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 AQUAculture USEr driven operational Remote Sensing information services Deliverable 3.5 Aquaculture Indicators Report FFCUL, VU/Vumc, NIVA, GRAS, DHI, SGM, PML 2014-­‐10 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, A
Grant Agreement No 607325) D3.5: Aquaculture Indicators Report 31/10/2014 Task 3.5: Development of Aquaculture Indicators Deliverable 3.5: Aquaculture Indicators Report Lead beneficiary FFCUL (4) Contributors VU/Vumc (2), NIVA (5), GRAS (6), DHI (7), SGM (8), PML(3) Due date Month 12 (31 October 2014) Actual submission date 31/10/2014 Dissemination level PU Change record Issue Date Change record Authors 0.1 1/10/2014 First draft FFCUL 0.2 31/10/2014 Final version FFCUL, VU/Vumc, NIVA, GRAS, DHI, SGM, PML 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 cites as Sá, C., Couto , A.B., Brito, A., Beltrán, C. Eleveld, M.A., Dale, T., Fragoso, B.D.D., Icely, J.D. (2014) “Aquaculture Indicators Report”, AQUA-­‐USERS deliverable D3.5, EC FP7 grant agreement no: 607325, 50p. © Copyright 2014, the member of the AQUA-­‐USERS consortium. All rights reserved. 2 D3.5: Aquaculture Indicators Report 31/10/2014 Task objective (from DoW) The objectives of this task are to: 1) Identify threshold levels of Chlorophyll a (as proxy for algal biomass), SST, TSM, CDOM, Kd etc. which correspond to alert levels for each region; 2) investigate the initiation and decline of the phytoplankton blooms; 3) Identify the dominant species and size groups of the bloom. 4) Develop further relevant indicators based on the combined knowledge gathered from EO and knowledge available from literature and user experience. Scope of this document The present document sets the basis for the development of aquaculture indicators considering all regions of interest for the Users. An overall description of each region is given, with emphasis on the main parameters affecting the dynamics of each area and the main difficulties associated to it. The data collected, both in situ and satellite, are described, as well as, the methods established for the development of aquaculture indicators. A case study is presented for the Portuguese region. The results presented in this document will be further analysed and methods refined in the implementation of aquaculture indicators task (4.3). Abstract This report is a contribution for the development of indicators to improve aquaculture development and sustainability. Both in situ and satellite data are used to establish the natural variability of several biogeochemical parameters in the regions of interest (i.e. areas where the Users sites are located) in order to identify potential benefits (e.g. food availability as could be indicated by chlorophyll a, Chl, abundance) and risks (e.g. blooms as generators of hypoxia conditions, presence of toxic species, suspended particulate matter) to the aquaculture activity. Five different regions were considered (i.e. south coast of Portugal, west coast of Scotland, the North Sea coast of Holland, the Baltic coast of Denmark and the northwest coast of Norway). In situ data were gathered for each region and statistical analyses were done in other to determine natural variability of the areas and to understand region-­‐specific correlation among parameters. Satellite data were also used with the same purpose and 10 years of Chl data were analysed to determine phytoplankton bloom phenology. For all regions, maps of bloom start day, bloom duration, bloom maximum, among other parameters, were generated. Integration of all information was tested in a case study for the Algarve area in the South of Portugal. 3 D3.5: Aquaculture Indicators Report 31/10/2014 List of abbreviations Abbreviation Description Chl Chlorophyll a SST Sea Surface Temperature TSM Total Suspended Matter CDOM Coloured Dissolved Organic Matter Kd Diffuse attenuation coefficient for downwelling irradiance NRT Near Real Time HABs Harmful Algal Blooms EO Earth Observation PIM Particulate Inorganic Matter POM Particulate Organic Matter DOC Dissolved Organic Carbon ODO Optical Dissolved Oxygen N Nitrogen P Phosphorus OC-­‐CCI Ocean Colour -­‐ Climate Change Initiative MERIS-­‐FR MEdium Resolution Imaging Spectrophotometer – Full Resolution SPM Suspended Particulate Matter adg 443 absorption due to gelbstof and detrital material at sensor wavelength 443 nm RMS Root-­‐Mean-­‐Square δ Bias 2
r Coefficient of determination φ Mean ratio T Temperature sal Salinity List of related documents Short Description Date DoW Grant Agreement Annex I (Description of Work) 01/07/2013 D1.1 Initial user requirements document 31/01/2014 D2.4 Database + application blueprint 28/02/2014 D2.6 Data policy, data communication and access protocols 30/04/2014 D5.1 IS data quality control 31/10/2014 4 D3.5: Aquaculture Indicators Report 31/10/2014 Table of contents 1 Introduction ................................................................................................................ 7 2 In situ data .................................................................................................................. 9 2.1 Portugal .................................................................................................................................... 10 2.1.1 Description of region ......................................................................................................... 10 2.1.2 Parameters and methods .................................................................................................. 11 2.2 The Netherlands ....................................................................................................................... 12 2.2.1 Description of region ......................................................................................................... 12 2.2.2 Parameters and methods .................................................................................................. 14 2.3 Denmark ................................................................................................................................... 15 2.3.1 Description of region ......................................................................................................... 15 2.3.2 Parameters and methods .................................................................................................. 16 2.4 Norway ...................................................................................................................................... 17 2.4.1 Description of region ......................................................................................................... 17 2.4.2 Parameters and methods .................................................................................................. 18 2.5 United Kingdom ........................................................................................................................ 19 2.5.1 Description of region ......................................................................................................... 19 2.5.2 Parameters and methods .................................................................................................. 21 3 Satellite data ............................................................................................................. 22 3.1 Description of Data ................................................................................................................... 22 3.2 Parameters ................................................................................................................................ 23 4 Phytoplankton Phenology .......................................................................................... 24 4.1 Method ..................................................................................................................................... 24 4.2 Results ....................................................................................................................................... 25 4.2.1 Portugal ............................................................................................................................ 25 4.2.2 UK ...................................................................................................................................... 25 4.2.3 The Netherlands, Denmark and Norway ........................................................................... 28 5 Phytoplankton response to environmental conditions ............................................... 29 5.1 The Netherlands ....................................................................................................................... 29 5.2 Denmark (DK) ............................................................................................................................ 33 5.3 Norway (NO) ............................................................................................................................. 36 6 Case Study – Sagres, Portugal .................................................................................... 39 6.1 Methodology ............................................................................................................................ 39 6.2 Results ....................................................................................................................................... 39 5 D3.5: Aquaculture Indicators Report 31/10/2014 7 7.1 8 Conclusions ................................................................................................................ 46 Future work .............................................................................................................................. 46 References ................................................................................................................. 47 6 D3.5: Aquaculture Indicators Report 31/10/2014 1
Introduction With the increased demand for high-­‐quality protein, fish farming is gaining importance to ensure food security and has become the fastest growing food production sector worldwide. Environmental conditions are crucial to ensure the growth and health of the produced species, while the production often releases large amounts of nutrients affecting the surrounding environment. Monitoring is therefore imperative on both levels; however, difficulties arise from lack of quality data, and inconsistent coverage. This projects aims to 1) providing the aquaculture industry with user-­‐relevant and timely information based on the most up-­‐to-­‐date satellite data and innovative optical in situ measurements 2) gather information on water quality for site selection to set up new locations for aquaculture/mariculture, and 3) provide NRT (Near Real Time) information on water quality, and alert on regional specific indicators for aquacultures already in activity to improve management and production. The present report will describe mainly the process of development and implementation of aquaculture indicators. This task involves the following steps (outlined in Figure 1): i) Identification of threshold levels of Chlorophyll a (Chl), Sea Surface Temperature (SST), Total Suspended Matter (TSM), Coloured Dissolved Organic Matter (CDOM) and Diffuse attenuation coefficient for downwelling irradiance (Kd), which correspond to alert levels for each region; and ii) investigation of the initiation and decline of phytoplankton blooms, in order to identify anomalies which could correspond to Harmful Algae Bloom events (HABs). Regions were specified according to the sites of the Users, which are listed in Table I. Figure 1 Schematic diagram illustrating the proposed method for the development of region specific Aquaculture indicators. 7 D3.5: Aquaculture Indicators Report 31/10/2014 Table I List of Users and site of interest User Country Coordinates Type of aquaculture Viveiros Ana Manjua Portugal (PT) 36.99N; 7.97W Clams Prins & Dingemanse The Netherlands (NL) 52.5-­‐53.2N; 5.0-­‐5.2E Mussels Hortimare Norway (NO) 61.05N; 4.87W Seaweed Finisterra PT 37.03N; 8.27W Mussels Erfjord Stamfisk NO 59.57N; 6.52W Salmon roe NO 59.52N; 6.40W Salmon roe Hjarno Havbrug Denmark (DK) 55.81N; 10.13E Trout CEFAS United Kingdom (UK) -­‐ -­‐ Scottish Salmon UK Salmon (Ommundsteigen) Erfjord Stamfisk (Teigane) 57.33N; 6.40W 8 D3.5: Aquaculture Indicators Report 31/10/2014 2
In situ data For the development of region-­‐specific aquaculture indicators it is essential to understand natural variability of each area. Historical in situ data were therefore gathered from partners and users for each region. The parameters available for each area varied and are summarized in Table II. Table II Summary of parameters available for each region Parameter DK NL UK NO PT Chlorophyll x x x x x Kd x SPM x x Temperature x x x x x Salinity x x x Secchi depth x x x Oxygen x x TSM x PIM x POM x DOC x ODO x External x Wind Direction x x Air Temperature x Pressure x Relative Humidity x Kd 490 x aCDOM x pH x Ammonium x Nitrate x Phosphate x Silicate x x Total N x x Total P x x Phytoplankton species information * only Phaeocystis sp. x* x x 9 D3.5: Aquaculture Indicators Report 31/10/2014 2.1
Portugal 2.1.1 Description of region In Portugal there are two aquaculture test sites, both located in the administrative region of the Algarve. The region continues to be the most relevant for aquaculture in Portugal, and in the year 2010 the production of clams contributed to 45% of the total national aquaculture production (INE, 2011). One of the study sites is located in Faro in the centre of the Algarve (Figure 2). Located in the middle of the Ria Formosa (RF) lagoon, the test site is an intertidal bivalve aquaculture farm belonging to Viveiros Ana Manjua Unipessoal, a famlily based company that runs for more than 40 years and its main production are clams (Ruditapes decussata) and Oysters (Ostrea edulis and Crassotrea spp.). The RF lagoon is part of the Ria Formosa Natural Park and its importance is internationally recognized and integrates the RAMSAR Convention and the Natura Network 2000 (Newton and Mudge, 2003). Ria Formosa is classified as mesotidal shallow lagoon (Bettencourt et al., 2004). Figure 2 Location of the Portuguese sites The second test site is located in Sagres in the Southwest Portugal (Figure 2). This area has been included as part of the Southwest Alentejo and Vicentine Coast Natural Park (PNSACV) since 1995. Recently, in 2011 the Natural Park Management Plan (POPNSACV) was published. The Portuguese Institute for the Nature and Forest Conservation (ICNF) is the responsible for the management and surveillance of the park, in articulation with the Maritime Police, and therefore as defined in POPNSACV, article nº 76; 1) the implantation of Marine cultures within the marine area of the park is subject to evaluation of the park authorities. Within the marine park is only allowed to develop aquaculture in the extensive or semi-­‐intensive regime and use exclusively indigenous species; and a regular monitoring plan of the water quality, sediments and benthic community has to be established. The area has minimal anthropogenic eutrophication due to the low resident population of 7000 inhabitants in an area of 179.76 km2. There are also no permanent rivers in this area, only torrential streams. Natural eutrophication in the form of upwelling episodes constitutes the main source of nutrients in these waters (Loureiro et al., 2011). The normal predominant wind is from the NW quadrant (Fragoso and Icely, 2009), which creates favorable conditions for the occurrence of upwelling (Loureiro et al., 2005). July is the month that 10 D3.5: Aquaculture Indicators Report 31/10/2014 shows the highest distribution of intensity of the Upwelling Index (Ramos et al., 2013). The water temperature is highly dependent on the wind direction and intensity. Average temperature values range the 14.9°C to 18.1°C (Ramos et al., 2012), average of 17°C reported between 2005-­‐2007 and minimum reported of 13.1°C and a maximum 23.7°C (Fragoso and Icely, 2009). Temperature fluctuations can be very dramatic in this area for instance a decrease of 5.6 °C degrees was reported in a 4 days period (Fragoso and Icely, 2009). This area is classified as a mesotidal, moderately exposed, Atlantic Coast (Bettencourt et al., 2004). Although it is considered a moderately exposed area, with waves ranging from 1.6 m to 2 m (Ramos et al., 2012) occasionally high energy waves can affect the area, a good example was the winter of 2013/14 during the Hercules storm, with waves higher than 10 m. Normally the water column is well mixed, but stratification events can occur with differences higher than 4°C between the surface and bottom. The Secchi depth is regularly recorded at the Finisterra S.A. aquaculture site ranging between 7 to 12 m (Goela et al., 2013) although values of 25 m were already measured. 2.1.2 Parameters and methods The parameters gathered for the Portuguese area are summarized in Table III. For details on methods please see document D5.1 IS data quality control. Table III In situ data available for the Portuguese site area Parameter Units Chlorophyll mg/m Feb. 2005 -­‐ Apr. 2014 Nr stations stations 5 Temperature º C Feb. 2005 -­‐ Oct. 2014 5 170 Salinity Feb. 2005 -­‐ Oct. 2014 5 153 Secchi depth m Feb. 2005 -­‐ Oct. 2014 2 91 3
-­‐1
Date Total measures meameasures 178 Kd 490 m Oct. 2008 -­‐ Mar. 2012 3 74 Wind Direction Sept. 2005 -­‐ Feb. 2008 1 63 -­‐1
aCDOM (443) m Oct. 2008 -­‐ Mar. 2012 3 81 pH Mar. 2014 -­‐ Oct. 2014 1 25 Oxygen mg/L Mar. 2014 -­‐ Oct. 2014 1 23 SPM mg/L Sept. 2011 -­‐ Sept. 2014 4 96 PIM mg/L Oct, 2013 -­‐ Sept. 2014 1 24 POM mg/L Oct. 2013 -­‐ Sept. 2014 1 24 Phytoplankton counts cell/ml Sept. 2008 -­‐ Mar. 2012 3 93 11 D3.5: Aquaculture Indicators Report 31/10/2014 2.2
The Netherlands 2.2.1 Description of region The Dutch case focuses on the shellfish culture that takes place in the estuarine waters in the southwest of the Netherlands, in the Oosterschelde (Easter Scheldt) estuary, and in the shallow Wadden Sea in the North of the country (Figure 3). Both areas are in direct connection with the North Sea. Figure 3 Location of the main research sites, Wadden Sea and Oosterschelde on backdrop map of average MERIS-­‐derived North Sea chlorophyll concentration for 2003 (based on a geometric mean derived from imagery of 15 February -­‐ 15 November 2003, source: Peters et al., 2005). The Oosterschelde is a shallow intertidal estuary characterised by relatively clear water. The entire region is impacted by the Delta Works, the vast estuarine engineering works that aim to prevent future flooding after the 1953 event. One of its masterpieces, the (normally open) storm surge barrier serves to protect against extreme events, whilst it enables the regular tidal movements required to sustain the tidal ecology of the Oosterschelde estuary, which is vital for aquaculture and fishing industry. The shellfish farming is primarily based on bivalves that are born in the wild (natural spatfall) and feed mainly on phytoplankton and less on resuspended microphytobenthos provided by the natural environment in which they are cultured (Prins et al., 1996). The bottom shellfish culture mainly consists of juvenile or adult native blue mussels (Mytilus edulis) and European flat oysters (Ostrea edulis), or foreign Pacific oysters (Crassostrea gigas) which are placed or relayed on the bottom for on-­‐growing in these shallow intertidal and subtidal areas with relatively low wave action. 12 D3.5: Aquaculture Indicators Report 31/10/2014 Similarly, the Wadden Sea, comprises of a large intertidal zone, and exchanges water with the North Sea through 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). Pacific oysters have been introduced into the Wadden Sea since the 1970s (Troost, 2010). Subsequently, natural spatfalls occurred and wild oyster populations became established (Diederich et al., 2005). These shellfish 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., 1998; Markert et al., 2006). Phytoplankton can also negatively impact these systems, notably in the case of HABs, when the harmfulness is of the algal blooms are related to the high biomass (Figure 4), leading to anoxia. Trophic significance of Phaeocystis depends on its size and form. Solitary cells can be filtered and ingested and act as a food source. Colonies are difficult to handle and result in reduced clearance rates and consequent food deprivation at higher densities (Smaal and Twisk, 1996). Peperzak and Poelman (2008) reported on an incidence in 2001 where anoxia caused by a bloom of Phaeocystis globosa had an estimated 15 to 20 million EUR impact (Peperzak and Poelman, 2008). Figure 4 The wax and wand of North Sea phytoplankton blooms, illustrated by monthly average North Sea chlorophyll concentrations (based on a geometric mean derived from imagery of 2003, source: Peters et al., 2005). Van der Woerd et al. (2011) conducted preliminary monitoring of these blooms, aided by remote sensing. The quantitative relevance of satellite information would be much higher if Phaeocystis detection in the optical reflection signal can be achieved. However, the pigment composition of Phaeocystis is not very different from that of other fucoxanthin-­‐ and fucoxanthin-­‐derivative-­‐
containing phytoplankton groups (Astoreca et al., 2009). Also its characteristically high scattering will not easily facilitate discrimination from diatoms in sediment-­‐laden waters (Peeters er al., 1991; Baretta-­‐Becker et al., 2009). Finally, the beds are located in areas that are difficult to monitor with 13 D3.5: Aquaculture Indicators Report 31/10/2014 state-­‐of-­‐the art case-­‐2 ocean colour remote sensing techniques, because the 300 x 300 m full resolution imagery is relatively course (Figure 5), and the optical model is likely impacted by bottom view, nearby tidal flats, or extreme case-­‐2 conditions with highly variable specific inherent optical properties (Hommersom et al., 2010; Van der Wal et al. 2010; Eleveld et al., 2014). Therefore, the focus has thus far been on blooms approaching from the North Sea, and on empirical algorithms that rely on training and likely benefit from harmful algal blooms being accompanied by other features that also have an optical impact on radiance or reflectance so that we can map them indirectly (Kurekin et al., 2014) Figure 5 The Oosterschelde estuary with sampling stations for chlorophyll, suspended matter and primary production measurements indicated by a red star. Mussel culture plots in the western and central parts are indicated in purple and oyster culture plots in the east (in orange) (Smaal et al., 2013). 2.2.2 Parameters and methods In situ data gathered for the Dutch coast were made available by the National monitoring program at http://live.waterbase.nl/waterbase_wns.cfm?taal=en. The list of parameters used in this document are listed in Table IV. 14 D3.5: Aquaculture Indicators Report 31/10/2014 Table IV In situ data gathered for the Dutch coast. Parameter Units Date Nr stations Total measures Chlorophyll µg/l Jan. 1997 -­‐ Feb. 2014 48 9368 Temperature º C Jan. 1997 -­‐ Dec. 2012 4 697 Salinity DIMSLS Jan. 1997 -­‐ Dec. 2012 43 8373 Oxygen mg/l Jan. 1997 -­‐ Dec. 2012 43 8603 TSM mg/l Feb. 1997 -­‐ Dec. 2012 42 8715 Secchi depth dm Jan. 1997 -­‐ Dec. 2012 23 4590 DOC mg/l Jan. 1997 -­‐ Dec. 2012 43 8829 Dissolved N mg/l Jan. 1997 -­‐ Dec. 2012 39 8047 Dissolved P mg/l Jan. 1997 -­‐ Dec. 2012 43 8435 Silicate mg/l Jan. 1997 -­‐ Dec. 2012 43 8829 Phaeocystis cell/l Jan. 2009 -­‐ Sept. 2011 35 633 2.3
Denmark 2.3.1 Description of region The aquaculture facilities in Denmark are located in the western Baltic Sea in the transition area between the North Sea-­‐Skagerrak and the Baltic Sea proper. Most marine aquaculture sites are found in the waters between the northwestern and southwestern Baltic Sea, but since recently a test facility is also operated at Bornholm (Figure 6). Figure 6 Location of marine aquaculture in Denmark 15 D3.5: Aquaculture Indicators Report 31/10/2014 The region has a high variability in physical, chemical and biological parameters both on spatial (horizontal and vertical) and temporal scales. Being the transition between the oligosaline Baltic Sea proper and the saline North Sea, the waters are characterized by a more or less sharp pycnocline separating the north-­‐going low-­‐saline surface waters from the south-­‐going more saline bottom waters. Close to the coasts and in the fjord (Danish word for estuary) freshwater input does also play a role in stratification. Periodically, strong winds interrupt the stratification – this happens in particular during autumn and wintertime. Besides salinity, stratification is also caused by vertical temperature variations caused by both the different origins of the surface and bottom waters and insolation heating the surface waters. Vertical distribution of nutrients and Chl follow the stratification. The nutrient concentrations of the surface waters reach minimum after the spring bloom and do not increase persistently until autumn when wind mix nutritious bottom water meaning that primary production is dominated by regenerated production. Shorter periods of nutrient enhancement do occur after heavy rainfall (nearshore and in estuaries) and entrainment of bottom water. The nutrient concentrations of the bottom waters are nearly always nutrient rich. The Chl in the surface follows the usual temperate seasonality with a spring bloom, a pre-­‐summer decline, and one or more maximums during summer-­‐autumn. Blooms, which are noxious to aquaculture, are particularly common in late winter and post the diatom spring bloom. The blooms are formed by flagellates like Dictyocha, Chrysochromulina and Pseudochattonella. The latest large bloom took place in 2011 and was formed by Pseudochattonella farcimen (Eikrem et al., 2009) Eikrem. Fortunately, the bloom occurred before the juvenile fish was inoculated into the fish cages. Earlier blooms of Chrysochromulina and Pseudochattonella in the 1980th and 1990th have caused major fish mortality due to oxygen depletion and/or algal toxins. During summer-­‐autumn other harmful species are also observed but for many years no events hazardous to aquaculture have occurred. Figure 7 Pseudochattonella farcimen (Eikrem et al., 2009) Eikrem. Foto from http://nordicmicroalgae.org 2.3.2 Parameters and methods Measurements at the aquaculture sites are few and not done regularly. Thus the most comprehensive data set derives from the Danish monitoring program NOVANA. The NOVANA program measures vertical profiles of salinity, temperature, light, oxygen and flurorescense using sensors. In addition water samples are collected for analyses of nutrients and chlorophyll and secchi depth is observed. The methodologies follow the national NOVANA guidelines (which are compliant with HELCOM and OSPAR guidelines). The parameters gathered for the Danish region are listed in Table V. 16 D3.5: Aquaculture Indicators Report 31/10/2014 Table V In situ data available for the Danish area Parameter Units Date Nr stations Total measures Chlorophyll µg/l Jan. 1990 -­‐ July 2014 51 11861 Temperature º C June 2007 -­‐ July 2014 15 1295 Kd 1/m Jan. 2002 -­‐ June 2014 401 25252 SPM mg/l Jan. 2002 -­‐ July 2014 40 3682 Ammonium µg/l Jan. 2002 -­‐ Sept. 2014 47 7294 Nitrate µg/l Jan. 2002 -­‐ Sept. 2014 42 8111 Phosphate µg/l Jan. 2002 -­‐ Sept. 2014 49 8582 Silicate µg/l Jan. 2002 -­‐ Sept. 2014 47 7624 Total N µg/l Jan. 2002 -­‐ Sept. 2014 49 8659 Total P µg/l Jan. 2002 -­‐ Sept. 2014 49 8532 2.4
Norway 2.4.1 Description of region The main test site in Norway is located in Sandsfjorden system. The Sandsfjord system is in the inner Ryfylke area, western Norway, and is composed of three fjordbranches, Sandsfjorden, Hylsfjorden, and Saudafjorden (Figure 8). The fjord system has a threshold of about 110 m just outside Nævøy and the deepest part, about 500 m depth, is situated in Hylsfjorden. Due to high freshwater input and restricted exchange with larger fjord areas outside, this fjord system is characterized by a marked brackish surface layer. The temporal variation of the freshwater run-­‐off is strongly influenced by the hydro electrical power plants in some of the main rivers in the area, which means that the freshwater run-­‐off may deviate from the natural patterns typical for this region. Besides salinity, the variability in fresh water run-­‐off results in an upper layer with much higher variability in temperature, oxygen, turbidity and nutrients concentrations, compared to the underlying coastal waters. 17 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 8 Study area in South-­‐western Norway. Red symbols indicate grow-­‐out sites (salmon, trout and rainbow trout), green symbols denote egg production sites (salmon, trout and rainbow trout), orange symbols denote production sites for fish other than salmnids, and blue denote shellfish sites. Purple squares denote slaughter sites. User’s sites are named (Teigane and Ommundsteigen). The fjords in Southwest Norway are classified as oligotrophic areas (Jansen et al., 2011). Due to the stratified water column and calm wind conditions there is restricted vertical mixing of nutrients into the euphotic layer (Aure et al. 1996; Asplin et al. 1999) and the euphotic zone is nutrient limited for extended periods of the year (Paasche and Erga, 1988; and Sætre, 2007). This fjord system is known for repeated blooms of the toxic haptophyte Prymnesium parvum, which historically has caused large fish mortalities in the system (Kaartvedt et al. 1991; Johnsen et al. 2010). This species is a brackish water species, and during the blooms in the Sandsfjorden system P.parvum is largely confined to the upper layer (Kaartvedt et al. 1991; Johnsen et al. 2010). Other algae species known to be harmful for finfish such as Chysochromulina spp. (Dahl et al. 1989), Karlodinium veneficum (Deeds et al. 2006), and Chaetoceros wighamii (Bruno et al. 1989; Treasurer et al. 2003), are regularly occurring in this fjord but there are no known cases where these species are the confirmed cause of mortality. The User in the region, Erfjord Stamfisk AS, is an egg producer and due to fish health considerations egg productions sites are located away from other aquaculture activities. However, the larger region has considerable aquaculture activities (Figure 8). The User Hortimare is located northern in the coast and is specialized in seaweed production. 2.4.2 Parameters and methods Temperature, salinity, oxygen and Chl fluorescence were measured using a profiling YSI probe. The probe was mounted on a rig inside the net pens of the farm. Secchi depth was measured according to standard methods. The Secchi depth is the depth at which we cannot distinguish the white disk anymore because it blends in the background (Preisendorfer, 1986). 18 D3.5: Aquaculture Indicators Report 31/10/2014 Water samples for analysis of phytoplankton abundance and species composition were taken at the farm site (Figure 8) at 0-­‐4 m and 6 m depth, using a hose. All analyses were carried out on an inverted microscope according to a modified Utermöhls method (NS-­‐EN 15972:2011). Cell numbers were transferred to NIVA’s marine algae database, PhytoMar, where calculations were done and the data were stored. Upon submission to PhytoMar the biovolume of each species is calculated according to HELCOM 2006 (Olenina et al. 2006). The biovolume is hereafter used to calculate cell carbon according to Menden-­‐Deuer and Lessard (2000). The parameters gathered are listed in Table VI. Table VI In situ data available for the Norwegian site area Parameter Units Date Nº stations Total measures Chlorophyll June 2007 -­‐ May 2010 2 3706 º C Feb. 2007 -­‐ May 2010 2 4638 DIMSLS Feb. 2007 -­‐ May 2010 2 4638 Feb. 2007 -­‐ May 2010 2 950 Diatoms µgC/l Mar. 2007 -­‐ Sept. 2014 2 167 Dinoflagellates µgC/l Mar. 2007 -­‐ Sept. 2014 2 167 Phytoflagellates µgC/l Mar. 2007 -­‐ Sept. 2014 2 167 Temperature Salinity Secchi depth 2.5
United Kingdom 2.5.1 Description of region The user in the UK is a salmon farm located in the Isle of Skye, in the inner Hebrides, west of Scotland (Figure 9). Overall, the Scottish Coastal Current (SCC) carries northward water from the Irish and Clyde Seas (Knight and Howarth, 1999). This current suffers a slight dilution by the fjords of the western coast of Scotland (McKay et al., 1986). The mean flow rate is approximately 3km d-­‐1 (McCubbin et al., 2002). From the analysis of 36 years of current meter and temperature data, it has been found that tides are the dominant mode of variability in the flow, whereas seasonal atmospheric forcing controls the temperature (Inall et al., 2009). Seasonality of nutrients related to the mixing and stratification, with higher concentrations in winter and decreased in the summer, due to consumption by phytoplankton. Chlorophyll concentration seasonal variations have a spring and autumn blooms (Figure 10). Concerning Harmful Algae Blooms species in the area, Karenia mikimotoi has regularly been identified in Scottish waters, with cell densities typically achieving five thousand cells per litre during the summer months, with few environmental consequences. This is in marked contrast to the regular blooms of shellfish toxin-­‐producing species in the region, with Pseudonitzschia being of particular recent concern. In 2006 a K. mikimotoi was detected concurrently from in situ and satellite with a peak in the Isle of Skye in August (Figure 11). 19 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 9 Location of the Scottish Salmon farm in the Isle of Skye and oceanography of the area (Scotland's Marine Atlas, 2011). Figure 10 Seasonality of chlorophyll concentrations for different waters masses in the western shelf and off-­‐
shelf areas (Kennington and Jones, 2006). Data for the off-­‐shelf and shelf regions are monthly mean concentrations from the ICES data-­‐sets 1970-­‐ 1996. 20 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 11 Time-­‐series of K. mikimotoi development at selected sites (Davidson et al., 2009). 2.5.2 Parameters and methods Monitoring at this site has only started in 2014 using an automatic using a commercial sonde. Previous data exist but record is unavailable. The current sonde measures in real time sea surface temperature, salinity, fluorescence and turbidity. It is trial with on-­‐line access to the Aqua-­‐users project, but no regular maintenance or calibration seems to be in place, so data should be used with caution. Data are listed in Table VII but its analysis is not reported in the following sections as further quality control is still required. Secchi disk record has also started in 2014, and regular sampling exists. There is also qualitative microscopic monitoring of presence/non presence of HABS that is being logged in the WISPweb along with the uploaded WISP data. Table VII In situ data available for the UK site. Parameter Units Date Nº stations Total measures Chlorophyll µg/l Mar. 2014 -­‐ May 2014 1 69 Temperature º C Mar. 2014 -­‐ May 2014 1 69 Salinity psu Mar. 2014 -­‐ May 2014 1 69 ODO Pct sat Mar. 2014 -­‐ May 2014 1 69 ODO mg/L Mar. 2014 -­‐ May 2014 1 69 Externa pwr/V Mar. 2014 -­‐ May 2014 1 69 WindDir Degrees Mar. 2014 -­‐ May 2014 1 68 WindSpeed m/s Mar. 2014 -­‐ May 2014 1 68 AirTemp º C Mar. 2014 -­‐ May 2014 1 68 BaroPress hPa Mar. 2014 -­‐ May 2014 1 68 RHumidity pcent Mar. 2014 -­‐ May 2014 1 68 21 D3.5: Aquaculture Indicators Report 31/10/2014 3
Satellite data Measuring the oceans by remote sensing has proven to be a unique resource to achieve local to global scale observations of a vast range of oceanic parameters (Robinson, 2007). Ocean Colour provides estimates of chlorophyll-­‐a concentration (Chl), which is a proxy for phytoplankton biomass and ocean primary productivity (e.g., Behrenfeld and Falkowski, 1997), and thus, it can be used as an ecological indicator for estuaries and coast sites (Platt et al., 2010). 3.1
Description of Data Several data records provided by satellite sensors are used throughout this report. Here, we used Chl estimates 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 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 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; to develop the proposed methodology, not requiring great computational effort; and 2) it provides reliable Chl phenology patterns for the large scale regions that enclosure the AQUA-­‐USERS user sites, contributing to the understanding of Chl variability regionally. The obtained MERIS-­‐FR data ranges from 19th july 2002 to 8th April 2012, and comprises of Chl estimates on a 300 m resolution for all sub regions defined under the AQUA-­‐USERS project. In the present report these data are only used for the Portuguese subregions (Iberia and Algarve) and UK (southwest of English Channel). For Portugal, 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. . For the UK, OC4v6 algorithm for Chla was used (O’Reilly et al, 2000). 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 is comprised of daily matrices that represent the ocean with a grid of 4.6 km2 per pixel. The temporal coverage used was the same as the OC-­‐CCI record. The regions of interest were defined as listed in Table VIII. 22 D3.5: Aquaculture Indicators Report 31/10/2014 Table VIII Coordinates of the areas defined for the satellite analysis. Site Minimum latitude Maximum latitude Minimum longitude Maximum longitude Country Iberia 37.5 41.5 -­‐8.5 -­‐10.5 Portugal Algarve 36.5 37.5 -­‐7.5 -­‐10.0 Portugal Cornwall 49.0 52.0 -­‐2.0 -­‐7.0 U.K. Scotland 55.0 60.0 -­‐4.0 -­‐9.0 U.K. NL/NO/DN* 50.0 62.0 0.0 12.0 Netherlands, Norway and Denmark * includes Waddenzee, Oosterschelde, Fureholmen (Hortimare), Ommundsteigen, Teigane. 3.2
Parameters Satellite derived parameters used in this report are listed in Table IX. Table IX List of satellite derived parameters. Parameter Acronym Source Spatial resolution Algorithm Chlorophyll-­‐a Chl OC-­‐CCI 4.6 km Oc4v6 Chlorophyll-­‐a Chl MERIS-­‐FR 300 m OC5 Diffuse attenuation coefficient at 490 nm Kd 490 MERIS-­‐FR 300 m Absorption due to gelbstof and detrital material at sensor wavelength 443 nm adg 443 MERIS-­‐FR 300 m Suspended particulate matter concentration SPM MERIS-­‐FR 300 m Pathfinder 4.6 km Sea surface temperature SST 23 D3.5: Aquaculture Indicators Report 31/10/2014 4
Phytoplankton Phenology Remote sensing of ocean colour constitutes an invaluable tool that allows the construction of time series at multiple spatial scales of phytoplankton biomass indexed as chlorophyll-­‐a concentrations, which can be used to assess a variety of ecological indicators (Platt et al, 2010). To identify the typical seasonal cycle of a region is a first step to detect anomalies of phytoplankton biomass and potential occurrence of an anomalous algal bloom, constituting an early warning for aquacultures, or to detect productive areas essential for aquaculture site selection. With that in view, an analysis of algal bloom phenology was performed based on historical satellite data for all the targeted regions. 4.1
Method Phytoplankton phenology was calculated from a computed annual climatology of the Chl data. The Chl climatology was calculated for both Chl 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 variation throughout the year for each pixel (Figure 12). 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 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 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 as illustrated in Figure 12. Computed phenology aspects were then ploted spatially for each grid point. Figure 12 Filtered and non-­‐filtered Time series of satellite driven chlorophyll-­‐a off the Iberia coast (latitude 41 ºN, longitude 10 ºW) showing the annual average and some of the phenology characteristics computed (bloom duration, area and maximum, and when occurred the maximum growth). 24 D3.5: Aquaculture Indicators Report 31/10/2014 4.2
Results 4.2.1 Portugal Results on bloom phenology are presented for the whole Iberia region, but focused in the south of Portugal, where the aquaculture sites are located. The annual Chl average for the Iberia region reveals maxima along the coastline, and an offshore latitudinal gradient with higher Chl mean in the northern areas (Figure 13). The bloom duration (in days), day of bloom start and bloom maximum (mg chlorophyll-­‐a m-­‐3) are plotted for the Algarve area (Figure 13). 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 (Figure 13). Figure 13 Climatological annual averages of chlorophyll a for the Portuguese coast (top left, from OC-­‐CCI data). Bloom duration (top right, from MERIS-­‐FR), day of bloom start (bottom left, from MERIS-­‐FR) and, maximum (bottom right, from MERIS-­‐FR) for Algarve sub-­‐region. Arrow shows Cape S. Vicente. 4.2.2 UK For the UK region, using the OC4v6 algorithm, results for the English Channel show average chlorophyll-­‐a values similar for the Cornwall and Scottish sub-­‐sets (Figure 14). South of Cornwall, where Karenia mikimotoi blooms have been described, summer blooms are identified (Julian day 189-­‐240) with Chl maxima surpassing 10 mg m-­‐3 and approximate bloom duration of 30-­‐60 days (Figure 14). 25 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 14 Climatological annual averages for chlorophyll a for the UK coast (top left, from OC-­‐CCI data). Bloom duration (top right, from MERIS-­‐FR), day of bloom start (bottom left, from MERIS-­‐FR) and, maximum (bottom right, from MERIS-­‐FR) for Cornwall sub-­‐region. The Scottish area reveals lower Chl concentrations maxima (lower than 10 mgm-­‐3), with blooms occurring earlier, in spring (Julian day 90-­‐150) and lasting longer (Figure 15). It should be taken in consideration that analyses in the Scottish area were done with OC-­‐CCI data that have courser resolution than the MERIS-­‐FR. 26 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 15 Climatological annual averages of chlorophyll a (top left), bloom duration (top right), day of bloom start (bottom left) and, maximum (bottom right) for the Scottish sub-­‐region. Analysis performed with OC-­‐CCI data. 27 D3.5: Aquaculture Indicators Report 31/10/2014 4.2.3 The Netherlands, Denmark and Norway Figure 16 Climatological annual averages of chlorophyll a (top left), bloom duration (top right), day of bloom start (bottom left) and, maximum (bottom right) for the North Sea region. Analysis performed with OC-­‐CCI data. The average annual Chl varied regionally, with lower concentrations in the Norwegian area and higher Chl concentrations in the Netherlands area, namely in the Wadden Sea sub-­‐area, and in the Danish coast (Figure 16). Chlorophyll maxima are particularly high, above 10 mg.m-­‐3, in the coast of Holland and in the Baltic coast of Denmark. All regions have early spring blooms, with the exception of the northern part of the region close the coast of Norway where blooms seem to occur in summer. We are aware that OC-­‐CCI data used in this analysis, and the retrieval algorithms might not be ideal for the optically complex waters occurring along the Dutch Coast. In the implementation phase WP 4.3 the AQUA-­‐USERS satellite archive will be applied to these areas. 28 D3.5: Aquaculture Indicators Report 31/10/2014 5
5.1
Phytoplankton response to environmental conditions The Netherlands Figure 17 Monitoring stations location. Station code: 1 'beerknmdn', 2 'bochtvwtm', 3 'boomkdp', 4 'dantzgt', 5 'doovbwt', 6 'goere2', 7 'goere6', 8 'grootgnd', 9 'hammot', 10 'hanswgl', 11 'harvss', 12 'huibgot', 13 'ijmdn1', 14 'lauwohvmd', 15 'lodsgt', 16 'marsdnd', 17 'noordwk10', 18 'noordwk2', 19 'noordwk20', 20 'noordwk70', 21 'rottmpt3', 22 'rottmpt50', 23 'rottmpt70', 24 'schardkdppt', 25 'schouwn10', 26 'ternzbi20', 27 'terslg10', 28 'terslg100', 29 'terslg135', 30 'terslg175', 31 'terslg235', 32 'terslg4', 33 'terslg50', 34 'vliesm', 35 'vlissgbissvh', 36 'walcrn2', 37 'walcrn20', 38 'walcrn70', 39 'wielgn', 40 'wisskke', 41 'zoutkplzgt', 42 'zuidolwot' Analysis of monitoring stations (Figure 17) revealed stations 4 and 13 as the ones with the highest Chl concentrations in the Dutch region (Figure 18, top). On the other hand, the highest nitrogen concentrations were observed at stations 2, 8, 10 and 26 (Figure 19, bottom). From all stations indicated so far, 4 is the only one which is mostly marine (median salinity of 30; Figure 19, top). In the Wadden Sea area, the largest blooms of Phaeocystis were observed at stations 2 and 4, as well as relatively large blooms at stations 16 and 21, located in the inlets (Figure 18, bottom). In the estuary of Oosterschelde (Easter Scheldt) the largest blooms of Phaeocystis occurred at station 35. Phaeocystis blooms are also frequent in the ‘bay’ (voordelta) just off shore of the Oosterschelde. 29 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 18 Box plot of Chlorophyll in logarithmic scale (top) and Phaeocystis cell counts in cell/L (bottom). See location of stations in Figure 17. 30 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 19 Box plot of Salinity in psu (top) and Dissolved nitrogen in mg/L (bottom) for the Netherlands monitoring stations. See location of stations in Figure 17. 31 D3.5: Aquaculture Indicators Report 31/10/2014 In situ data indicate that the highest Chl concentrations are observed for temperatures higher than 5 ºC and for a wide range of salinities, i.e. from 0 to higher than 30. Some of the phytoplankton blooms observed in the Dutch area, especially the ones with chlorophyll concentrations between ~5-­‐45 mg.m-­‐3 were caused by large Phaeocystis blooms (Figure 20). Phytoplankton blooms with concentrations higher than 50 mg.m-­‐3 were not caused by Phaeocystis. In situ data also indicate that Phaeocystis blooms frequently occur with salinities higher than 20, especially higher than 26-­‐27. It is likely that Phaeocystis blooms occured when salinities were higher than average at station 2. Unfortunately, data on temperatures during Phaeocystis blooms are not available. 3
Figure 20 Chlorophyll a concentrations (mg/m ) against temperature (ºC) and Phaeocystis abundance (cells/L), 3
as well as salinity against chlorophyll a concentrations (mg/m ) and Phaeocystis abundance (cells/L). All stations considered in this graph. Points to consider: Given the high concentration of nutrients (nitrogen and phosphate), as well as high salinity values observed at station 2, this location can be at risk of suffering from the negative effect of Phaeocystis blooms. Phaeocystis blooms could be observed when temperatures are higher than 5 and salinities higher than 20. 32 D3.5: Aquaculture Indicators Report 31/10/2014 5.2
Denmark (DK) Figure 21 Monitoring stations location. Station code: S is the location of the site, 1 'ARH170002', 2 'ARH170006', 3 'ARH170117', 4 'ARH190004', 5 'FRB1877', 6 'FRB1939', 7 'FRB1993', 8 'FRB2013', 9 'FYN6100016', 10 'FYN6100051', 11 'FYN6300043', 12 'FYN6500033', 13 'FYN6500053', 14 'FYN6700009', 15 'FYN6700053', 16 'FYN7301703', 17 'FYN7701207', 18 'KBH1723', 19 'KBH1942', 20 'KBH431', 21 'KBK3002', 22 'KBK3005', 23 'NOR40302', 24 'NOR409', 25 'NOR4410', 26 'RKB43', 27 'ROS1727', 28 'SJYKFF5', 29 'STO0101023', 30 'STO0101047', 31 'STO0101124', 32 'STO0103052', 33 'STO0601088', 34 'STO0801008', 35 'STO0801049', 36 'VEJ0004273', 37 'VEJ0005790', 38 'VEJ0006489', 39 'VEJ0006870', 40 'VEJ0006883', 41 'VSJ20004', 42 'VSJ20925', 43 'VSJ30002', 44 'VSJ30006', 45 'VSJ41007', 46 'VSJ41008', 47 'VSJ42009', 48 'VSJ43010', 49 'VSJ45012', 50 ' VSJ50015', 51 'VSJ53016' Monitoring stations around the Danish coast (Figure 21) revealed clear Chl concentrations variations across the Danish region considered in this project (Figure 22, top). Stations around the End-­‐User site are some of the most productive ones (Stations 37-­‐40). Temperatures and Kd values are generally similar in these monitoring stations, with some differences observed in nutrient concentrations (Figure 22 and Figure 23). Station 37, in the adjacent inshore area of the End-­‐User site, is the station with the highest nitrogen (Figure 23, top) and phosphate (not shown) concentrations reported. 33 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 22 Box plot of Chlorophyll in logarithmic scale (top) and Temperature in ºC (bottom) for the Danish monitoring stations. See location of stations in Figure 21. 34 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 23 Box plot of Dissolved nitrogen in μg/L (top) and Kd in m-­‐1 (bottom) for the Danish monitoring stations. See location of stations in Figure 21. 35 D3.5: Aquaculture Indicators Report 31/10/2014 3
-­‐1
Figure 24 Chlorophyll a concentrations (mg/m ) against temperature (ºC) and Kd (m ) for all stations and only for stations 38, 39 and 40. Points to consider: The highest concentrations of Chl (>20 mg.m-­‐3) were observed at temperatures lower than 3.5ºC, especially during late winter and early Spring (February-­‐April). This in agreement with the period of bloom initiation identified in the phenology section. This increase in phytoplankton biomass is likely to be associated with ice melting. Large phytoplankton blooms (>20 mg.m-­‐3) were also found to be associated with Kd values higher than 0.6 m-­‐1. Kd values higher than 1 and temperatures below 3.5ºC are likely to be associated with very large phytoplankton blooms (>40 mg.m-­‐3). 5.3
Norway (NO) The highest Chl concentration were observed at Ommunndsteigen, where they varied from ~0 up to 38 mg.m-­‐3. In situ data indicated that at Ommundsteigen the highest Chl concentrations (>15 mg.m-­‐3) were observed when temperatures were between 6 and 12.5ºC, which generally occur during late summer. This pattern was also observed in the bloom initiation analysis presented in the phenology section. This in situ dataset also provided an indication that diatoms respond positively to temperature, with the highest abundances of diatoms (>200 cells.mL-­‐1) observed at temperatures higher than 12.5ºC. 36 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 25 Box plot of Chlorophyll (in logarithmic scale) and temperature (ºC) for Norwegian sites. Station 1: Teigane, Station 2: Ommundsteigen; see location in Figure 8. 37 D3.5: Aquaculture Indicators Report 31/10/2014 3
Figure 26 Chlorophyll a concentrations (mg/m ) and diatom biomass (µgC/l)) against temperature (ºC) at stations Ommundsteigen and Teigane. 38 D3.5: Aquaculture Indicators Report 31/10/2014 6
Case Study – Sagres, Portugal Sagres site, within the Algarve sub-­‐region in Portugal, has been the subject of a study where several analyses have been performed and integrated, as a testing case. These analyses should then be implemented in the other regions of interest of the AQUA-­‐USERS project. 6.1
Methodology Following the same data preparation as mentioned in section 4 (Phytoplankton Phenology) for satellite derived Chl estimates, a filtered daily climatology covering the annual period per grid point was computed for SST (sea surface temperature), Kd 490 (diffuse attenuation coefficient at 490 nm), adg 443 (absorption due to gelbstof and detrital material at sensor wavelength 443 nm), and SPM (Suspended particulate matter concentration). For each grid point the climatological Chl time series was correlated with SST, Kd 490 adg 443 and SPM at zero lag, and at the best negative lag, i.e., shifting the time series to predict Chl. The best negative lag was computed using a simple cross-­‐
correlation method. Additionally, for each pixel, values of SST, Kd 490, adg 443, and SPM were computed when bloom initialized, ended, and their average during the bloom period. Further, MERIS-­‐FR derived Chl estimates were compared against in situ measurements at Sagres site. Several statistics were computed to assess the difference between both data, including root-­‐mean-­‐
square (RMS), bias (δ), coefficient of determination (r2), and mean raio (φ). For the in situ data, regression analyses were used to explore the dataset and evaluate annual and inter-­‐annual trends of biological and environmental data. In order to explore the relationships between the phytoplankton data and environmental conditions, a canonical correspondence analysis (CCA) was performed in the CANOCO 4.5 software. The statistical significance was evaluated through a Monte Carlo permutation test. This analysis is effective in revealing the relationships between the spatial and temporal structure of communities and the environmental factors that might be responsible for that. 6.2
Results Sagres site is situated at the south west of the Portuguese coast (37.018 ºN, 8.883 ºW) and is characterized by an annual mean of 1.36 mg.m-­‐3 Chl, that varies 3.72 mg.m-­‐3 Chl throughout the climatological year Figure 27. Figure 27 Annual average of MERIS-­‐FR derived Chl (calculated using the OC5 algorithm developed for coastal waters) showing the location of the Sagres site. 39 D3.5: Aquaculture Indicators Report 31/10/2014 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 seasonal varibility. The opposite was observed in the adjacent area (Figure 28). Figure 28 Map showing the correlation coefficients between Chl and SST for each grid point within the Algarve region. 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 was no smaller than 0.60. Nevertheless, stronger relationship was observed to follow closely the bathymetry lines mostly between the 100 -­‐ 250 m (Figure 29). Figure 29 Map showing the correlation coefficients between Chl and Kd 490 for each grid point within the Algarve region. 40 D3.5: Aquaculture Indicators Report 31/10/2014 A further analysis on the climatological time series for the 300 m2 area that includes Sagres site (Figure 30), indicate that no lag correlation between Chl and SST is better than the lag 0 correlation (r = 0.25) when shifting the SST time series backwards in time. Additionally, a siilar relationship was found between Chl and Kd 490 at the satellite derived value covering the 300 m2 that imcompass Sagres site (Figure 31). Figure 30 Climatological time series of satellite derived Chl and SST for the location of the Sagres site, ilustrating the identified bloom period as well as the day when the maximum growth was observed. 41 D3.5: Aquaculture Indicators Report 31/10/2014 Figure 31 Climatological time series of satellite derived Chl and Kd 490 for the location of the Sagres site, illustrating the identified bloom period as well as the day when the maximum growth was observed. To note that for this site satellite derived Chl estimates are significantly (P<<0.01) and strongly (r2 = 0.75) correlated to the in situ observations (Figure 32). Figure 32 Linear regression analysis between Chl estimates from MERIS-­‐FR and in situ observations at Sagres site (37.0 ºN, 8.9ºW). The number of matched observations between both sources was 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. In the figure is also shown the 1:1 relation (–). 42 D3.5: Aquaculture Indicators Report 31/10/2014 The in situ data correlation matrix indicated that total Chl is negatively associated with temperature and change in temperature during 1-­‐week period, as well as salinity and Secchi depth (Table X). These relationships are in agreement to what was presented above. Table X Correlation matrix for total chlorophyll a concentrations, change in temperature, temperature, salinity and Secchi depth. Correlations Marked correlations are significant at p < .05000 N=63
Variable
Means
Std.Dev.
TChla
dT
T
Sal
Tchla
0.94996
0.945967
1.000000
dT
-0.12540
2.378463
-0.435641
1.000000
0.478961 0.211103
0.348399
T
17.34921
2.481797
-0.442148
0.478961
1.000000 0.686902
0.351229
Sal
36.10952
0.171063
-0.396048
0.211103
0.686902 1.000000
0.523251
Secchi
10.30159
3.048717
-0.402786
0.348399
0.351229 0.523251
1.000000
-0.435641 -0.442148 -0.396048
Secchi
-0.402786
Diatoms were observed to be associated with low values of temperature and negative values of change in temperature (Figure 33). It is the main group contributing to pool of phytoplankton Chl. Some occurences of Phaeocytis were found to be associated with high values of temperatures and salinities. It is likely that Phaeocystis would occur after the bloom of diatoms, when waters become less turbulent and warmer. Other phytoplankton groups, such as coccolitophores and dinoflagellates seem to be associated with high temperatures and low concentrations of Chl. Figure 33 Canonical Correspondence Analysis (CCA) ordination diagram relative to data on absolute contributions of phytoplankton. Arrows refer to environmental variables (Temperature (Temperat), change in 43 D3.5: Aquaculture Indicators Report 31/10/2014 temperature (dTemp), salinity, Kd (490), total chlorophyll a (Tchla), Total Suspended Matter (TSM) and Coloured Dissolved Organic Matter (aCDOM (443)). The first two ordination axes represent 97.0% of the species-­‐
environment relationship and 19.0% of species variability. Satellite data has shown that Chl concentrations increase with high temperatures in the coastal waters off Sagres. Generally, the highest concentrations of chlorophyll a are observed during spring, with cooler temperatures, and summer, during occasional upwelling events when temperatures drop temporally but the overall temperatures are higher. In situ data indicate phytoplankton increase in response to decreased water temperatures in this region (Figure 34). For chlorophyll concentrations higher than 2 mg.m-­‐3, it is possible to identify two different groups, one that responds to occasional temperature decreases, which is likely to correspond to summer upwelling events, and another group which is not associated with any change in temperatures, which is likely to correspond to the spring bloom that occurs in lower temperature waters (Figure 34). These in situ data also indicate that Kd values in these waters are strongly associated with total chlorophyll a concentrations, which is also in accordances with the correlations presented using satellite data. -­‐3
Figure 34 Total chlorophyll a concentrations (mg/m ) against temperature (ºC), change in temperature in 1-­‐
-­‐1
week period (ºC) and Kd (m ). During summer, the highest Chl concentrations were observed in association with high decreases in temperature (Figure 35). The same pattern was observed for diatom abundances. Diatoms are 44 D3.5: Aquaculture Indicators Report 31/10/2014 known to proliferate in turbulent upwelling conditions in the Portuguese coast, which seems to be the case. 3
Figure 35 Total chlorophyll a concentrations (mg/m ) against change in temperature in 1-­‐week period (ºC), as well as diatom abundances (cells/mL) against change in temperature in 1-­‐week period (ºC). Only data from summer period was used. Points to consider: In general terms, the seasonal pattern of Chl seems to be strongly influenced by high water temperatures. In situ data has indicated that high phytoplankton biomass can be expected during spring, when water temperature is still ~15ºC. During summer, high Chl concentrations can be observed when there is a decrease of at least 2-­‐3ºC in water temperature 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, this dataset does not have enough resolution to evaluate them. 45 D3.5: Aquaculture Indicators Report 31/10/2014 7
Conclusions Phytoplankton spring bloom is a very important feature of phytoplankton dynamics in the coastal areas of Europe. Cornwall and Norway are the regions with later bloom initiation day. The other regions have a bloom initiation days between February and late April/May. Phytoplankton blooms in Norway are generally short. The highest chlorophyll a concentrations (>15 mg.m-­‐3) were observed when temperatures were between 6 and 12.5ºC. The highest annual Chl concentrations in our study regions were observed in the Netherlands and in Scotland, where the blooms were also the ones with higher duration periods (120-­‐210 days). In the Netherlands, high chlorophyll concentrations (> 20 mg/m3) were observed for temperatures higher than 5ºC and salinities from 0 to 30. Large Phaeocystis blooms have also been reported in the Netherlands. According to the analyses preented herein Phaeocystis blooms could be observed when temperatures are higher than 5 ºC and salinities higher than 20. In Denmark, high annual Chl concentrations were also observed. Generally, phytoplankton blooms occur early, in February-­‐March during ice melting conditions and have short bloom duration periods, of about 60 days or less. The highest concentrations of Chl (>20 mg.m-­‐3) were observed at temperatures lower than 3.5ºC (February-­‐April). Large phytoplankton blooms (>20 mg.m-­‐3) were also found to be associated with Kd values higher than 0.6 m-­‐1. Kd values higher than 1 and temperatures below 3.5ºC are likely to be associated with very large phytoplankton blooms (>40 mg.m-­‐3). The Portuguese 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. Phytoplankton blooms are also observed during summer, when water temperature is higher. Strong correlations between coastal sea surface temperature and chlorophyll concentrations were observed using satellite data, showing the overall seasonal pattern. In summer, negative correlations between in situ Chl and temperature have highlighted occasional upwelling events, with a temperature decrease of at least 2-­‐3ºC during a 1-­‐week period. Most phytoplankton blooms occuring due to upwelling events are dominated by diatoms. However, Phaeocystis blooms may also happen during high temperature and salinity conditions. This deliverable is a contribution for the development of indicators for aquaculture sustainability. It will provide the basis for task 4.3, which is dedicated to the implementation of aquaculture indicators. This report will also contribute for the development of methods for HAB detection, within the scope of Task 3.3. In addition, the detailed information provided by this deliverable (3.5) and future deliverable 4.3 will be explored to develop: 1) methods and maps for site selection according to food availability; 2) eutrophication indicators (risk of hypoxia and other symptons); 3) maps of riverine input, based on CDOM and SPM. 7.1
Future work The case study presented for Sagres represent a detailed method for the development of aquaculture indicators. The same approach will be applied, provided that available data are sufficient, to the other study sites in the Netherlands, Norway, Denmark and United Kingdom. This will include: 1) seasonal pattern for chlorophyll a, SST, Kd, SPM, wind, PAR and nutrients; 2) 90%ile analysis of main parameters (Chl); 3) spatial correlation between parameters; 4) Lag analysis; 5) regression analysis; and 6) Multivariate analysis. Results should allow the suggestion of integrated thresholds for all regions, both for site selection and for daily management. In the end, a ‘cookbook’, with a complete description of the methodology, should be produced to help future adjustments and further development of these indicators. 46 D3.5: Aquaculture Indicators Report 31/10/2014 8
References Asplin, L., Salvanes, A.G.V., Kristoffersen, J.B.(1999). Nonlocal wind-­‐driven fjord–coast advection and its potential effect on plankton and fish recruitment. Fisheries Oceanography, 8, 255-­‐263. Astoreca, R., Rousseau, V., Ruddick, K., Knechciak, C., Van Mol, B., Parent, J.Y., Lancelot, C. (2009). Development and application of an algorithm for detecting Phaeocystis globosa blooms in the case 2 southern North Sea waters. Journal of Plankton Research, 31, 287-­‐300. Aure, J., Molvær, J., Stigebrandt, A. (1996). Observations of inshore water exchange forced by a fluctuating offshore density field. Marine Pollution Bulletin, 33, 112-­‐119. Baretta-­‐Bekker, J.G., Latuhihin, M., Prins, T.C., Baretta, J.W. (2009). Long-­‐term temporal and spatial distribution of phytoplankton carbon biomass in the Dutch North Sea. Journal of Sea Research, 61, 50–59. Behrenfeld, M., Falkowski, P. (1997). A consumer’s guide to phytoplankton primary productivity models. Limnology and Oceanography, 42 (7), 1479–1491. Bettencourt A.M., Bricker S.B., Ferreira J.G., Franco A., Marques J.C., Melo J.J., Nobre A., Ramos L., Reis C.S., Salas F., Silva M.C., Simas T., Wolff W. J. (2004). Typology and Reference Conditions for Portuguese Transitional and Coastal Waters, Develoment of Guidelines for the Application of the European Union Water Framework Directive. 99 pp. Bruno, D.W., Dear, G., Seaton, D.D., (1989). Mortality associated with phytoplankton blooms among farmed Atlantic salmon, Salmo salar L., in Scotland. Aquaculture, 78, 217-­‐222. Casey, K.S., Brandon, T.B., Cornillon, P., Evans, R. (2010). The Past, Present and Future of the AVHRR Pathfinder SST Program, in (Eds.) Barale, V., Gower, J.F.R. & Alberotanza, L. Oceanography from Space: Revisited, Springer, pp. 273-­‐287. Cranford, P.J., Kamermans, P., Krause, G.; Mazurié, J., Buck, B.H., Dolmer, P., Fraser, D., Van Nieuwenhove, K., O'Beirn, F.X., Sanchez-­‐Mata, A., Thorarinsdóttir, G.G., Strand, Ø. (2012). An ecosystem-­‐based approach and management framework for the integrated evaluation of bivalve aquaculture impacts. Aquaculture Environment Interactions 2(3), 193-­‐213. Dahl, E., Lindahl, O., Paasche, E., Throndsen, J. (1989). The Chrysochromulina polylepis bloom in Scandinavian waters during spring 1988, Novel phytoplankton blooms. Springer, pp. 383-­‐405. Dankers, N., Zuidema, D.R. (1995). The role of the mussel (Mytilus-­‐Edulis-­‐L) and mussel culture in the Dutch-­‐Wadden Sea. Estuaries 18(1A), 71-­‐80. Davidson K., Miller P., Wilding T.A., Shutler J., Bresnan E., Kennington K., Swan S. (2009). A large and prolonged bloom of Karenia mikimotoi in Scottish waters in 2006. Harmful Algae, 8, 349-­‐361. Deeds, J.R., Reimschuessel, R., Place, A.R. (2006). Histopathological effects in fish exposed to the toxins from Karlodinium micrum. Journal of aquatic animal health, 18, 136-­‐148. Diederich, S., Nehls, G., van Beusekom, J.E.E., Reise, K. (2005). Introduced Pacific oysters (Crassostrea gigas) in the northern Wadden Sea: invasion accelerated by warm summers? Helgoland Marine Research, 59(2), 97-­‐106. Eikrem, W., Edvardsen, B. & Throndsen, J. (2009). Renaming Verrucophora farcimen Eikrem, Edvardsen et Throndsen (Research note). Phycological Research 57(3), 170. Eleveld, M.A., van der Wal, D., van Kessel, T. (2014). Estuarine suspended particulate matter concentrations from sun-­‐synchronous satellite remote sensing: Tidal and meteorological effects and biases. Remote Sensing of Environment, 143, 204–215. 47 D3.5: Aquaculture Indicators Report 31/10/2014 Fragoso B., Icely J.D. (2009). Upwelling events and recruitment patterns of the major fouling species on coastal aquaculture (Sagres, Portugal). Journal of Coastal Research, ICS 2009 (Proceedings), Lisbon, SI 56, 419 – 423. Goela, P.C., Icely, J.D., Cristina, S., Newton, A., Moore, G., Cordeiro, C. (2013). Specific absorption coefficient of phytoplankton off the southwest coast of the Iberian Peninsula: A contribution to algorithm development for ocean colour remote sensing. Continental Shelf Research, 52(0), 119 – 132. Gohin, F., Saulquina, B., Oger-­‐Jeanneret, H., Lozac'h, L., Lampert, L., Lefebvre, A., Rioud, P., Bruchond, F. (2008). Towards a better assessment of the ecological status of coastal waters using satellite-­‐derived chlorophyll-­‐a concentrations. Remote Sensing of Environment, 112 (8), 3329-­‐3340. Hommersom, A., Peters, S., van Der Woerd, H.J., Eleveld, M.A., de Boer, J. (2010). Chi-­‐square spectral fitting for concentration retrieval, automatic local calibration, quality control, and water type detection. Canadian Journal of Remote Sensing, 36 (6), 650-­‐670. Inall M., Gillibrand P., Griffiths C., MacDougal N., Blackwell K. (2009). On the oceanographic variability of the North-­‐West European Shelf to the West of Scotland. Journal of Marine Systems, 77, 210-­‐226. INE (2011). Estatísticas da pesca 2010. Instituto Nacional de Estatística, Lisboa, Portugal. Jansen, H., Strand, Ø., Strohmeier, T., Krogness, C., Verdegem, M., Smaal, A. (2011). Seasonal variability in nutrient regeneration by mussel Mytilus edulis rope culture in oligotrophic systems. Marine Ecology Progress Series, 431, 137-­‐149. Johnsen, T.M., Eikrem, W., Olseng, C.D., Tollefsen, K.E., Bjerknes, V. (2010). Prymnesium parvum: The Norwegian Experience1. JAWRA Journal of the American Water Resources Association, 46, 6-­‐13. Kaartvedt, S., Johnsen, T.M., Aksnes, D.L., Lie, U., Svendsen, H. (1991). Occurrence of the Toxic Phytoflagellate Prymnesium parvum and Associated Fish Mortality in a Norwegian Fjord System. Canadian Journal of Fisheries and Aquatic Sciences, 48, 2316-­‐2323. Kennington K., Jones, D. (2006). SAHFOS report on Plankton Ecology SEA7. 47pp. Knight, P.J., Howarth, M.J. (1999). The flow through the north channel of the Irish Sea. Continental Shelf Research, 19 (5), 693–716. Kurekin, A.A., Miller, P.I., van der Woerd, H.J. (2014). Discrimination of Karenia mikimotoi and Phaeocystis blooms in European coastal waters: merged classification of MODIS and MERIS ocean color data. Harmful Algae, 31, 163-­‐176. Loureiro, S., Newton, A., Icely, J.D. (2005). Microplankton composition, production and upwelling dynamics in Sagres during the summer of 2001. Scientia Marina, 69, 323-­‐341. Loureiro, S., Reñé, A., Garcés, E., Camp, J., Vaqué, D. (2011). Harmful algal blooms (HABs), dissolved organic matter (DOM), and planktonic microbial community dynamics at a near-­‐shore and a harbour station influenced by upwelling (SW Iberian Peninsula). Journal of Sea Research, 65(4), 401 – 413. Markert, A (2006). Untersuchungen der Lebensgemeinschaft eines durch die Pazifische Auster (Crassostrea gigas) neu entstandenen Austernriffs im Ostfriesischen Wattenmeer. Diplomarbeit, Freie Universität Berlin. McKay, W.A., Baxter, J.M., Ellett, D.J., Meldrum, D.T. (1986). Radiocaesium and circulation patterns west of Scotland. Journal of Environmental Radioactivity, 4, 205–232. 48 D3.5: Aquaculture Indicators Report 31/10/2014 McCubbin,D., Leonard, K.S., Brown, J., Kershaw, P.J., Bonifield, R.A., Peak, T. (2002). Further studies of the distribution of technetium-­‐99 and caesium-­‐137 in UK and European coastal waters. Continental Shelf Research 22, 1417–1445. Menden-­‐Deuer, S., Lessard, E.J. (2000). Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography, 45, 569-­‐579. Newton, A., Mudge, S.M. (2003). Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuarine, Coastal and Shelf Science, 57, 73 – 85. Olenina, I. (2006). Biovolumes and size-­‐classes of phytoplankton in the Baltic Sea. HELCOM Baltic Sea Environment Proceedings, 106, 144pp O’Reilly, J. E., Maritorena, S., Siegel, D., O’Brien, M. C., Toole, D., Mitchell, B. G., et al. (2000). Ocean color chlorophyll a algorithms for SeaWiFs, OC2, and OC4:. Tech. rep. In S. B.Hooker, & E. R. Firestone (Eds.), SeaWiFS postlaunch technical report series. Vol. 11. SeaWiFS postlaunch calibration and validation analyses, part 3. Greenbelt, Maryland: NASA, Goddard Space Flight Center. 923 pp. Paasche, E., Erga, S.R. (1988). Phosphorus and nitrogen limitation of phytoplankton in the inner Oslofjord (Norway). Sarsia, 73, 229-­‐243. Peeters, J.C.H., Haas, H., Peperzak, L. and Wetsteyn, L.P.M.J. (1991) Limiting factors for phytoplankton in the North Sea. Water Science and Technology, 24, 261-­‐267. Peters, S.W.M., Eleveld, M., Pasterkamp, R., van der Woerd, H., Devolder, M, Jans, S., Park, Y., Ruddick, K, Block, T., Brockmann, C., Doerffer, R, Krasemann, H., Röttgers, R., Schönfeld, W., Jorgensen, P.V., Tilstone, G., Martinez-­‐Vicente, V., Moore, G, Sørensen, K., Høkedal, J., Johnsen, T.M, Lømsland, E.R., Aas, E. (2005).Atlas of chlorophyll-­‐a concentration for the North Sea based on MERIS imagery of 2003. Vrije Universiteit, Amsterdam,121 pp. Peperzak, L., Poelman, M. (2008). Mass mussel mortality in the Netherlands after a bloom of Phaeocystis globosa (prymnesiophyceae). Journal of Sea Research, 60, 220–222 Platt, T., Sathyendranath, S., White, G.N., Yaco, C.F., Zhai, L., Devred, E., Tang, C. (2010). Diagnostic Properties of Phytoplankton Time Series from Remote Sensing. Estuaries and Coasts, 33, 428-­‐439 Preisendorfer, R.W. (1986). Secchi disk science: Visual optics of natural waters. Limnology and Oceanography, 31, 909-­‐926. Prins, T.C., Smaal, A.C., Dame, R.F. (1998). A review of the feedbacks between bivalve grazing and ecosystem processes. Aquatic Ecology, 31, 349–359. Prins, T.C., Smaal, A.C., Dankers, N., Pouwer, A.J. (1996). Filtration and resuspension of particulate matter and phytoplankton on an intertidal mussel bed in the Oosterschelde estuary (SW Netherlands). Marine Ecology Progress Series, 142, 121–134 Ramos, E., Juanes, J.A., Galvão, C., Neto, J.M., Melo, R., Pedersen, A., Scanlan, C., Wilkes, R., van den Bergh, E., Blomqvist, M., Karup, H.P., Heiber, W., Reitsma, J.M., Ximenes, M.C., Silió, A., Méndez, F., González, B., (2012). Coastal waters classification based on physical attributes along the (NE) Atlantic region. An approach for rocky macroalgae potential distribution. Estuarine, Coastal and Shelf Science, 112(0), 105 – 114, 2012. Ramos, A.M., Pires, A.C., Sousa, P.M., Trigo, R.M., (2013) The use of circulation weather types to predict upwelling activity along the western Iberian Peninsula coast. Continental Shelf Research,, 69(0), 38 – 51. Reise, K. (1981). High abundance of small zoobenthos around biogenic structures in tidal sediments of the Wadden Sea. Helgolander Meeresuntersuchungen, 34(4), 413-­‐425. 49 D3.5: Aquaculture Indicators Report 31/10/2014 Robinson, I. S. (2007). Discovering the Ocean from Space -­‐ the Unique Applications of Satellite Oceanography. Springer-­‐Verlag Berlin and Heidelberg GmbH & Co. KG. ISBN: 9783540244301. 684 pp. Sætre, Roald, ed. (2007). The Norwegian Coastal Current – Oceanography and Climate. Trondheim: Tapir Academic Press. ISBN 978-­‐82-­‐519-­‐2184-­‐8. Scotland's Marine Atlas (2011). Information for The National Marine Plan. .Scottish Government. Smaal, A.C., Twisk, F. (1997). Filtration and absorption of Phaeocystis cf globosa by the mussel Mytilus edulis L. Journal of Experimental Marine Biology and Ecology, 209, 33–46. Smaal, A. C., Schellekens, T., van Stralen, M.R., Kromkamp, J. C., 2013. Decrease of the carrying capacity of the Oosterschelde estuary (SW Delta, NL) for bivalve filter feeders due to overgrazing? Aquaculture 404, 28-­‐34. Storm, T., Boettcher, M., Grant, M., Zühlke, M., Fomferra, N., Jackson, T., Sathyendranath, S. (2013). Product User Guide, Ocean Colour Climate Change Initiative, 51 pp., www.esa-­‐oceancolour-­‐
cci.org/?q=webfm_send/317aqua-­‐users_d3-­‐5_28_10_2014.doc. Treasurer, J.W., Hannah, F., Cox, D. (2003). Impact of a phytoplankton bloom on mortalities and feeding response of farmed Atlantic salmon, Salmo salar, in west Scotland. Aquaculture, 218, 103-­‐
113 Troost, K. (2010). Causes and effects of a highly successful marine invasion: Case-­‐study of the introduced Pacific oyster Crassostrea gigas in continental NW European estuaries. Journal of Sea Research, 64, 145–165. Van der Wal, D., van Kessel, T., Eleveld, M.A., Vanlede, J. (2010). Spatial heterogeneity in estuarine mud dynamics. Ocean Dynamics, 60(3), 519-­‐533. doi:10.1007/s10236-­‐010-­‐0271-­‐9 (Open Access) Van der Woerd, H.J, Blauw, A., Peperzak, L., Pasterkamp, R., Peters, S. (2011). Analysis of the spatial evolution of the 2003 algal bloom in the Voordelta (North Sea). Journal of Sea Research, 65 (2), 195-­‐204. 50