Capturing the Value of Ecosystem Services Jon D. Erickson - - PowerPoint PPT Presentation

Alternative Ways to Understand and Assess the Impacts of Atmospheric Pollutants

Jon D. Erickson Rubenstein School of Environment & Natural Resources Gund Institute for Ecological Economics • University of Vermont Colin M. Beier Adirondack Ecological Center SUNY College of Environmental Science & Forestry

Capturing the Value of Ecosystem Services

Capturing the Value of Ecosystem Services

• Evolution of “Ecosystem Services”
• Role of Modeling
• Cases

– Climate Change & Disturbance Regulation – Human Impact & Recreation Amenities

• ES & NYSERDA

Evolution of Ecosystem Services

Stock-Flow Fund-Service vs.

ECOSYSTEM SERVICES Gas regulation Climate regulation Disturbance regulation Water regulation Water supply Erosion control and sediment retention Soil formation Nutrient cycling Waste treatment Pollination Biological control Refugia Food production Raw materials Genetic resources Recreation Cultural ECOSYSTEM FUNCTIONS Regulation of atmospheric chemical composition. Regulation of global temperature, precipitation, and other biologically mediated climatic processes at global, regional, or local levels. Capacitance, damping and integrity of ecosystem response to environmental fluctuations. Regulation of hydrological flows. Storage and retention of water. Retention of soil within an ecosystem. Soil formation processes. Storage, internal cycling, processing, and acquisition of nutrients. Recovery of mobile nutrients and removal or breakdown of excess or xenic nutrients and compounds. Movement of floral gametes. Trophic-dynamic regulations of populations. Habitat for resident and transient populations. That portion of gross primary production extractable as food. That portion of gross primary production extractable as raw materials. Sources of unique biological materials and products. Providing opportunities for recreational activities. Providing opportunities for non-commercial uses. From: Costanza, R. R. d'Arge, R. de Groot, S. Farber, M. Grasso, B. Hannon, S. Naeem, K. Limburg, J. Paruelo, R.V. O'Neill,

• R. Raskin, P. Sutton, and M. van den Belt. 1997. The value of the world's ecosystem services and natural capital. Nature

387:253-260

Source: Costanza et al., “The Value of the World’s Ecosystem Services and Natural Capital,” Nature 387: 253-260, 1997.

Millennium Ecosystem Assessment

• 5-10% of the area of five

biomes was converted between 1950 and 1990

• More than two thirds of

the area of two biomes and more than half of the area of four others had been converted by 1990

Net Present Value ($/hectare)

Source: Millennium Ecosystem Assessment

Role of Modeling

Three Levels of Modeling

• 1. Scoping Models

High generality, low resolution, broad participation by all stakeholder groups.

• 2. Research Models

More detailed and realistic attempts to replicate the dynamics of a particular system of interest, with emphasis on calibration and testing.

• 3. Management Models

Medium to high resolution. Emphasis on producing future management scenarios. Can be exercising #1 or #2, or require further elaboration to apply management questions.

Increasing Complexity, Cost, Realism, and Precision

Source: Costanza, R. and M. Ruth, “Using Dynamic Modeling to Scope Environmental Problems and Build Consensus,” Environmental Management 22: 183-195, 1998.

A systems framework for ES assessment…

Ecosystem Processes Benefit Flows

Direct feedbacks to society Provisioning Regulating Cultural

External Drivers External Drivers

Beier et al. (2008) Ecosystems

Beier et al. (2008) Ecosystems

Cases

Climate Change & Disturbance Regulation (Scoping Model)

Source: Stern review on the economics of climate change, 2006

Picture taken by an automatic camera located at an electrical generating facility on the Gulf Intracoastal Waterway (GIWW) where the Route I-510 bridge crosses the GIWW. This is close to where the Mississippi River Gulf Outlet (MRGO) enters the GIWW. The shot clearly shows the storm surge, estimated to be 18-20 ft. in height..

Past and Projected Wetland Loss in the Mississippi Delta (1839 to 2020)

NEW ORLEANS Coastal Louisiana

History of coastal Louisiana wetland gain and loss over the last 6000 years, showing historical net rates of gain of approximately 3 km2/year over the period from 6000 years ago until about 100 years ago, followed by a net loss of approximately 65 km2/yr since then.

2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

• 7000
• 6000
• 5000
• 4000
• 3000
• 2000
• 1000

1000 Years Before Present

3 sq km/yr

Net wetland gain

65

sq km/yr Net wetland loss

Global Storm Tracks 1980 - 2006

Figure 1. Typical hurricane swath showing GDP and wetland area used in the analysis.

฀  TDi  e  gi

 1  wi  2 GDP i

฀  TDi  e  gi

 1  (wi 1)  2  wi  2

 GDP

i

Predicted total damages from storm i Avoided cost from a change of 1 ha of coastal wetlands for storm i

The value of coastal wetlands for hurricane protection

ln (TDi /GDP i)=  + 1 ln(gi) +  2ln(wi) + ui

(1) Where: TDi = total damages from storm i (in constant 2004 $US); GDPi = Gross Domestic Product in the swath of storm i (in constant 2004 $US). The swath was considered to be 100 km wide by 100 km inland. gi = maximum wind speed of storm i (in m/sec) wi = area of herbaceou s wetlands in the storm swath (in ha). ui = error

Figure 2. Observed vs. predicted relative damages (TD/GDP) for each of the hurricanes used in the analysis.

0.0001 0.0010 0.0100 0.1000 1.0000 0.0001 0.0010 0.0100 0.1000 1.0000 10.0000 TD/GDP observed

Opal 2005 Jeanne 2004 Andrew 1992 Fran 1996 Katrina 2005 Allen 1980 Hugo 1989 Isabel 2003 Elena 1985 Erin 1995

Bill 2003 Allison 1989 Charley 1998 Isidore2002 Dennis 1999 Alberto 1994 Bob 1991 Gloria 1985 Gaston 2004 Keith 1988 Jerry 1989 Irene 1999 Danny 1997 Chantal 1989 Allison 2001

Floyd 1999 Bret 1999 Emilly 1993

Alicia 1983 Frances 2004

Lili 2002 Bonnie 1998 Charley 2004

Ivan 2004

R2 = 0.60

• A loss of 1 ha of wetland in the model corresponded to an

average $33,000 increase in storm damage (median = $5,000) from specific storms.

• Taking into account the annual probability of hits by hurricanes
• f varying intensities, the annual value of coastal wetlands

ranged from $250 to $51,000/ha/yr, with a mean

• f

$8,240/ha/yr (median = $3,230/ha/yr).

• Coastal wetlands in the U.S. were estimated to currently provide

$23.2 Billion/yr in storm protection services.

Costanza, R., O. Pérez-Maqueo, M. L. Martinez, P. Sutton, S.

• J. Anderson, and K. Mulder, “The value of coastal wetlands

for hurricane protection,” Ambio 37:241-248, 2008.

Cases

Human Impact & Recreation Amenities (Research/Management Model)

Adirondack Park

• 6-million acre state park,

established in 1880s.

• No harvesting or timber

management on public land.

• Public land managed almost

exclusively for wilderness / recreation.

• Matrix of mountains/lakes.
• Interspersed with a population
• f 131,000 (14 people/sq. mi.)
• Public land managed by NY

Department of Environmental Conservation (DEC).

• 53 management units.
• Wilderness, Wild Forest,

Primitive, Canoe, Intensive Use Areas

Adirondack Forest Preserve

Beier et al. (2008) Ecosystems

Provision Model

Add rasters together, slice into 10 equal-area classes

+ + +

Slice each raster into 20 equal-area classes

Distance to exemplary aquatic communities

Provision Index

Distance to Megawetlands Ecosystem Rarity Distance to State Threatened/Endangered Animal Habitat

1-10 scale Blue = High Provision Red = Low Provision

Provision Index

Use Model

+ +

Use Index

Distance to Roads Distance to Trails Distance to Recreation Points (Lean-tos, Boat Launches, etc.)

Use Index

1-10 scale Blue = High Use Red = Low Use

Disturbance Model

+ +

Disturbance Index

+

Acid Deposition Distance to Structures Distance to Aquatic Invasives Index of Biotic Integrity

Disturbance Index

1-10 scale Blue = Low Disturbance Red = High Disturbance Islan ands ds Core

• re Areas

Combining rasters illuminates relationships between provision, use & disturbance

Provision minus Use Green = High Provision, Low Use Red = Low Provision, High Use

PCA1 PCA2 3 2 1

• 1
• 2
• 3

2 1

• 1
• 2
• 3

Wild Forest Wilderness/Primitive

William_Whitney West_Canada_Lake

Use

Using index scores to classify management units

PCA1 PCA2 3 2 1

• 1
• 2
• 3

2 1

• 1
• 2
• 3

Wild Forest Wilderness/Primitive

William_Whitney West_Canada_Lake

“Average” Units

Use

Using index scores to classify management units

PCA1 PCA2 3 2 1

• 1
• 2
• 3

2 1

• 1
• 2
• 3

Wild Forest Wilderness/Primitive

William_Whitney West_Canada_Lake

High Prov. Low Use Low Dist.

Use

Using index scores to classify management units

PCA1 PCA2 3 2 1

• 1
• 2
• 3

2 1

• 1
• 2
• 3

Wild Forest Wilderness/Primitive

William_Whitney West_Canada_Lake

Silver_Lake Siamese_Ponds Sentinel_Range Raquette_River Pigeon_Lake Pharoah_Lake Pepperbox McKenzie_Mt Jay_Mt Hoffman_Notch High_Peaks Ha_De_Ron_Dah Giant_Mt Five_Ponds Dix Blue_Ridge Wilmington Wilcox_Lake Whitehill Watson_East_Triangle Vanderwhacker Taylor_Pond Split_Rock Shaker_Mt Sargent_Ponds Saranac_Lakes Raquette_Jordan_Boreal Moose_River_Plains Lake_George Jessup_River Independence_River Hammond_Pond_West Hammond_Pond Grasse_River Fulton_Chain Ferris_Lake Debar_Mt Cranberry_Lake Chazy_Highlands Blue_Mt Black_River Aldrich_Pond West_Canada_Mt Wakeley_Mt Round_Lake Lake_Champlain_Islands Hurricane_Mt Hudson_Gorge Bog_River Whiteface_Mt St_Regis

Low Prov. High Use High Dist.

Use

Using index scores to classify management units

ES & NYSERDA

Scoping  Research  Management