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# Bayesian networks for environmental management including fisheries The BayFish suite of models

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Presented at the Basin Focal Project Fisheries Workshop at Cali, Colombia, Feb 2008

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### Bayesian networks for environmental management including fisheries The BayFish suite of models

1. 1. people science environment partners Bayesian networks for environmental management including fisheries The BayFish suite of models Eric BARAN Martin VAN BRAKEL
2. 2. Bayesian models Networks of variables Example: medical diagnosis of a baby with a probability of interaction between variables • Each box is a variable with 2 or 3 states • Choice of variables and weight of each variable is based on hard data OR on expert knowledge • Once the network is set up and each variable parametrized, the software computes probabilities resulting from multiple interactions
3. 3. Probabilities based on a time series analysis COMBINATION OF PROBABILITIES Probabilities based on mapping
4. 4. Scenario analysis 1 Scenario analysis 2
5. 5. Bayesian Networks • BNs define a system as a network of variables linked by probabilistic interactions • For each variable a small number of states is defined • Variables can be driving (parent) or driven (child) Network variables Attached probability tables Justification (driving variables) (based on data or on knowledge) Natural fish stock Number of fishing boats Natural fish stock There are 74% chances that the fish >100,000 tons > 500 units <100000 tons 300 to 500 units stock is superior to 100,000 tons >100,000 tons 74 < 300 units There are 26% chances that the fish <100000 tons 26 stock is inferior to 100,000 tons There are 25% chances that the number of Number of fishing boats fishing boats is superior to 500 units > 500 units 25 There are 50% chances that the number of Fish catch 300 to 500 units 50 fishing boats is between 300 and 500 units > 50,000 tons There are 25% chances that the number of < 300 units 25 25,000 - 50,000 tons < 25,000tons fishing boats is inferior to 300 units
6. 6. Driven Variables Driving variables and probability Justification Computer calculation table of the driven variables (detailed example for one combination) (based on Bayes formula) of the probability of having a certain catch given all the Natural fish stock Number of fishing boats previous given probabilities >100 ,000 tons > 500 units <1 00 000 tons 300 to 500 units < 300 units If the stock is >100,000 tons If there are 300 to 500 boats Stock Boats Catch Then > 50,000 tons 25,000 to 50,000 tons 25,000tons < Fish catch - there are 60% chances that >100,000 tons> 500 units 90 10 0 >100,000 tons300 to 500 units 60 30 10 the catch is superior to 50,000 tons > 50,000 tons >100,000 tons< 300 units 20 20 60 - there are 30% chances that 25,000 - 50,000 tons <100000 tons > 500 units 10 70 20 25,000 tons < catch < 50,000 tons < 25,000tons <100000 tons 300 to 500 units 10 30 60 - there are 10% chances that <100000 tons < 300 units 0 10 90 the catch is inferior to 25,000 tons
7. 7. Building a Bayesian network 1. Network development • Identify the major variables of the system studied • Delete variables for which no information is available • Arrange remaining variables in a meaningful way 2. Variables specification • Define the relevant states of each variable • Specify the weight of each variable 3. Parameterization (“elicitation of probabilities”) • Define the probability of each state of each driving variable • For each driven variable, define the weight of each driving variable.
8. 8. Thus Bayesian models • Quantify trends resulting from multiple influences • Allow integration of quantitative and qualitative information (e.g. databases and local knowledge) • Are made for scenario analysis • Allow analyzing trade-offs • Act as decision support tools
9. 9. BayFish Bac Lieu
10. 10. Bac Lieu: inland coastal system characterized by: - a saline intrusion; - a series of sluice gates; - conflicting land uses (basically rice vs. shrimp farming) 1 Shrimp farming area 2 Shrimp+rice area 3 Rice farming area 3 2 1 Cau Sap r oa d Vinh My stal Coa Thanh Tri Ho Phong Pho Sinh Sea Lang Tram
11. 11. Objectives 1. Help optimize operation of sluice gates 2. Assist decision making about water management options 3. Inform stakeholders about SLUICE GATE production trade-offs 4. Involve stakeholders in the PHYSICAL WATER SOURCES management process WATER AND SOIL CHEMICAL PROPERTIES RICE FISH CRAB SHRIMP TOTAL PRODUCTION
12. 12. Management options, outcomes and trade-offs Economic return Food security Ecosystem health SLUICE GATE SLUICE GATE SLUICE GATE PHYSICAL WATER SOURCES PHYSICAL WATER SOURCES PHYSICAL WATER SOURCES WATER AND SOIL CHEMICAL PROPERTIES WATER AND SOIL CHEMICAL PROPERTIES WATER AND SOIL CHEMICAL PROPERTIES TOTAL PRODUCTION TOTAL PRODUCTION TOTAL PRODUCTION
13. 13. Gate operation Sea vs. Mekong water flows Saline vs. fresh water Pollution (acid soils, farming, shrimp factories) IF freshwater IF no pollution IF saline water RICE FISH/CRAB SHRIMP Low income Medium income High income Bad environment Good environment Bad environment Food security Food security No food security TRADE OFFS
14. 14. Sluice gate operation Baseline All open Sluice gates scenarios LT open LT HP open All closed Mekong inflow Marine inflow Rainfall Above mean 39.1 Above mean 39.1 Above 89mm 43.0 Below mean 60.9 Below mean 60.9 Below 89mm 57.0 Water quantity 3 ± 16 -53 ± 97 114 ± 93 Water salinity Water acidity Above 10 40.6 Soil acidity Water pollution Between 4-10 28.8 Severe 20.6 Water quality Acceptable 93.1 Unacceptable 6.88 Below 4 30.6 Important 60.5 Medium 39.5 Negligible 39.5 13 ± 12 No 39.9 FWQuantity for rice WQual. for rice WQual. for aquaculture High 40.3 Good 30.0 WQual. for est. fish WQual. for FW fish Good 54.6 Low 59.7 Bad 70.0 Good 52.7 Good 23.7 Bad 45.4 Bad 47.3 Bad 76.3 Estuarine fish Freshwater fish Fish Aquaculture Production Good 43.2 Good 26.8 Good 48.2 Bad 56.8 Bad 73.2 Bad 51.8 Wild fish Good 41.4 Bad 58.6 Rice production Fish production Crab Production Shrimp production Good 48.4 Good 43.7 Good 54.6 Good 55.6 Bad 51.6 Bad 56.3 Bad 45.4 Bad 44.4 TOTAL INCOME FOOD SECURITY ENVIRONMENT Global outcomes Good 53.4 Good 48.0 Good 46.9 Bad 46.6 Bad 52.0 Bad 53.1
15. 15. Outcomes of BayFish – Bac Lieu All Gates Closed • Salinity very low, problems with acidity • Aquaculture production very low • Rice production increases a little (baseline +15%) • Household income decreases (not balanced by better food security) • Environmental conditions deteriorate Lang Tram and Ho Phong sluice gates open • High marine inflow • High aquaculture production (baseline +80%) • Decreased rice production (baseline –10%) • Household income increases (baseline +50%) • Food security and Environmental health held at baseline levels etc
16. 16. Details about BayFish – Bac Lieu: Principles and structure Google: “Developing a consultative Bayesian model”
17. 17. BayFish Tonle Sap
18. 18. Objectives • To identify relationships between river hydrology, floodplain habitats and fish production • To predict the impact of environmental modification on fish production • To raise awareness among stakeholders about variables to be encompassed in the management process
19. 19. MIGRATIONS HYDROLOGY HABITAT Stock Catch FISHERIES Production
20. 20. TS rainfall TS runoff Tonle Sap Flooded Mekong inflow water level Floodplain O2 vegetation HYDROLOGICAL SCENARIOS Overland flow O2 for O2 for O2 for TS migrants Mekong migrants residents Built Flood beginning Flood duration Flood Structures MIGRATIONS HABITAT for level residents Floodplain refuges HABITAT for MIGRATIONS TS migrants of residents Flood for fishes MIGRATION of TS migrants HABITAT for Mekong migrants MIGRATIONS of Mekong migrants HYDROLOGY HABITAT STOCK of residents STOCK of # Khmer MS fishers TS migrants Pressure from LS fishery MS gear efficiency STOCK of # migrant MS fishers Mekong migrants # MS fishers CATCH of PRESSURE residents Stock on residents Pressure from # Viet./Cham MS fishers MS fishery Activity of SS fishers CATCH of # Viet./Cham SS fishers TS migrants PRESSURE on TS migrants Pressure from # SS fishers SS fishery # Khmer SS fishers CATCH of Gear size Mekong migrants PRESSURE of SS fishers on Mekong migrants Catch FISHERIES TOTAL FISH CATCH
21. 21. TS Runoff Over Mean 42.5 Under mean 57.5 2.5e+004 ± 1.4e+004 Mekong flow Over mean 47.6 Under mean 52.4 3.1e+004 ± 1.5e+004 Water level Kampong L ... >9m 52.7 Overland flow <9m 47.3 Bank structures Over Mean 42.9 7.9 ± 3.2 Many 5.00 Under mean 57.1 Few 95.0 7.3e+003 ± 4.7e+003 Floodplain oxygen Floodplain vegetation > 4 mg/l 29.8 Grass 56.3 Flood beginning Flood duration Floodplain flood level 2<mg/l <4 27.6 Shrub 41.8 Before 20 June 30 > 13 weeks 30 > 9m 57.2 2 mg/l 42.6 Forest 1.94 Betwen 55 5< weeks <13 60 < 9m 42.8 After 20 Aug 15 < 5 weeks 10 O2 for Black fish O2 for White fish Acceptable 58.4 Acceptable 67.9 HYDROLOGY Impossible 41.6 Impossible 32.1 HABITAT WUP-JICA JICA model GIS WUP-JICA MRC WUP-FIN database Consultation databases model Bibliography
22. 22. HYDROLOGY HABITAT TS rainfall TS runoff Above 1000 45.5 Above 30000 53.7 Below 1000 54.5 Below 30000 46.3 1.2e+003 ± 8.7e+002 2.8e+004 ± 1.4e+004 TS water level Floodplain O2 Flooded vegetation Mekong inflow Above 10m 29.6 Above 4mgl 16.6 Grass 54.6 HYDRO. SCENARIOS Above 34300 60.0 From 8 to 10 47.8 From 2 to 4 ... 23.5 Shrub 43.4 Baseline 100 Below 34300 40.0 Below 8m 22.6 Below 2mgl 59.9 Forest 1.98 High 0 3.4e+004 ± 1.6e+004 8.6 ± 2.6 Dam 0 Overland flow O2 for TS migrants O2 for Mekong migrants O2 for residents Above 6400 60.0 Acceptable 28.4 Acceptable 16.6 Acceptable 40.1 Below 6400 40.0 Impossible 71.6 Impossible 83.4 Impossible 59.9 Flood beginning Flood duration Flood level Before mid J ... 40.0 More 11 we ... 26.2 Mid July to ... 40.0 Around 8 we ... 66.1 High 51.3 MIGRATIONS Built Structures HABITAT for residents Low 48.7 Blocking 3.64 After mid Au ... 20.0 Less 6 weeks 7.70 Open 96.4 Good 26.3 Bad 73.7 MIGRATIONS of residents Floodplain refuges Flood for fishes Free 65.0 Perennial 73.2 HABITAT for TS migrants Good 60.6 Blocked 35.0 Temporary 26.8 Good 18.5 Bad 39.4 Bad 81.5 MIGRATION of TS migra ... Free 70.2 Blocked 29.8 HABITAT for Mekong mi ... MIGR. of Mekong migra ... Good 10.7 Free 52.1 Bad 89.3 STOCK of residents Blocked 47.9 Abundant 48.5 Scarce 51.5 STOCK of TS migrants Abundant 49.8 Scarce 50.2 Stock MS gear efficiency Increasing 75.0 # Khmer MS fishers STOCK of Mekong migr ... Stable 25.0 Increasing 50.0 Abundant 43.9 Pressure from LS fishery Stable 50.0 PRESSURE on residents Scarce 56.1 Nil 41.0 Increasing 67.5 Blockage 59.0 # MS fishers # migrant MS fishers CATCH of residents Stable 32.5 Increasing 60.0 Increasing 50.0 High 58.0 Stable 40.0 Stable 50.0 Low 42.0 PRESSURE on TS migra ... # Viet./Cham MS fishers Increasing 69.6 Pressure from MS fishery Increasing 75.0 CATCH of TS migrants Stable 30.4 Increasing 64.5 Stable 25.0 High 59.7 Stable 35.5 Activity of SS fishers Low 40.3 More Fishing 63.4 Catch More Farming 36.6 # Viet./Cham SS fishers PRESSURE on Mekong ... Increasing 75.0 CATCH of Mekong migra ... Increasing 69.6 # SS fishers Pressure from SS fishery Stable 25.0 High 56.7 Stable 30.4 Increasing 98.7 Increasing 73.4 Low 43.3 Stable 1.30 Stable 26.6 # Khmer SS fishers Increasing 100 Gear size of SS fishers Stable 0 Increasing 25.0 TOTAL FISH CATCH Stable 75.0 High 57.0 FISHERIES Low 43.0
23. 23. Production of the dai fishery (x1000 tonnes) Probability of a high stock in BayFish (%) 16 60 14 50 12 40 10 30 8 6 20 4 10 2 0 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 Mekong migrant fish TS resident fish Dai fishery catch
24. 24. Details about BayFish – Tonle Sap: Principles and structure http://www.mssanz.org.au/modsim05/papers/baran.pdf Comprehensive report http://www.ifredi.org/BS_project.asp
25. 25. BayFish SWOT STRENGTHS • The only kind of decision support tool that can integrate quantitative as well as qualitative information • Can overcome the paucity of statistics and biological information in zones poorly studied • A way for a diversity of stakeholders to meet and talk about management decisions • Tool intuitive (no programming language needed) and user-friendly. Open sources, models of very small size. Software freely accessible on Internet to read models (www.norsys.com)
26. 26. BayFish SWOT WEAKNESSES • Not a dynamic model (snapshot of a system instead) • Probabilities subjective -> good stakeholders selection is essential • The tool must be simple enough to be acceptable -> difficult balance between simplification and realism OPPORTUNITIES • Build models and partnerships between fish- and agriculture- related institutions • Address trade-offs in water uses and management
27. 27. Thank you !
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