InVEST A Tool for Mapping and Valuing Ecosystem Services

Emily McKenzieDriss EnnaanayStacie WolnyGregg Verutes

– Evidence: Test tools, improve decisions, write stories

– Tools: Make it easy to quantify ecosystem services

– Influence: Achieve broader policy change

Outline

• What questions is InVEST designed to answer?

• What are InVEST’s key characteristics?

• What are common challenges using InVEST?

Filling the Gap

Policy decisions:Region/landscape scale

Short timelineForward 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

Challenge: Integrated decision-making

“You can only manage what you can measure”

– Ecosystem services ‘invisible’ in decisions– Need to evaluate choices, quantify tradeoffs

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

How might shoreline armoring affect Erosion/flooding from storm events?

Coastal and marine recreation?Nursery habitat for key species?

Fisheries?

How would a new cattle management approach affect agricultural revenues?

How would a new cattle management approach affect agricultural revenuesdrinking water qualityerosion controlcarbon sequestrationand biodiversity?

InVEST

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

InVEST within decision making

Stakeholders

ScenariosResults

Policy input

Mapping toolNow 2050

Now 2050

C (

tons

)Policy implementation

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

1. Applicable anywhere - Terrestrial

Upper Yangtze

Tanzania

California

Hawai’i

Colombia Sumatra

Virungas

Amazon

Applicable anywhere – Marine

Vancouver Island

Belize

Chesapeake BayPuget Sound

Galveston BayMonterey Bay

Applicable anywhere - Many kinds of decision context

Decision Context Geography

Spatial PlanningTanzania, Indonesia, British

Columbia, Hawai’i, China, Belize

Ecosystem-based management (terrestrial-marine links)

USA (Puget Sound, Galveston & Chesapeake Bays)

Climate adaptation USA - Galveston & Monterey Bays

Payments for ecosystem servicesColombia (water funds), Indonesia

(REDD), Borneo, Tanzania

Impact assessment, permitting, licensing Colombia (mining)

Multilateral development bank investments World Bank in Malawi

Corporate strategy Lafarge in Michigan, USA

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)

3. Spatially explicit

Python scripts packaged into an ArcGIS toolbox

How might ecosystem services change with different:

4. Scenario-based

• Interventions• Possible futures• Visions of the future• Future baselines• Quantitative modeled scenarios

Complementary scenario tools

• Scenario primer for InVEST users

• Scenario generator linked to InVEST

• Linking to IDRISI Land Change Modeler

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)

6. Biophysical & socio-economic outputs

Water for Irrigation Crop PollinationSupplyMaximum potential services

Total surface runoff from each land parcel on landscape (vol. ha-1)

Insect abundance (# insects ha-1)

UseIntermediate service

Amount of water used for crop irrigation (vol. ha-1)

Insect abundance contributing to crop (# of insects ha-1)

UseFinal service

Additional crop yield given additional water available for irrigation (kg ha-1)

Crop yield due to insects (kg crop ha-1)

Value NPV of additional crop yield($ ha-1)

NPV of additional crop yield ($ ha-1)

Valuation

Value of captured

wave energy

Avoided damages

NPV of fish &

shellfish

Values of recreation activities

Avoided areaEroded/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 Outputecosystem services & values

ModelsInput Datareflect scenarios

Marine

7. Flexible data requirements

Tier 1 Tier 2 Tier 3

Models

Data

Simple Complex

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

8. Strong Scientific Foundation

100 + authors

Oxford University

Press

Published April 2011

Many disciplines

• Hydrology• Economics• Policy• GIS analysis• Software

engineering

• Ecology• Marine biology• Coastal engineering• Fisheries• Programming• Oceanography

9. Free and open source

Ready to use, but customizable

http://invest.ecoinformatics.org

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

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Challenges

–Data – even for Tier 1 models

–Capacity to interpret and apply

–Water-related services

–Governmental silos

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)

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

More information

http://invest.ecoinformatics.org

www.naturalcapitalproject.org

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Thanks…People

Andrew BalmfordTaylor RickettsNeil Burgess

Gretchen DailyBrendan FisherPeter KareivaEric Lonsdorf

Guillermo MendozaRobin NaidooErik Nelson

Nasser OlweroSteve Polasky

Jim RegetzAmy RosenthalMathieu Rouget

Mary RuckelshausHeather Tallis

Buzz ThompsonKerry Turner

…

SupportNSF

NSF-NCEASNASA

Leverhulme TrustGoogle

Packard FoundationMacArthur Foundation

Moore FoundationSummit Foundation

Roger and Vicki SantPeter and Helen Bing

…

PeopleAnne Guerry

Jodie ToftKatie ArkemaRich SharpJon FoleyCK Kim

Gregg VerutesDriss Ennaanay

Stacie WolnyAmy Rosenthal

Nirmal BhagabatiJim SalzmanChris Colvin

Mike PapenfusGreg GuannelJoey BernhardtSpencer WoodPam Matson

…

Any questions?

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

Testing/Verification of Water Yield

0 100 200 300 400 500 6000

100

200

300

400

500

600

R² = 0.824150696427196

Comparison of Annual water yield between SWAT and InVEST

in Texas Gulf Basin

InVEST

SW

AT

InVEST

SWAT