSTs aim to operationalise the implementation of the
ecosystem-based approach (EBA) (Depellegrin et al., 2021) through the
understanding of socio-ecological system dynamics. At the same time,
they provide a mechanism to evaluate management strategies prior to
their implementation (Fulton et al., 2011; Stelzenmüller et al., 2013;
Janβen et al., 2019).
Examples of DSTs range from specic sectoral programmes such as
Marxan for MPA design (G¨
oke et al., 2018) or InVEST for ecosystem
service valuation (Montero-Hidalgo et al., 2023). Another example is the
use of the Automatic Identication System (AIS) to track commercial
and shing vessels (Le Tixerant et al., 2018). Other geographic infor-
mation systems such as SEANERGY have been used to assess synergies
and conicts between activities (Bonnevie et al., 2020); INDIMAR for
suitability zoning for e.g. for offshore wind farms (Abramic et al., 2021),
Mytilus for cumulative impact assessment (Hansen, 2019), or
Tools4MSP that integrates different spatial analyses (Menegon et al.,
2018).
A review of DSTs by Pınarbas¸ ı et al. (2017) showed that these tools
are mainly used by planners and marine users during specic MSP
phases and steps of MSP. These include tasks such as: examining existing
conditions and future scenarios for planning, and alternative manage-
ment measures for plan development. Consequently, the main purpose
of DSTs is to assess: environmental impacts; communication; interaction
between planners and stakeholders; and site identication and scenario
building. Nevertheless, large data requirements and specic technical
capabilities hinder the use of DSTs in all MSP steps (Stamoulis and
Delevaux, 2015). Despite the existence of a growing user-developer
community (Depellegrin et al., 2021), there is still signicant potential
to improve DSTs to support operational MSP processes (Pınarbas¸ı et al.,
2017).
The objective of this study is to structure the relevant data and key
analyses that need to be carried out in the planning and marine plan
development phases, in order to generate a suitable zoning methodology
for the future development needs of existing and emerging maritime
activities. The second objective is to test this methodology for different
maritime activities in three use cases, namely three oceanic archipelagos
with different environmental, social and economic conditions, to
conrm that the methodology is exible, adaptable and replicable.
The aim is to provide a basis for implementing suitability zoning
within an ecosystem-based approach to MSP, taking into account the
MSFD:
•Potential impact on the marine environment and degradation of the
MSFD Good Environmental Status.
•Inconsistencies with marine conservation objectives.
•Optimal and limiting oceanographic conditions for the development
of maritime activities.
Fig. 1. Suitability zoning framework including its ve fundamental -
key components.
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
3
•Land-sea and sea-land interactions between coastal uses and mari-
time activities.
•Synergies and conicts between maritime sectors.
2. Methodology
2.1. Framework for suitability zoning
The maritime sector suitability zoning approach is based on ve
fundamental or key components aimed at achieving environmental
sustainability, identifying natural potential and avoiding conicts with
nature conservation, coastal and maritime sectors. To analyse each
component, the process selects relevant parameters that characterise
them and determine their ‘suitability’ in relation to the maritime sector
in question for which we are seeking suitable locations for development.
The ve components considered in this study are visually illustrated in
Fig. 1 and are described in more detail below.
2.1.1. Environmental sensitivity
This component includes information to analyse the sensitivity of the
marine environmental components in relation to the pressures arising
from the maritime sector under consideration. This involves visualising
areas of robust environmental conditions where the expected environ-
mental impacts are minimised.
In order to introduce the MSFD more deeply into the methodology
and to list the parameters needed to assess environmental sensitivity, it
is decided here to follow the Good Environmental Status (GES). The GES
is described in COM 2017/848/EU and consists of 11 Qualitative De-
scriptors (QDs) and 39 related criteria elements, divided into essential
features and characteristics of marine waters, as well as predominant
pressures and impacts. Thus, the GES served as a checklist to go through
the 11 QDs and examine the potential impacts of the maritime sectors on
the marine environment.
2.1.2. Marine conservation
This section analyses the potential incompatibility of the maritime
activity with marine conservation, considering the possibility that the
activity may contribute to the achievement of conservation objectives.
For example, marine birds conservation is in direct conict with offshore
wind energy (OWE) installations (Larsen and Guillemette 2007). How-
ever, OWE parks can act as shery exclusion zones, thereby contributing
to the conservation of biological resources (Hammar et al., 2016). When
assessing the compatibility of the analysed maritime activity with ma-
rine conservation, it is imperative to include data on the expansion of
marine protected areas (MPAs) and their associated conservation ob-
jectives and targets.
2.1.3. Potential/constraints of natural oceanography
It is also necessary to assess the (unfavourable) oceanographic con-
ditions for the development of the maritime activity under consider-
ation. Variables such as depth, wave height or current strength can
constrain or facilitate the development of activities. For example, cur-
rents are essential for the dispersion of nutrient inputs from aquaculture
sites, while limiting the anchoring of structures (Tsiaras et al., 2022).
Similarly, wind speed is crucial for OWE as visualised by energy po-
tential maps (Wind Europe 2020; Emeksiz and Demirci, 2019; Costoya
et al., 2020; Kumar et al., 2020), but the installation of turbines is
limited by bathymetry.
For the list of oceanographic parameters, the Copernicus Ocean
Monitoring Indicators were used as they meet the operational re-
quirements for monitoring and assessing ocean conditions.
2.1.4. Land-sea interactions
This component analyses the potential synergies and conicts be-
tween the maritime activity under consideration and existing land use in
coastal areas. This assessment should take into account sectors such as
urban and coastal tourism development, as well as ports, land transport
infrastructure, industrial areas, rural and agricultural areas and other
relevant uses. In this study we have used land use or land cover data sets
that provide high resolution information following a classication of
anthropogenic activities within the coastal zone.
2.1.5. Operational maritime uses
Finally, as in the previous component, the potential synergies and
conicts with operational maritime uses and activities need to be ana-
lysed. For this assessment, it is necessary to collect spatial information
on the distribution of existing maritime activities in order to spatially
analyse potential multi-use and co-use areas with the maritime activity
under consideration.
2.2. Analysing the suitability of sites through multi-criteria analysis
To generate the nal suitability maps for the analysed activity, the
result map of each resulting analysis was overlaid as described above.
The spatial overlapping process requires the relative importance of each
component and associated parameter. For this purpose, we used the
Analytical Hierarchy Process (AHP) (Goepel, 2014; Saaty, 1990). Saaty
(1987) stated that two levels are fundamental in the use of AHP, namely
Fig. 2. -Hierarchical structure for the maritime sectors analysis (Shinoda et al., 2019) where the signicance of each cluster and each maritime sector is analysed.
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
4
a hierarchical structure (Fig. 2) to represent the problem being modelled
and the pairwise comparisons to establish relationships - in our case
between the key components and their associated parameters or spatial
data sets collected (see Appendix 1; Fig. 4). Five pairwise matrices were
thus developed. These matrices determine a set of weights that quanti-
tatively reect the relative importance or strength of each component or
parameter considered in the rst and second AHP hierarchical levels,
respectively. In this context, at the rst level, key components are
compared against each other, e.g. comparing whether marine conser-
vation is more/less relevant than conicts between operational mari-
time sectors when analysing suitability zoning for a particular maritime
activity. Then, at the second level, each parameter is compared with
each other for the analyses within each key component, e.g. to deter-
mine the sensitivities of different environmental components, to assess
(un)favourable oceanographic conditions in relation to the activity
under consideration, or to assess synergies and conicts between the
activity under consideration and all other coastal and maritime uses and
activities. It should be emphasised that the various analyses should be
carried out in relation to a single maritime activity.
The AHP pairwise comparison technique allows quantitative
assessment of the relative importance (i.e. weights) between parameters
and key components through the knowledge of experts and different
stakeholders. In this study, a structured process was followed to gather
expert knowledge. First, a structured survey was developed to determine
the weights of the rst and second AHP levels using the expert
knowledge within the consortium of the PLASMAR project. To facilitate
the pairwise comparison of the surveys, an AHP Excel le was adapted
from Goepel (2013).
Secondly, a round of expert discussion was conducted in order to
reach a consensus on the determination of the different weights. The
expert panel was recruited from the regional institutes and within the
stakeholder workshops (following Quesada-Silva et al., 2019, and
described in Abramic et al., 2021). Prior to the discussion, a
non-exhaustive review of scientic and grey literature, including tech-
nical reports (see the Supplementary Material for more details on the
literature review conducted for aquaculture, OWE and sand extraction),
was conducted to facilitate the discussion among the experts and to
support their judgement with empirical data whenever possible. For
example, in order to analyse the potential environmental impacts on the
GES, publications reviewing them in the context of aquaculture
(Png-Gonzalez et al., 2019) and offshore wind energy (Abramic et al.,
2018) were followed.
Compatibility with marine conservation was analysed, taking into
account recommendations published by the International Union for
Nature Conservation (IUCN) (Day et al., 2019). If the IUCN recom-
mendation included options for the development of maritime activities
within the MPA, further scientic and technical reports on specic
topics were reviewed (see Supplementary Material).
With regard to the oceanographic conditions that could limit or
favour the development of maritime activities, analyses were made of
Fig. 3. Location map showing the European Macaronesian archipelagos of the Azores, Madeira and the Canary Islands. The study area (i.e. 30 km off the coast of the
islands) corresponds to the spatial extent of the INDIMAR DSS.
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
5
physical aspects (e.g. sea temperature and salinity, air pressure, ba-
thymetry, winds, currents, waves, etc.) and chemical aspects (e.g. oxy-
gen, nutrients, chlorophyll a).
Potential synergies and conicts have also been analysed for both
coastal land uses and maritime activities. For coastal sectors, the liter-
ature on land-sea interactions was reviewed, with a particular focus on
the coastal distance component, while for current maritime sectors the
debate revolved around conicts and potential multiple uses with other
maritime activities. Details of these reviews can be found in Appendix 2
(offshore wind review), Appendix 3 (aquaculture review) and Appen-
dix 4 (sand mining review).
2.3. INDIMAR decision support system and integrated site suitability
model
To facilitate the application of the suitability zoning methodology,
the INDIMAR Decision Support System (DSS) will be used, as developed
specically for the case study regions (Abramic et al., 2021). The
INDIMAR DSS is based on Geographic Information System (GIS) tech-
nology and uses spatial data layers representing the different parameters
of the key components. The methodology for calculating the suitability
index (R) is based on the weighted overlay technique, where each spatial
data layer (i.e. parameter) is assigned a weight according to its impor-
tance for the corresponding assessment (i.e. key component) with
respect to the maritime activity under consideration. This index can
have a value between 0 and 10, where R =0 reects a totally unsuitable
location and R =10 represents the most suitable locations or sites.
In order to calculate the suitability index, it is necessary to incor-
porate the collected data into the system. An additional requirement is
to dene the type of contribution (CV) or “suitability” relationship of
each parameter to the maritime activity. For more precise analyses,
numerical values of parameters can be divided into ranges (e.g.
considering suitable ranges of wind speed between 7 and 8.5 m/s and
excluding <7 m/s and >8.5 m/s). Furthermore, qualitative parameters
were divided into categories (e.g. habitat types, species or different
types of MPAs). For the purpose of calculating the suitability index, CV is
associated with values using the following coding: Positive contribution
(CV =1). Neutral contribution (CV =0). Negative contribution (CV =-
1). Excluded value (R =0).
Finally, for each parameter, it is necessary to establish the weights
(pW) calculated by the AHP. In this sense, the suitability index (R) is
calculated as the sum of the parameter weights (pW) multiplied by the
parameter contributions (CV):
R =ΣpWi* CVi, where ΣpWi =100
Once DSS INDIMAR has been congured with all the parameter
Fig. 4. Number of datasets available for archipelagos - Detailed table of data available for the Canary Islands, Madeira and the Azores in Appendix 1.
Table 1
Conguration of weights for the three scenarios considered in the Canary
Islands.
Key components First level weights for the different scenarios
Expert
consensus
(A)
Environmentalist
(B)
Environmentalist with
MPA’s restrictions (C)
Environmental
sensitivity
20.08 50.08 50.08
Marine
conservation
12.88 12.31 12.31
Coastal Land Use 11.68 11.11 11.11
Natural-
oceanographic
potential
38.98 11.39 11.39
Maritime
Activities
16.38 15.11 15.11
TOTAL 100 100 100
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
6
weights and the type of contribution, the system calculates the suit-
ability scores for the entire study area. The system denes a grid of
discrete elements (300 m ×300 m) and calculates the suitability index
for each one. In order to increase the efciency of the system and reduce
the computation time, the suitability index is calculated for areas up to
30 km from the coast of the archipelagos of the Azores (37,500 km2),
Madeira (12,500 km2) and the Canary Islands (45,000 km2).
2.4. Use cases and scenarios
The AHP process was repeated for three maritime activities and
tested in three different use cases (Fig. 3). One suitability zoning map
was produced for offshore wind farms in Madeira, another for aggregate
extraction in the Azores and a third for aquaculture in the Canary
Islands.
INDIMAR DSS also allows users to dynamically adjust the weights of
the parameters and components and visualize the resulting suitability
maps. This feature facilitates the comparison of different scenarios and
changes in the conguration of parameters and key components to
identify optimal locations for the analysed maritime activity. For
example, after establishing the relative importance (i.e. weights)
through AHP, the weights among the key components of the rst AHP
level can be modied to analyse different development scenarios.
To assess the robustness of the suitability zoning methodology,
different policy scenarios are developed within each use case:
•Expert consensus scenario. The weights are developed by expert
opinion, with the panel calculating the weights using AHP. This re-
ects a sustainable development zoning that balances the weights
between all environmental, social and economic considerations
associated with all key components.
•Conservative scenario. Where the weights associated with environ-
mental sensitivity and marine conservation are maximized to mini-
mize potential adverse impacts on the ecological components.
•Development scenario. Where the weights associated with favour-
able natural oceanographic conditions and proximity to strategic
coastal infrastructure are maximized to minimize costs and promote
the development of the particular activity.
•Conict minimisation scenario. Based on higher weights given to
land-sea interactions and operational maritime uses to minimize
conicts with all other activities.
These scenarios were applied to each use case according to the
availability of spatial data collected for each archipelago (Table 1).
Thus, the different scenarios were applied unevenly as a means of
illustrating and discussing the applicability of the proposed zoning
methodology (rather than assuming it) which is the ultimate goal of this
article.
2.5. Data collection according to the suitability zoning framework
The data of the three use cases (Azores, Madeira, Canary Islands)
were collected according to the ve suitability components: marine
environmental data according to the MSFD GES, distribution of MPAs
and their conservation targets, oceanographic features, coastal land use
and current maritime activities (Fig. 4; Appendix 1). There was a sig-
nicant lack of spatial coverage for the marine environment data sets in
the Portuguese archipelagos (Fig. 4, Appendix 1). In comparison, the
spatial coverage for the Canary Islands is higher due to the availability of
data related to the GES of the MSFD shared by the Spanish National
Spatial Data Infrastructure. MPAs, coastal land use and oceanographic
conditions were obtained using data products from the European Envi-
ronment Agency and Copernicus (Copernicus Marine Service and Land
Monitoring Service). Data on operational maritime activities and sectors
are mainly obtained from local data providers/developers, with the
exception of information on maritime transport, which was obtained
from the EMODnet Human Activities Portal.
The use of data products from European data initiatives has two
advantages. Firstly, they cover large areas - including the entire Maca-
ronesian region. Secondly, these data sets are provided in a single,
unied data model, which means that the data sets are harmonized.
The data collection process often reveals numerous data gaps. One
approach to address this is to use indirect or proxy information. This
type of data is used to infer or estimate a particular variable or phe-
nomenon of interest when direct measurements are not available. In this
structured data collection, land cover data is used as a proxy for infor-
mation on human activities, in particular land cover. In addition, we
considered the protected area of a particular marine species as an in-
dicator of increased potential for that species to be present, in line with
the recommendations of various authors such as Abramic et al., (2023),
Zhang et al., (2022); Flower et al., (2020); Maccarrone et al., (2015);
O’Mahony et al., (2009).
3. Results
3.1. Applying the suitability zoning methodology to the use cases
3.1.1. Suitability zoning for aquaculture in the Canary Islands
Aquaculture is a well-established sector in the Canary Islands.
Therefore, the rst use case aimed to identify potential suitable areas for
the expansion of the marine aquaculture sector in the region according
to the different scenarios designed.
Table 1 shows the weights used in the rst level of the analytical
hierarchy process when comparing the relative importance of the key
components in analyzing suitable sites for aquaculture in the Canary
Islands for the different scenarios: expert consensus (A), environmen-
talist (B) and environmentalist considering restrictions resulting from
the designation of marine protected areas (C). Higher weight values
indicate a higher relevance of all the parameters considered within each
key component.
The rst model, Expert Consensus (Fig. 4A, Table 2), primarily looks
for suitable oceanographic conditions, such as a temperature that pro-
motes growth of the product, site depths that do not exceed 50 m,
suitable currents and wave conditions that allow construction and
maintenance of the facilities without excessive costs. Oceanographic
conditions are indirectly linked to economic viability, along with
proximity to any ports or even smaller ports to minimize maintenance
and operational economic costs. In this prole, environmental sustain-
ability is considered, the model avoids sensitive areas (e.g. specic
benthic habitats and vulnerable species), but with twice less weight than
oceanographic conditions. The other three components, conservation
(avoiding but not excluding MPAs with seabird conservation objectives),
land-sea interaction (e.g. avoiding conicts with coastal tourism) and
potential conicts with other maritime activities (e.g. searching the
distance to offshore submarine outows) are included in the model but
with much lower weights.
For the Canary Islands, it was possible to collect spatial data avail-
ability on benthic habitats (PLASMAR Consortium, 2020) and food web
models on different ecological components (Couce-Montero et al., 2015;
Montero et al., 2021). This allowed the testing of applied generic
governance policy scenarios, the development of the sector with less
possible impact on the marine environment. INDIMAR DSS, through the
environmentalist scenario, applied the highest importance to the
sensitivity of ecological components. The same scenario also included
the restriction of marine This was done to minimize the potential
negative impact on marine protected areas over other considerations,
such as higher production costs due to the remoteness of the coast.
Firstly, the environmentalist scenario in INDIMAR showed that most of
the zones suitable for aquaculture are located beyond a depth of 50 m
(Fig. 4B, Table 2). This is due to the availability of detailed maps of
benthic habitats (e.g. seagrass or maerl beds) covering this depth,
beyond which they were only mapped through broader habitats without
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
7
Fig. 4A. Suitability maps for aquaculture resulting from Expert consensus scenario considered for the Canary Islands.
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
8
Fig. 4B. Suitability maps for aquaculture resulting from Environmental scenario considered for the Canary Islands.
A. Abramic et al.
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information at the community level (e.g. circalittoral ne sand or deep
sea bed).
The third scenario, unlike the previous environmentalist scenario
where aquaculture was only constrained by the spatial distribution of
sensitivity of the ecological components considered, illustrates the
application of specic conservation management measures resulting in
fewer suitable areas (Fig. 4C, Table 2).
Fig. 4C. Suitability maps for aquaculture resulting from Environmental and MPAs legislation scenario considered for the Canary Islands.
Table 2
Total extension (Km2) by suitability categories of the resulting zoning for each of the scenarios considered in the
Canary Islands.
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
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3.1.2. Suitable zoning for offshore wind energy development in Madeira -
second use case
At the time of writing, offshore wind energy (OWE) has not been
established in Madeira. However, given the lack of a continental shelf
around the archipelago, it is likely that offshore wind farms (OWFs) will
be developed using oating wind turbines. These will need to be placed
where the wind is suitable for this activity, while staying out of the
feeding grounds and migration corridors of seabirds and sensitive
benthic habitats (Abramic et al., 2022). In this context, the INDIMAR
expert consensus generated a suitability prole that prioritised envi-
ronmental sensitivity and natural oceanographic potential (Table 3).
However, in this scenario, a notable expansion of the suitable area, i.e.
largely suitable or unrestricted zones (Fig. 5A, Table 4), can be observed,
resulting from a signicant lack of marine environmental data (Fig. 4;
Appendix 1). Signicant gaps in the available environmental informa-
tion were observed, particularly with respect to the distribution of
coastal habitats and associated species. The limited data coverage made
it difcult to identify, and therefore avoid, areas where OWE facilities
would have a high impact. This may also explain the similarities be-
tween the sustainable development scenario (i.e. expert consensus) and
the development scenario (Fig. 5A and Fig. 5B respectively, Table 4).
Table 3 shows the weights used in the rst level of the analytical
hierarchy process when comparing the relative importance of the key
components in analysing suitable sites for offshore wind farms in
Madeira for the different scenarios: (A) expert consensus, (B) develop-
ment, (C) conict minimisation. Higher weight values indicate higher
relevance of all parameters considered within each key component.
Due to the limited availability of environmental data (see Fig. 4C,
Appendix 1), further scenarios were developed to analyse suitable
zoning that would maximise the natural potential for development of the
OWE sector while minimising social conicts (e.g. with coastal tourism -
aesthetic visual impacts) and marine conservation issues Table 3. Suit-
able sites for OWE would need to be close to certain terrestrial electrical
facilities to connect the turbines to the island’s electrical grid, and
within favourable wind speed and depth ranges (Fig. 5B).
OWE is often perceived as a threat to coastal areas heavily used for
recreational and tourist activities due to visual impacts (Lloret et al.,
20–22). Thus, the third prole scenario (Fig. 5C) reects a policy of
“avoiding conict between OWE facilities and coastal tourism”.
Accordingly, this prole maximized the weights related to the land-sea
interaction, while minimising the weights of all other components to less
than 10%. As expected, this model increased the distance of suitable
areas from the coast, especially from urban areas where coastal tourism
is developed.
3.1.3. Appropriate zoning for aggregate extraction in the Azores - third use
case
In the Azores, public policy on aggregate extraction (i.e. mainly
sand) excludes from extraction all areas with potential conict with
other operational maritime activities. Thus, in the scenarios considered
for this use case (Table 5), suitability zoning excludes all areas currently
used by other activities, thus avoiding any type of potential conict. This
had a direct impact on the consensus of the regional experts consulted,
who gave greater relevance to coastal and marine uses and activities
when analysing suitable locations for marine aggregates extraction
(Fig. 6A).
Data on marine ecological spatial distribution were also lacking for
the Azores (Fig. 4; Appendix 1), indicating that the sensitivity of marine
ecological components is overlooked, resulting in suitable zoning that
most likely disregards environmental impacts (Fig. 6B). A conict
minimisation scenario was also carried out, taking into account the
spatial distribution of natural aggregate deposits (Fig. 6C). Due to the
Table 3
Conguration of the weights for the three scenarios under consideration in
Madeira.
Key components First level weights for the different scenarios
Expert
consensus (A)
Developmental
(B)
Conict
minimisation (C)
Environmental
sensitivity
35 1 9.8
Marine conservation 15 23.5 9.8
Coastal Land Use 16 24.5 60.8
Natural-
oceanographic
potential
30 38.5 9.8
Maritime Activities 4 12.5 9.8
TOTAL 100 100 100
Fig. 5A. Suitability maps for offshore wind energy resulting from the expert consensus scenario considered in Madeira (see Table 3).
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
11
lack of data for this case study, the spatial results are very similar (see
Table 6).. However, in this scenario the suitability zoning showed lower
scores in zones closer to harbours to avoid conicts with high intensity
shipping lanes.
Table 5 shows the weights used in the rst level of the analytical
hierarchy process when comparing the relative importance of the key
components to analyse suitable locations for aggregate extraction in the
Azores for the different scenarios: (A) expert consensus, (B) environ-
mental, (C) conict minimisation. Higher weight values indicate a
higher relevance of all parameters considered within each key
component.
4. Discussion
The results of this study demonstrated the applicability of the pro-
posed suitability zoning method through the three case studies for
different maritime sectors. This method considers the natural potential
of oceanographic conditions and land-sea interactions to identify suit-
able development areas for the maritime sectors analysed, with the aim
of minimising impacts on the marine environment, promoting compat-
ibility with marine conservation and reducing potential conicts with
other coastal and maritime activities. Thus, all ve components of the
suitability framework are considered (Fig. 1). Moreover, the integration
of all these aspects in the DSS INDIMAR has resulted in an easy to use
Fig. 5B. Suitability maps for offshore wind energy resulting from the Developmental scenario considered in Madeira (see Table 3).
Fig. 5C. Offshore wind suitability maps resulting from the conict minimisation scenario considered in Madeira (see Table 3).
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
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and exible tool for scenario development through the conguration of
weights that allow a technical decision on whether the marine envi-
ronment and/or conservation and/or natural potential and/or avoid-
ance of conicts with maritime and/or coastal sectors should be
prioritised.
More than a conceptual development, the method was successfully
tested on three different maritime sectors. The results showed that the
developed method using the INDIMAR DSS model is able to provide
advanced results if properly fed with data and aggregated information,
following the suitability framework. A model fed with collected data
that fulls the requirements of ve components provides accurate re-
sults for the introduction or expansion of the maritime sector. For
example, in the case of aquaculture in the Canary Islands case study, the
availability of detailed and accurate data allowed a precise assessment
of suitable areas. The ability to access a wealth of information on each of
the ve data components ensured that the model could identify areas
with optimal growth potential while minimising the risks associated
with unsuitable conditions. Reliable data availability facilitated the
assessment of trade-offs between different suitability components, such
as oceanographic conditions and environmental sensitivity.
The fully operational model is capable of enhancing the relevance of
individual or different components (e.g. marine environment and nature
conservation) while still taking into account the other components
included in the analysis. This choice provides the opportunity to tailor
the model according to the governance strategy, producing different
zoning outcomes that are consistent with a range of planning objectives.
Policy-based (weight) proles can facilitate or constrain trade-offs,
increasing or decreasing the relevance of specic components, but all
are considered in the analysis. In this way, the results provided contain
highly useful information required for the decision-making process, as
the applied method and DSS INDIMAR are aligned with the needs of the
decision maker (Bolman et al., 2018).
In addition, this methodology can be used to go beyond following
already established governance policy planning objectives. The INDI-
MAR DSS, together with the dened model, is capable of dening the
MSP governance strategies. The model can provide suitable areas for the
analysed sectors, test different scenarios and test components for a va-
riety of options for trade-offs. The testing of scenarios with different
constraints and limitations (e.g. related to the environment, marine
conservation or minimising conicts with other coastal and maritime
activities) showed spatial changes in the distribution of suitable areas for
the assessed maritime sectors depending on the congured trade-offs
between the key components of the framework. This provided insights
to assess whether the marine space requirements of the maritime sector
are secured and what trade-offs are necessary between each of the
components considered. As noted by Gimpel (et al., 2015), scenario
analysis can facilitate the denition of governance and planning pol-
icies, enhance or limit component trade-offs, or, if possible, simply apply
a balance of environmental, conservation, and oceanographic condi-
tions’ potentials and conicts.
The offshore wind and sand extraction use cases in Madeira and the
Azores, respectively, faced data availability challenges that affected
their suitability zoning results. Both suitability models were signi-
cantly less restrictive due to the lack of environmental spatial informa-
tion. This clearly shows how model results depend on data availability.
After conducting a structured data collection for each case study,
missing data were identied (see Fig. 4; Appendix 1). In this context,
spatial results should be taken with caution, considering whether the
available information is sufcient to adequately assess each of the key
suitability components. Furthermore, the presence of data gaps will
indicate the suitability of policy scenarios for modelling purposes.
For areas with signicant data gaps on the marine environment, it
was possible to model specic policies to avoid conicts with coastal (i.
e. OWE in the Madeira use case) and maritime sectors (i.e. sand
extraction in the Azores use case). These proles are suitable for
developing scenarios for policy planning, with identiable options, al-
ternatives and suitable areas when considering specic trade-offs. The
third OWE model, applied in Madeira, includes specic policies to avoid
any conict with coastal tourism, to consider multi-use, co-use or even
trade-offs with maritime sectors, to develop the offshore wind farm with
lower natural potential areas and foreseeable impacts on the marine
environment during construction and maintenance.
However, the exibility of the methodology developed for the other
sectors does not apply to sheries, as it poses certain challenges due to
its dynamic nature, which is highly dependent on the availability and
movement of resources and stocks. Unlike the other maritime sectors
analysed, sheries management requires continuous monitoring and
adaptive strategies to respond to changing environmental conditions
and stock dynamics. As a result, it can be more difcult to obtain all the
necessary information and ensure its accuracy for effective decision
Table 4
Total extension (Km
2
) by suitability categories of the resulting zoning for each of the scenarios considered in
Madeira.
Table 5
Conguration of the weights for the three scenarios considered in the Azores.
Key components First level weights for the different scenarios
Expert
consensus (A)
Environmental
(B)
Conict
minimisation (C)
Environmental
sensitivity
19 50 9.25
Marine conservation 15 7.25 1.5
Coastal Land Use
(Land–sea
interactions)
26 18.26 27.51
Natural-oceanographic
potential
10 2.25 11.5
Maritime Activities 30 22.23 50.22
TOTAL 100 100 100
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
13
making within the developed methodology.
In this study, we tested a new methodology for three different
maritime sectors, applied to three Macaronesian archipelagos with
different environmental, social and economic conditions. The results
showed that the suitability framework developed and applied by INDI-
MAR DSS is exible and can be implemented throughout the EU Mac-
aronesia region. In this context, the geographical coverage of INDIMAR
DSS is the whole marine region, similar to the application of MYTILUS or
SEANERGY developed for the whole Baltic Sea (Bonnevie et al., 2020,
2022) or the MSP Challenge simulation platform (Abspoel et al., 2021)
covering the whole North Sea.
Although the method can be applied to any use case, adapting the
INDIMAR DSS system to a new region is not easy. When applying the
INDIMAR DSS to a different environment, several factors need to be
considered:
- Inconsistencies or gaps in data can limit the effectiveness of the re-
sults provided by the DSS. It is essential to assess the availability and
quality of data and spatial information specic to the new region.
- Each region has unique social, economic and environmental char-
acteristics that shape its priorities in MSP. Adapting the system to
take account of these contextual differences is necessary to ensure its
relevance and applicability in the new region. The zone suitability
framework does not include socio-economic components. This is a
signicant gap that needs to be considered in future development
and in attempts to increase the adaptability of the system (Abramic
et al., 2023).
- Legal and governance frameworks for MSP may vary from region to
region. It is important to understand and integrate the specic legal
and governance requirements of the new region into the DSS to
ensure compliance and effectiveness. Similar to the socio-economic
component, the future suitability framework should include a
governance component that takes into account the administrative
competence related to maritime sectors (e.g. competence for mari-
time sheries) and analyses the marine area (e.g. who has compe-
tence for the territorial sea, the contiguous zone and the exclusive
economic zone).
Fig. 6A. Suitability maps for the extraction of aggregates resulting from the expert consensus scenario considered in the Azores (see Table 5).
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
14
- The implementation of a DSS requires sufcient institutional ca-
pacity and expertise to operate and maintain the system and properly
interpret the results. It is important to assess the existing institutional
capacity in the new region in order to provide the necessary training
and resources to support the successful implementation of the DSS.
- If the new region has cross-border or transboundary dimensions,
spatial interactions and coordination with neighbouring regions
become important. These considerations may not have been
adequately addressed in the use case specically due to the nature of
archipelagos, and adapting the DSS to incorporate cross-border in-
teractions may be a challenging feature for further development.
In summary, while the INDIMAR DSS has broad applicability, its
implementation in a new region requires careful attention to data
availability, contextual differences, legal and governance frameworks,
institutional capacity and spatial interactions. Taking these factors into
account, this DSS will enhance the effectiveness and relevance of the
DSS in the MSP processes of the new region.
5. Conclusion
In this study, the tested methodology has shown a high degree of
adaptability and applicability, whether for the introduction or expan-
sion of the maritime sector, or for covering the diversity of maritime
sectors included in the study. Using the developed methodology, it is
possible to analyse additional sectors that exploits wave, tide or currents
energy, maritime transport or various maritime tourism activities such
as whale watching, diving, kite surng and others. The methodology
provides reliable results that can be effectively applied in practical
scenarios.
It is also important to note that methodologies developed for specic
locations can be adapted to other contexts. However, this adaptation
should take into account various factors such as data availability,
contextual differences, legal and governance frameworks, institutional
capacity and spatial interactions, especially in transboundary contexts.
The zoning methodology developed here takes into account all ve
components of the suitability framework, adjusting the weights and
seeking a balance between the marine environment, conservation,
Fig. 6B. Suitability maps for the extraction of aggregates resulting from the environmental scenario considered in the Azores (see Table 5).
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
15
Fig. 6C. Suitability maps for the extraction of aggregates resulting from the Conict minimisation scenario considered in the Azores (see Table 5).
Table 6
Total expansion (Km2) by suitability categories of the resulting zonation for each of the scenarios considered in
the Azores.
A. Abramic et al.
Ocean and Coastal Management 251 (2024) 107051
16
natural oceanographic potential, land-sea interactions and marine and
coastal users.
During the application of the zoning methodology to three use cases,
it became clear that the results of the model were heavily inuenced by
the availability of data. To address this challenge, a structured data
collection process following the zoning suitability framework was
implemented. However, generating the new data required to improve
the quality of spatial suitability analyses was beyond the scope of this
study. Nevertheless, this structured process facilitated a clear under-
standing of which components of the framework had signicant data
gaps and the impact of these gaps on the results. This information
allowed for a more informed interpretation of the zoning results and
provided insights into areas that may require further data collection or
improved data management strategies.
In conclusion, the methodology provides the exibility to adapt the
model and produce zoning results that are consistent with the gover-
nance strategy and meet planning objectives. By incorporating policy-
dened proles with associated weights and constraints, the method-
ology allows trade-offs to be facilitated or regulated, thereby increasing
or decreasing the importance of specic components considered in the
analysis. This approach enables the creation of precise policy scenarios
tailored to specic maritime sectors.
These policy scenarios can be used for various purposes, such as
informing the development or renement of policies within the MSP
framework, adapting governance strategies for maritime sectors and
improving environmental management practices. By using these sce-
narios, decision-makers can evaluate different options, assess their im-
pacts and make informed decisions that are consistent with the
objectives of sustainable maritime development.
Author contributions
Ricardo Haroun: Writing – review & editing, Project administration.
Deborah Shinoda: Methodology, Investigation, Conceptualization.
Natacha Nogueira: Methodology, Investigation. Andrej Abramic:
Writing – review & editing, Writing – original draft, Validation, Super-
vision, Project administration, Methodology, Investigation, Formal
analysis, Data curation, Conceptualization. Helena Calado: Writing –
review & editing. Carlos Andrade: Writing – review & editing,
Conceptualization. Gilberto Carreira: Project administration. Sachi
Kaushik: Writing – review & editing, Conceptualization. Victor Cor-
dero_penin: Writing – review & editing, Writing – original draft. Ale-
jandro Garcia Mendoza: Software, Methodology. Yazia Fernandez-
Palacios: Writing – review & editing. Maria Magalh˜
aes: Methodology,
Investigation
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data available and searchable through metadata catalogue:
http://www.geoportal.ulpgc.es/geonetwork/srv/eng/catalog.
search#/home.
Acknowledgements
This work was supported by the PLASMAR project (grant number
MAC/1.1a/030); PLASMAR +project (grant number MAC2/1.1a/347)
under the INTERREG V-A Spain-Portugal MAC 2014–2020 (Madeira-
Azores-Canarias) programme of the European Regional Development
Fund (ERDF) of the European Union.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ocecoaman.2024.107051.
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