1. InVEST A Tool for Mapping and Valuing Ecosystem Services Emily McKenzie Driss Ennaanay Stacie Wolny Gregg Verutes

2. –Evidence: Test tools, improve decisions, write stories –Tools: Make it easy to quantify ecosystem services –Influence: Achieve broader policy change

3. Outline What questions is InVEST designed to answer? What are InVEST’s key characteristics? What are common challenges using InVEST?

4. Filling the Gap Policy decisions: Region/landscape scale Short timeline Forward looking, comparative Policy decisions: Region/landscape scale Short timeline Forward looking, comparative GLOBAL, SYNTHETIC 60% of global ES in decline (Millennium Assessment) $33 Trillion/y (Costanza et al. 1997 Nature) LOCAL, SPECIFIC 2 forest patches: $60K/year (Ricketts et al. 2004. PNAS) 22 others (just for pollination!)

5. 5 Challenge: Integrated decision-making “You can only manage what you can measure” –Ecosystem services ‘invisible’ in decisions –Need to evaluate choices, quantify tradeoffs

6. 6 Decision-maker questions –How would a proposed dam or logging project affect ecosystem services and biodiversity? –What would be the best land use plan for balancing different stakeholders’ visions for the future? –How would upstream deforestation affect the quality & quantity of water downstream? –Where might REDD and payments for watershed services projects be feasible? ANSWERS: Accounting tools for quantifying ES

7. How might shoreline armoring affect Erosion/flooding from storm events? Coastal and marine recreation? Nursery habitat for key species? Fisheries?

8. How would a new cattle management approach affect agricultural revenues?

9. How would a new cattle management approach affect agricultural revenues drinking water quality erosion control carbon sequestration and biodiversity?

10. InVEST Quantify, map & value ecosystem service impacts of alternative resource decisions

11. InVEST within decision making Stakeholders Scenarios Results Policy input Mapping tool Now2050 Now2050 C (tons) Policy implementation

12. 7.Flexible data requirements 8.Free and open source 9.Scientific foundation 10.Accompanying policy tools “Top Ten” InVEST attributes 1.Applicable anywhere 2.Multiple services 3.Spatially explicit 4.Scenario based 5.Production functions 6.Biophysical & socio- economic outputs

13. 1. Applicable anywhere - Terrestrial Upper Yangtze Tanzania California Hawai’i Colombia Sumatra Virungas Amazon

14. Applicable anywhere – Marine Vancouver Island Belize Chesapeake Bay Puget Sound Galveston Bay Monterey Bay

15. Applicable anywhere - Many kinds of decision context Decision ContextGeography Spatial Planning Tanzania, Indonesia, British Columbia, Hawai’i, China, Belize Ecosystem-based management (terrestrial-marine links) USA (Puget Sound, Galveston & Chesapeake Bays) Climate adaptationUSA - Galveston & Monterey Bays Payments for ecosystem services Colombia (water funds), Indonesia (REDD), Borneo, Tanzania Impact assessment, permitting, licensing Colombia (mining) Multilateral development bank investments World Bank in Malawi Corporate strategyLafarge in Michigan, USA

16. 2. Multiple Ecosystem Services Fisheries (0) Aquaculture: finfish (1) Coastal Vulnerability (0) Recreation (0) Wave Energy (1) Carbon Sequestration (1) Habitat Risk Asst (1) Aesthetic Quality (1) Water Quality (1) Water purification (1) Sediment retention (1) Crop pollination (1) Hydropower (1) Irrigation water (1) NTFPs (1) Flood control (1) Commercial timber (1) Coastal Protection (1) Aquaculture: shellfish (1) Biodiversity (1) Carbon sequ’n (1) Agricultural prod’n (1)

17. 3. Spatially explicit Python scripts packaged into an ArcGIS toolbox

18. How might ecosystem services change with different: 4. Scenario-based Interventions Possible futures Visions of the future Future baselines Quantitative modeled scenarios

19. Complementary scenario tools Scenario primer for InVEST users Scenario generator linked to InVEST Linking to IDRISI Land Change Modeler

20. 5. Production function Carbon storage ~ f(veg, storage/ha, harvest, decay) Inputs: land use/cover, C densities, harvest rates, decay rates of harvested wood. Outputs: C stored/ha Valuation: damage costs avoided Sediment retention ~ f(soil, slope length, veg, rain, neighbors) Inputs: land use/cover, topography, soils, precip, basins Outputs: tons sediment retained/ha Valuation: replacement costs avoided (dredging)

21. 6. Biophysical & socio-economic outputs Water for IrrigationCrop Pollination Supply Maximum potential services Total surface runoff from each land parcel on landscape (vol. ha -1 ) Insect abundance (# insects ha -1 ) Use Intermediate service Amount of water used for crop irrigation (vol. ha -1 ) Insect abundance contributing to crop (# of insects ha -1 ) Use Final service Additional crop yield given additional water available for irrigation (kg ha -1 ) Crop yield due to insects (kg crop ha -1 ) ValueNPV of additional crop yield($ ha -1 )

22. Valuation Value of captured wave energy Avoided damages NPV of fish & shellfish Values of recreation activities Avoided area Eroded/flooded Harvested biomass Captured wave energy Visitation Landed biomass Fisheries Recreation Model Aquaculture Wave energy Coastal Protection Production functions Socioeconomic Oceanography Habitat type Species distribution Bathymetry Topography Model Output ecosystem services & values Model Output ecosystem services & values Models Input Data reflect scenarios Marine

23. 7. Flexible data requirements Tier 1Tier 2Tier 3 Models Data SimpleComplex Tier 0 I am out of the office until 15 June, with little email access. I will get back to you on my return. Thanks, Emily InVEST

24. 8. Strong Scientific Foundation 100 + authors Oxford University Press Published April 2011

25. Many disciplines Hydrology Economics Policy GIS analysis Software engineering Ecology Marine biology Coastal engineering Fisheries Programming Oceanography

26. 9. Free and open source Ready to use, but customizable http://invest.ecoinformatics.org

27. 10. Accompanying policy tools InVEST in Practice e.g. SEA InVEST Tip Sheets e.g. REDD Scenario Primer & Generator Screening Criteria TEEB (& other) Case Studies …. Meet the SPIes

28. 28 Challenges –Data – even for Tier 1 models –Capacity to interpret and apply –Water-related services –Governmental silos

29. Data preparation Expertise needed –GIS expertise for gathering and formatting data –Subject matter experts e.g. hydrology, economics, carbon –Both applying InVEST and subsequent analysis Time estimate vary depending on –location –scale of work –expertise of working team Full run of model, from data gathering to results –As little as 1 to 3 months –But often much longer!! (18-24 months and counting)

30. Data collection In some countries –Good resolution data often freely available online –From government sources and research institutions. For other regions –Free global data online (Global landcover, Tier 1 carbon, HydroSHEDS etc) –But often coarse scale –Finer scale data may be available –Partnerships with regional organizations very helpful

31. More information http://invest.ecoinformatics.org www.naturalcapitalproject.org

32. 32 Thanks… People Andrew Balmford Taylor Ricketts Neil Burgess Gretchen Daily Brendan Fisher Peter Kareiva Eric Lonsdorf Guillermo Mendoza Robin Naidoo Erik Nelson Nasser Olwero Steve Polasky Jim Regetz Amy Rosenthal Mathieu Rouget Mary Ruckelshaus Heather Tallis Buzz Thompson Kerry Turner … Support NSF NSF-NCEAS NASA Leverhulme Trust Google Packard Foundation MacArthur Foundation Moore Foundation Summit Foundation Roger and Vicki Sant Peter and Helen Bing … People Anne Guerry Jodie Toft Katie Arkema Rich Sharp Jon Foley CK Kim Gregg Verutes Driss Ennaanay Stacie Wolny Amy Rosenthal Nirmal Bhagabati Jim Salzman Chris Colvin Mike Papenfus Greg Guannel Joey Bernhardt Spencer Wood Pam Matson …

33. Any questions?

34. InVEST testing and validation Currently being tested against SWAT and FIESTA (peer-reviewed water yield models) Comparison with ARIES in progress Arizona (DOI) and Oregon (EPA) Ground-truthing in multiple sites In Baoxing, China, modeled water yield is > 90% of observed Applied water pollution model in Minnesota – InVEST only 9% off actual observed loading into basin

35. Testing/Verification of Water Yield InVEST SWAT