1. IMAR – Portugal http://www.imar.pthttp://www.imar.pt www.ecowin.org/ecasa ECASA www.ecasa.org.uk ECASA SC group meeting in Rome, 7 th – 8 th November 2006 A. Sequeira J.G. Ferreira Ecosystem Approach for Sustainable Aquaculture

2. GIS Loch Creran division into boxes Physical data Homogenous physical conditions for Morphology Currents Vertical stratification Water bodies defined for Water Framework Directive (WFD) implementation Due to management requirements for EQS, water body boundaries should fit model box limits Aquaculture sites When possible include aquaculture areas into boxes (rather than across boxes) EcoWin2000 model Loch Creran 1 Water Body

3. Model coupling: Spatial aggregation Delft3D Hydrodynamic model EcoWin2000 ecological model several boxes 20  layers 30 boxes 3 layers

4. Loch Creran Wild species - GIS model Loch Creran total wild species distribution Loch Creran total wild species distribution Total wild shellfish individuals: 2 585 x 10 6 Wild species - I) Data Processing

5. Loch Creran Wild species model Marine biological interest in Loch Creran: Tidal rapids Tidal rapids Biogenic reefs of Biogenic reefs of –Modiolus modiolus –Serpula vermicularis Habitats Directive (SAC) Serpula vermicularis reef associated with fauna in Loch Creran from Black et al, 1999 Wild species - II) Regulation analysis

6. Loch Creran Wild species model Total bivalves 2 585 x 10 6 ind Filtration rate 1.5 – 2.6 L ind -1 h -1 Filtration by wild populations Min: 93.1 x 10 6 m 3 d -1 Max: 98.6 x 10 6 m 3 d -1 Loch Creran volume 240 x 10 6 m 3 ≈ 40% of Loch volume is filtered per day ≈ 40% of Loch volume is filtered per day ≈ 2.4 – 2.6 days to filter the Loch Volume filtered per day Wild species - III) Resource partitionig assessment

7. Loch Creran Wild species model Filtration by wild populations Min: 93.1 x 10 6 m 3 d -1 Max: 98.6 x 10 6 m 3 d -1 Loch Creran volume 240 x 10 6 m 3 Chl a concentration* 1 µg L -1 Total chl a in Loch Creran 240 kg Chl a cleared by wild species 93.1 – 98.6 kg chl a d - 1 Baseline food requirements * Mean value, from KEYZONES project data - 2005 Wild species - III) Resource partitionig assessment

8. C learance rates specific for the species in analysis C learance rates specific for the species in analysis – Rates used here were based on the Modiolus modiolus filtration. Clearance rates will vary according to species, body size and season. O ther wild species O ther wild species – Consider other filter feeders that are nor shellfish (e.g. red tube worn). M ap of sediments and biotopes M ap of sediments and biotopes – A good map of the type of sediments and biotopes is important to improve the interpolation surfaces. I ncrease number of sampling stations I ncrease number of sampling stations – Species density is a highy variable parameter. Results shown here are useful to give a rough idea of the type of results one can obtain. Wild species - GIS model improvements :

9. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Eutrophication and Aquaculture in Coastal Systems Application of Screening Models for Assessment Farm-scale screening models International Symposium on Research and Management of Eutrophication in Coastal Ecosystems. Nyborg, Denmark Session 12 – Eutrophication and Aquaculture http://www.farmscale.org/ http://www.farmscale.org/ 20 th -23 rd June 2006 J.G. Ferreira, S.B. Bricker, A.J.S. Hawkins, R. Pastres, A. Newton

10. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Farm-scale conceptual diagram Current Farm length Width Depth Chl a POM Chl a POM Sections 1 2 3 n-1 n Shellfish

11. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Farm-scale modelling Application to shellfish aquaculture lDefine farm dimensions lDefine environmental parameters (e.g Chl a, POM, TPM, O 2 ) lSelect species and culture density lTransport food across farm segments lCalculate food depletion and oxygen consumption lOutput cultivation yield lAssess eutrophication status

12. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Results – Different culture siting FarmDimensions (m)SpeciesModel 300X20X10C. gigasPML Cultivation period (d)454545 FoodChl a (  g L -1 )POM (mg L -1 ) TPM (mg L -1 ) 10525 Environment Density (ind m -3 ) T ( o C) O 2 (mg L -1 ) Sections 1,2,3 500,500,500158.7 Current speed HighMediumSlow (m s -1 ) 0.5 0.10.02 Total seed (X10 3 ind)30000 3000030000 Total harvest (TFW)727.1692.4323.9 Biomass ratio485462216 Final mean Chl a (  g L -1 ) 7.94.72.1 Final min. O 2 (mg L -1 )8.47.76.9 Income (k€)365634621619

13. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Results – Different culture densities FarmDimensions (m)SpeciesModel 300X20X10C. gigasPML Cultivation period (d)180180180 FoodChl a (  g L -1 )POM (mg L -1 ) TPM (mg L -1 ) 5525 EnvironmentCurrent (m s -1 ) T ( o C) O 2 (mg L -1 ) 0.02158.7 Cultivation scenarioLowMediumHigh Density (ind m -3 )25 (all)100 (all)500 (all) Sections 1,2,3 Total seed (X10 3 ind) 1500 600030000 Total harvest (TFW)34.3137.3 400.2 Biomass ratio458458 267 Final Chl a (  g L -1 ) 4.32.8 0.9 Income (k€)171.5686.52001

14. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Results – ASSETS model FarmDimensions (m)Species Cultivation (d) 300X20X10Generic45 FoodChl a (  g L -1 )POM (mg L -1 ) TPM (mg L -1 ) 11525 EnvironmentCurrent (m s -1 ) T ( o C) O 2 (mg L -1 ) 0.02157.0 Cultivation scenarioLowMediumHigh Density (ind m -3 ) 25 (all)100 (all)500 (all) Total seed (X10 3 ind) 1500 600030000 Total harvest (TFW)13.136.8 39.1 Final mean Chl a (  g L -1 ) 9.56.01.3 Final min. O 2 (mg L -1 )5.93.8 1.8 ASSETS gradeGoodModerate Poor Income (k€)65.5184195 WFD

15. GEM – Geochemical and Ecological Modelling http://www.farmscale.org/Address Synthesis lFARM is a screening model directed both at the farmer and the regulator; lFARM has three uses: (i) Prospective analysis for siting or distribution; (ii) Ecological and economic optimisation of existing farms; (iii) Assessment of farm-related eutrophication effects (including mitigation); lThe seamless integration of ASSETS TM, allowing eutrophication assessment, means that FARM is effectively a screening model both for shellfish productivity and water quality; lThe model’s simple interface hides complex internal processing, including transport equations, shellfish individual growth, population dynamics, dissolved oxygen balance and the calculation of ASSETS TM ; lThe FARM model will go live in the Fall of 2006, and will include the possibility of adding fish cages and seaweeds to explore polyculture effects. Different combinations of shellfish polyculture will be implemented in 2007; lThe FARM model is at the forefront of the latest generation of client-server models, part of the rapidly emerging paradigm of Software as a Service (SaaS). http://www.farmscale.org

16. A differential DPSIR approach for coastal ecosystem management A.M. Nobre, J.G. Ferreira IMAR – Institute of Marine Research http:// www.ecowin.org/ Research and management of eutrophication in coastal ecosystems 20-23 June 2006, Nyborg Strand,Nyborg, Denmark ana@salum.netana@salum.net, joao@hoomi.comjoao@hoomi.com

17. Differential DPSIR description Objective Inform managers about the several problems identified in the coastal zones due to an increase of human pressure, including both ecological and economic components Questions to answer: Is it possible to establish a relation between water quality of a coastal ecosystem and its economic value? If so, how it changes with the pressure from the drainage basin and with other pressures inside the ecosystem? Ecological indicators Economic indicators t t +  t t + 2.  tt + 3.  t Response implementation periods

18. Methodology Assessment of the ecosystem in a given year (t)  DPSIR Drivers  Pressures  State Identification of the most relevant issues Socio-economic activities and land uses Loads and other forcing functions Appropriate ecological indicators Quantification Ecological Research -Pressure indicators State indicators Manag. -Management Level State Classification Economic VDrivers-VEcosystem Quantification of both environmental and economic variables using the differential DPSIR approach Year t+  t Year t tt

19. Methodology Assessment of the impacts in the ecosystem after a given period (  t)  DPSIR Response  Drivers  Pressure  State = Envir. Impact Identification of the most relevant issues Management actions and measures Changes in drivers: Changes in pressures: Changes in state = Impacts in the ecosystem: Quantification Ecolog. Res. --  Pressure indicators  State indicators Man. --Management Level (t+  t)  State Classification Economic ResponseCost  VDrivers -VImpact Ecosyst    Evaluation of changes in  t Year t+  t Year t tt

20. Application to a coastal lagoon Eutrophication symptoms Low pelagic primary production, limited by the fast water turnover Benthic eutrophication symptoms as a result of nutrient peaks, large intertidal areas and short water residence times Main economic activities: Bivalve aquaculture (extensive) Fisheries (extensive) Salt production Tourism Agriculture and livestock Industry High ecological value: Ramsar Convention (1971) site Natural park (1978) Cites Convention (1975) Birds Directive (1979) Habitats Directive (1992) Natura 2000 network Ria Formosa

21. Management issues Period of analysis The most important issues for management in Ria Formosa: Seasonal variation of population Water quality in intertidal areas and channel upper reaches Excessive macroalgal growth Decrease of clam stocks since mid 80’s due e.g. to the appearance of the parasite Perkinsus atlanticus (Azevedo, J.Parasitol.1989) t, annual average (1980-1985) ∆t, period 1985-1995 t+∆t, annual average (1995-1999)

22.  DPSIR Scenarios Economic and ecological changes in  t Response value t1t1 t2t2 t3t3 -60% -40% -20% 0% 20% 40% 60% 80% ExternalEnvCost ResponseCost  Drivers ExternalEnvCost / Impact value t1t1 t2t2 t3t3 Although management actions were taken between 1985 and 1995 the state decrease Other measures could have been adopted that might had reduced the lost of the ecosystem economical value