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Part 3. S ummary of spatial tools and models in use for aquaculture zoning, site selection and area management

This part starts with a subsection on spatial data types, quality and sources to highlight the importance of using good data and information for aquaculture zoning, site selection and area management. Although high-quality primary and secondary data requirements are highlighted under a subsection on “Spatial data types, quality and sources” below, the requirements for good data apply equally to development of all tools and models.

Subsequent subsections provide a summary of a few of the available tools and models. The development of aquaculture zoning, site selection and area management often results from the application of crosscutting tools and models, with a capability to deal with varying spatial scales. Often, the outputs from small scale models can be used at the larger scale, as indicated by the modelling framework in Figure A4.1.
Some of the farm scale and ecosystem models selected tend to reflect the fact that the majority of such development has taken place in Europe and North America, except perhaps for the application of GIS, remote sensing and freshwater modelling, which have a more global application.
Given the vast range of tools and models, only some of the main ones are listed in this document .
Occasionally, tools and models are developed for aquaculture for zoning, site selection and area management, but are not identified by a distinct “name” (of the model), which makes them difficult to identify and highlight. Occasionally, a suite of tools is developed but the literature refers to them only as being part of a general “decision support tool” used in a particular location, which again makes them difficult to include here. Other models contribute in a smaller way, such as models that simulate fish and shellfish growth, which provide useful information, but do not in themselves contribute to zoning, site selection

FIGURE A4.1. Generic framework that shows how various tools and models can be used to provide the drivers, boundary conditions or fluxes into larger scale models and vice versa. Good quality data at each stage are vital.

Generic framework that shows how various tools and models can be used to provide the drivers, boundary conditions or fluxes into larger scale models and vice versa. Good quality data at each stage are vital.
and area management activity. There is no doubt the development and application of GIS, remote sensing and dynamic models, in particular, will increase over time as aquaculture develops and expands further.
This section generally covers the tools and models used within the case studies (Annex 5) mentioned in Table A4.1 or selected for inclusion by the authors.
Table A4.2 also identifies a non-exhaustive list of projects, where tools and models are being developed or used in the application of aquaculture zoning, site selection and area management, that will stimulate investigation. Each subsection, however, also contains a reading list, generally covering more recent publications and Web resources, where applicable. There is

TABLE A4.2. Selection of recent international collaborative research projects where tools and models were applied to aquaculture zoning, site selection and area management.

Selection of recent international collaborative research projects where tools and models were applied to aquaculture zoning, site selection and area management

also a short additional reading section at the end of this Annex.

Spatial Data Types, Quality and Sources

Data are facts and statistics collected together for reference or analysis. There are two key types: (i) primary data; and (ii) secondary data. Primary data include information collected directly through measurement, or otherwise collected directly for a specific purpose. Primary data would include on-site measurements of water quality, for example, or stakeholder feedback through project questionnaires. Secondary data are data that have been collected by someone else, or the primary data that have been processed in some form (e.g., through statistics, or input into GIS, or a model) with the outputs constituting secondary data. Although primary data are available via remote sensing, such data are generally a good example of secondary data, for example, collected via satellite imagery with measurement and processing completed by the collecting organization (e.g., through NASA or other similar agencies) and sold or made available free as databases for use by third parties (such as GIS specialists or modellers).
Any tool or model applied is only as good as the data that are used to develop and then apply that tool or model. The old adage “poor data in = poor information out” should be heeded, and people applying tools and models should make every effort to incorporate high-quality and relevant data for the tool or model applied. The main factors influencing data quality are the money, time and effort that are put into data collection.
Data quality is largely scale dependent, whereby if data were collected for a small-scale (large area) project, then the use of the same data for a large-scale project would almost certainly be inappropriate because the resolution of the data would be insufficient. Any scaling done needs to be certain that the tool or model remains valid. In developing and applying a fish growth model covering the whole production cycle for a 1 kg fish, the data used for model calibration and validation also need to cover this size, and not simply scaling measured data for juvenile stages up to 100 g and assuming this will be sufficient.
Data quality must also take into consideration: (i) the accuracy of data used and to their precision (how precisely has a measurement been recorded); (ii) standardizing the methods used for data collection; (iii) the use of appropriate classification systems and thematic categories; (iv) the timeliness of the data; and (v) possible sources of error in any data collected (Meaden and Aguilar-Manjarrez, 2013).
Sources of data are many and varied, and the extent of available data depends on the location, previous work done and on their application. Increasingly, there are a large number of databases that are becoming freely available on climate, from remote sensing and for mapping purposes, for example. If not freely available, then such data can also be purchased for a fee.
Good data sources come from the refereed literature: journals that present the results from years of research conducted by universities, research institutions, and others globally. Governments will often maintain a large body of data for their country. FAO maintains large databases on fisheries and aquaculture production globally through its FIGIS database, comprising strategic data, information, analyses, and reviews of issues and trends on a broad range of fisheries subjects. It is important that any data used come from a reliable source with appropriate vetting to ensure the key requirements on data quality outlined above are met. In the end, if data are not available, then the only alternative is direct data collection, remembering that this form of data collection is probably the most expensive and time consuming to carry out.
There are many online sources of spatial data. Each data set is usually described and categorized so that the user can understand what the data set contains and represents—this information is known as metadata.
An example of a metadata portal is the United Nations Environment Programme online portal of environmental data sets (http://geodata.grid.unep .ch). Hosted data are searchable by keywords, and can be filtered by thematic category, priority issue and geographic region, etc.
For a more thorough review of data types and sources specific for application through GIS, see Meaden and Aguilar-Manjarrez (2013). The Web site http://gisinecology.com/gis_data_sources.htm also provides some examples of data sources available.

Web Resources

FAO . 2016. GeoNetwork—The portal to spatial data
and information. [online]. Rome. [Cited 12 January
2017]. www.fao.org/geonetwork/srv/en/main.home
FAO . 2016. Global aquaculture production
1950–2014. In: FAO Fisheries and Aquaculture
Department [online]. Rome. [Cited 12 January
2017]. www.fao.org/figis/servlet/TabLandArea?tb_
ds=Aquaculture&tb_mode=TABLE&tb_
act=SELECT&tb_grp=COUNTRY
UNE P. 2016. Environmental data explorer. In: United
Nations Environment Programme [online]. Geneva.
[Cited 12 January 2017]. http://geodata.grid.unep.ch
Further Reading
Aguilar-Manjarrez, J., Kapetsky, J. M. & Soto, D.
2010. The potential of spatial planning tools to
support the ecosystem approach to aquaculture.
FAO/Rome. Expert Workshop. 19–21 November
2008, Rome, Italy. FAO Fisheries and Aquaculture
Proceedings No. 17. Rome, FAO. 176 pp. (also
available at www.fao.org/docrep/012/i1359e/
i1359e00.htm).
Meaden, G. J. & Aguilar-Manjarrez, J., eds. 2013.
Advances in geographic information systems and
remote sensing for fisheries and aquaculture.
Summary version. FAO Fisheries and Aquaculture
Technical Paper No. 552. Rome, FAO. 98 pp.
Includes a CD–ROM containing the full document.
425 pp. (also available at www.fao.org/
docrep/017/i3102e/i3102e00.htm).

Geographic Information Systems

The application of geographic information systems (GIS) has a long history, carried out by GIS practitioners.
GIS is a spatial tool that uses geospatially referenced information to visualize, question, analyse and interpret data to understand the relationships, patterns and trends in the data at various geographic scales, within or across administrative or ecosystem boundaries.
Common outputs include maps that highlight the data analysis undertaken, along with underlying information about the geospatially referenced area under investigation. Digital maps can come from a number of sources and include topographic mapping, digital elevation model data and satellite imagery, for example. Mapping the bathymetry (water depth) of the sea is slightly more specialized, with maps available from the National Oceanic and Atmospheric Administration (NOAA) (https://ngdc.noaa.gov/mgg/ bathymetry/maps/nos_intro.html) and the General Bathymetric Chart of the Oceans (GEBCO, www.gebco.net), among others.

Aquaculture zoning and site selection are often evaluated through a multi-criteria evaluation (MCE) model that incorporates layers of information (attributes) within the GIS. These include, for example, digital maps; physical, chemical and biological attributes, such as water depth (bathymetry), wave height, distance to port and population centres (markets); application of remote sensing data (e.g. water temperature and chlorophyll concentration); identification of other water uses (fisheries, oil extraction, navigation routes and aggregate dredging, to name a few); and areas that are off limits for other reasons (tourism areas and marine protected areas, for example), often verified through field surveys (called ground truthing). These data are used collectively to then define areas suitable for aquaculture development and map out suitable aquaculture zones and sites. At a simple level, the MCE overlaps layers and identifies areas where conflicting use is low. More typically, the MCE weighs the importance of each element in the overall scheme, to then determine areas suitable for aquaculture identified by a higher overall score and those not suitable for a range of reasons achieving a lower score. There are various ways in which that weighting can be completed.
As well as providing information in its own right, GIS can also integrate a range of other data and information for presentation, including, for example, hydrographic information on waves, tides and currents, which can form a further layer in the technical understanding of finding the best locations for aquaculture development to take place.
The majority of the case studies in Table A4.1 have used GIS as part of their respective zoning, site selection and/or area management activity and thus GIS is a critical tool in the development of aquaculture. It is not possible in the available space, however, to outline all possible uses and applications to aquaculture, or indeed to describe how GIS works.
For a more thorough review of how GIS can support aquaculture development, the publications by Kapetsky and Aguilar-Manjarrez (2005), Aguilar- Manjarrez, Kapetsky and Soto (2010), and Meaden and Aguilar-Manjarrez (2013) are recommended. GIS is often the first tool to be applied in zoning and site selection before the application of more specific local area and farm-scale models are applied, which assess likely impacts from a specific level of production at a site or within an area.
Table A4.3 provides a list of popular GIS software available. Also, there are a number of aquaculture inventory initiatives (i.e., Web mapping applications) already being undertaken by many countries; see FAO’s National Aquaculture Sector Overview (NASO) map collection compilation at www.fao.org/fishery/ naso-maps/country-initiatives/en.

Web Resources

The reader is also recommended to investigate:
FAO GISFish publications database (www.fao.org/ fishery/gisfish/index.jsp), which provides a large resource of information on the application of GIS, remote sensing and mapping for aquaculture and fisheries.
The Institute of Aquaculture in Scotland, the United Kingdom of Great Britain and Northern Ireland, is a rich source of research information focused on GIS, remote sensing and spatial applications for resource management for aquaculture (www.aquaculture.stir .ac.uk/GISAP/gis-group).

TABLE A4.3. Popular general-use GIS software.

Popular general-use GIS software.

Satellite Remote Sensing

Remote sensing is the science of obtaining information about objects or areas from a distance, typically from aircraft or satellites where sensors detect energy reflected from the earth’s surface, using either passive means (such as sunlight) or active means (such as lasers). The most often used remote sensing information for aquaculture comes from satellites, which are able to measure sea surface temperature and chlorophyll concentration, detect harmful algal blooms and sea surface height, and provide wind data over large areas; the information can then be analysed using GIS.
Satellite data generally consist of raster images, with associated data in each pixel of the image. Image pixels represent areas of sea (or land) at different spatial scales depending on the system used; they can have a vast range of spatial resolutions, ranging from a very high resolution, such as 1 m 3 1 m, or low resolution, such as 1 000 m 3 1 000 m per pixel, which is generally too large for specific site selection activity, but is useful for larger spatial scales considered for zoning. Raw data are generally referred to as Level 1 information and constitute the primary data received from the images. Conversion using GIS, for example, into mapped images and then model outputs represent secondary data that can, through the GIS applications outlined above, provide useful information on areas of interest for aquaculture based on the parameters measured and any other criteria used in the assessment.
Table A4.4 provides a summary of the commonly used remote sensing software, and for an overview of how remote sensing can support aquaculture development, please see Dean and Popolus (2013).

Further Reading

Dean, A. & Popolus, J. 2013. Remote sensing and
GIS integration. In G. J. Meaden & J. Aguilar-
Manjarrez, eds. Advances in geographic information
systems and remote sensing for fisheries
and aquaculture. CD–ROM version. FAO Fisheries
and Aquaculture Technical Paper No. 552. Rome,
FAO. 425 pp. (also available at www.fao.org/
docrep/017/i3102e/i3102e00.htm).
Dean, A. & Salim, A. 2013. Remote sensing for the
sustainable development of offshore mariculture.
In J. M. Kapetsky, J. Aguilar-Manjarrez & J. Jenness.
A global assessment of offshore mariculture
potential from a spatial perspective, pp. 123–181.
FAO Fisheries and Aquaculture Technical Paper
No. 549. Rome, FAO. 181 pp. (also available at
www.fao.org/docrep/017/i3100e/i3100e00.htm).
Kapetsky, J. M. & Aguilar-Manjarrez, J. 2007. Geographic
information systems, remote sensing and
mapping for the development and management
of marine aquaculture. FAO Fisheries Technical
Paper No. 458. Rome, FAO. 125 pp. (also available
at www.fao.org/docrep/009/a0906e/a0906e00
.htm).
Kapetsky, J. M. & Aguilar-Manjarrez, J. 2010.
Geographic information systems, remote sensing
and mapping for the development and management
of marine aquaculture. In: FAO. The State
of World Fisheries and Aquaculture, pp. 150–154.
Rome, FAO. (also available at www.fao.org/
docrep/013/i1820e/i1820e00.htm).
Meaden, G. J. & Kapetsky, J. M. 1991. Geographical
information systems and remote sensing in inland
fisheries and aquaculture. FAO Fisheries Technical
Paper No. 318. Rome, FAO. 262 pp. (also available
at www.fao.org/docrep/003/t0446e/t0446e00
.htm).
Platt T., Shah, P., George, G., Menon, N., Mohammed,
N., Thottan, M. P. & Sathyendranath, S.
2015. Use of remote sensing in the context of
cage aquaculture. 5th International Symposium
on Cage Aquaculture in Asia. (also available at
http://eprints.cmfri.org.in/10588/1/CAA5%20
Souvenir_Grinson.pdf).
Saxena, M. R., Gangulya, K., Sunder, B. S., Rani, P.,
Rao, A. & Shankar, G. R. 2014. Monitoring
land use with reference to aquaculture in
Chinna Cherukuru village of Nellore District,
Andhra Pradesh, India—A remote sensing and
GIS based approach. The International Archives
of the Photogrammetry, Remote Sensing and
Spatial Information Services, Volume XL-8, 2014.
pp. 927–931. (also available at www.int-archphotogramm-
remote-sens-spatial-inf-sci.net/
XL-8/927/2014/isprsarchives-XL-8-927-2014.pdf).
Travaglia, C., Kapetsky, J. M. & Profeti, G. 1999.
Inventory and monitoring of shrimp farms in Sri
Lanka by ERS SAR data. FAO Environment and
Natural Resources Working Paper No. 1. 34 pp.
Travaglia, C., Profeti, G., Aguilar-Manjarrez, J. &
Lopez, N. 2004. Mapping coastal aquaculture and
fisheries structures by satellite imaging radar: case
study of the Lingayen Gulf, the Philippines. FAO
Fisheries Technical Paper No. 459. Rome, FAO.
45 pp. (also available at www.fao.org/docrep/007/
y5319e/y5319e00.htm).
Valentini, E., Filipponi, F., Xuan, A. N., Passarelli,
F. M. & Taramelli, A. 2016. Earth observation for
maritime spatial planning: measuring, observing
and modelling marine environment to assess
potential aquaculture sites. Sustainability, 8(6),
No. 519. doi:10.3390/su8060519.
Wijenayake, W. M. H. K., Gunaratne, A. B. A. K.,
De Silva, S. S. & Amarasinghe, E. S. 2014. Use
of geographical information system and remote
sensing techniques for planning culture-based
fisheries in non-perennial reservoirs of Sri Lanka.
Lakes and Reservoirs Research and Management,
19(3): 183–191.

Risk Mapping, Including Climate Change

Management of aquaculture operations, even in normal times, is complex because of the range of factors that can affect production. All the segments of the aquaculture production and supply chain are vulnerable to disaster events, a situation which makes the tasks of disaster risk reduction, emergency response, and recovery and rehabilitation particularly demanding in the aquaculture sector.

Aquaculture is practised in varied environmental and physical settings, but several factors (many of which are linked to location) affect vulnerability:
1. Many aquaculture sites are relatively exposed compared with other industries owing to competition for coastal resources and production locations.
2. Many aquaculture sites are situated in fragile ecosystems that can be affected by hydrometeorological changes.
3. Conditions for cultured species easily deteriorate with changes in temperature, precipitation, and other water quality parameters. The cultured species are often sensitive or have low tolerance to these changes.
4. Aquaculture is often the “last user” of freshwater and usually accorded low priority in its allocation.
5. In many countries, aquaculture is mostly carried out by small-scale and resource-poor farmers with weak resilience and adaptive capacity to disasters.
The use of spatial tools and models is increasingly prevalent in society. Spatial tools acquire, manage and analyse data that have geographic or geospatial context. This includes remote sensing technology, including satellites images, aerial surveys, global positioning systems (GPS), GIS and information and communications technology (ICT) tools more broadly, such as mobile communication devices, and other data gathering sensors such as meteorology sensors. Some tools and models are explicitly targeted to disaster management and/or the aquaculture sector.
Changes in climate will affect aquaculture in freshwater and marine systems as a consequence of increased air and water temperatures, the lack of or change in resources, particularly affecting rainfall and freshwater availability in certain regions, changes in ocean acidification, changes in frequency and intensity of storms and harmful algal blooms, among others, which will impact aquaculture stocks, infrastructure and livelihoods. The extent of impact will vary regionally and requires the application of GIS and remote sensing to establish likely risks.

Web Resources

See Climate Change Vulnerability Index (www
.natureserve.org/conservation-tools/climatechange-
vulnerability-index) for plant and animal
species vulnerable to climate change impacts.
See ClimateWizard (www.climatewizard.org) for a
summary of global temperature and precipitation
change expected in the mid and end of the
twenty-first century.

Further Reading

Risk Mapping

Brown, D. & Poulain, F. 2013. Guidelines for the fisheries
and aquaculture sector on damage and needs
assessments in emergencies. FAO, Rome. 114 pp. (also
available at www.fao.org/3/a-i3433e/index.html).
Cattermoul, B., Brown, D. & Poulain, F. 2014.
Fisheries and aquaculture emergency response
guidance. Rome, FAO. 167 pp. (also available at
www.fao.org/3/contents/64f74d96-3323-4795-
880d-0c9399e6f049/i3432e00.htm).
Joyce, K. E., Wright, K. C., Samsonov, S. V. &
Ambrosia, V. G. 2009. Remote sensing and the
disaster management cycle. Advances in geoscience
and remote sensing. G. Jedlovec, ed. InTech,
pp. 318–346. (also available at www.intechopen
.com/books/advances-in-geoscience-andremotesensing/
remote-sensing-and-the-disastermanagement-
cycle).

Climate Change

De Silva, S. S. & Soto, D. 2009. Climate change and
aquaculture: potential impacts, adaptation and
mitigation. In K. Cochrane, C. De Young, D. Soto &
T. Bahri, eds. Climate change implications for
fisheries and aquaculture: overview of current
scientific knowledge, pp. 151–212. FAO Fisheries and
Aquaculture Technical Paper No. 530. Rome, FAO.
Hamdan, R., Othman, A. & Kari, F. 2015. Climate
change effects on aquaculture production performance
in Malaysia: an environmental performance
analysis. International Journal of Business and
Society, 16(3): 364–385.
Handisyde, N. T., Lacalle, D. S., Arranz, S. & Ross,
L. G. 2013. Modelling the flood cycle, aquaculture
development potential and risk using MODIS data:
a case study for the floodplain of the Rio Paraná,
Argentina. Aquaculture, 422–423: 18–24.
Handisyde, N. T., Ross, L. G., Badjeck, M.-C. &
Allison, E. H. 2014. The effects of climate change
on world aquaculture: a global perspective.
Technical report. University of Stirling and Department
for International Development, UK. 152 pp.
Handisyde, N., Telfer, T. C. & Ross, L. G. 2016.
Vulnerability of aquaculture-related livelihoods
to changing climate at the global scale. Fish and
Fisheries. doi:10.1111/faf.12186 (also available
at http://onlinelibrary.wiley.com/doi/10.1111/
faf.12186/abstract).
Lebel, L., Lebel, P. & Lebel, B. 2016. Impacts,
perceptions and management of climate-related
risks to cage aquaculture in the reservoirs of
Northern Thailand. Environmental Management,
58(6): 931–945.
Liu, Y., Saitoh, S.-I., Igarashi, H. & Hirawake, T.
2014. The regional impacts of climate change
on coastal environments and the aquaculture of
Japanese scallops in northeast Asia: case studies
from Dalian, China, and Funka Bay, Japan. International
Journal of Remote Sensing, 35: 4422–4440.

There are a number of journal publications on climate change and aquaculture; too many to mention here. A useful means to gain further information is to conduct a Web search for “aquaculture climate change zoning site selection” or variations thereof.

Mapping Aquaculture Facilities to Improve the Effectiveness of Planning and Management

Inventories and monitoring of aquaculture facilities provide decision makers with important baseline data and trends on production, area boundaries, size distribution of farms, environmental conditions and impacts, and spatial risks to the ecosystem and to the farming systems, and so on. Mapping facilities improves the effectiveness of planning and management interventions to increase production, improve emergency preparedness (including for diseases) and reduce risks in general.
The mapping of aquaculture facilities can be performed accurately, regularly (i.e., days, months or years) and at selected spatial scales by remote sensing.
Remote sensing—using satellites, aircraft, drones or fixed sensors—enables observations of large and often remote or inaccessible areas at a fraction of the cost of traditional surveys. It provides a large range of observation data that complement and extend data acquired from in situ observations to support aquaculture management.
FAO has been assisting countries in recording the location and type of aquaculture structures so they can improve their aquaculture zoning, site selection and area management. The work of FAO by Travaglia et al. (1999) and Travaglia et al. (2004) has demonstrated the mapping of coastal aquaculture and fisheries structures using radar satellite images in Sri Lanka and the Philippines. Aquaculture structures and their evolution can be assessed against locations of sensitive ecosystems and habitats to highlight potential impacts, and they can be used to assess spatial risks to aquaculture. They can also be linked to the licencing process to identify unregistered or illegal facilities and to land tenure issues. FAO’s National Aquaculture Sector Overview (NASO) map collection (www.fao.org/fishery/nasomaps/ naso-home/en) provides a spatial inventory of aquaculture with attributes, including species, culture systems and production (FAO, 2016). Based on Google Earth/Maps technology, its aim is to develop ways to assist developing countries, and so to encourage them to conduct their own inventories, at minimal cost, as part of their strategic planning for sustainable aquaculture development.
Google Earth is a good starting point for spatial inventories of aquaculture, as it makes high-resolution data (e.g., satellite images or historical aerial photographs) freely available to the general public without requiring any remote-sensing expertise.
More advanced approaches based on image analysis require the use of GIS or remote-sensing software and access to satellite images in their original format.
For example, images from the Sentinel-1A satellite are being used to monitor aquaculture in the Mediterranean (ESA, 2016).

Further Reading

Application of Models and Indices—Introduction

Table A4.5 provides a summary of models and indices used in the case studies and additional models of relevance to aquaculture zoning, site selection and area management. Descriptions of each of these models and indices are provided below. Other models and indices are available.

Models for Freshwater Environments

The largest impact in freshwater lake systems is most likely eutrophication potential, with the addition of nutrients from waste feed, faeces and dissolved wastes, increasing algal growth (i.e., higher chlorophyll and, in extreme cases, phytoplankton blooms) and reducing oxygen availability, especially when the phytoplankton die and are consumed by bacteria.
This limits productivity in aquaculture, but more fundamentally damages the ecosystem for wild species and other uses. The primary limiting factor for phytoplankton growth in freshwater systems is phosphorus, which is generally added in excess to fish feeds because fish lack phytase to be able to process phosphorus efficiently. When released to the environment, this increases the concentration so that algal growth is no longer restricted. The most common approach to assess freshwater lake systems and aquaculture is application of the Dillon-Rigler mass-balance model. At larger river basin scales, the SWAT model is also used, and described here. Other models provide more generic assessment of freshwater environments at system scales (e.g., see Panuska and Kreider, undated), and for classification of estuarine systems (e.g., see www.eutro.org).

Dillon-Rigler Model

Lakes are generally sensitive waterbodies, where shifts in trophic status and the potential for eutrophication affect the likely uses that lakes can be put to, including aquaculture. Lakes are classified according to their trophic state, as oligotrophic, mesotrophic, eutrophic or hypereutrophic, depending on the level of phosphorus or chlorophyll present.

TA BLE A4.5. Models and indices used for zoning, site selection and area management

Models and indices used for zoning, site selection and area management

Hypereutrophic lakes are unsuitable for aquaculture, as phytoplankton growth and the potential for eutrophication is already high and fish production would simply increase the problems. For other conditions, the question of how much aquaculture is feasible depends to a large extent on what maximum phosphorus level is acceptable for the water body, whether a change in trophic status is acceptable for that production, which is in part a social decision, and whether secondary effects of production such as lowering oxygen concentration have a self-harming effect. Freshwaters therefore need an evaluation to assess the likely impacts.
The Dillon-Rigler model was not developed with aquaculture in mind, but is nonetheless applied globally to evaluate the changes resulting from the proposed aquaculture production. Application of the Dillon-Rigler model (see Brazil case study, Annex 5) requires prior knowledge on the existing status of the water body, so data collection of primary data is an important prerequisite. Data are needed on surface area and depth, which allow an estimate of water volume, along with flow rate into and out of the lake so that residence time can be calculated. There also needs to be some estimate of the likely input to the lake system from a certain level of fish culture, and whether this is in particulate form and buries in the sediment, or in dissolved form and is available to phytoplankton in the water column.
Estimates of capacity for aquaculture will also depend on the addition of phosphorus from other sources, natural and human-induced, which then allows apportionment of loading to different activities while remaining within the maximum environmental limit imposed. See further reading on the application of this model.

Further Reading

Beveridge, M. C. M. 1984. Cage and pen fish farming.
Carrying capacity models and environmental
impact. FAO Fisheries Technical Paper No. 255.
131 pp. (also available at www.fao.org/
docrep/005/ad021e/ad021e00.htm).
Johansson, T. & Nordvarg, N. L. 2002. Empirical
mass balance model calibrated for freshwater fish
farm emissions. Aquaculture, 212(1–4): 191–211.
Mhlanga, L., Mhlanga, W. & Mwera, P. 2013.
The application of a phosphorus mass balance
model for estimating the carrying capacity of Lake
Kariba. Turkish Journal of Veterinary and Animal
Sciences. (also available at http://journals.tubitak
.gov.tr/veterinary/issues/vet-13-37-3/vet-37-3-12-
1110-37.pdf).
Punuska, J. C. & Kreider, J. C. Undated. Wisconsin
lake modelling suite: program documentation
and user manual. Version 3.3 for Windows. (also
available at http://dnr.wi.gov/lakes/Model/
WiLMSDocumentation.pdf).
Riasco, J., Diaz, D., Beltran, L. & Gutierrez, F. 2012.
Dynamical model to estimate carrying capacity in
reservoirs with fish farming/Modelo dinamico para
estimar la capacidad de carga de cuerpos de agua
con piscicultura. Revista U.D.C.A Actualidad &
Divulgación Científica, 15 (1): 135–145. (In Spanish)
(also available at www.scielo.org.co/pdf/rudca/
v15n1/v15n1a15.pdf).
Zhang, Y-F., Wang, D.-P., Wei, G.-Y. & Wei, H.-Y.
2012. Cage culture capacity analysis of Dahua
Yantan reservoir in Guangxi. Journal of Southern
Agriculture, 43(11). (In Chinese).

Soil and Water Assessment Tool (SWAT )

The Soil and Water Assessment Tool (SWAT) is a public domain hydrological model jointly developed by the USDA Agricultural Research Service (USDA-ARS) and Texas A&M AgriLife Research (http://swat.tamu.edu).
SWAT simulates the quality and quantity of surface and groundwater and predicts the environmental impact of land use, land management practices, and climate change on that water quality and availability.
The model is widely used at the scale of a small watershed to river basin to assess soil erosion prevention and control, non-point source pollution control, and regional management in watersheds.
Its inclusion here results from its use to evaluate the contribution of freshwater flows on aquaculture site selection and production taking place in estuarine and marine systems as a bridge between both ecosystems.
Under these circumstances, the SWAT model is used to evaluate freshwater water flows and volumes and concentration of nutrients and sediments as they impact the marine environment, which then support the further evaluation of the marine system at the zonal scale.

Further Reading

Ferreira, J. G., Saurel, C., Lencart e Silva, J. D.,
Nunes, J. P. & Vasquez, F. 2014. Modelling
of interactions between inshore and offshore
aquaculture. Aquaculture, 426–427: 154–164.
Ferreira, J. G., Saurel, C., Nunes, J. P., Ramos, L.,
Lencart e Silva, J. D., Vazquez, F., Bergh,
Øivind, Dewey, W., Pacheco, A., Pinchot, M.,
Ventura Soares, C., Taylor, N., Taylor, W.,
Verner-Jeffreys, D., Baas, J., Petersen, Jens
Kjerulf, Wright, J., Calixto, V. & Rocha, M.
2013. FORWARD—Framework for Ria Formosa
water quality, aquaculture and resource development.
111 pp. (also available at http://orbit.dtu.dk/
ws/files/102164373/Publishers_version.pdf).
Marinov, D., Galbiati, L., Giordani, G., Viaroli, P.,
Norro, A., Bencivelli, S. & Zaldívar, J-M. 2007.
An integrated modelling approach for the
management of clam farming in coastal lagoons.
Aquaculture, 269 (1–4): 306–320.
Nobre, A. M., Ferreira, J. G., Nunes, J. P., Yan, X.,
Bricker, S., Corner, R. A., Groom, S., Gu, H.,
Hawkins, A. J. S., Hutson, R., Lan, D., Lencart e
Silva, J. D., Pascoe, P., Telfer, T. C., Zhang, X. &
Zhu, M. 2010. Assessment of coastal management
options by means of multilayered ecosystem
models. Estuarine, Coastal and Shelf Science, 87(1):
43–62.

Application of Dynamic Farm and Ecosystem- Scale Ecological Models for Zoning and Site

Selection in Marine Systems

Dynamic ecological models are used in aquaculture to assess the capacity of an area to support cultured species, most typically by providing information and predictions on the growth of species on culture, estimations of waste generated, and how the environment will respond to that waste—essentially, an assessment of the siting of a certain level of production in a certain area. This can be done at both the farm scale (site selection) and more widely at the ecosystem scale, incorporating multiple farms (zoning).
For the models developed, these have been applied almost universally to the marine environment. Models combine submodels on hydrodynamics (water flows), species growth (primarily based on local water temperature in fed species like fish, and temperature and food availability in unfed species like bivalves), and mass balance (the balancing of energy or nutrients in and out of the system) together with baseline environmental information (such as measures of specific parameters, including temperature, existing nutrient loading and so on).
In freshwaters, model use is more limited, but given that most freshwater sites are more fragile ecosystems than corresponding marine environments, further work needs to be undertaken to evaluate the longerterm effects of aquaculture development, particularly in cages in lakes.
The following are some examples of dynamic models currently applied to aquaculture zoning, site selection and carrying capacity assessment. Byron and Costa- Pierce (2013) provide a short review on the application of such models (including others not listed here) in carrying capacity assessment. One important point of note is that all models require calibration and validation when applied within a new situation, or require a certain level of enhancement when applied to new aquaculture species. As such, application to “new” circumstances often need a period of development, including primary and secondary data collection where necessary.

Further Reading

Byron, C. J. & Costa-Pierce, B. A. 2013. Carrying
capacity tools for use in the implementation
of an ecosystems approach to aquaculture. In
L. G. Ross, T. C. Telfer, L. Falconer, D. Soto &
J. Aguilar-Manjarrez, eds. Site selection and
carrying capacities for inland and coastal aquaculture.
FAO/Institute of Aquaculture, University of
Stirling, Expert Workshop, 6–8 December 2010.
Stirling, United Kingdom of Great Britain and
Northern Ireland. FAO Fisheries and Aquaculture
Proceedings No. 21. Rome, FAO. 46 pp. Includes a
CD–ROM containing the full document. 282 pp.
(also available at www.fao.org/docrep/018/i3322e/
i3322e.pdf).

Hydrodynamic Measurement and Modelling

Fundamentally, hydrodynamic measurement and modelling is an assessment of water movement and water flows, tides and waves. Currents are water density, tidally driven or caused by wind, and waves are caused predominantly by wind. The National Oceanic and Atmospheric Administration (NOAA) maintains a number of fixed buoys at different locations around the world (www.ndbc.noaa.gov). These buoys are loaded with instruments to measure water quality and other parameters, such as wave height, period and spectra. At a broad scale, for assessment of zones, the use of remote sensing data, as outlined above, is useful in determining waves and wind effects on ocean currents.
At its simplest level, current speeds and direction can be measured using a small float, timer and hand-held GPS. At a larger scale, discrete current meters can be deployed (examples are electromagnetic current meters, impeller-type meters and the Acoustic Doppler Current Profiler or ADCP), fixed in one location for a defined period, which measure current speed and direction at different water depths at fixed time intervals throughout the deployment.
Most large-scale hydrodynamic models incorporate tidal harmonics (for a description, see Tidal Analysis Software Kit, TASK, at http://noc.ac.uk/using-science/ products/tidal-harmonic-analysis), which are mathematical formulations of water flows based principally on Navier-Stokes equations. (See www.nauticalcharts.noaa.gov/csdl/learn_models.html for a description of what hydrodynamic models are used for).
Examples of flow models include FLOW-3D (www .flow3d.com/commercial-aquaculture-systems), Delft3D-FLOW (http://oss.deltares.nl/web/delft3d), Finite-Volume Coastal Ocean Model (FVCOM) (www .int-res.com/articles/aei2014/5/q005p235.pdf), and ECOM-si (http://woodshole.er.usgs.gov/operations/ modeling/ecomsi.html).
Current speed and direction and wind and wave activity are critical for site selection. Wind, waves and water movement (tidal flow effects) affect the cage and mooring design, the spread of farm wastes from aquaculture activity, which impacts the seabed and surrounding water column. Good water movement through the site is needed to ensure sufficient oxygenated water flows through the cages and on-site infrastructure.
Hydrodynamic models also often underpin ecological models that assess the impacts of aquaculture on the local and regional environment. Such models generally refer to the coupling of hydrodynamic and ecosystem models.

Further Reading

The following papers are a few examples where hydrodynamic modelling has been applied in site selection and zoning activity for aquaculture.
Ferreira, J. G, Caurel, C., Lencart e Silva, J. D., Nunes, J. P. & Vazques, F. 2014. Modelling the interactions between inshore and offshore aquaculture. Aquaculture, 426–427: 154–164.
(also available at www.fojo.org/papers/forward/forward.pdf).
Ferreira, J. G., Hawkins, A. S. J., Monteiro, P., Moore, H., Service, M., Pasco, P. L., Ramos, L. & Seueira, A. 2008. Integrated assessment of ecosystem-scale carrying capacity in shellfish growing areas. Aquaculture, 275: 138–151.
(also available at www.researchgate.net/profile/ Ana_Sequeira6/publication/222582349_ Integrated_assessment_of_ecosystem-scale_ carrying_capacity_in_shellfish_growing_areas/ links/541f710f0cf2218008d3e8bd.pdf).
Foreman, M. G. G., Chandler, P. C., Stucchi, D. J., Garver, K. A., Guo, M., Morrison, J. & Tuele, D. 2015. The ability of hydrodynamic models to inform decisions on the siting and management of aquaculture facilities in British Columbia. DFO Can. Sci. Advis. Sec. Res. Doc. 2015/005. vii 1 49 pp. (also available at www.researchgate.net/ profile/M_Foreman/publication/275523139_The_ ability_of_hydrodynamic_models_to_inform_ decisions_on_the_siting_and_management_ of_aquaculture_facilities_in_British_Columbia/ links/553ec0a00cf210c0bdaaacca.pdf).
Ge ˆ cek, S. & Legovi ´c, T. 2010. Towards carrying capacity assessment for aquaculture in the Bolinao Bay, Philippines: a numerical study of tidal circulation.
Ecological Modelling, 221(10): 1394–1412.
Symonds, A. M. 2011. A comparison between far-field and near-field dispersion modelling of fish farm particulate wastes. Aquaculture Research, 42(S1): 73–85.
Wu, Y., Chaffey, J., Law, B., Greenberg, D. A., Drozdowski, A., Page, F. & Haigh, S. 2014.
A three-dimensional hydrodynamic model for aquaculture: A case study in the Bay of Fundy.
Aquaculture Environmental Interactions, 5: 235–248.

The Farm Aquaculture Resource Management (FAR M) Model

The FARM model is designed to determine the sustainable level of production for aquaculture farms culturing a range of species (fish, shellfish and algae) in marine and freshwater environments and pond systems, and to improve sustainability, profitability and environmental stewardship. The model can be applied to a variety of species and environmental conditions.
The underlying models use equations to describe feeding in fed species or food/nutrient availability in non-fed species (such as shellfish and algae); regulation of feeding (such as feeding rate and feed conversion ratio (FCR); species growth; energy input and loss through harvestable products, wastes and biological processes; oxygen consumption through anabolic and catabolic processes; and mass balance equations to reflect and account for inputs and outputs to the production system.
The FARM model thus predicts growth, nutrient uptake and release to the environment, calculates a mass balance to partition where the waste ends up, and is able to assess the changes in water quality over a growth cycle. The approach and the equations used reflect the species concerned and whether they are fed, such as with fish production, or rely on localized primary productivity and nutrient availability, such as with shellfish and algae. Modelling can be undertaken on individual species produced in monoculture and multiple species in integrated multi-trophic aquaculture (IMTA) systems.
The general model framework includes individual growth integrated with environmental drivers and other data on farm practices to produce the outputs.
When growing more than one species in an IMTA system, for example, the species, their activity (e.g., growth) and outputs (e.g., wastes) interact over time (e.g., shellfish using some of the particulate waste generated by the fish farm) to provide an overall impact on growth of each species and the impacts for the local environment.
The FARM model is not particularly data intensive in terms of what data are needed to produce tangible results. It is a screening model used to evaluate: (i) optimal carrying capacity (i.e., the greatest sustainable yield of market-sized animals within a given time period); (ii) ecological and economic optimization of culture practice for shellfish and finfish and algae; (iii) information on the effects of changing the timing of seeding and harvest; and (iv) is used for assessment of farm-related eutrophication effects on local water, among other outputs. A limited online version of the FARM model is available for use at www.farmscale.org.
The FARM model has been applied in a number of locations globally, including China, Europe, Thailand and the United States of America (www.longline .co.uk/site/products/aquaculture/farm).

Further Reading

Cubillo, A. M., Ferreira, J. G., Robinson, S. M. C., Pearce, C. M., Corner, R. A. & Johansen, J. 2016. Role of deposit feeders in integrated multi-trophic aquaculture—A Model analysis.
Aquaculture, 453: 54–66. doi:10.1016/j.aquaculture.2015.11.031.
Ferreira, J. G., Grant, J., Verner-Jeffreys, W. & Taylor, N. G. H. 2013. Carrying capacity for aquaculture, Modelling frameworks for the determination of. In P. Christou, R. Savin, B. Costa-Pierce, I. Misztal & B. Whitelaw, eds. Sustainable Food Production, pp. 417–448.
Ferreira, J. G., Hawkins, A. J. S. & Brocker, S. B. 2007. Management of productivity, environmental effects and profitability of shellfish aquaculture— the Farm Aquaculture Resource Management (FARM) model. Aquaculture, 264: 160–174.
Ferreira, J. G., Hawkins, A. J. S., Monteiro, P., Moore, H., Service, M., Pascoe, P. L., Ramos, L. and Sequeira, A. 2008. Integrated assessment of ecosystem-scale carrying capacity in shellfish growing areas. Aquaculture, 75: 138–151.
Saurel, C., Ferreira, J. G., Cheney, D., Suhrbier, A., Dewey, B., Davis, J. and Cordell, J. 2014.
Ecosystem goods and services from Manila clam culture in Puget Sound: a modelling analysis.
Aquaculture Environment Interactions, 5: 255–270.

ECOWIN Model

ECOWIN is an ecological model for large-scale aquatic systems. The basic structure is that of a spatial (2D and 3D) framework of boxes, within which relevant biogeochemistry (e.g., nutrient concentrations) and population dynamics are resolved. There is an underlying hydrodynamic model (such as Deflt3D-FLOW) that imparts water movement characteristics in the model, which allows each of the model boxes to interact and change as a consequence of that interaction so that, for example, changes in nutrients can be assessed over large spatial and temporal scales.
Aquaculture is added into the appropriate model boxes where it exists in physical space through objects corresponding to hierarchies for simulating, for example hydrodynamics, air temperature, shellfish growth, seeding and harvesting processes, and so on. The location of aquaculture production has effects (such as a changed nutrient condition) on surrounding boxes through the application of the hydrodynamic model.
The net effect is to assess the impacts of aquaculture development being conducted in relatively limited spatial locations, over a whole area or zone under assessment, thus providing an indication of the overall carrying capacity for individual areas within the zone.
This ecological model is applicable to the ecosystem scale, covering waterbodies or coastal areas, and can be run over time scales of several years to decades to assess the changing situation over the model period.
The outputs from the ECOWIN model can also support site specific assessment because the outputs from ECOWIN can be applied within the FARM model (described above) and other models (Figure A4.1).
The ECOWIN model has been applied in a number of locations globally, including China, Ireland, Portugal and the United States of America (www.longline .co.uk/site/products/aquaculture/ecowin).

Further Reading

Ferreira, J. G. 1995. EcoWin—An object-oriented ecological model for aquatic ecosystems. Ecological Modelling, 79: 21–34.
Ferreira J. G., Andersson, H. C., Corner, R. A., Groom, S., Hawkins, A. J. S., Hutson, R., Lan, D., Nauen, C., Nobre, A. M., Smits, J., Stigebrandt, A., Telfer, T. C., de Wit, M., Yan, X., Zhang, X. L. & Zhu., M. Y. 2006. SPEAR Sustainable options for People, catchment and Aquatic Resources. ISBN 972-99923-0-4. 71 pp.
Ferreira, J. G., Hawkins, A. J. S., Monteiro, P., Moore, H. Service, M., Pascoe, P. L., Ramos, L., & Sequeira, A. 2008. Integrated Assessment of Ecosystem-Scale Carrying Capacity in Shellfish Growing Areas. Aquaculture, 275, 138–151.
Ferreira, J. G., Hawkins, A. J. S., Monteiro, P., Service, M., Moore, H., Edwards, A., Gowen, R., Lourenco, P., Mellor, A., Nunes, J. P., Pascoe, P. L., Ramos, L., Sequeira, A., Simas, T. & Strong, J. 2007. SMILE—Sustainable Mariculture in Northern Irish Lough Ecosystems—Assessment of carrying capacity for environmentally sustainable shellfish culture in Lough ecosystems. Institute of Marine Research 100 pp. (also available at www.ecowin .org/smile/documents/smile%20book.pdf).
Nobre, A. M., Ferreira, J. G., Newton, A., Simas, T., Icely, J. D. & Neves, R. 2005. Management of coastal eutrophication: Integration of field data, ecosystem-scale simulations and screening models.
Journal of Marine Systems, 56 (3/4), 375–390.
Nunes, J. P., Ferreira, J. G., Bricker, S. B., O’Loan, B., Dabrowski, T., Dallaghan, B., Hawkins, A. J. S., O’Connor, B. & O’Carroll, T. 2011. Towards an ecosystem approach to aquaculture: Assessment of sustainable shellfish cultivation at different scales of space, time and complexity. Aquaculture 315(3–4) 369–383.
Modelling-Ongrowing Fish Farms-Monitoring (MOM) Model and FjordEnv Model The MOM model was developed initially in 1997 as a means to assess the environmental impacts of single salmon farming sites in Norway.
One of the key components of the MOM model is consideration of the quantity of particulate material released from a fish farm site and the spread of that waste on the seabed for estimation of the likely changes in sediment oxygen concentration from the deposition of that particulate matter, which varies with the level of production and amount of feed used.
Likely changes in sediment conditions are predicted and compared to a minimum environmental quality standard (EQS) defined in legislation. The model is run iteratively, increasing or decreasing salmon production until the EQS is not crossed, which then sets the maximum allowable production of fish (www.ancylus .net) for the site. The model contains a range of species that can be modelled in different environments.
At a larger scale, the FjordEnv model gives an estimate of environmental conditions of a marine water body, including physical circulation in fjords and other inshore areas. The model computes rates of mixing intensity, water exchange and residence times in different depth strata. It also computes the expected rate of oxygen consumption and oxygen minimum in the basin water. Furthermore, the model computes changes of water quality due to changes in the supply of nutrients and organic matter from fish farms and other sources through combining estimates defined by the MOM model.
Before the model is applied to a specific area, information on topography and forcing functions must be gathered. Some of the forcing is derived from offshore conditions, such as tidal amplitude, density variations in the water column and the natural vertical flux of organic matter. As these vary on regional and larger scales, means data can only be stored in a database and used by referencing or calling this information through the model. As tidal amplitude is used, hydrographic measurements collected directly using current meters are not specifically needed to complete the model computations, but can be used to improve the quality of the computations carried out.

Further Reading

Anon. Undated. Ancylus MOM version 3.2 user manual. 27 pp. (also available at www.ancylus .net/Filbas/MOM/Manual_MOM_v3_2.pdf).
Ervik, A., Kupka-Hansen, P., Aure, J., Stigebrandt, A., Johannessen, P. & Jahnsen, T. 1997. Regulating the local environmental impact of intensive marine fish farming 1. The concept of the MOM system (Modelling-Ongrowing fish farms-Monitoring).
Aquaculture 158: 85–94.
Stigebrant, A. 2001. FjordEnv—a water quality model for fjords and other inshore waters.
Goteborg University. 44 pp. (also available at www.ancylus.net/Filbas/Fjord_dynamics.pdf).
Stigebrant, A. 2011. Carrying capacity: general principle of model construction. Aquaculture Research, 42(S1), 41–50.

DEPOMOD/MERAMOD/TROPOMOD/CODMOD and Shellfish DEPOMOD

DEPOMOD (see Scottish case study, Annex 5) was developed in Scotland, the United Kingdom of Great Britain and Northern Ireland, as a means to regulate marine fish farming activity. Particulate wastes from fish farms are a controlled substance and require permission for discharge—most recently through the Water Environment (Controlled Activities) (Scotland) Regulations 2011 (called CAR licence) administered by the regulatory body, the Scottish Environment Protection Agency.
The DEPOMOD model is a site selection model, originally developed in 2002 as a means to regulate the maximum production permissible on salmon sites.
DEPOMOD (Cromey et al., 2002) is a particle tracking model for predicting the flux and resuspension of particulate waste material (food and faeces). The model evaluation is based on an assessment of the associated benthic community impacts resulting from the deposition of solid wastes onto the seabed, resulting changes in sediment condition, and impact on number and type of species present on the seabed.

The model algorithms define the spread of particulate waste on the seabed based on the production of the site, quantities of particulate material released and its dispersion based on particle settling velocity, current speed and direction information (gained through a current meter deployment) and water depth (through a bathymetric survey) to determine where the waste will deposit on the seabed. Applying limits on the impacts in terms of quantity of solids depositing and the application of a benthic index, the model is run iteratively from a starting biomass of fish on site, and that quantity is reduced and/or the cage configuration altered until the site is “passed.” The permitted peak biomass is set at this limit. The model defines an Allowable Zone of Effect (AZE) based on the settlement of waste feed and faeces. Within the AZE, a limited amount of impacts is permitted, with government-imposed Environmental Quality Standards (EQSs) defining minimum quality standards expected (examples are minimum number of species present in the sediment and maximum deposition per m2). The production carrying capacity of the site is therefore limited by comparing model runs against EQSs for benthic species count, medicine concentration and sediment nutrient concentration. Deposition of in-feed medicines, used against sea-lice infestation, is included as a controlled waste and is modelled through a DEPOMOD derivative called AutoDEPOMOD.
More recently, NewDEPOMOD has been developed as a replacement for the original version, which has undergone recoding using new software, new calibration, and validation and implementation of improvement based on a better understanding of deposition and resuspension for high water flow dispersive sites.
The MERAMOD and TROPOMOD models are derived from the original DEPOMOD model for application in Mediterranean and tropical environments, respectively, adapted and calibrated for the fish species and environmental conditions exhibited in those areas (see Philippines case study, Annex 5). CODMOD is a model that completes the same activity for cod species, and Shellfish-DEPOMOD for assessing the impacts of mussel (Mytilus sp.) longlines.

Further Reading

Black, K. D., Carpenter, T., Berkeley, A., Black, K. & Amos, C. 2016. Redefining seabed process models for aquaculture: NewDEPOMOD. Final report. Scottish Association of Marine Science.
200 pp. (also available at www.sams.ac.uk/kennyblack/REFINING%20SEA-BED%20PROCESS%20 MODELS%20FOR%20AQUACULTURE%20 Final%20Report%20for%20web.pdf).
Cromey, C. J., Nickell, T. D. & Black, K. D. 2002. DEPOMOD—modelling the deposition and biological effects of waste solids from marine cage farms. Aquaculture, 214: 211–239. (also available at www.i-mar.cl/noticias/2009/descarga/Cromey_etal_Aqua2002.pdf).
Cromey, C. J., Nickell, T. D., Treasurer, J., Black, K. D. & Inall, M. 2009. Modelling the impact of cod (Gadus morhua L.) farming in the marine environment—CODMOD. Aquaculture, 289(1–2): 42–53.
Weise, A. M., Cromey, C. J., Callier, M. D., Archambault, P., Chaimberlain, J. & McKindsey, W. 2009. Shellfish-DEPOMOD: Modelling the biodeposition from suspended shellfish aquaculture and assessing benthic effects. Aquaculture, 228(3–4): 239–253.

ACExR-LESV Model

The ACExR-LESV(SF) model simulates the effects of finfish and shellfish aquaculture at the water body (zonal) scale, and is applicable to regions of restricted water exchange, such as the fjordic sea lochs found in Scotland, the United Kingdom of Great Britain and Northern Ireland. The model contains a number of submodels to simulate changes in biology and chemistry, an aquaculture fish waste submodel to estimate dissolved and particulate nutrient additions, and a pelagic ecosystem submodel to define water quality characteristics, including dissolved oxygen, chlorophyll, nitrogen and phosphorus. The model averages the results per 24 hours, but simulates the changes that occur over the period of a year. The net effect is a model that defines modelled changes to water conditions that result from fish farm activity.

Further Reading

Tett. P. 2014. Guide to the implementation of the ACExR-LESV(SF) model for aquaculture in sealochs and other regions of restricted exchange.
Scottish Association of Marine Science, Oban. 22 pp. (also available at www.sams.ac.uk/paultett/ acexr-lesv-guide-2014).
Tett, P., Portilla, E., Gillibrand, P. A. & Inall, M. 2010. Carrying and assimilative capacities: the ACExR-LESV model for sea-loch aquaculture. Aquaculture Research 42: 51–67.
Tett, P., Portilla, E., Inall, M., Gillibrand, P. A., Gubbins, M. & Amundrod, A. 2007. Modelling the Assimilative Capacity of Sea-Lochs. Final report to the Scottish Aquaculture Research Forum, project SARF012. (also available at www.sarf.org.uk/Project%20Final%20Reports/ SARF012%20-%20Final%20Report.pdf).

Ecopath with Ecosim

Ecopath with Ecosim is a free ecological modelling software suite (available at http://ecopath.org). The suite has three main components: Ecopath, which is a static, mass-balanced snapshot of the system being modelled; Ecosim, which is a time dynamic simulation module; and Ecospace, a spatial and temporal dynamic module primarily designed for exploring impact and placement of protected areas. This modelling suite was not developed for aquaculture, but has been applied to aquaculture, most notably for shellfish production and considerations of carrying capacity as part of zoning and site selection activity.

Further Reading

Byron, C. J., Jin, D. & Dalton, T. M. 2015. An integrated ecological-economic modelling framework for the sustainable management of oyster farming. Aquaculture 447: 15–22.
Ferriss, B. E., Reum, J. C. P., McDonald, P. S., Farrell, D. M. & Harvey, C. J. 2016. Evaluating trophic and non-trophic effects of shellfish aquaculture in a coastal estuarine food web. ICES Journal of Marine Science, 73(2): 429–440.
Kluger, L. C., Taylor, M. H., Mendo, J., Tam, J., & Wolff, M. 2016. Carrying capacity simulations as a tool for ecosystem-based management of a scallop aquaculture system. Ecological modelling, 331: 44–55.
McKindsey, C. 2013. Carrying capacity for sustainable bivalve aquaculture. In P. Christou, R. Savin, B. Costa-Pierce, I. Misztal & B. Whitelaw, eds. Sustainable Food Production, pp. 449–466. Springer, New York.
Zhang, T. W., Su, Y. P. & Ma, S. 2011. A preliminary study of Ecopath with Ecosim to the shrimp pond ecosystem. Applied Mechanics and Materials, 88–89: 423–426.
Qualitative Network Model Applied to Shellfish Reun et al. (2015) have applied a Qualitative Network Model (QNM) to the consequences of bivalve culture and application of management decisions on species community structure within Puget Sound. This is not a site selection or zoning methodology per se, but decisions on these rely also on assessment of consequences of aquaculture development as part of the ecosystem approach, which is why it has been included here.
The process is a probability model using a graphical method that defines a simplified matrix of complex ecological interactions between species responses along with abiotic and other (e.g., social, economic) linkages. The model assesses specific scenarios by evaluating the impacts of changes to the system.
In an aquaculture context, this has been applied to additional bivalve aquaculture production, removing predators of bivalves from an area, or assessing changes in nutrient concentrations to then evaluate the consequences on the overall ecosystem structure.

Further Reading

Reum, J. C. P., McDonald, P. S., Ferriess, B. E., Farrell, D. M., Harvey, C. J. & Levin, P. S. 2015. Qualitative network models in support of ecosystem approaches to bivalve aquaculture. ICES Journal of Marine Science, 72(8): 2278–2288.

Example of a Decision Support System

SYSMAR (see Indonesia case study) is a decision support system (DSS) developed for decision makers in the management of finfish marine aquaculture facilities for sites with scarce data availability. The DSS was designed to regulate the development of facilities where the activity is already well established for assessment of suitable locations and estimation of potential environmental impacts of existing cage clusters. In addition, it provides guidance on planning and identification of potential areas for expansion of the activity.
SYSMAR is comprised of modules for estimation of finfish farm emissions, site selection and carrying capacities. Data from various sources and numerical simulation models for flow, waves and water quality are embedded within a graphical user interface using ArcGIS, with the addition of open source modelling systems used to facilitate further developments.
Site selection and carrying capacity limits are defined by adoption of cost-effective methods based on the results of simulation models for water flows, waves and water quality. Freely available bathymetric data are combined with data from remote sensing and ocean forecast systems, enabling cost-effective model developments in places with scarce data. Specific site selection is based on best flushing, protection to hazards and farm operation. Thematic maps based on in situ measurements, simulation models and zoning schemes are prepared and imported to the DSS. Templates built using ArcGIS are overlaid for generating suitability maps for marine finfish facilities.
Recommendations concerning the relocation and/or best location of farms are provided.
Farm carrying capacity is based on hydrodynamics from measurements or model simulations. The method estimates the maximum fish production for the given flushing at the fish farm location, falling velocities of waste, and a user-defined threshold for carbon deposition on the seabed; and cumulative carrying capacity is dictated by the rate at which nutrients can be added without triggering eutrophication. The emitted load of dissolved inorganic nitrogen (DIN) from all the farms should not exceed a user-defined percentage of the DIN load entering the water body. Recommendations regarding farm and overall carrying capacity of marine finfish aquaculture sites are delivered.
SYSMAR has been successfully applied to several sites in Indonesia. The modules for site selection and carrying capacity have been validated using in situ observations in South-East Asia.

Further Reading

Mayerle, R., Windupranata, W. & Hesse, K. J. 2009. A decision support system for a sustainable environmental management of marine fish farming.
In Y. Yang, X. Z. Wu & Y. Q. Zhou, eds. Cage aquaculture in Asia, pp. 370–383. Proceedings of the Second International Symposium on Cage Aquaculture Asia, 3–8 July 2006, Hangzhou, China. Vol. 2. Asian Fisheries Society, Manila, Philippines, and Zhejiang University, Hangzhou, China.
Niederndorfer, K. In press. Proposal of a practical method to estimate the ecological carrying capacity for finfish mariculture with respect to particulate carbon deposition to the sea floor.
Research and Technology Centre, University of Kiel, Kiel, Germany. (Ph.D. Thesis).
Van der Wulp, S. A. 2015. A strategy to optimize the arrangement of multiple floating net cage farms to efficiently accommodate dissolved nitrogenous wastes. Dissertation. Research and Technology Centre, University of Kiel, Kiel, Germany. 111 pp. Windupranata, W. 2007. Development of a decision support system for suitability assessment of mariculture site selection. Research and Technology Centre, University of Kiel, Kiel, Germany. pp. 125. (Ph.D. Thesis).

Application of Index Tools

Indices are a method of aggregating univariate or multivariate parameters to define an overall index score within a range of possible scores, with the score defining an overall impact, status or condition. Indices have been used in ecology for many years; examples include diversity measures such as the Shannon-Weiner Index and AMBI (http://ambi.azti.es). Below are some examples of indices that have been used within the aquaculture case studies (Annex 5) summarized in Table A4.1.

TRIX Index

Within the Turkey case study and in regions across the Mediterranean, the TRIX index has been applied to aquaculture site selection and zoning activity. TRIX was originally developed by Vollenweider et al. (1998) as an index that defines trophic conditions in marine systems based on generalized water quality parameters, notably the linear addition of the logs of chlorophyll-a concentration, oxygen saturation, total nitrogen concentration and total phosphorus concentration.
The index is not widely used, but is applied to aquaculture in various countries growing fish in the Mediterranean to evaluate changes in water conditions as a result of cage aquaculture deployment. In Turkey (Turkey case study, Annex 5), for example, it has been applied to define areas where aquaculture is permitted and not permitted, based on the existing water quality to parameterize the TRIX model, combined with minimum distance to shore, water depth and current speed criteria. TRIX is used to assess the likely consequences of cage aquaculture through changes to the index score resulting from additional culture.
Applied through GIS, with interpolation between data points, the result is a map showing locations where aquaculture is permitted and demarcation of zones that are strictly applied.

Further Reading

Vollenweider, R. A., Giovanardi, F., Montanari, G. & Rinaldi, A. 1998. Characterization of the trophic conditions of marine coastal waters, with special reference to the NW Adriatic Sea. Proposal for a trophic scale, turbidity and generalized water quality index. Environmetrics, 9, 329–357.
Yucel-Gier, G., Pazi, I., & Kucuksezgin, F. 2013.
Spatial analysis of fish farming in the Gulluk Bay (Eastern Aegean). Turkish Journal of Fisheries and Aquatic Sciences, 13: 737–744. (also available at www.trjfas.org/uploads/pdf_188.pdf).
Yucel-Gier, G., Pazi, I., Kucuksezgin, F. & Kocak, F. 2011. The composite trophic status index (TRIX) as a potential tool for the regulation of Turkish marine aquaculture as applied in the eastern Aegean coast (Izmir Bay). Applied Ichthyology, 27: 39–45.

Nutrient Enhancement and Benthic Index

The nutrient enhancement and benthic index has been applied to Scottish sea lochs as a means to categorize whether or not aquaculture is permissible in a particular loch system (body of enclosed marine water, similar to the Norwegian fjordic system). The index uses underlying models of nutrient enhancement, referred to as the equilibrium concentration enhancement (ECE) model, to predict the nitrogenous nutrients arising from fish farming conducted within the loch; and a carbon deposition model to predict the area of seabed liable to be covered by the settlement of particulate waste material (feed and faeces). For interpretation of results, the predicted ECE values and the percentage areas of “degraded” seabed are combined in a manner which identify the relative potential sensitivity of sea lochs to further fish farming development.
The approach adopted is a semi-logarithmic scaling of ECE values from 0–5, such that each sea loch can be assigned an index of nutrient enhancement. In a similar manner, the percentage area of degraded seabed is scaled from 0–5, allowing each sea loch to be assigned an index of benthic impact. These two scaled indices are then added together to give a single combined index for each sea loch. The resultant single index, scaled from 0–10, is used to provide an indication of the relative sensitivity of a sea loch system to further fish farming development by assigning three categories. Category 1 sea lochs are the most sensitive, and precaution is applied for not allowing further aquaculture development (where aquaculture already exists). Precaution is also applied to Category 2 water bodies, but allows for some further development,

where a developer has shown (through EIA and modelling work) that no additional harm will be caused.
For Category 3 lochs, further development of aquaculture is permitted, although EIA is still necessary, as it is for all aquaculture development in Scotland, the United Kingdom of Great Britain and Northern Ireland.
The system has been applied to all Scottish sea lochs on the west coast (aquaculture is not permitted on the east coast of Scotland, the United Kingdom of Great Britain and Northern Ireland), and an atlas also produced to show stakeholders the results as part of the overall strategic plan.

Further Reading

Gillibrand, P. A., Gubbins, M. J., Greathead, C. & Davie, I. M. 2002. Scottish executive locational guidelines for fish farming: predicted levels of nutrient enrichment and benthic impact. Fisheries Research Service, Marine laboratory, Aberdeen.
53 pp. (also available at www.gov.scot/Uploads/Documents/Report63.pdf).

Brazilian Aquaculture Sustainable Development Index

Brazil has undertaken development of an Aquaculture Sustainable Development Index (ASDI), used to rank the overall sustainability of aquaculture development in freshwater lakes. Four sub-indices are incorporated, covering social sustainability, environmental sustainability, institutional sustainability and economic sustainability, each graded between 1 (poor) and 5 (high), with 3 being average. Each sub-index has up to five criteria that are evaluated and scored using an average weighting to calculate the overall sub-index score. Each of the four sub-indices are then weighted (social = 5, environmental = 4, institutional = 3 and economic = 2) to calculate the final score for the ASDI. A score of 3 to 4 suggests the lake is considered medium sustainability.
Further reading: No references are available. See case study in Annex 5.

Additional Reading

The following provide some additional references not
specifically identified in Part 3, which offer additional
routes to find required information on models and
tools available.
Bueno, G. W., Ostrensky, A., Canzi, C. & de Matos,
F. T. 2013. Implementation of aquaculture parks
in federal government waters in Brazil. Reviews in
Aquaculture, 7(1): 1–12.
Byron, C., Link, J., Costa-Pierce, B. & Bengston, D.
2011. Modelling ecological carrying capacity of
shellfish aquaculture in highly flushed temperate
lagoons. Aquaculture, 314(1–4): 87–99.
Filueira, R., Guyondet, T., Comeau, L. A. & Grant, J.
2014. A fully-spatial ecosystem-DEB model of
oyster (Crassostrea gigas) carrying capacity in the
Richibucto Estuary, Eastern Canada. Journal of
Marine Systems, 136: 42–54.
Lauer, P., López, L., Sloan, S. & Doroudi, M. 2015.
Learning from the systematic approach to aquaculture
zoning in South Australia: A case study of
aquaculture (zones-lower Eyre Peninsula) policy
2013. Marine Policy, 59: 77–84.
Ren, J. S., Stenton-Dozey, J., Plew, D. R., Fang, J. &
Gall, M. 2012. An ecosystem model for optimizing
production in integrated multitrophic aquaculture
systems. Ecological Modelling, 246: 34–46.
Salama, N. K. G., Murray, A. G. & Rabe, B. 2015.
Simulated environmental transport distances of
Lepeophtheirus salmonis in Loch Linnhe, Scotland,
for informing aquaculture area management structures.
Journal of Fish Diseases, 39(4): 419–428.
Shi, J., Wei, H., Zhao, L., Yuan, Y., Fang, J. &
Zhang, J. 2011. A physical-biological coupled
aquaculture model for a suspended aquaculture
area in China. Aquaculture, 318(3–4): 412–424.
To assist in the preparation of this publication, case
studies from ten countries—Brazil, Chile, China,
Indonesia, Mexico, Oman, the Philippines, Turkey,
Uganda and the United Kingdom of Great Britain and

Tironi, A., Marin, V. H. & Campuzano, F. J. 2010.
A management tool for assessing aquaculture
environmental impacts in Chilean Pataginian
fjords: Integrating hydrodynamic and pellets
dispersion models. Environmental Management,
45(5): 953–962.
Yusoff, A. 2015. Status of resource management
and aquaculture in Malaysia. In: Romana-Eguia,
Parado-Estepa, Salayo, & Lebata-Ramos, eds.
Resource enhancement and sustainable aquaculture
practices in Southeast Asia: challenges
in responsible production of aquatic species,
pp. 53–65. Proceedings of the International
Workshop on Resource Enhancement and Sustainable
Aquaculture Practices in Southeast Asia 2014
(RESA). Tigbauan, Iloilo, Philippines, Aquaculture
Dept., Southeast Asian Fisheries Development
Center.

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