Case study 1 “Site selection”

AQUAculture USEr driven operational

Remote Sensing information services

Deliverable 6.1

Case study 1:

Site characterisation/selection using historic data

AQUA-USERS is funded under the European Community’s 7th Framework Program (Theme SPA.2013.1.1-06: Stimulating development of downstream services

and service evolution, Grant Agreement No 607325)

DHI, NIVA, WI, PML, FCUL, SGM

2015-11

DHI,WI,PML

2015-01

2

D6.1: Case study 1: Site characterisation/selection using historic data

15/11/2015

Task 6.1/D6.1:

Site characterisation/selection using historic data

Lead beneficiary DHI

Contributors Wi, VU, PML, FFCUL, NIVA, SGM

Due date 30/04/2015 (month 18)

Actual submission date 15/11/2015

Dissemination level PU

Change record

Issue Date Change record Authors

0.1 10/11/2014 Initial version DHI

0.2 29/05/2015 Draft, case story addition DHI

0.3 16/9/2015 Case story addition DHI

0.4 25/9/2015 Case story addition WI

0.5 28/9/2015 Case story addition PML

0.6 15/10/2015 Case story addition NIVA

1.0 31/10/2015 Report for submission DHI, WI

Consortium

No Name Short Name

1 Water Insight BV WI

2 Stichting VU-VUMC VU/VUmc

3 Plymouth Marine Laboratory PML

4 Fundação da Faculdade de Ciências da Universidade de Lisboa FFCUL

5 Norsk institutt for vannforskning NIVA

6 DHI GRAS GRAS

7 DHI DHI

8 Sagremarisco-Viveiros de Marisco Lda SGM

To be cited as

Kaas, H., Møhlenberg, F., Heinänen, S., Holst, M.H., Dale, T., Miller, P., Icely, J., Poser, K., Marieke Eleveld, M. and Laanen, M. (2015) “Site characterisation/selection using historic data”, AQUA-USERS deliverable D6.1, EC FP7 grant agreement no: 607325, 86p.

© Copyright 2015, the member of the AQUA-USERS consortium. All rights reserved.

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Task objective

To provide users with spatial and temporal site characterization or site selection information based on the processed archive of remote sensing observations, historic in-situ and deterministic modelling data based on the results of WP2 and WP3.

Scope of this document

This document describes the results of site characterisation/selection case studies performed by the partners together with their national end users. The case studies shall demonstrate the transfer and integration of the knowledge, data and methodologies developed in the project into management and decision support for aquaculture as well as central and local authorities.

Abstract

Together with the end users, a number of case studies are conducted in order to demonstrate the practical usability of the results of the project in operation of marine aquaculture. The developed knowledge-data-methodologies are integrated and transferred into management and decision support information. The focus is on georeferenced information and a key analytical instrument is GIS. The tasks has investigated the application of the spatial information for site characterisation or site selection for different production types and locations; involving identification of relevant data, testing optimal data processing and how to communicate the information. Site characterisation supports the daily operation of the aquaculture production by increasing the knowledge of the production. Site selection does on the other hand have a longer perspective by supporting the optimal future production sites. Site selection may also support administrative management in regulation and licensing. The case studies are defined in close cooperation with the endusers. In all, 5 studies are conducted with focus on fish and mussel production and geographical scales from local through national to regional. Based on the objectives, determinant factors for the site characterisation/selection are identified and (historical) data collected. In most cases, the primary data source is earth observation data, but in situ data measured by the project and external parties as well as deterministic modelling data are also important. For each case, an analytical framework is defined (dependent on case and data types involved) and implemented and analysed in a GIS environment. The outcome of the GIS analyses is maps describing production sites and identifying sites suitable for aquaculture production of the given species and in the given geographical area and scale.

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List of abbreviations

Abbreviation Description

EO Earth Observation

GIS Geographical Information System

EMODnet European Marine Observation and Data network

MyOcean An European project dedicated to the implementation of the GMES Marine Service for Ocean Monitoring & Forecasting. The project is closed and in April 2015 followed up by the Copernicus Marine Environment Monitoring Service. Web access to data http://marine.copernicus.eu/.

List of related documents

Short Description Date

D2.3 GIS site selection application blue print 28/02/2014

D2.1 and 2.7 User requirements 31/01/2014

23/03/2015

D3.1 Optical algorithms 30/10/2015

D3.2 SST algorithms 24/07/2014

D3.3 HAB detection 03/03/2015

D3.4 Specification of MET Ocean data 27/10/2015

D3.5 Aquaculture indicators 01/11/2014

D4.1 MERIS + S3 product archive 05/09/2014

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Table of contents

1 Introduction ................................................................................................................ 7

2 Case: Site characterization and selection for salmon production in Scottish waters using the AQUA-USERS portal .................................................................................................. 11

2.1 Objective.................................................................................................................................................................. 11

2.2 Important administrative and environmental factors determining suitability......................... 11

2.3 Description of applied data ............................................................................................................................. 13

2.4 The analytical framework ................................................................................................................................ 14

2.5 Results ...................................................................................................................................................................... 16

3 Case: Site selection for rainbow trout production in the Danish waters combining environmental modelling and spatial restrictions ............................................................ 17

3.1 Objective(s) ............................................................................................................................................................ 17


3.3 Description of the applied data ..................................................................................................................... 21

3.3.1 Administrative constrains ................................................................................................................................21

3.3.2 Optimal production .............................................................................................................................................21

3.3.3 Environmental impacts .....................................................................................................................................22

3.4 Indicators and suitability criteria ................................................................................................................. 23

3.4.1 Administrative constrains ................................................................................................................................23

3.4.2 Optimal production .............................................................................................................................................23

3.4.3 Environmental impacts layer ..........................................................................................................................24

3.5 Resulting suitability maps ............................................................................................................................... 24

3.5.1 Conversion into multiplied suitability layers ...........................................................................................24

3.5.2 Suitability based on administrative restrictions ....................................................................................24

3.5.3 Suitability based on production data ..........................................................................................................27

3.5.4 Suitability based on environmental impact data ..................................................................................28

3.5.5 Composite of all suitability layers, which equals site selection .......................................................29

3.6 Perspective ............................................................................................................................................................. 30

4 Case: Potential new sites for Salmon Aquaculture in Rogaland County Norway based on combination of EO data and spatial restrictions .............................................................. 32

4.1 Objective(s) ............................................................................................................................................................ 32

4.2 Analytical framework ........................................................................................................................................ 32

4.3 Data ........................................................................................................................................................................... 33

4.3.1 Types of data ...........................................................................................................................................................33

4.4 Data selection ........................................................................................................................................................ 35

4.5 Data analyses and selection of suitability criteria ................................................................................. 35

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4.6 Conversion into multiplied suitability layers .......................................................................................... 37

4.7 Results ...................................................................................................................................................................... 37

4.7.1 Suitability layer based on legal/administrative restrictions ...........................................................37

4.7.2 Suitability layer based on production data ..............................................................................................41

4.7.3 Combined spatial suitability layer ................................................................................................................41

5 Case: Site characterization for mussel farming in the Dutch Wadden Sea using EO data 43

5.1 Objective(s) ............................................................................................................................................................ 43




5.5 Results ...................................................................................................................................................................... 46

5.6 Discussion ............................................................................................................................................................... 49

6 Case: Mussel farming in Portugal ............................................................................... 51

6.1 Objective(s) ............................................................................................................................................................ 52


6.2.1 Armona offshore park Ria Formosa.............................................................................................................54

6.2.2 Finisterra concession Sagres ...........................................................................................................................54


6.3.1 Licensing agencies ................................................................................................................................................55

6.3.2 EU Directives (some examples) .....................................................................................................................55

6.3.3 Suitability of the Algarve coast for culture of the Mediterranean Mussel (Mytilus galloprovincialis) .................................................................................................................................................................56


6.4.1 Armona offshore park Ria Formosa.............................................................................................................57

6.4.2 Finisterra concession Sagres ...........................................................................................................................58

6.4.3 Obligatory monitoring for the Agência Portuguesa do Ambiente (APA) ................................59

6.4.4 Data on mussel growth to calibrate FARM growth model for Mytilus galloprovincialis. ..60

6.5 Results ...................................................................................................................................................................... 60

6.5.1 Armona offshore park Ria Formosa ...........................................................................................................60

6.5.2 Results focusing on the USER Finisterra and their specific requirements towards understanding HAB events in Sagres ..........................................................................................................................62

7 References ................................................................................................................ 68

Appendix 1. Case on site selection for rainbow trout production in Danish waters .......... 71

Appendix 2. Case on site selection for salmon production in a Norwegian fjord ............... 83

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1 Introduction

The AQUA-USERS project aims at providing the aquaculture industry with relevant and timely information from Earth observation combined with other available data such as in-situ measurements and dynamic modelling. The key purpose of AQUA-USERS is to develop, together with users, a software system that brings together satellite information on optical water quality and temperature with in-situ observations on water quality, temperature and ecological parameters. Additionally, the application will collect relevant weather prediction data and met-ocean data (wind, waves etc.) from external data (model) sources.

In WP6, the developed knowledge-data-methodologies are integrated and transferred into management and decision support information. For this, three case studies (T6.1-T6.3) are conducted applying the developed tools of the software system and integrating the collected data, i.e. optical water data from satellites (earth observations, EO), in situ measurements from farmers and data sources accessible on the internet (e.g. EMODNET) as well as met-ocean acquired from relevant internet data service (e.g. MyOcean). All case studies are conducted in tight collaboration with the end-users as it is considered paramount to demonstrate the practical usability of the results of the project in operation of marine aquaculture.

In Task 6.1 collected data are applied for site characterisation/selection. Tasks 6.2 and 6.3 apply the app developed by the AQUA-USERS project for daily operation at different productions sites. The app brings together the different data collected within the project and provides a decision support system, which will link the observed environmental conditions to a set of (user-defined) possible management options as well as an evaluation of earlier management decisions. In Task 6.2 case study the primary data source is in situ optical water quality made by the end users while in case WP6.3 also include other types of data. The objective of the present report (D6.1) is to describe the analyses and outcome of Task 6.1.

Site characterisation/selection is a key factor in any aquaculture operation, affecting both the growth, the success and sustainability of the industry. Site characterisation can in particular support (daily) operation. Knowledge of the conditions at the production sites decreases the uncertainty in the daily decision making. Which data to apply depends on the decision to take. Both historical data showing the usual patterns at the site, and real time data are important. For example regarding algal blooms, historical data tell about the risk of blooms, the frequencies on seasonal or year-to-year scales, the common species (toxic/non-toxic), earlier economic consequences, etc. Such knowledge should be built by integration of different types of data and data sources (EO, measurements, models). To make decision on mitigation, real time data such as EO data on distribution and level of chlorophyll are essential.

Site selection focuses on the wider perspective by supporting the selection of optimal future production sites. It is not only a tool for the aquaculture farmers to ensure economic viability. It is an integral part of spatial planning and supports administrative management, regulation and licensing. Doing proper site selection minimises conflicts between the wide range of stakeholders involved in the exploitation of the sea; other industries also requesting marine space, recreational users, guardians of nature values, etc. Complete site selection involves integration of data addressing all aspects of the intersection between spatial planning for marine waters and the requirements of parties exploiting the sea. Spatial restrictions, logistic requirements of the industries (distance to shore, etc.) as well as the ecosystem services expressed e.g. as carrying capacity (e.g. food availability, temperature range) and assimilative capacity (e.g. dilution/recycling of organic matter and nutrients) are essential topics of concern. Particularly, the dual ecosystem approach considering both suitability of the environments to the aquacultures, and the impacts of the industries on the environment are a requirement today. Worldwide, legislation emphasises the importance of an ecosystem-based approach when managing marine activities (Hyder et al, 2015).

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The specific objective of Task 6.1 is to make critical analyses of specific user sites (site characterisation) and of wider areas providing basis for selection of optimal locations for marine aquaculture production (site selection). The basis for the analyses is the blueprint for application of GIS for site selection prepared in D2.3 and the user requirements mapped in D2.1 and D2.7. Frameworks for the analyses are given by D2.3 as well as D3.5, the latter giving an overview of useful aquaculture indicators. Data are acquired from a number of data providers. Besides earth observation data processed by the project (MERIS, MODIS, AATSR, see D2.2, D3.1, D3.2 and D4.1), other involved data derived from in situ measurement at aquaculture farms (Task 5.3) and at e.g. sites visited as part of national monitoring programmes as well as data from dynamic modelling (important external in situ and model data sources are described in D3.4).

A key analytical tool in more of the cases is provided by geographical information systems (GIS). GIS is a powerful tool for storing, organizing, transforming and displaying data. It does also provide strong functionalities for analysing georeferenced data. GIS has several advantages for aquaculture applications, not only providing a visual inventory of e.g. physical restrictions and environmental characteristics of the marine waters, but also allowing generation of maps for different uses, e.g. suitability maps. GIS is thus optimal for decision support systems.

Exactly which data to collect and apply in site characterisation/selection analyses depend on the purpose of the analysis, e.g. the targeted species, the location/region, and not the least on the data availability. We will just mention a selection of relevant data. Generally the following may be considered with regard to the spatial restrictions (IUCN, 2009):

o Areas with underwater cables or conduits o Areas of interest for tourism(e.g., beaches) o Areas of military interest o Artificial areas with stone reefs o Coastline, distance to shoreline (close access, limited visibility from land) o Dumping points and underwater outlets along the coast, wastewater sources o National and regional legislation related to aquaculture development o National and regional standards for water quality, etc. o Other aquaculture facilities o Port areas and infrastructures o Protected areas (natural parks, heritage sites, Natura2000, underwater areas of

archaeological interest) o Traditional fishing areas o Vessel anchorage areas, ship traffic and routes

For analyses of the ecosystem services the end-users were asked which physical, chemical and biological parameters they consider as important. The accumulated list is given below.

o Algal species, concentration of microalgae (upwelling indicator) o Bathymetry (for anchoring and potential immersion of cages) o Chlorophyll-a †‡ o Currents (water exchange, spread of nutrients, not too strong to cause fish mortality) o Algal blooms (including HAB events) †‡ o Nutrients (NO3, NH3, PO4 - eutrophication issues) o Oxygen levels (health) o Parasites and bacteria o pH o Salinity o Seabed type and composition (e.g., for anchoring; erosion/accumulation site) o Temperature (SST; health and feeding of e.g. salmon) †‡

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o Tidal amplitude o Turbidity (light/shadow limits the photosynthetic activity of seaweed) † o Waves (degree of exposure) ‡ o Wind speed and direction (risk of damaging storms) ‡ o Suspended sediment †‡

(Variables retrieved from EO data (†) and models (‡) in the AQUA-USERS project)

Comprehensive site characterisation/selection is a non-trivial time consuming exercise and is outside the scope of the AQUA-USERS project. The objective of the project is to apply different approaches to characterise/select sites and to demonstrate the possibilities for different cases, i.e. different purposes/type of productions/geographical locations, of using and integrating available data and specifically, the applicability of earth observation data. To fulfil this, 5 case studies are defined in close cooperation with the end-users. The studies have focus on different species of fish and mussel and comprise different geographical scales from local through national to regional. Based on the objectives, key factors for the site characterisation/selection are identified and (historical) data collected. In most cases, the primary data source is earth observation data, but in situ data measured by the project and external parties as well as deterministic modelling data are also important. For each case, an analytical framework is defined (dependent on case and data types involved) and implemented. In the table below the objectives of the define cases are given and in the following chapter that analyses and outcome is described.

Table 1 Overview of the cases analysed in Task 6.1.

Case Objectives

Site characterization and selection for salmon production in Scottish waters

The objective is to consider to what extent the parameters relevant to site selection and characterisation will be accessible using the AQUA-USERS data portal, rather than having to obtain additional regional datasets or to require stand-alone GIS software and GIS expertise.

Site selection for offshore rainbow trout production in Danish territorial waters

The objective is to demonstrate the use of multiple data categories (spatial restrictions, optimal production and environmental impacts) and of environmental 3D model data (numerical modelling) for identification of optimal sites for rainbow trout production using GIS as the spatial suitability analysing tool.

Site selection for salmon production in a fjord in Rogaland county, Norway

The objective is to identify suitable new sites based on a spatial suitability analysis combining EO data from the MERIS archive and data on marine restrictions and area use (occupied areas or areas of interest for other sectors) using a simple model based on GIS data layers.

Site characterisation for mussel production in the Dutch Wadden Sea

The objective is to show the applicability of environmental EO data to characterise licenced production sites in order to select suitable plots for each production step and thereby optimising production.

Site characterisation for mussel production in the Algarve region, Portugal

The objective is to demonstrate application of historical model data for the characterisation of the biggest concession in Portugal, offshore aquaculture at the Armona offshore park (APPAA-15Km2), and usability of Earth Observation data in combination with in situ

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data for characterisation of the smaller concession Finisterra (1 Km2) with particular emphasis on the Harmful Algal Bloom problems (HABs).

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2 Case: Site characterization and selection for salmon production in Scottish waters using the AQUA-USERS portal

Salmon farming is a major and growing industry in Scotland, producing 200,000 tonnes per year, and employing over 2,200 people. Scotland is the largest farmed salmon producing country in the EU, and salmon is Scotland’s largest food export with a worldwide (60 countries) retail value of over £1 billion per year. This economic success has been accompanied by public and regulatory concerns over the environmental and health risks of sea cage fish farming: waste, escapes, diseases and parasites, chemicals and feed (Staniford, 2002). Hence the selection of new sites for salmon farming is increasingly challenging.

2.1 Objective

The objective of this case study is to consider to what extent the parameters relevant to site selection and characterisation will be accessible using the AQUA-USERS data portal, rather than having to obtain additional regional datasets or to require stand-alone GIS software and GIS expertise.

Hence the proposed approach was to first distil user requirements provided by The Scottish Salmon Company (TSSC) on the AQUA-USERS questionnaire (D2.7); then to compare these requirements with what is accessible through the Data Portal. As PML is also the partner developing the data portal, this facilitates discussion with the WebVis team regarding additional functionality. We presented the concept to the Scottish and other users at the AQUA-USERS User Board Meeting, Venice, June 2015.

2.2 Important administrative and environmental factors determining suitability

Finding a site for a new finfish farm involves subjecting potential sites to a series of filters (ECASA, 2007):

geographical: a farm needs good access, shelter from wind and waves, and distance from sources of pollution and other water use;

socio-economic: it will be easier to gain permission to install a farm in regions where there is a shortage of employment and where the public favours fish-farming;

environmental: deposition of waste, and the maintenance of good water quality within and around the farm.

In Table 2 some indicators are listed to be of use for finding optimal locations for salmon farming in Scotland, together with some considerations that may be involved in site selection this region. These indicators are based on the experience of PML and the findings of the user requirements questionnaire (D2.1).

Table 2: Examples of indicators for salmon farming in Scotland.

Factor Considerations

Depth Deep enough for flushing, but not too deep for bottom mounted instruments

Current speed and direction Suitable for frequent exchange of water between site and wider estuary, but not too strong to cause fish mortality

Wind / wave Risk of damaging storms, and protection from prevailing wind/waves.

Nutrients Not too high already, eutrophication issues Normal ratios for healthy phytoplankton growth

Chlorophyll-a Not too high already, eutrophication issuesArea with

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medium-high abundance

SST Preference range for target species

Frequency of HAB events Relatively low for region

Water clarity / turbidity

Seabed type Some types better than others

Distance from shoreline May be at shore for easy access, or offshore for improved exchange

Proximity to other farms, protected areas, public usage, other animal habitats, shipping, etc.

Administrative layers

Oxygen levels Healthy

Salinity

Based on these indicators, a list of variables, characteristics and properties were discussed with TSSC to identify respective thresholds for site selection, summarised below in Table 3. Further useful information arose in discussions: “Could Chlorophyll-a give an indication of nutrient levels? The staff measures Secchi disk for daily management, but this is not considered for site-selection so much, would be helpful for eutrophication. Nutrient calculations are based on assumptions of a background 116 µg nitrate per litre, but there is no evidence for that. We don’t know how much of the nutrients are digested and released to the water column. HAB distribution would be useful for site selection, so we know to avoid areas where there were jellyfish or red-tide blooms previously (e.g. last 20 years). Currently this is anecdotal, so something more quantitative would be useful for new staff.”

Table 3. List of variables/characteristics/properties that define a good salmon farming site in Scotland and the respective thresholds indicated by TSSC.

Parameter/characteristic/properties Thresholds

Scottish Salmon Company (mostly salmon) Depth

Ok 20m

Ideal ≥25m

Currents:

Measured over 15 day’s period at:

-top

-middle

-bottom

Currents should be strong enough to ensure particle dispersion.

Not less than 5 cm.s-1

Problematic when higher than 10 -15cm.s-1

Wave climate

5 -6m (site with highest approved wave height)

Distance from shore

Controlled by bathymetry.

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2.3 Description of applied data

Regarding the likelihood of HABs at potential sites, this can be addressed using in situ phytoplankton sampling, or with greater uncertainty using EO HAB discrimination. A large dataset of the Karenia mikimotoi HAB cell counts were collected in Western Scottish waters from 2008-2011 (Davidson et al., 2015). Figure 2-1 shows the frequency of this HAB species: there are few consistent differences between the regions, for example Region 7 ‘East coast’ experiences fewer HABs, and Region 9 ‘St Kilda’, the most offshore and westerly area has much higher HAB likelihood. Also, certain individual sites within each region appear to be lower risk, with HAB frequency lower than 5% of samples.

Figure 2-1. Frequency of Karenia mikimotoi (HAB) events (above threshold of 500 cells l-1

) analysed by location (pod) and region. Regions are: 1: Dumfries & Galloway; 2: Clyde (Ayrshire & South Argyll); 3: North Argyll & South Lochaber; 4: North Lochaber, Skye, Ross & Cromarty, West Sutherland; 5: Western Isles; 6: North coast & Orkneys; 7: East coast; 8: Shetland; 9: St Kilda.

Using the charting tools in the AQUA-USERS Data Portal we will attempt to expand this risk analysis in the next stage of the project. Figure 2-2 depicts a monthly risk map for a HAB species in the prototype Data Portal. It should be borne in mind that most HAB events are inherently sporadic, so will never be predicted reliably from climatology.

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Figure 2-2. Harmful algal bloom risk for west Scotland for Jun. 2009, showing a possible bloom of the Karenia mikimotoi HAB species approaching the coasts of the outer Hebrides and Tyree.

2.4 The analytical framework

Site selection issues have been considered previously for Scottish salmon farming, and there is useful information that can be gathered. For example, Figure 2-3 summarises the objective and criteria that should be considered in maximising the socio-economic benefits while minimising environmental damage (Whitmarsh, 2007).

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Maximise net

benefits from

salmon farming

Maximise

socio-

economic

benefits

Minimise

environmental

damage

Sustaining

employment and

livelihoods

Contributing to edible

supplies of fish

Minimising pollution

and water quality

reductions

Minimising visual

intrusion and

landscape impacts

Minimising impact on

wild salmon stocks

Minimising demand

for fishmeal-based

salmon feed

Goal (Level One) Objectives (Level Two) Criteria (Level Three)

Figure 2-3. : Hierarchy of objectives for salmon aquaculture (Whitmarsh, 2007)

Of course in any site selection there are many factors to consider simultaneously in the decision making, and so a full implementation would require an approach such as multi-criteria analysis (MCA). For example, an extensive project considered all factors involved in recommending areas for future aquaculture exploitation (of all types) in south and east England (MMO 2013, Figure 2-4).

Figure 2-4. Areas of future potential for aquaculture, shown by green, magenta and yellow regions denoting different aquaculture types (MMO, 2013)

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In proposing the Data Portal for the challenge of site selection, a simpler combination of parameters may be sufficient. Figure 2-5 describes a simple framework that could be implemented within the Data Portal, combining a user-selected set of parameters and threshold values to calculate an output layer of potential sites that meet all the criteria. Although this function was not a requirement for the Data Portal deliverable, we will work with the PML WebVis team to encourage the implementation of this capability. Further tools such as histogram, region statistics and extreme value analysis would also assist the site selection process.

> 20m

1<Chl<10

Depth

Chl-a

Potentialsites

Figure 2-5. Proposed framework for site selection using the Data Portal, with thresholds applied to a user-selected combination of EO, bathymetry or metocean layers to generate an output map of potential sites.

2.5 Results

We have matched the user requirements for site selection with the EO-based indicators that are currently or planned to be available on the AQUA-USERS Data Portal:

• Sea surface temperature

• Water transparency (particles and dissolved materials concentration);

• Chlorophyll (as a proxy for algal biomass);

• Significant wave height;

• Sea depth;

• Other metocean data: surface currents, wind speed;

• HAB distribution;

• Nutrient enrichment (from model).

As the Data Portal continues development during the second half of the project, we aim to extend its capabilities and available datasets. Preliminary map results for the wider Scottish coast will be presented to Users and iteratively improved, so that the AQUA-USERS Data Portal can be applied directly to future site selection case studies.

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3 Case: Site selection for rainbow trout production in the Danish waters combining environmental modelling and spatial restrictions

Danish marine trout farming is a small industry with a high potential. The industry is producing 12,000 tonnes per year, and employing approximately 200 people. Denmark is the oldest farmed trout producing country in Europe, and the industry is expected to grow from 12,000 tonnes to 60,000 tonnes in the coming years. The expected growth is accompanied by public and regulatory concerns over the environmental and health risks of sea cage fish farming: waste, escapes, diseases and parasites, chemicals and feed (Staniford, 2002). Hence the selection of new sites for trout farming is increasingly challenging.

3.1 Objective(s)

The objective of this case is to demonstrate the use of multiple data categories (spatial restrictions, optimal production and environmental impacts) and of environmental model data for identification of optimal sites for rainbow trout production using GIS as the analysis tool for determining spatial suitability. The goal is to define a usable analytical framework for producing suitability maps for this specific case, i.e. suitable waters for “large” aquacultures (large cages compared to present, with a capacity above 1500 tonnes of rainbow trout).


The aim is to create a simple model based on GIS data layers. In the present case, each GIS layer represents the suitability for a specific parameter relevant for large cage rainbow trout production in Danish waters. For other cases, e.g. production of other species, the selection of parameter should be modified as relevant. The end goal (outside the scope of the AQUA-USERS project) is to embed the model in a generic site selection tool that the users can feed with data relevant for their specific site selection.

The overall analytical framework defined for identification of spatial suitability is outlined in Figure 3-1. The overall frame is modified after Nath et al. (2000). A key step is to select the data required for the case (here rainbow trout production). In general for site selection the data searched for should describe the spatial distribution of the key characteristics identified. The key characteristics should as far as possible address all key aspects both concerning administrative/spatial constrains, production requirement for the species to be produced, and ecosystem services/impacts. Economic issues may also be relevant to address specifically. It is recommended to involve the stakeholder, such as the aquaculture producer community as well as the authorities responsible for licensing production sites. The stakeholders should also be involved in the specification of indicators to apply (involving spatial coverage and spatial and temporal aggregations (e.g. yearly means). An important part of the specification is to take account of data availability. When laying down the framework for the preparation of suitability maps, the analytical methods must be considered. These can involve classification, GIS map overlay, proximity analysis, conditional processing, etc. In D2.3, more details on these methodologies are given. The aim is to find the best way to combine and integrate the given spatial information into a useful format for analysis and decision making.

The specific scheme for the site selection analysis for offshore production of rainbow trout in Danish waters is shown in Figure 3-2. The basic concept is to produce maps covering the Danish territorial waters for the selected indicators and subsequently, using GIS, analyse the suitability and prepare suitability maps. To optimise and systemise the data selection and acquisition, three overall topics was defined: administrative constraints identifying spaces already occupied by other marine activities such as ship routes and wind parks, suitability for production identifying area with optimal physical and ecological water conditions for rainbow trout, and environmental impact based on indicators of environmental conditions decisive to the effects of an offshore production. Since the

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scope of task 6.1 has been to demonstrate the applicability of the suggested methodologies for site characterisation/selection, the goal has not been to make a complete compilation of relevant indicators. For instance socio-economic issues are not considered. Similarly, logistic issues like distance to harbours, road infrastructure, etc. are not included although such are potentially important for the suitability seen from the producer’s side. For Denmark it is however not considered very critical as the infrastructure is well developed and the distances on land are always short. So in all, it is considered that the indicators applied present a realistic picture of suitable sites for the production of rainbow trout.

Figure 3-1. Graphic representation of the seven phases in a GIS project for site selection (modified after Nath et al., 2000).

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Two type of suitability determinations are applied:

1. Binary (Boolean) suitability, where the data or area is either suitable (1) or not suitable (0),

2. Continuous suitability (binomial distribution), where the suitability is graduated from 0 (not suitable) to 1 (suitable).

Administrative constrains are in all cases classified according to the Boolean suitability; i.e. spaces already occupied by other activities or by regulatory restrictions are attached the suitability 0 while ‘free’ areas are labelled as suitability 1. For production and environmental impact indicators, both approaches are used; i.e. some are converted into Boolean suitability and others into Continuous suitability. As a prerequisite for the conversion into Continuous suitability, suitability criteria are defined for each indicator (see following section).

Figure 3-2. Flow diagram of the analytical framework of the case on offshore production of rainbow trout in Danish waters.

After conversion of all selected parameters/indices into suitability classes and preparation of indicator suitability maps, the classifications are integrated by multiplying the suitabilities within each of the three defined topics: administrative constrains, suitability for production and environmental impact. This is done to make it possible to evaluate the suitabilities related to each of the individual topics.

Finally, an overall suitability maps is prepared based on the three topic maps. As for the three topics, the combined spatial suitability is estimated by multiplying the suitability of the three topic. Thereafter, the combined spatial suitability is converted into four categories, representing four quantiles, the classes 0 and 1 represents in total 1st and 2nd quantile:

0: Not suitable (suitability class = 0) 1: Low suitability (suitability class = >0-0.5) 2: 25% of the second highest suitability (3rd quantile; suitability class = 0.5-0.8) 3: 25% of the highest suitability (4th quantile; suitability class = 0.8-1)

The ‘suitability scheme’ is illustrated in Figure 3-3.

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Administrative constrains suitability (layers multiplied)

Production suitability (layers multiplied)

Impact suitability (layers multiplied)

Combined spatial suitability for offshore marine aquaculture (suitability layers multiplied)

Figure 3-3. Illustration of the scheme applied to reach at a final suitability map for offshore rainbow trout production in Danish territorial waters based on the data included in the present project.

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3.3 Description of the applied data

3.3.1 Administrative constrains

Administrative constrains may arise from legal restrictions as conservation areas and security zones and occupation by other activities at sea as sea cables, dumping sites, extraction sites, shipping routes, etc. In these area there can be no aquaculture facilities. The constraints included in the analysis are listed in Table 4.

Table 4. Inventory of administrative constrains layer involved in the site selection analyses for Danish offshore production of rainbow trout. All data layers are extracted from “Miljøportalen” managed by the Danish Geodata Agency.

Data layer Description of data

Sea cables Underwater cables

Dumping sites Areas designated for dumping of seabed material

Extraction sites Areas designated for sand and gravel excavation

Banned areas Areas which for various reasons are not available

Military zones Areas reserved for military purposes

Shipping routes Areas designated as major shipping lanes; including the surrounding security zones

Oil & Gas fields Areas of offshore oil and gas production

Offshore windfarms Areas of offshore windfarms

3.3.2 Optimal production

The data basis for the indicators used for suitability analyses related to the optimal conditions for trout production as well as environmental impacts is numerical modelling results. A ten year period from 2000-2011 has been modelled with DHIs model suite for the open marine waters. To avoid start up noise, only data from 2004 is used. The model suite comprises a combined MIKE 3D FM hydrographical and biogeochemical model covering the Baltic Sea areas and a MIKE 3D FM hydrographical model covering the North Sea areas of Danish territorial waters. Both models are established for the Danish Ministry of Environment and Food for use in marine management and decision making. The resolution is from 100m to 5 km dependent on location (highest in coastal waters). Wave data are extracted from historical runs with DHIs operational wave model based on MIKE SW modelling tool also covering the Baltic Sea and North Sea waters respectively. The resolution of the model is 1 km. Data from all models are saved on an hourly basis (i.e. in 1 hour time steps).

The indices involved in the analysis of the suitable sites seen from the finfish production view are listed in Table 5. Data are selected based on expert judgement and data availability. Both the indicators and the suitability classes are analyses are done in collaboration with the marine trout producers of Denmark. It was considered to include low water temperature as an indicator since this may have an impact on the smolts success at the beginning of the production period (April-November). The farmers did however not find it decisive for the selection of production sites. Similarly, the level of chlorophyll based on EO data was considered but rejected by the farmers as they do not judge this as a decisive condition. Marine aquaculture problems by phytoplankton in Danish water are related to the potential post spring bloom occurrence of flagellates as the ichthyotoxic Pseudochattonella and Chrysochromulina and are not very common. In addition, since the concentration of these algae is usually low, even when ichthyotoxic effects are observed, the ‘traditional’ HAB indicator (occurrences of high biomasses) is not optimal. In order to take this risk into consideration, a new indicator must be developed. The importance of strong winds has also been considered but based on analysis of wind and wave data, it is concluded that the deriving wave

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height more precisely indicates the optimal production conditions, hence this parameter have been chosen over wind speed.

Table 5. Inventory of optimal production layers included in the site selection analyses for Danish offshore production of rainbow trout. All data are extracted from results files of numerical 3D modelling of Danish marine waters.


Water depth Water depth in meters. Data from the bathymetry map included in the models. Water depths between 15 and 45 m are considered most optimal.

Current speed Modelled water current speed for surface layer 0-10 m (mean values). Data from: 2004 to 2011. Too low current influence the water exchange in the cages and the dilution/dispersion of waste products. Too high current cause too high stress on the cage nets and affect daily operation. Both too low and too high current may have negative impacts on fish welfare.

Wave height Modelled significant wave heights. Data from 2000 to 2011. The wave height is important to maintenance and operation of the farms.

Water temperature Modelled water temperature for the surface layer 0-10 m (mean value; cages are up till 12m deep). Data from 2004-2011. High temperature reduce the consumption of feed and impact fish welfare and production.

Salinity/ mean temperature

A derived indicator describing the osmotic stress that the rainbow trouts are exposed to. The osmotic stress (from salinity) is dependent on the water temperature. The higher the value, the less suitable.

3.3.3 Environmental impacts

The data basis for the analyses related to environmental impacts is similar to the one used for optimal production condition and is described above. The indices involved in the analysis are listed in Table 6. This leg of the spatial analysis should be strengthened before the site selection is satisfactory. This requires a tight collaboration with the environmental authorities licensing the rights to produce fish in marine aquacultures, which has been out of scope of the AQUA-USERS project. Other EU research projects are targeting this topic, such as AQUA-SPACE and the coming TAPAS in which the results of the present project will be followed up.

Table 6. Inventory of environmental impact layers included in the site selection analyses for Danish offshore production of rainbow trout. All data are extracted from results files of numerical 3D modelling of Danish marine waters.


Bed shear stress Bed shear stress is a derived indicator; estimated as a function of the roughness of the seabed and near-bed current velocity – both extracted from the model. It is a measure of the risk of accumulation of material (incl. faeces) on the seabed below the cages. The higher value the lower risk. Only data from Baltic Sea areas. Daily data from 2000 and 2002 and every second day data from 2003, and 2006-2011.

Dissolved oxygen Modelled data on dissolved oxygen (DO) are extracted for the bottom layer in the Danish Baltic Sea areas. Data from 2004-2011.

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3.4 Indicators and suitability criteria

3.4.1 Administrative constrains

Data for Administrative layers are extracted from the “Miljøportalen” as raster files, which are subsequently converted into Boolean (or binary) raster files with values of 0 (not suitable) and 1 (suitable).

3.4.2 Optimal production

Applied indicators and suitability criteria for the data targeting optimal production conditions are shown in Table 7.

For the environmental parameters, threshold values are defined and exceedance maps produced using the embedded postprocessing functionality of DHIs MIKE models. The exceedance maps show how the percentage of the time that the parameter is above or below the threshold, respectively. The model files are converted into GIS layers expressing the suitability according to a Binary (Boolean) or Continuous scale. For the Continuous suitability scale, the intermediate suitability between not suitable = 0 and suitable = 1, the suitability value is defined by a linear function.

Table 7. The indicators and suitability criteria applied for the data targeting optimal production conditions. Not suitable = 0; suitable = 1.

Data layer Indicator Suitability criteria

Water depth Depth intervals: >15m, 15-45m, > 45m:

Continuous If <15m = 0 If 15-45m = 1 If > 45m: intermediate: ysuitability = (-0.01818x) + 1.8181

Current speed Data from 1 Mar – 1 Dec (production period) Thresholds: 0.1 m/s and 0.4 m/s

Continuous. Based on 2 raster files Raster 1: If < 20% of time exceeding 0.1m/s = 0 If >40% exceeding 0.1m/s =1 Intermediate: ysuitability = 0.05x-1 Raster 2: If >80% exceeding 0.4m/s = 0 If <40% exceeding 0.4m/s =1 If intermediate: ysuitability = -0.025x+2 Final: Raster 1 x Raster 2

Wave height Data from 1 Mar - 15 Dec (production period) Threshold: 2m Mean exceedance for all years

Binary (Boolean) If mean exceedance >3% = 0 Otherwise = 1

Water temperature Data from July and August (high temperature months) Threshold: 20oC Exceedance for individual months

Continuous If >50% exceeding 20 oC = 0 If <15% exceeding 20 oC = 1 If intermediate: ysuitability = -0.0286x + 1.4286 Final: minimum class of July and August

Maximum salinity/ mean temperature

Data from April (smolts introduced) Coefficient of variation (CV) of salinity divided by mean

Binary (Boolean) If < 0.079 = 0 Otherwise = 1

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temperature Threshold: 0.079

3.4.3 Environmental impacts layer

Applied indicators and suitability criteria for the data targeting optimal production conditions are shown in Table 8.

Table 8. The indicators and suitability criteria applied for the data targeting optimal production conditions. Not suitable = 0; suitable = 1.


Bed sheer stress Data from Dec, Jan, Feb (following production season; before next season) Threshold: 0.025

Continuous If <0 exceeding 0.025 = 0 If >15% exceeding 0.025 = 1 If intermediate: ysuitability = .0667x + (-5e-08) Final: maximum class of the three months

Dissolved oxygen Data from 1 Jul – 31 Oct (highest risk of oxygen depletion) Thresholds: 4 mg/l (mean per month)

Binary (Boolean) Per month, all years If >15% of time exceeding 4mg/l = 0 Otherwise = 1 Final: suitability for the each month multiplied

3.5 Resulting suitability maps

3.5.1 Conversion into multiplied suitability layers

As described earlier, a combined suitability map is produced for each of the three topic administrative constrains, optimal production and environmental impacts and subsequently a final suitability map is produced indicating the spatial distribution of suitable sites for offshore production of rainbow trout in Danish territory waters. The conversion is done by multiplying the relevant data layers as shown in Figure 3-3.

3.5.2 Suitability based on administrative restrictions

The suitability layer covering the administrative restrictions shows that a large part of the Danish waters are “occupied” (Figure 3-4). The “occupation” is due to a diverse range of legal restrictions and exploration of the marine ecosystem services (Figure 3-5).

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Figure 3-4. Areas with administrative restrictions.

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Figure 3-5 Maps of administrative restrictions in Danish waters. Large versions of the maps can be found in Appendix 1.

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3.5.3 Suitability based on production data

Regarding locations with optimal conditions for production of rainbow trout, the combined suitability map shows that suitable areas can be selected in Danish Baltic Sea Waters: in Kattegat, Belt Sea, western Baltic Sea and the coastal areas north-east of Bornholm (Figure 3-6). When waters the Danish part of the Baltic Sea are classified as not suitable, the reasons are in most areas water depth and current speed. In the brackish waters around the east coast of Sjælland and the southeastern islands Lolland and Falster (south of Sjælland), the maximum salinity/mean temperature is the restrictive factor. In the North Sea, Skagerrak and south and east of Bornholm, high frequency of large waves classifies the areas as not suitable (Figure 3-7).

Figure 3-6. The combined suitability based on indicator for optimal production of rainbow trout in Danish territorial waters.

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Figure 3-7. Suitability maps for the indicators applied for the topic optimal production conditions. Large versions of the maps can be found in Appendix 1.Large versions of the maps can be found in Appendix 1.

3.5.4 Suitability based on environmental impact data

The suitability maps for the topic environmental impacts show where the environmental conditions do not support production of finfish because the aquaculture production can impact the environmental status negatively. Low bed sheer stress poses a risk of accumulation of organic material on the seabed below the culture. Sedimentation of organic material in areas characterised by periods with low oxygen content (oxygen depletion) are also sensitive. The combined suitability map shows that such areas are especially found in the western Baltic Sea, in the deeper waters of the southern Belt Sea, i.e. south of the islands Fyn and Sjælland and further south, south of Lolland-Falster (Figure 3-8). From the suitability maps for the individual indicators, it appears that both indicators indicate unsuitable waters (Figure 3-9).

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Figure 3-8. The suitability based on indicators on the environmental impacts.

Figure 3-9. Suitability maps for the indicators applied for the topic environmental impacts. Large versions of the maps can be found in Appendix 1.

3.5.5 Composite of all suitability layers, which equals site selection

The overall composite of the suitability maps based on the three topics and thus on all included indicators is shown in Figure 3-10. It appears that the North Sea is in general not suitable for the type of production investigated here. The reason is in particular the occurrence of large waves. In the Danish part of the Baltic Sea, the most suitable areas (green on map) are in the Kattegat, in Great Belt (the Belt Sea strait between Fyn and Sjælland) and in the waters southeast of Sjælland.

It must be emphasised that the composite map is a draft and further analyses are required to produce a final consolidated mapping (see more in section on Perspectives).

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Figure 3-10 Composite spatial suitability layer including an indication of the Danish Water Bodies. Natura 2000 sites are included. 0: Not suitable (suitability class = 0), 1: Low suitability (suitability class = >0-0.5), 2: 25% of the second highest suitability (3

rd quantile; suitability class = 0.5-0.8), 3: 25% of the highest suitability (4

th

quantile; suitability class = 0.8-1). See also zoom in Figure 3-11. It should be noticed that further analyses are required for a final consolidated mapping (see more in section on Perspectives).

3.6 Perspective

The Danish aquaculture industry is already applying for new production sites and the present site selection maps support that the chosen areas are in general suitable for offshore rainbow trout production. The applications have however underpinned the shortfall in not including a suitability layer related to Natura 2000 and Water Framework water bodies. Presently, it is however not clear how these areas should be included. In Figure 3-11 zooming in on the Baltic Sea waters, the composite suitability map (Figure 3-10) is overlaid with the Danish marine Natura 2000 and WFD water bodies areas. Overlying administratively restricted areas where aquaculture cannot take place, it is seen that the “free scope” area is in particular narrowed down in the Kattegat and in the Great Belt area. In general, only offshore areas, far away from the coast, in Kattegat and the western Baltic Sea remain. Considering that the regulations laid out for Natura 2000 areas and WFD water bodies do not exclude human activities as long as these does not violate the conservation and environmental goals respectively, the way to go is to developed indicators which can ensure these objectives, while supporting the EU as well as national Danish policy of predicting growth of the sector. This is a very important and challenging future research requirement.

The present case is based on time series modelling and demonstrates that such data form a valuable basis for site selection. Advantages of 3D modelling data are the availability of data on many environmental parameters with values from the whole depth interval and in a high temporal frequency. The requirement is time series model runs with a model providing a good (validated) description of the marine environments in sufficient resolution. EO data is another potential source of data. Compared to model data, particular shortfalls are the limitations in number of parameters, the restriction to the surface layer and at least on northern latitudes, the occurrence of cloud sometimes reducing the number of usable scenes significantly. Advantages are the reliable provision of easy accessible data (excluding periods of replacement of satellites as recently experienced with the MERIS to SENTINEL shift which was not satisfying from a data user view point), and the expected new high resolution data.

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Figure 3-11. Zoom of suitable areas in the combined spatial suitability layer including an indication of the Danish Water Bodies and the Natura 2000 sites. 0: Not suitable (suitability class = 0), 1: Low suitability (suitability class = >0-0.5), 2: 25% of the second highest suitability (3

rd quantile; suitability class = 0.5-0.8), 3:

25% of the highest suitability (4th

quantile; suitability class = 0.8-1).

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4 Case: Potential new sites for Salmon Aquaculture in Rogaland County Norway based on combination of EO data and spatial restrictions

Norway is Europe’s biggest aquaculture producer with a production of 1.3 million tons in 2014. Salmonids constitute more than 80% of this production. The steady growth of the industry during the last three decades is partly a result of a combination of strict health regulations, close safety monitoring and continuous work to develop the industry. However, the biggest asset in Norway’s aquaculture industry remains the excellent natural conditions. The country’s large sea areas and more than 51,500 miles of coastline have provided good conditions for year-round seafood cultivation. There is a political goal of a continued growth in the aquaculture industry, but increased production presents the industry with a range of challenges. Environmental concerns related to sea lice and escaped fish have remained unresolved. Other challenges have grown more apparent in recent years, such as area scarcity. Among the existing sites, it is well known that some are “golden sites” with high production and few environmental problems. Finding new good sites is a prerequisite for sustainable growth.

4.1 Objective(s)

The objective of this case study is to identify suitable areas where new sites can be placed. We have carried out a spatial suitability analysis that combines data from the satellite data archive on temperature, physical parameters and data on marine restrictions and area use (occupied areas or areas of interest for other sectors).

The idea is to create a simple model based on GIS data layers, where the spatial suitability for aquaculture can be identified in an easy way. Each GIS layer represents the suitability for a specific parameter relevant for salmon production, here exemplified by Rogaland county Norway.

4.2 Analytical framework

The analytical framework for identifying the spatial suitability is similar to the framework described in the Danish case study. First data relevant for salmon production is identified through a screening process. Then data is extracted. The data is mapped in GIS and analysed by scientific experts followed by a post-processing of the physical and chemical data by a set of selection criteria set by the same experts. Then the layers are converted into suitability layers of the selected parameters. The GIS-layers are then combined to give a combined spatial suitability map. The analytical framework of this case study is outlined in Figure 4-1.

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Figure 4-1. Flow diagram of the analytical framework.

4.3 Data

4.3.1 Types of data

A number of administrative and environmental factors determine the suitability of an area.

Administrative factors can be restrictions of legal character, which exclude the area from aquaculture production, or be areas with many administrative restrictions but where aquaculture potentially can be placed. In the latter case the suitability must be assessed in each specific case. Areas are excluded if they are allocated to other activities in area plans according to the Planning and Building Act, if they are protected according to the Diversity Act (e.g. National salmon fjords, national parks) or protected according to the Culture Heritage Act. In these areas there can be no (new) aquaculture facilities. Areas with strong interest from other sectors, such as fisheries and tourism will also have restrictions (e.g. spawning and fishing grounds, kelp beds and recreational areas), and here the possibility for new aquaculture sites will be assessed in each case.

Environmental factors represent the physical and chemical parameters that are important for salmon production and animal welfare.

Through a screening process we have selected data that can be important for site selection for salmon farming. Data are selected based on best available knowledge, expert judgement and last but not least and data availability at this point.

In Table 9 the data identified during the screening process is presented:

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Table 9 Overview of data and data layers selected for screening.

Data layer Description of data Data source

Administrative data

Other aquaculture sites

Positions of already existing aquaculture sites

Recommended minimum distance of 5 km to fish slaughterers and processing plants, broodstock facilitities, water intakes to and land based producing sites, sea based fry production sites, net cleaning sites, and big on-growing fish farms (>6000 t MTB)

Recommended minimum distance of 2,5 km to other on-growing fish farms, land-based on-growing facilities and important salmon rivers

Directorate of Fisheries

Security zones around shipping routes

Areas restricted due to security zones around major shipping lanes. Normally 1 km on each side of routes.

Norwegian Coastal Administration

National salmon fjords (salmon protection)

This dataset contains national salmon fjords created by decisions made in Parliament on 25 February 2003 (round 1) and 15 May 2007 (round 2). The purpose of national salmon fjords is to provide a variety of the most important salmon stocks in Norway a special protection against intervention and activities in rivers and the fish farms in the nearby fjords and coastal areas


“Låssettingsplass” (penning in areas)

“Låsettingsplass” is defined as a space close to the shoreline where topographic and hydrographic conditions are such that a sea cage for storage of wild caught fish can be stored.

Farms can only be placed within a distance of 1,5 km from a “låssettingsplass”


Spawning grounds Defined areas with increased density of eggs and/or increased density of sexual mature adults.


Feeding/nursery grounds

Defined areas where the density of adult individuals is greater than in other areas. A nursery area is a grazing area used by fry / small fish.


Fishing areas Present and historic fishing areas (vocational, leisure and tourism) that can be expected used in the future. Includes areas for both active (seine, purse seine or shrimp trawls) and passive gear (gillnets and longlines).


Marine protected areas

Areas protected under the following laws : Diversity Act 2009, biotope protection by the Wildlife Act of 1981, the Nature Conservation Act 1970 , the Nature Conservation Act of 1954 Act on Jan Mayen of 1930 and Law on Nature Conservation of 1910. In addition, the areas protected under the following legislation on Svalbard: Svalbard Act of 1925 and the Svalbard Environmental Act of 2002. The data set also provides access to the legal requirements

Norwegian Environment Agency

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Data layer Description of data Data source

applicable to each individual protection decisions .

Production data

Water depth Water depth in meters. Authorities’ requirement is a minimum of 20 meters from the bottom of the net pen to the seafloor.

The Norwegian Mapping Authority

Temperature (surface layer) below 3 °C

Temperature for the surface layer 0-10 m (mean values) as periods where the temperature is below 3°C. Temperature is important for the fish physiology and influence circulation, feeds intake, digestion and growth. Temperature is also important for the solubility of oxygen Requirements from Norwegian Food Authorities: Smolt stage > 3 °C Adult stage > 1°C Mature adult broodstock > 2 °C

Period: Feb-March 2004-2011

Historical EO data

Temperature (surface layer) above 18 °C

Temperature for the surface layer 0-10 m (mean values) as periods where the temperature is above 18 °C. Requirements from Norwegian Food Authorities: Smolt Stage < 18, Adult stage <18 Mature adult broodstock < 12 °C Period: July - August 2004-2011.

Historical EO data

4.4 Data selection

All initial selected data have been analysed by an aquaculture expert by analyses of data and by visual studies of GIS maps. All data except for temperature below 3 °C have been selected for further use. Visual data analyses of the temperature below 3°C showed that temperatures in this range do not occur during the period where we have satellite data. Hence the data layer is not relevant to include, since the optimal water depth for production will be defined as above 10 m.

4.5 Data analyses and selection of suitability criteria

Based on the selected data and analyses of the data a set of criteria for creating suitability classes have been employed for each parameter.

To be able to combine all layers to a combined spatial suitability map all raster layers have been created to have the same extent and resolution and “scale” (suitability 0-1).

All data has been converted to a suitability layer. There are in general two types of suitability data layers:

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1. Boolean (or Binary) suitability, where the data or area is either suitable (1) or not suitable (0), or

2. Continuous suitability (binomial distribution), where the suitability is graduated from 0 (not suitable) to 1 (suitable).

The Boolean suitability areas cover areas, where there are legal/administrative restrictions on how the areas can be used. These are e.g. already existing aquaculture activities, and national parks/protected habitats.

Continuous suitability areas, are areas where the suitability of a physical or chemical parameter have been assessed (based on literature, experience and expert judgement) and transformed to a suitability index.

For all parameters the value 0 = not suitable. Values > 0 and = 1 is suitable, 1 being the most suitable area. Selected data is summarised in Table 10.

Legal/administrative restrictions layers

Data for legal/administrative layers has been converted into a raster files, creating Boolean (or binary) raster files with values of 0 (not suitable) and 1 (suitable). These layers include already existing aquaculture activity, national salmon fjords (salmon protection), protected areas and coastal waterways.

Other sector interest restrictions

Data on “areas of interest for other sectors” layers are converted into raster files treated as continuous since they are to be assessed in every case and cannot be flagged as either suitable or unsuitable in the analysis. They are however all defined as 0.5 as the probability of a positive outcome of a proposal is unknown in advance. These layers include spawning (nursery grounds), fishing areas, penning-in sites.

Production layers

Water depth

Depth is treated as a continuous suitability layer. Authorities’ requirements are a minimum of 20 meters from the bottom of the net pen to the seafloor. Although available on the marked, shallow nets are not much used in Norway any longer. In western Norway typical depth of net pens is 35-40 m but pens as deep as 55 m are increasingly common. Areas shallower than 50 m will therefore be of very limited interest, while areas with depths deeper than 75-80 meters will have no restrictions on the cage type. There are no max depth requirements or recommendations from authorities. Norwegian fish farms are already located at sites that span most of the depths range found in Norwegian fjords and coastal waters, and we have no empirical data that can grade the suitability of areas deeper than the minimum required depth from authorities. The depth data available had polylines with depth increments of 50 meters, and a depth limit for suitability of 80 meters was not feasible within the time frame of this task.

Water depth was classified into a suitability layer where: shallower than 50 m was defined as 0, between 50 and 100 meters was defined 0.5 and deeper than 100 m was defined as 1.

Temperature (surface layer) above 18 °C

Monthly exceedance for each year for 18 °C was calculated. A monthly mean was further calculated, i.e. mean per month for all years. According to the Norwegian Food authorities guidelines only sites with temperatures below 18 °C is recommended (even if adult salmon can survive 21°C). To assess suitability for full production cycle sites we used the recommended temperature for smolt as

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threshold. Based on visual inspection of these layers the 18 °C layers for July and August were used as a suitability criteria for temperature. Temperatures above 18 °C was defined as 0, temperatures below 18 °C was defined as 1.

Table 10. Overview of data and data layers selected for the site selection GIS model. Suitability type: 1-Boolean suitability, 2-Continuous suitability, see explanation in section 4.5.

Data layer Data post processing

Suitability type

Administrative restrictions layers

Aquaculture sites - 1

Security zones around shipping routes (coastal waterways)

- 1

National salmon fjords (Salmon protection) - 1

“Låssettingsplass” (Penning in sites) - 2

Spawning grounds - 2

Feeding/nursery grounds - 1

Fishing areas 2

Marine Protected areas - 1

Production layers

Water depth - 2

Temperature (surface layer) above 18 °C - 1

4.6 Conversion into multiplied suitability layers

The conversion of the different suitability layers was performed by multiplying the selected data layers into combined spatial suitability layers (as illustrated in Figure 4-4).

4.7 Results

4.7.1 Suitability layer based on legal/administrative restrictions

The suitability layer covering the administrative restrictions shows that a relatively large part of the Rogaland County is covered by legal restrictions and other administrative restrictions (Figure 4-2). In these areas, applications for new aquaculture sites will be automatically rejected.

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Figure 4-2. Areas with administrative/legal restrictions where there can be no further aquaculture sites (admin no). Large versions of the maps can be found in Appendix 2.

In areas with strong interest from other sectors, such as fisheries the possibility for new aquaculture sites will be assessed in each case. Areas with “case by case” assessment restrictions are shown in Figure 4-3.

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Figure 4-3. Areas with other users restrictions where new aquaculture sites might be given (admin assess).

Figure 4-4 shows that there are few areas left with no legal/administrative restrictions and potential restrictions due to area interests from other sectors. The statistics from Directorate of Fisheries also confirm that the numbers of licences in Rogaland has been quite stable the last 10 years and few new have been granted.

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Figure 4-4. Areas with legal/administrative and other user restrictions

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4.7.2 Suitability layer based on production data

Figure 4-5. Data layers for the suitability layer of production parameters. Large versions of the maps can be found in Appendix 2.

There are relatively large areas in Rogaland with depths suitable for aquaculture (Figure 4-4), both in the inner areas of the county and in the more open fjord areas. Regarding temperatures the outer coast stays within the recommended temperature range (Figure 4-4). In the inner fjord areas some reach higher temperatures than 18 °C during summer months, while the outer parts stays, while other stays within the recommended temperature range (Figure 4-4). This is due to the strong and local effect of freshwater run-off on temperature in the fjords.

4.7.3 Combined spatial suitability layer

As mentioned in section 3.5, the combined spatial suitability layer has been created by multiplying the suitability layers presented above. The combined spatial suitability for salmon production in Rogaland is shown in Figure 4-6.

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Figure 4-6. Combined spatial suitability layer for Rogaland County. 0: Not suitable (suitability class = 0), Intermediate suitability (suitability class=<0.5) and suitable (suitability class= 0.5-1).

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5 Case: Site characterization for mussel farming in the Dutch Wadden Sea using EO data

In the Netherlands, shellfish culture takes place in the estuarine waters in the southwest Netherlands and in the shallow Wadden Sea in the North of the country. The cultured species are mussels and oysters, with blue mussels economically being the most important species. The Dutch site characterisation study focuses on the mussel culture that takes in the Wadden Sea. The Wadden Sea is a large intertidal zone, and exchanges water with the North Sea though its tidal inlets that are located between the barriers islands. Historically, native mussel and oyster beds have been present in the Wadden Sea, but blue mussels have been seeded and harvested in the Wadden Sea since the 1950s, when a parasite threatened the harvest in the Oosterschelde (Dankers and Zuidema, 1995).

These mussel populations rely on the natural availability of nutrients and phytoplankton for their growth. A healthy, but highly productive ecosystem is, therefore, of utmost importance for shellfish aquaculture. These areas are also often rich in biodiversity and act as important nursery grounds for fish and crustaceans and feeding areas for birds. Hence, carrying capacity is considered in terms of the maximum sustainable yield of the bivalve culture, and on potential changes in ecosystem structure and functioning and ecological variability over different spatial and temporal scales (Cranford et al., 2012). The mussel and oyster reefs themselves play a pivotal role as ecosystem engineers in such a system (Reise, 1981; Prins et al.). Phytoplankton can also negatively impact these systems, notably in the case of harmful algal blooms (HABs), when the harmfulness is of the algal blooms are related to the high biomass leading to anoxia.

Mussels are mostly cultivated in bottom culture in the Wadden Sea. The mussels are grown on specific plots that the aquaculture operators rent from the government (van Ginkel, 1990). The mussel seed is traditionally dredged from natural beds, but increasingly, ropes are used to collect mussel seed. Mussel production in the UNESCO World Heritage Site Wadden Sea is highly disputed due to its ecological impacts (Dankers and Zuidema, 1995). Therefore, no substantial extension of the production areas is expected in the coming years.

5.1 Objective(s)

There is no site selection as such happening in the Wadden Sea as no new plots for production are being granted. There is also little trade in parcels between producers. Therefore, the member of the user board is interested in a characterisation of the sites they are currently renting. This characterisation could help them to quantify which of their plots is suitable for which production step. So far this is based on experience alone and on the yields from previous years. They are also interested in developments over time, because the Wadden Sea is a very dynamic environment (only 20-30% of plots can be used at any given time).

An overview of the location of the user’s production plots in the Wadden Sea is given in Figure 5-1.

Figure 5-1: Location of the mussel plots used in this analysis

http://en.wikipedia.org/wiki/Wadden_Sea

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As this case study is dealing with characterisation of existing sites rather than the selection of new sites, only environmental factors are taken into account in this analysis. Administrative constraints have been taken into account when the locations of the current sites were determined; for the operational management of the sites they are less relevant. The factors that have been identified as relevant by the user in the two user questionnaires (cf D2.1 and D2.7) are:

Algae as food (most important factor)

Temperature

Water depth

Currents

Salinity

Oxygen

Presence of mussel seeds

Two additional factors have been taken into account, namely

Concentration of total suspended matter as this has been identified in numerous studies as a very relevant parameter for mussel growth (e.g. Widdows et al. 1979, van Stralen 1995, Cranford et al. 2011)

The ratio Chl-a/TSM has been calculated as an index for food quality as suggested by van Stralen (1995) and Capelle (in prep).


Two main data sources were used for the analysis: Water quality information derived from the AQUA-USERS satellite data archive and physical data derived from a modelled data set kindly provided by Duran-Matute et al. (2014) of NIOZ. In the following, the data sources and derived data sets are described.

A number of environmental variables were derived from the MERIS sensor by Water Insight. MERIS full resolution images (spatial resolution of 300m) were processed using the CoastColour processor in extreme case mode (cf D3.1). These data are based on the entire available MERIS FR archive, i.e. from 2002-2012.

Concentration of chlorophyll-a (Chl), Concentration of total suspended matter (TSM), Feed quality (ration Chl-a/TSM)

Turbidity

Examples of maps are shown in Figure 5-2.

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Figure 5-2: Concentration of Chl-a in mg/m³ (left) and TSM in mg/l (right) derived from MERIS using the CC processor for 01/05/2007.

Other variables were derived from model results produced by Duran-Matute et al. (2014) of NIOZ. The results are obtained from three-dimensional numerical simulations carried out with the GeneralEstuarine Transport Model (GETM), at a horizontal resolution of 200m and with terrain-following vertical coordinates with 30 layers. Their model runs cover the years 2009-2011. Data were received as monthly aggregated values.

Salinity (Sal) at surface and close to the bottom

Temperature (Temp) at surface and close bottom

Maximum velocity (Vmax) at the surface and in the bottom layer

Exposure time (Expo) of tidal flats

Examples of these maps are shown in Figure 5-3.

Figure 5-3: Mean salinity (left) and maximum velocity in the bottom layer derived from the GETM model for May 2009 (data from Duran-Matute et al., 2014)


For 12 mussel fields in the Wadden Sea (see Figure 5-1), long term averages and monthly averages of environmental parameters were derived from satellite images/models.

To avoid bias in the satellite-derived data due to seasonal variation in cloud cover, first monthly averages were calculated from all images available in each month, and then these monthly averages were averaged to yield long-term averages. An overview of the processing is given in Figure 5-4.

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Figure 5-4: Processing of satellite and model data for site characterisation in the Dutch Wadden Sea

The threshold values for optimal and acceptable production conditions used in this analysis are given in Table 11 (kindly supplied by JJ Capelle of the Delta Academy/Hogeschool Zeeland based on Capelle et al (2015), De Wit & Capelle (2015), Philippart et al (2010) and Van Stralen & Dijkema (1994)).

Table 11. Thresholds used as indicators for suitability for mussel production in the Dutch coastal waters

Parameter Optimal Acceptable

Temperature (°C) 10-20 5-26

Salinity (psu) 28-34 >22

Current speed (m/s) 0.5-0.6 <0.5

Chl-a concentration (mg/m³) 10-30 >2

TSM concentration (mg/l) <15 <50

It should be noted that these values refer to concentrations in the water column. For Chl and TSM, it was shown by Smaal & Van Stralen (1990) that the concentrations 5cm above the mussel beds were 1.5 to 2.5 times higher than in the water column.

5.5 Results

In Table 12 the long term average values for all environmental variables considered are presented for all 12 mussel fields. The values are classified into three classes indicating most favourable (green), average (yellow) and least favourable (red) conditions. This table gives an easy overview that allows to compare the environmental conditions at all mussel fields.

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Table 12. Long-term averages of environmental conditions at mussel fields in the Wadden Sea (green fields indicate most favourable, yellow average and red least favourable conditions)

Field Chl TSM Food

quality Turbidity

Sal (bottom)

Sal (Surface)

Vmax (bottom)

Vmax (surface)

Temp (bottom)

Temp (surface)

Expo

1 13.2 14.3 1.42 14.4 28.4 28.4 0.5 1.0 10.6 10.6 0.0

2 13.2 14.3 1.37 14.1 28.4 28.4 0.4 0.9 10.5 10.5 0.0

3 15.2 16.7 1.25 15.1 28.5 28.5 0.3 0.7 10.4 10.4 0.0

4 16.3 18.5 1.22 16.3 27.9 27.9 0.2 0.6 10.4 10.4 0.0

5 16.9 16.6 1.43 14.3 21.5 21.4 0.2 0.6 10.6 10.6 0.0

6 19.0 18.4 1.32 15.0 21.2 21.0 0.3 0.7 10.6 10.6 0.0

7 13.0 14.0 1.41 13.9 28.6 28.6 0.3 0.6 10.7 10.7 0.0

8 13.7 13.8 1.47 14.0 29.1 29.1 0.3 0.8 10.8 10.8 0.0

9 14.3 14.4 1.44 14.1 28.9 28.9 0.3 0.7 10.7 10.7 0.0

10 13.2 13.3 1.34 12.6 23.8 23.6 0.3 0.7 10.6 10.6 0.0

11 25.1 21.1 1.50 15.2 19.6 19.1 0.3 0.8 10.7 10.7 0.0

12 25.4 18.2 1.86 13.7 16.8 13.9 0.2 0.7 10.6 10.6 0.0

It was noted that the differences in current speed seem rather low. This is most likely due to the spatial resolution of the model of 200m, which does not resolve the differences between tidal flats and the channels between them, which are often just meters to tens of meters wide.

In addition to the long term average, also the annual cycle based on monthly averages has been derived for all parameters. Exemplarity, data for the concentration of chlorophyll-a, TSM and food quality are given in Figure 5-5. This kind of plot gives an impression of the annual variability within a field and the differences in annual cycle between the fields. This information is expected to be useful in planning when to move which mussels to which field.

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Figure 5-5: Annual cycle of chlorophyll-a concentrations and temperature at selected mussel fields

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5.6 Discussion

The results of our analysis were compared with the theoretical production value (Theoretische Productie Waarde - TPW) of mussel beds in the Wadden Sea. The TPW is a measure of the productivity of a mussel field as a basis for determining the rent a mussel grower has to pay for a certain mussel field. It was determined by the Ministry of Agriculture, Nature and Food Quality (LNV), the latest values are from 2002. A map of the TPW for the mussel fields in the Wadden Sea is given in Figure 5-6 (from Capelle et al. 2015).

Figure 5-6: Theoretical production value (TPW) of mussel fields in the Wadden Sea (from Capelle in prep.)

According to the TPW, the most productive parcels considered in this analysis are the ones close to the inlet Vliestroom between the islands of Vlieland and Terschelling. The least productive ones are the ones are at the eastern side of the Wadden Sea. Comparing these values with the results in Table 12 the following observations can be made:

Of the environmental variables considered in the analysis, salinity and TSM seem to have the greatest influence on the productivity: Salinity is highest in the fields which have the highest production value, whereas these fields show the lowest TSM values

Surprisingly, the least productive fields show the highest concentrations of Chl-a, these values are still within the range considered optimal. A number of reasons could explain this. On the one hand, the high Chl concentrations could be offset by high concentrations in TSM, which lowers the efficiency of food take up by mussels. Other factors may have a higher influence altogether than Chl concentration in an environment such as the Wadden Sea where Chl concentrations are rather high in general. On the other hand, it should also be noted that the values presented in Table 12 are long term averages. As there is a strong seasonal variabiliity in Chl concentrations (see Figure 5-5), the higher concentrations beyond the optimal range during spring, summer and autumn could be a limiting factor.

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Temperature and current velocity seem to have little influence on the productivity. This may be a result of the small variation observed due to the spatial resolution of the model (see section 5.5)

To link the environmental variables considered in this analysis more directly with the mussel yield, it is planned to use the data as input for a growth model for mussels in collaboration

with Hogeschool Zeeland.

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6 Case: Mussel farming in Portugal

Traditional fishing and aquaculture activities are declining in Portugal and this is of concern to the government as Portugal has 14.5% of Europe’s maritime Exclusive Economic Zone (1 656 402 Km2) Thus, the strategic national plan for fisheries in Portugal between 2007-2013 has a specific section for supporting , innovating and diversifying aquaculture production with measures listed below

(MADRP – DGPA (2007) :

1) Establish a strategic plan for the development of aquaculture;

2) Promote and improve the communication between the institutions involved in the licensing

procedure for aquaculture;

3) Provide incentives to obtain certification for high quality production processes and

products;

4) Encourage an increase in production, diversification of species, and the development of new

products;

5) Promote innovation and encourage partnerships between the research and the production

entities involved in aquaculture;

6) Promote professional courses to improve the necessary skills of the workforce .

However, at present the contribution of aquaculture relative to the capture fisheries is low. Thus in 2012, the total aquaculture production in Portugal was of 10 317ton with a value of 54 Million Euros, with the production of bivalves representing 38% of the total production, of which clam (Tapes decussata) was the dominant species (INE, 2014). Nonetheless, Table 13 shows there has been substantial investment by Portuguese government with the support of the European Union through the PROMAR programme (MADRP – DGPA ,2007).

Table 13. Portuguese companies or individuals receiving a 60% subsidy from the EU in Euros between 2007-2013 for aquaculture projects.

LOCATION No of entities Subsidy paid out Total investment

Continental 57 12 779 685€ 21 299 475€

Algarve 34 10 319 396€ 17 198 993€

Bivalves >50000 € 13 7 119 752€ 11 866 253€

Fish > 50000€ 6 2 976 432€ 4 960 720€

With regard to bivalves, 13 enterprises with an investment over 50 000 euros per enterprise have received approximately 11,900,000 Euros between 2007-2013. Some examples of the largest ones are shown in Figure 6-1 and include the concession of Finisterra (one of the Users in the AQUA_USERS project ). The other offshore concession shown in the Figure 6-1, one in the east coast of the Algarve, the Tavira aquaculture area (APA de Tavira, Despacho n.º 4223/2014 de 21 de Março) later renamed as Monte Gordo aquaculture area (APA de Monte Gordo, Despacho n.º 9049-A/2014 de 14 Julho), and other in the centre of Portugal (APA do Centro, Despacho n.º 4222/2014 de 21 de Março), these areas are mainly directed to the production of biogenetic cultures/ marine bivalves. Compared to clams and oysters, mussels (Mytilus galloprovincialis) have only recently been considered for aquaculture in Portugal probably due to their low market value. However, the expansion to offshore areas has encouraged the expansion in the production of this species, including by one of the Users in the AQUA-USERS project – Finisterra.

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Figure 6-1: Location of the recently established aquaculture production area in the Algarve shown in Table 13, from west to east in the figure: Finisterra Lda (1Km

2), Testa&Cunhas (1.6 Km

2), Pilot Area of Aquaculture

Production of Armona (APPAA) (15 Km2) and APA de Monte Gordo (10Km

2).

6.1 Objective(s)

Site selection for mussels and other bivalves has already been made on a substantial scale (section 6.0 above) based on decisions that have already been taken by the government. The objective of this section is to demonstrate some historical data on the characterisation of the biggest concession in Portugal for offshore aquaculture at the Armona offshore park (APPAA-15Km2) using a range of different modelling approaches (Figure 6-1, Figure 6-2) and then focusing on the interests of Finisterra (1 Km2) by using Earth Observation data in combination with in situ data, with particular emphasis on the Harmful Algal Blooms (HABs) problem (Figure 6-3).

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Figure 6-2. Spatial domain for the modelling framework, including the catchment area, inshore and offshore boxes of the ecological model, and the 15 km

2offshore aquaculture park, (Ferreira et al, 2014)

Figure 6-3. a) Location of Southwest Alentejo and Vicentine Coast Natural Park Finisterra concession with the grey band showing the 2Km extension of the park into the maritime zone www.icnf.pt/portal/ap/resource/img/pnsacv/map-pq adapted by B.Thaman; b) the red square indicates the concession available to Finisterra for aquaculture (with thanks Lourenço Ribeiro) .

a) b)

http://www.icnf.pt/portal/ap/resource/img/pnsacv/map-pq

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6.2.1 Armona offshore park Ria Formosa

Figure 6-4 shows the modelling Framework, including GIS, used to gain a better understanding of the interactions between aquaculture and the environment between the inshore and offshore habitat of the Ria Formosa. The detailed description of how these models were used are available in (Ferreira et al, 2012; 2014).

Figure 6-4 Modelling framework from the FORWARD and COEXIST projects for analysing interactions between the inshore coastal zone and offshore aquaculture (Ferreira et al, 2014).

6.2.2 Finisterra concession Sagres

With regard to the focus on Sagres at the location of Finisterra’s concession, Earth Observation and in situ data have been used to develop and implement indicators for aquaculture sustainability following the scheme outlined in Figure 6-5. The approach is to: 1) to identify the thresholds of Chlorophyll-a (Chl-a); Sea Surface Temperature (SST); Total Suspended Matter (TSM); Coloured Dissolved Organic Matter (CDOM) - also called Yellow Substance (YS); and the Diffuse attenuation coefficient for downwelling irradiance (Kd), which correspond to alert levels for each region; and 2) investigate the initiation and decline of phytoplankton blooms to try to identify anomalies corresponding to Harmful Algae Bloom event (HABs)

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Figure 6-5. Schematic diagram illustrating the proposed method for the development of regional specific indicators for aquaculture.


Although site selection is not the objective of this Case Study, it is important to recognise that there are a number of government bodies and agencies involved in the licensing of the concessions. There also EU Directives with which aquaculture concessions have to comply.

6.3.1 Licensing agencies

• Direcção Geral das Pescas • Ministério do Ambiente e do Ordenamento do Território • Agência Portuguesa do Ambiente (APA) • Administração da Região Hidrográfica do Algarve (a subdivision within APA) • Instituto da Conservação da Natureza e da Biodiversidade • Capitanias do Porto de Faro e Lagos • Instituto Hidrográfico • PROMAR - Pojectos de apoio ao Investimento Produtivos na Aquacultura

6.3.2 EU Directives (some examples)

Water Framework Directive (C.E.C., 2000)

Shellfish Directive (C.E.C., 2006)

Marine Strategy Framework Directive (C.E.C., 2008)

Maritime Spatial Planning (C.E.C, 2014)

The licensing and monitoring requirements are more demanding when a concession, is linked to protected area, as is the case for Finisterra. Apart from normal requirements required by the authorities for licensing (see list above), any concessions within a national park will be subject to additional restrictions, as is the case for the Portuguese User - Finisterra. Their concession is located in the PNSACV (Southwest Alentejo and Vicentine Coast Natural Park – 110km), as the park extends 2Km off the coast (Figure 6-3). A management plan was established 1995 (ICNF) and updated in 2011 (POPNSACV, ICNF). Under Article nº 76 of this plan for the implantation of Marine Culture there has

https://www.google.pt/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CB8QFjAAahUKEwjI7pXmsI3JAhUCQxoKHb9rBtg&url=http%3A%2F%2Fwww.apambiente.pt%2F&usg=AFQjCNFIgHM_M1lhdKdUorbvH-7drS3UZg&sig2=adqu2FBMXypbmq0rUdmlJw

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to be an environmental impact study, only indigenous species can be cultured under an extensive or semi-extensive regime, and there has to be a monitoring plan for regular measurements of water quality, sediments and benthic community.

6.3.3 Suitability of the Algarve coast for culture of the Mediterranean Mussel (Mytilus galloprovincialis)

The EU FP-7 COEXIST project (www.coexistproject.eu, Deliverable 1.1) has looked at the suitability of European coastal waters for the cultivation of a range of fish and shellfish, including the Mediterranean mussel (Mytilus galloprovincialis). Figure 6-6 shows the coast of the Algarve in green i.e an area highly suitable for cultivation of the Mediterranean mussel. In addition, this project has also outlined thresholds for environmental requirements, such as temperature, salinity, chlorophyll, and depth for the culture. Table 14 shows the parameters and thresholds from the COEXIST, but also those selected by Finisterra at Sagres. Although the thresholds for most of the parameters are similar, there are differences; for instance, the maximum salinity at Sagres is 36,5psu compared to the 30 selected by COEXIST.

Figure 6-6. Suitability map for the cultivation of Mediterranean mussel (www.coexistproject.eu)

http://www.coexistproject.eu/

http://www.coexistproject.eu/

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Table 14. Parameters and thresholds for the Mediterranean mussel (Mytilus galloprovincialis)



The extensive data set that has been used by the COEXIST and FORWARD projects to carry out modelling exercise for APPAA are described fully in the published literature (e.g. Ferreira et al, 2012; 2014) and will not be presented here as the main objective is to provide an illustration how these models can contribute to characterisation of sites for mussel aquaculture along the Algarve coast.

Parameters

Thresholds

Finisterra (SW Portugal) Coexist project

(D1.1)

Salinity (PSU) Min 20

Max 36,5 30

Temperature (°C) Min 13 17

Max 20 20

Depth (m) Min 20 2,5

Max 35 50

Chlorophyll-a (mg.m3)

Min 0,5 0,5

Max 10

Opt >1,0

Oxygen (mg.dm3) Min 5

Max 10

Wave height (m) Min

Max 4 5

Current speed (m.sec) Min >0,1

Max

Wind speed (m.sec) Min

Max 10 10

Sediment type artificial structures over

sandy bottom

sandy /muddy bottoms, or, artificial

hard substrates

Sediment slope (%) not >5

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6.4.2 Finisterra concession Sagres

Several data records provided by satellite sensors are have been used in conjunction with in situ data collected from the Finisterra concession between 2008-2014. Chl-a estimates are provided by two different sources. One is a product of the ESA funded project Ocean Colour - Climate Change Initiative (OC-CCI); and data provided by the MEdium Resolution Imaging Spectrometer at full resolution, from hence forward referred to as MERIS-FR. Further, a record of Sea surface temperature (SST) estimates from the Pathfinder program (Casey, 2010) was also used.

The OC-CCI is a merged dataset that combines observations from different individual missions (Sea-Viewing Wide Field-of-View Sensor, Moderate Resolution Imaging Spectroradiometer on board the Aqua Earth Observing System, and MEdium Resolution Imaging Spectrometer) to create a more robust time series with increased temporal and spatial coverage (Storm et al, 2013). The OC-CCI record starts 4th of September 1997 and finishes on the 31st of July 2012, providing daily global observations of Chl in a 4.6 km bin grid. The OC-CCI Chl-a product is estimated using the OC4v6 (O'Reilly et al., 2000), an algorithm developed for Case I waters. The OC-CCI record is used for the following reasons: (1) OC-CCI provides a high quality and robust estimations of Chl-a; to develop the proposed methodology, not requiring great computational effort; and 2) it provides reliable Chl-a phenology patterns for the large scale regions that include the Sagres .

The MERIS-FR data ranges from 19th July 2002 to 8th April 2012, and comprises Chl-a estimates on a 300 m resolution for Sagres regions, where Chl estimates were processed with the OC5, developed for coastal waters (Gohin et al, 2008). Additional to Chl product, diffuse attenuation coefficient at 490 nm (Kd 490), absorption due to gelbstof and detrital material at sensor wavelength 443 nm (adg 443), and suspended particulate matter concentration (SPM) MERIS-FR products were also used here, which hold the same spatial and temporal characteristics as the former.

The Pathfinder program provides global SST data that is accurate and consistent, for use in climate research. Pathfinder SST data (V5.2) was obtained from the National Oceanographic Data Center (www.nodc.noaa.gov/SatelliteData/pathfinder4km/), and comprised daily matrices that represented the ocean with a grid of 4.6 km2 per pixel. The temporal coverage used was the same as the OC-CCI record.

Phytoplankton phenology was calculated from a computed annual climatology of the Chl-a data. The Chl-a climatology was calculated for both Chl-a records (OC-CCI and MERIS-FR) for each region of interest, by averaging the value for each day of the year per pixel, thus producing a daily annual (n=366) average product representing the typical Chl-a variation throughout the year for each pixel The following conditions were established in order to accept the time series for posterior analysis: i) only locations that had more than 100 observations; ii) Chl-a values ranged between 0 and 50 mg.m-

3; iii) values higher than three times the time series standard deviation, throughout the climatological year were discarded. Chl-a time series for each location was filtered with a harmonic regression (fitting 15 harmonics). The annual bloom was defined as the period of the highest peak above the mean value of that pixel for the whole climatological year. Several phenology characteristics were calculated, namely, the day of the bloom initiation and end, bloom duration, bloom maximum and day of occurrence, bloom range, day of maximum growth and value, and finally, the perimeter and area of the bloom. Computed phenology aspects were then plotted spatially for each grid point.

The other source of data that has contributed to the characterisation of the Finisterra site at Sagres is the MERIS validation exercise where radiometric parameters and concentrations of optically active water constituents were estimated between September2008 and March2012. The measurements were consistent with the MERIS validation protocols (Doerffer, 2002; Barker, 2011). The measurements were timed to coincide with the MERIS overpass, within 30 min at Station A (Finisterra concession for aquaculture). The clear skies and flexible access to boats at Sagres enabled approximately 300 matches between MERIS products and in situ data (Cristina et al, 2014; Goela et

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al, 2014). Greater detail about how these data where obtained can be found in the publications (Cristina et al, 2014; 2015) and (Goela et al, 2013; 2014; 2015).

6.4.3 Obligatory monitoring for the Agência Portuguesa do Ambiente (APA)

Table 15 shows the parameters that are monitored by the Finisterra to comply with APA’s requirements for licensing the concessions. This data is part of the in situ data that contribute to management of the system, although in practice many of them are not frequent enough. In order to overcome this Finisterra has set up the oceanographic buoy that is shown in Figure 6.7 which for some parameter will transmit hourly measurements by Ftp (file transfer protocol) to a website.

Table 15. Parameters , analytical methods and frequency of observation required by APA

Parameter Analytical method Frequency/Observations

Water column

Basic

Salinity Direct reading Tri-monthly

pH Direct reading Tri-monthly

Temperature Direct reading Tri-monthly

Organic load estimate

Nutrients: ammonia,

nitrites, nitrates,

phosphorus.

Tri-monthly

Productivity estimate

Chlorophyll Filtration and

spectrophotometry Tri-monthly

Turbidity Direct reading Tri-monthly

Oxygenation Winkler Tri-monthly

Superficial sediments

Physico-chemical

properties

Granulometry Sieving instrument for fine sediments

analysis Annual

Organic matter content

Annual

Determination of carbon

concentration, total nitrogen and organics,

redox potential.

Analysis of carbon and total nitrogen

and organics analysis

Annual

Oxygen Annual

Biological parameters

Composition, abundance and

biomass of macro benthic

fauna

Optic microscopy and calculations

Annual

https://www.google.pt/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0CB8QFjAAahUKEwjI7pXmsI3JAhUCQxoKHb9rBtg&url=http%3A%2F%2Fwww.apambiente.pt%2F&usg=AFQjCNFIgHM_M1lhdKdUorbvH-7drS3UZg&sig2=adqu2FBMXypbmq0rUdmlJw

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Continuous Monitoring What is being monitored

• Temperature (hourly)

• Salinity (hourly)

• Turbidity (hourly)

• Wave height (hourly)

• Flourescence (hourly)

• Nutrients (weekly?)

• Pigments (weekly?

• Microplankton (weekly?)

• Meteorological conditions

• Atmospheric conditions

Figure 6.7. Monitoring programme including an oceanographic bouy providing hourly readings.

6.4.4 Data on mussel growth to calibrate FARM growth model for Mytilus galloprovincialis.

As part of the characterisation of the Finisterra site, in situ data for temperature, salinity, Chl-a, POM and TPM have been related to data on the growth of spat mussel over the period of a year to calibrate growth models for WinShell and FARM.

Figure 6.8. In situ data collected to calibrate the WinShell and FARM model for M. Galloprovincialis

6.5 Results


Figure 8.8 shows some examples of the outcomes of the COEXIST and FORWARD projects (Ferreira et al, 2012; 2014), that could be produced by using different aspects of the modelling framework . Thus fluxes and boundary conditions have been modelled with the

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Delft .3D-hydrodynamic model to show the tidal circulation and exchange in the Ria Formosa lagoon (Figure 6-9 ,top left). The EcoWin2000 system scale model has been used establish the spatial framework for the watershed, the lagoon, and the adjacent coastal waters. The system is divided into 34 boxes, two vertical layers. Boxes have been defined using GIS based on uses, legislation, water quality, and hydrodynamics Figure 6-9, top right) A different example of the uses of this model is the simulation of changes in clam harvest due to offshore aquaculture of mussels (Figure 6-9, bottom left).This is potentially due to the depletion of food from offshore, which is removed by farmed mussels before the water enters the Ria Formosa on the flood tide and thereby reducing the potential for clam production (Ferreira et al., 2014), particularly where the lagoon entrance faces APPAA. A final example is the comparison of the environmental performance of monoculture and integrated multi trophic aquaculture IMTA, using particulate organic matter (POM) as an indicator of the role of filter feeders in mitigating the negative aspects of fed aquaculture due to excess feed and particulate waste (Figure 6-9., Bottom right).

Figure 6.9. (top left) connectivity between offshore and the lagoon , (top right )spatial domain for the

modelling framework, including the catchment area, inshore and offshore boxes of the ecological model, and the 15 km2offshore aquaculture park; (bottom left) change to in inshore production of the clam(Tapes decussatus) with the addition of mussel culture (Mytilus galloprovincialis) in the offshore aquaculture park (Ferreira et al, 2012; 2014), Fig. 8. environmental performance of the offshore aquaculture park with various

culture combinations (no culture, monoculture, integrated multi trophic aquaculture-IMTA)Results focusing on the USER Finisterra and their specific requirements towards understanding HAB events in Sagres

This section of the Case Study results could be much more detailed, but as this work has already been published (see Ferreira et al 2014), the idea is to illustrate the kind of effort that has already

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taken place for mussel culture along the Algarve coast with a modelling framework at different scales, which in the future could be used by Finisterra at Sagres.

6.5.2 Results focusing on the USER Finisterra and their specific requirements towards understanding HAB events in Sagres

The work on the aquaculture indicators from Deliverable 3.5 (AQUA_USERS) is highly relevant to Finisterra and although they have already been submitted they bear some repetition in demonstrating the benefit of the Earth Observation work at Sagres which is focusing specifically on the environmental conditions affecting HABs that is the overriding problem concerning the User a t Sagres, on the Portuguese coast (37.018°N, 8.883°W).

Results on bloom phenology are focused in the south of Portugal, where the Finisterra aquaculture site is located. The annual Chl-a average for the Iberia region reveals maxima along the coastline, and an offshore latitudinal gradient with higher Chl-a mean in the northern areas (Figure 6.10). The bloom duration (in days), day of bloom start and bloom maximum (mg chlorophyll-a m-3) are plotted for the Algarve area Bloom maxima with Chl > 1 mg m-3 occur near the coast and around Cape S.Vicente where the onset of the growth period is dependent on upwelling events (Fragoso and Icely, 2009) which occur more intensively in late spring/summer months. Bloom start day in this area varied from Julian day 120 to 250 with duration (120 days) longer than its surrounding areas (see set of images in Figure 6.10). It is notable that that satellite derived Chl-a estimates are significantly (P<0.01) and strongly (r2 = 0.75) correlated to the in situ observations (Figure 6 11 d).

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Figure 6.10. Climatological annual averages for Algarve sub-region: (top left) chlorophyll a for the Portuguese coast from OC-CCI data;. (top right) Bloom duration from MERIS-F; (bottom left) day of bloom start from MERIS-FR and, maximum (bottom right) day of bloom maximum from MERIS-FR . Arrow shows Cape S. Vicente.

The annual average of MERIS-FR at derived Chl Tand is characterized by an annual mean of 1.36 mg.m-3 Chl-a, that varies +/- 3.72 mg.m-3 Chl-throughout the climatological year (Figure 6.11.,a).

Strong and positive correlations indicate that Sagres is within an area where cool waters, either from the Portuguese current or due to the seasonal upwelling, vary directly and significantly with the Chl-a seasonal variability, which is opposite to what is observed in the adjacent area (Figure 6.11,b). The relationship between satelite derived Chl and Kd 490 indicates a different pattern. Chl and Kd 490 correlates very strongly and significantly throughout the whole Algarve region, where r values are nolower r than 0.60; the strongest relationship is observed to follow closely the bathymetry lines mostly between the 100 - 250 m (Figure 6.11, c). The number of matched observations between satellite Chla and in situ Chl sources is shown in Figure 6.11 d); a linear regression analysis is based on n = 84 (green crosses), φ = 1.734, RMS = 0.34, δ = 0.12, r = 0.75, P << 0.01, best fit line (-) has a slope of 0.74 and a constant 0.09, with (–) marking the 1:1 relation .

Figure 6-11. Annual average of MERIS-FR derived Chl (calculated using the OC5 algorithm developed for coastal waters) showing the location of the Sagres site; b) Map showing the correlation coefficients between Chl and Kd 490 for each grid point within the Algarve region. c), Map showing the correlation coefficients between Chl and Kd 490 for each grid point within the Algarve region; .d) Linear regression analysis between Chl estimates from MERIS-FR and in situ observations at Sagres site (37.0 °N, 8.9°W).

A further analysis of the of the climatological time series for the 300m2, including the Sagres site (Figure 6-12a) indicate that a ‘no lag correlation’ between Chl-a and SST is better than ‘lag 0’

a) b)

c) d)

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correlation when shifting the SST time series backwards in time; a similar relationship has been found between Chl-a and Kd 490 (Figure 6-12 right).

Figure 6-12 (left) Climatological time series of satellite derived Chl and SST for the Sagres site, illustrating the bloom period and the days when maximum growth was observed.; (right) Climatological time series of satellite derive Chl and Kd490 for the Sagres site again illustrating the bloom period and the days when there was maximum growth

Figure 6-13 summarises some of the analysis of the in situ data that has been collected at Sagres between 2008 until 2012, whereby the shape of the spectrum for coefficient of absorption for phytoplankton, as well as the level during bloom periods (TChla > than 2.98 µg.l-1) is much greater than for no-bloom periods (TChla < 0.54 µg.l-1). Furthermore, CHEMTAX has demonstrated a substantial difference been the microplankton community with larger-sized diatoms dominating through the bloom periods and other groups, generally, smaller sized groups during the no bloom periods (Goela et al 2014). In summary, the pigment results showed that diatoms dominated from early spring to summer, coinciding with upwelling conditions, while cryptophytes, prymnesiophytes and prasinophytes dominated in autumn and winter, coinciding with seasonal stratification. This type of information is very useful for Finisterra for both availability of primary production for mussel growth and also the potential for HABs and the types of species contributing to the toxicity. For example, during bloom periods the diatom Pseudo-nitzschia spp might produce toxins, whilst in contrast during no bloom periods other groups including dinoflagellates such as Ceratium spp. might become a problem.

Useful information can also be obtained from the ternary diagram in Figure 6-13b about the bio-optical characteristics of the Finisterra site which from all the data obtained for the Sagres site shows that the site is characterised by phytoplankton (aph) and not non-algal particles (anap). However there are periods when the absorption of yellow substance (ays) (equivalent to coloured dissolved organic matter-CDOM) dominate, but this fraction of absorption is mostly attributed to coastal influences rather than phytoplankton dynamics.

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Figure 6-13. a) Comparison between spectra for coefficients of absorption and the abundance distribution microplankton groups estimated by CHEMTAX under bloom and no bloom conditions at Sagres (Goela et al, 2014); b) ternary diagram comparing the coefficients of absorption for phytoplankton aph), yellow substance (ays) and non-algal particles (anap) from the MERIS validation collect between 2008-2103 (Cristina et al, 2014)

The series of images in Figure 6-14 demonstrate how satellite images can show the evolution of blooms at the Finisterra site (see higher resolution within the insets, Cristina et al, 2015). When linked with in situ data (e.g WISP-3) this approach may become an important management tool in the future

Figure 6-14. MERIS Reduced Resolution satellite images of Algal Pigment Index 1 (API 1) showing the development of an algal bloom between February and March 2012. The Insets highlight the Sagres region (Cristina et al, 2015).

In conclusion to section 6.5.1, the Sagres study site is characterized by a typical spring bloom, which can last for 60-90 days. Chlorophyll concentrations can occasionally go up to 10 mg.m-3.

a) b)

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Phytoplankton blooms are also observed during summer, when water temperature is higher. Strong correlations between coastal sea surface temperature and chlorophyll-a concentrations are observed using satellite data, showing the overall seasonal pattern. In summer, negative correlations between in situ Chl-a and temperature have highlighted occasional upwelling events, with a temperature decrease of at least 2-3ºC during a 1-week period. Most phytoplankton blooms occurring due to upwelling events are dominated by diatoms. However, Phaeocystis blooms may also happen during high temperature and salinity conditions. Other important harmful species can be observed in the waters off Sagres, however, only those datasets with samples of phytoplankton have enough resolution to evaluate them.(Goela et al, 2014).

No data has been presented in this results section on mussel growth to calibrate FARM growth, as essentially this work is in its early stages. However, the idea is to test a minimum threshold for chlorophyll ensuring mussel survival for the Sagres region, using the individual model WindShell (http://www.longline.co.uk/winshell/). Winshell is a shellfish model to determine individual shellfish growth for oysters, clams and mussels. The objective was to test which were the minimum Chlorophyll-a, TPM, POM values, where the anabolism of the mussel equals the catabolism. This value can be for instance useful to use in the aquaculture indicators, as well for creating suitability maps/areas using GIS and EO data. Other drivers included in the analysis are the average temperature and salinity for Sagres.

Overall conclusion

Objective 1: The modelling work related to in situ data by Ferreira and colleagues demonstrate the importance of this type of study for characterising the suitability of a site for aquaculture and also the potential effects of aquaculture sites on the surrounding environment at different scales.

Objective 2: This characterisation of the site at Sagres has focused mainly on the development of phytoplankton blooms with the potential to contain Harmful Algal Blooms (HABs) at the request of the User. The commercial success of Finisterra is seriously affected by the closure of the sale of bivalve products due to toxins from HABs, particularly, if the closure continues over a long period of time. The Instituto Português do Mar e Atmosfera - IPMA (https://www.ipma.pt/en) is responsible for monitoring these events and carrying out weekly monitoring of bivalves from sections along the Portuguese coast (Figure 6-15). For example, the Ria Formosa Pilot Area of Aquaculture Production of Armona (APPAA) concession is within section L8 and the Sagres concession for Finisterra is within L7c. Sections under closure are highlighted in red. There is an argument that with the increasing investment and development that many of these areas are too large as variations of toxin levels can vary considerably within a specific region and it only needs one toxic event to have the entire section closed down. Thus any information from both Earth Observation and in situ data is important for addressing these issues with objective data.

Instituto%20Português%20do%20Mar%20e%20Atmosfera

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16-10-2015

2014 - closure events at area L7c

Open

Partially closed

Fully closed

No information available

www.ipma.pt/pt/pescas/bivalves/

Figure 6-15. IPMA map for the 16th

October 2015 showing sections of the Portuguese coast that are partially or fully closed due to toxins; historical records are also available such as the one for section L7c in 2014 showing days affected by toxins.

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Appendix 1. Case on site selection for rainbow trout production in Danish waters

Suitability maps based on administrative constrains

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Suitability maps based on optimal production condition

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Suitability maps based on data indicating environmental impacts

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Production layers

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Environmental impact layers

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Appendix 2. Case on site selection for salmon production in a Norwegian fjord

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Case study 1 “Site selection”

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Transcript of Case study 1 “Site selection”