et al. 2012; Filgueira et al. 2014). Suitability modeling refers to the spatial overlay
of geo-data layers to identify suitable aquaculture sites by identifying, for instance,
favorable environmental factors or constraints. Such studies can determine the
suitability for aquaculture development at various intensities (Longdill et al. 2008)
or can distinguish suitability by type of aquaculture cage (Falconer et al. 2013).
GIS-based suitability modeling is one of the most frequent DSS applications
used to evaluate potential aquaculture sites. The first applications of these techniques date back to 1985 when the siting of aquaculture and inland fisheries using
GIS and remote sensing was conducted by the FAO. Until the mid-1990s, most
studies continued to target data-rich, small-scale environments (Gifford et al. 2001).
Early studies looked primarily at coastal or land-based aquaculture related to
oysters (shellfish) and shrimps and by using simple siting models (Fisher and Rahel
2004). The main drawback of applying suitability models in offshore environments
was a lack of fine-scale data with the necessary temporal and spatial resolution
(Fisher and Rahel 2004). Since GIS applications and models have become significantly more complex, and the resolution and quality of data has greatly improved
(Fisher and Rahel 2004), one might expect the focus to have shifted to offshore
areas. However, as yet, the majority of GIS-based site selection efforts are still
focused on shrimp aquaculture in coastal areas around Asia, while studies in offshore environments remain rare.
Once spatially resolved data are available, a GIS-based Multi-Criteria Evaluation
(GIS-MCE)—also referred to as area weighted rating (Malczewski 2006)—can be
used as a flexible and transparent DSS for potential aquaculture siting. Applications
of GIS-MCE in offshore areas were undertaken in a study by Perez et al. (2005) in
which suitable sites were modelled for offshore floating marine fish cage aquaculture in Tenerife, Canary Islands. The untapped potential of offshore mariculture
is addressed in a global assessment wherein all spatial analyses of suitability and
constraints were conducted with the help of GIS (Kapetsky et al. 2013).
Recently, the combination of GIS and dynamic models to identify suitable sites,
as done by Silva et al. (2011) for shellfish aquaculture, is becoming more popular.
Superimposed models such as FARM (Farm Aquaculture Resource Management;
www.farmscale.org) aim to support the siting process with detailed analyses of
production, socio-economic outputs, and environmental effects (Silva et al. 2011).
In Nunes et al. (2011), the implementation of an ecosystem approach to aquaculture
has been advanced by testing various complementary analytical tools. The tools
were used to assess multiple aspects of blue mussel cultivation in Killary Harbour,
Ireland at different spatial scales (farm- to system-level), times (seasonal to annual
to long-term analyses) and levels of complexity (from simple to complex processbased modelling). The selected tools included a system-scale, process-based ecological model (EcoWin 2000; www.ecowin2000.com), a local-scale carrying
capacity and environmental effects model (FARM), and a management level
eutrophication screening model (ASSETS). Further examples of combining different ecosystem tools for decision support for aquaculture is presented in Filgueira
et al. (2014).
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V. Stelzenmüller et al.
of geo-data layers to identify suitable aquaculture sites by identifying, for instance,
favorable environmental factors or constraints. Such studies can determine the
suitability for aquaculture development at various intensities (Longdill et al. 2008)
or can distinguish suitability by type of aquaculture cage (Falconer et al. 2013).
GIS-based suitability modeling is one of the most frequent DSS applications
used to evaluate potential aquaculture sites. The first applications of these techniques date back to 1985 when the siting of aquaculture and inland fisheries using
GIS and remote sensing was conducted by the FAO. Until the mid-1990s, most
studies continued to target data-rich, small-scale environments (Gifford et al. 2001).
Early studies looked primarily at coastal or land-based aquaculture related to
oysters (shellfish) and shrimps and by using simple siting models (Fisher and Rahel
2004). The main drawback of applying suitability models in offshore environments
was a lack of fine-scale data with the necessary temporal and spatial resolution
(Fisher and Rahel 2004). Since GIS applications and models have become significantly more complex, and the resolution and quality of data has greatly improved
(Fisher and Rahel 2004), one might expect the focus to have shifted to offshore
areas. However, as yet, the majority of GIS-based site selection efforts are still
focused on shrimp aquaculture in coastal areas around Asia, while studies in offshore environments remain rare.
Once spatially resolved data are available, a GIS-based Multi-Criteria Evaluation
(GIS-MCE)—also referred to as area weighted rating (Malczewski 2006)—can be
used as a flexible and transparent DSS for potential aquaculture siting. Applications
of GIS-MCE in offshore areas were undertaken in a study by Perez et al. (2005) in
which suitable sites were modelled for offshore floating marine fish cage aquaculture in Tenerife, Canary Islands. The untapped potential of offshore mariculture
is addressed in a global assessment wherein all spatial analyses of suitability and
constraints were conducted with the help of GIS (Kapetsky et al. 2013).
Recently, the combination of GIS and dynamic models to identify suitable sites,
as done by Silva et al. (2011) for shellfish aquaculture, is becoming more popular.
Superimposed models such as FARM (Farm Aquaculture Resource Management;
www.farmscale.org) aim to support the siting process with detailed analyses of
production, socio-economic outputs, and environmental effects (Silva et al. 2011).
In Nunes et al. (2011), the implementation of an ecosystem approach to aquaculture
has been advanced by testing various complementary analytical tools. The tools
were used to assess multiple aspects of blue mussel cultivation in Killary Harbour,
Ireland at different spatial scales (farm- to system-level), times (seasonal to annual
to long-term analyses) and levels of complexity (from simple to complex processbased modelling). The selected tools included a system-scale, process-based ecological model (EcoWin 2000; www.ecowin2000.com), a local-scale carrying
capacity and environmental effects model (FARM), and a management level
eutrophication screening model (ASSETS). Further examples of combining different ecosystem tools for decision support for aquaculture is presented in Filgueira
et al. (2014).
136
V. Stelzenmüller et al.