In terms of advanced, web-based interactive DSSs, AkvaVis includes site
selection, carrying capacity, and management monitoring modules (Ervik et al.
2008, 2011). With suitable adaptations, it appears to be a promising tool for
estimating offshore aquaculture potential at national levels and for managing its
subsequent development. Filgueira et al. (2014) proposed dynamic, fully-spatial
modeling, scenario-building, and optimization tools such as PEST (modelindependent Parameter ESTimation, www.pesthomepage.org) as an ideal combination of tools for effective MSP. In the context of MSP decision support, other
tools such as MaxEnt (Maximum Entropy modelling) or MARXAN have been
utilized in combination with GIS to identify trends, opportunities and concerns
related to sustainable management and farm locations (http://dspace.stir.ac.uk/
handle/1893/19465) or to identify fisheries areas (Schmiedel and Lamp 2012).
ARIES (ARtificial Intelligence for Ecosystem Services) assists in mapping service
flows of the ecosystem such as aquaculture benefits (ariesonline.org/docs/
ARIESModelingGuide1.0.pdf) and InVEST (Integrated Valuation of Ecosystem
Services and Tradeoffs) enables the user to evaluate how aquaculture can affect
production and value of marine ecosystem services (www.naturalcapitalproject.org/
models/models.html).
As described in Ferreira et al. (2012), the data requirements for DSS expand with
the scale of the aquaculture operation. Thus, it will be challenging to use
aquaculture-specific DSS in a broader spatial planning system, such as MSP or
ICZM, where a large ecosystem scale is required. Indeed, as might be expected, and
as articulated by Gifford et al. (2001), there are no “ideal” sites for aquaculture and
compromise will always be required. Fortunately, a range of GIS-based DSS exist
already to help to find this compromise and to support MSP using transparent data
management and advanced visualization.
Several tools have been developed to help assess conflicts and synergies between
fisheries, aquaculture, and other marine sectors and to advance practical applications based on that knowledge (Stelzenmüller et al. 2013b). Given the multiple-use
context of many sea areas, the identification of suitable sites for aquaculture will
depend on location-specific understanding of conflicts and synergies between
various proposed types of sea use. While conflicts should be minimized, the discovery of synergies can help identify areas suitable for co-location (Stelzenmüller
et al. 2013b; Griffin et al. 2015).
6.4 The Co-location Scenario: Combining Offshore
Wind Energy and Aquaculture
Both English and German marine plans encourage the combination of aquaculture
with other uses. In the UK, a strong national policy statement calls for consideration
of the “significant opportunities for co-existence of aquaculture and other marine
activities” (Government 2011, 3.9.6). The UK’s East Inshore and Offshore Marine
6 Aquaculture Site-Selection and Marine Spatial Planning …
137
selection, carrying capacity, and management monitoring modules (Ervik et al.
2008, 2011). With suitable adaptations, it appears to be a promising tool for
estimating offshore aquaculture potential at national levels and for managing its
subsequent development. Filgueira et al. (2014) proposed dynamic, fully-spatial
modeling, scenario-building, and optimization tools such as PEST (modelindependent Parameter ESTimation, www.pesthomepage.org) as an ideal combination of tools for effective MSP. In the context of MSP decision support, other
tools such as MaxEnt (Maximum Entropy modelling) or MARXAN have been
utilized in combination with GIS to identify trends, opportunities and concerns
related to sustainable management and farm locations (http://dspace.stir.ac.uk/
handle/1893/19465) or to identify fisheries areas (Schmiedel and Lamp 2012).
ARIES (ARtificial Intelligence for Ecosystem Services) assists in mapping service
flows of the ecosystem such as aquaculture benefits (ariesonline.org/docs/
ARIESModelingGuide1.0.pdf) and InVEST (Integrated Valuation of Ecosystem
Services and Tradeoffs) enables the user to evaluate how aquaculture can affect
production and value of marine ecosystem services (www.naturalcapitalproject.org/
models/models.html).
As described in Ferreira et al. (2012), the data requirements for DSS expand with
the scale of the aquaculture operation. Thus, it will be challenging to use
aquaculture-specific DSS in a broader spatial planning system, such as MSP or
ICZM, where a large ecosystem scale is required. Indeed, as might be expected, and
as articulated by Gifford et al. (2001), there are no “ideal” sites for aquaculture and
compromise will always be required. Fortunately, a range of GIS-based DSS exist
already to help to find this compromise and to support MSP using transparent data
management and advanced visualization.
Several tools have been developed to help assess conflicts and synergies between
fisheries, aquaculture, and other marine sectors and to advance practical applications based on that knowledge (Stelzenmüller et al. 2013b). Given the multiple-use
context of many sea areas, the identification of suitable sites for aquaculture will
depend on location-specific understanding of conflicts and synergies between
various proposed types of sea use. While conflicts should be minimized, the discovery of synergies can help identify areas suitable for co-location (Stelzenmüller
et al. 2013b; Griffin et al. 2015).
6.4 The Co-location Scenario: Combining Offshore
Wind Energy and Aquaculture
Both English and German marine plans encourage the combination of aquaculture
with other uses. In the UK, a strong national policy statement calls for consideration
of the “significant opportunities for co-existence of aquaculture and other marine
activities” (Government 2011, 3.9.6). The UK’s East Inshore and Offshore Marine
6 Aquaculture Site-Selection and Marine Spatial Planning …
137