http://invest4triangle.wikispaces.com/
Ecosystem Service Valuation and Biodiversity Preservation in the Upper Neuse Watershed of
North Carolina: Are ecosystem service markets likely to preserve biodiversity in a highly
urbanizing region?
Hess, G.R., K. Bigsby, C. Clary, N. Harris, M. Lawler, M. McHale, A. Raimondi, S. Shifflett, N.
Wisenbaker
INTRODUCTION:









Regulatory Markets - market based approach for environmentally sound solutions
Market Diversity - potential for many markets are developing - from wetlands and stream
restoration, to endangered species, to carbon credits
Stacking Services - some propose stacking of services - why would this be something we
would want to do?
InVEST - developed to be a regional scale model with moderate resolution that provides
a spatial medium for stacking services across a landscape
Why is this a new thing? What else has been done? (i.e. up until invest was available the
studies on ES tended to be across very broad spatial scales (constanza as an example) or
at very small scales and on a service at a time - neither seemed to be very valuable for
market creation.
How has invest been used? What have the results shown? (show where the models have
been used, what questions they were able to answer (mainly do ES and Biodiversity
match up?), and if they actually lead to the preservation of open
space/ecosystems/biodiversity and where.
Some other places ecosystem service markets have worked are.....
We would like to build on these analyses - see if biodiversity and ES overlap (and all the
other fancy terms george talks about)
and then ALSO ask the question - is it likely in an area with rapid development and
increasingly high land values - would the value of stacked services be able to compete?
Therefore - if the markets were established would it be likely to save land from
development?
<<Notice - you start with the big picture ideas and dwindle your way down to the last paragraph
which is the "our goals were" paragraph>>
METHODS:

I think this is obvious - talk about the study area, the different models (including input
variable tables), and also the land value analyses
Biodiversity:
Areas of conservation concern
In theory, if conservation areas correspond to areas of high economically-valued ecosystem services,
motivations for land conservation justifiably encompass benefits to nature and human welfare. We
evaluated this assumption for in our Upper Neuse study area. We used two different independent
methods to designate areas of conservation area. First, we used species distribution models developed
by North Carolina GAP analysis project (www.basic.ncsu.edu/ncgap) to calculate species richness
estimates of terrestrial vertebrates. Second, we used significant natural heritage areas and element
occurrences identified by the North Carolina Natural Heritage Program (www. ncnhp.org) following
methods developed by the Nature Conservancy. We employed three metrics to evaluate concordance
between areas designated as conservation concern and those representing the highest quartile for
stacked ecosystem services following Hess et al (2006). Completeness was calculated as the proportion
of high staked ecosystem services areas encompassed within each conservation area type (based on
species richness, significant natural heritage areas, and element occurrences). Representation was
calculated as the proportion of areas of high staked ecosystem services included in at least one area of
conservation concern. Overlap was calculated as the proportion of areas concordant between high
ecosystem services and designated areas of high conservation value.
Carbon:
We used the definitions provided by InVEST 1.005 Beta User’s Guide to determine input
parameters,
Aboveground biomass comprises all living plant material above the soil (e.g., bark,
trunks, branches, leaves). Belowground biomass encompasses the living root systems of
aboveground biomass. Soil organic matter is the organic component of soil, and
represents the largest terrestrial carbon pool. Dead organic matter includes litter as well
as lying and standing dead wood. (citation for InVEST guidebook)
Based on the definitions above, we searched the literature for relevant input data. When the literature
presented data ranges, we determined the average of the range for our input. Where necessary data were
converted to Mg C ha-1 for the model.
Table 1. Input data for the Carbon Model in Mg C ha-1. See appendix for detailed calculations and citations.
LULC Code
1
2
3
4
5
6
7
LULC Name
Open Water
Urban
Barren
Forest
Grassland
Agriculture
Wetland
C_above
0
33
0
69.95
0.239
0.98
52
C_below
0
6.6
0
14.68
0.16
0
10
C_soil
0
82.5
0
109
117
4.56
163
C_dead
0
0
0
0
0
0
0
APPENDIX: Input Data for the C Model
URBAN C_above
George’s class found 1,595,000 tons C
1 short ton = 907 ,184.74 grams (Google calculator)
1 gram = 1 X 10-6 Megagrams (Mg) (Google calculator)
Therefore George’s class found 1,446,959.66 Mg C in Raleigh
Raleigh is 182 square miles (http://quickfacts.census.gov/qfd/states/37/3755000.html) (114 plus
extraterritorial jurisdiction)
1 square miles = 258.998811 hectares (Google calculator)
Therefore Raleigh is 47137.78 hectares
Therefore 33.83698 MgHa-1
URBAN C_below
Calculated C_below by finding 20% of C_above
Therefore C_below is 6.6 Mg Ha-1
URBAN C_soil
1 meter squared = 0.0001 hectares (Google calculator)
1 kilogram = 0.001 megagrams (Google calculator)
We used Soil Organic Carbon (SOC) to a depth of 1 m (100cm) from the literature of other urban
areas in the south:
7.1 kg m-2
Park use
Pouyat et al 2006
14.4 kg m-2
residential grass
Pouyat et al 2006
8.2 kg m-2
urban
Pouyat et al 2002
3.3 kg m-2
residential grass
Pouyat et al 2006
We took the average of those numbers therefore: 8.25 kg m-2
Which converts to 82.5 Mg Ha-1
FOREST C_above
68.68 Mg ha-1
North Carolina Han et al 2005
70.75 Mg ha-1
SC Piedmont
Wang et al 2008
63.63 Mg ha-1
SC Piedmont
Wang et al 2008
76.75 Mg ha-1
SC Piedmont
Wang et al 2008
The average of the above numbers is: 69.9525 Mg ha-1
FOREST C_below
Calculated C_below by finding 21% of C_above (Saugier et al 2001)
Therefore C_below is 14.68 Mg ha-1
FOREST C_soil
We used Soil Organic Carbon (SOC) to a depth of 1 m (100cm) considering a Temperate Warm
forest:
71
Pouyat et. al. 2002
147
Lorenz and Lal 2009, Robinson 2007, Saugier et. al. 2001
The average is therefore: 109 Mg ha-1
GRASSLAND C_above
0.239 Mg ha -1 Han et al 2005
GRASSLAND C_below
Calculated C_below by finding 67% of the C_above (Saugier et al 2001)
Therefore: 0.16013 Mg ha -1
GRASSLAND C_soil
117 Mg ha -1 Lorenz and Lal 2009
AGRICULTURE C_soil
We used Soil Organic Carbon (SOC) to a depth of 1 m (100cm)
4.2
mid-atlantic cropland
Pouyat et al 2002
8
Crops
Lorenz et Lal 2009, Robinson 2007, Saugier 2001
1.5
average for silage cropped soils
Franzluebbers 2007
The average of which is: 4.56 Mg ha -1
AGRICULTURE C_above
0.983 Mg ha -1 Han et al 2005
WETLAND C_above
52 Mg ha-1 (Geise et al 2003 having found an average of biomass numbers presented and dividing
that average by 50% carbon)
WETLAND C_below
Assumed below was 20% of above Saugier et al 2001
Therefore: 10 Mg ha-1
WETLAND C_soil
Calculated average of SOC numbers presented in Giese et al. 2003: therefore 163 Mg ha-1
Pollination:
The INVEST pollination model requires a list of bee species or guilds and a land cover map. The model
uses various parameters to relate bee species characteristics (e.g., foraging distance and nesting site
preference) to habitat and pollination resource availability and outputs a spatially explicit map of
pollination service value. For our study area, we compiled a list of bee species and parameters by
consulting the literature and local apiologists (Table 1, Bambara, Hines, Roos, Roulston). We then
identified parameters for habitat and pollination resource availability by synthesizing our knowledge of
the literature (Steffan-Dewenter et al. 2002; Ricketts et al. 2008) with expert opinion (Bambara, Hines,
Roos, Roulston).
Table 1.
Species
Body Size
(IT, mm)
Foraging
Distance
(m)
Ground
Nesting
Cavity
Nesting
Spring
Activity
Summer
Activity
Fall
Activity
Apis
XX 18
929
No
Yes 13
.3
.5
.2
Bombus
401 17
Yes 14
No
.3
.6
.1
Andrena
206 15
Yes
No 15
.6
.2
.2
Halictus
XX 19
260
Yes
No 16
.4
.5
.1
Osmia
3.5 1
1990
No
Yes 5
0.9 1
0.1
0
1451
Yes
No 6
0
0.7
0.3
Xenoglossa
Peponapis
3.5 2
1320
Yes
No 7
0
0.8
0.2
Xylocopa**
7.25 3
720
No
Yes 8
0.4 11
0.4
0.2
Habropoda
4.29 4
2560
Yes
No
1 12
0
0
Cavity
Nesting
Ground
Nesting
Floral
Resources
Spring
9
Table 2.
Land Use
Land Cover
Floral
Resources
Summer
Floral
Resources
Fall
0
0
Water
0
0 0
Urban
1
0.25 .25
.25
.25
Barren
0.25
0.5 .15
.15
.15
Forest
1
0.75 1
.1
.1
.25
1 .5
.75
.75
Grassland
Agriculture
Wetlands
0
0.5 .5
0.75
0 .4
.75
.5
.4
.4
1 – Gordon et al. 2003 Life history and nest biology of the mason bee Osmia (Acanthosmioides) Integra
Cresson in coastal dunes (Hymenoptera : Megachilidae)
2 – Bullock et al. 1999 Relationships among Body Size, Wing Size and Mass in Bees from a Tropical Dry
Forest inméxico
3 – Skandalis et al. 2009 Body Size and Shape of the Large Carpenter Bee, Xylocopa virginica (L.)
(Hymenoptera: Apidae)
4 – Jane Monroe thesis 2010 (under the direction of Michael Mesler) effects of resource availability on
foraging trip duration in the silver bee, habropoda miserabilis
5 – Maccagnani et al. 2007 Osmia cornuta management in pear orchards
6 – Julier et al. 2009 Wild Bee Abundance and Pollination Service in Cultivated Pumpkins:Farm
Management, Nesting Behavior and Landscape Effects
7 – Willis et al. 1995 foraging dynamics of peponapzs pruznosa (hymenoptera:
anthophoridae) on pumpkin (cucurbzta pepo) in southern ontario
8 – Jenkins et al. 2004 Cavity-nesting Hymenoptera in Disturbed Habitats of Georgia and
South Carolina: Nest Architecture and Seasonal Occurrence
9 – Barthell et al. 1998 Nesting Biology of the Solitary Digger Bee Habropoda depressa (Hymenoptera:
Anthophoridae)in Urban and Island Environments
10 – Michelba et al. (1968) late season foraging activities of xenoglossa gabbii crawfordi cockerell (hymenoptera - apoidea) And Julier et al. (2009) Wild Bee Abundance and Pollination Service in
Cultivated Pumpkins: Farm Management, Nesting Behavior and Landscape Effects – best guess based on
Michelbae title (couldn’t actually find the article yet… and the discussion in Julier)
11 – Gerling et al. (1978) Biology and Mating Behavior of Xylocopa virginica L. (Hymenoptera,
Anthophoridae) (best guess based on info in the article)
12 – Cane et al. (1998) Estimation of Bee Size Using Intertegular Span (best guess on info in the article)
13 – Seeley 1978
14 – Ricketts, T.H. et al. (2008), Landscape effects on crop pollination services: are there general
patterns?. Ecology Letters, 11: 499–515.
15 – Gathmann 2002
16 – Rozen 2008
17 – Charman 2010
18 – Bullock 1999
19 – Sahli 2007
Water Yield/Run Off:
Potential Costs due to Developing Agriculture and Forest Land Covers
“Retention systems capture a volume of runoff and retain that volume until it is displaced in part or in
total by the next runoff event. Retention systems therefore maintain a significant permanent pool
volume of water between runoff events” (USEPA 1999). Based on published cost data of actual storm
water better management practices (BMPs) in North Carolina, the method that is described below was
created to make estimates of mitigation costs associated with various storm-water BMPs if Forest and
Agricultural areas were developed.
The water quality volume is often defined as the volume of runoff that the storm-water BMP is designed
to store and/or treat, which is often based on a design precipitation depth or depth of runoff (Weiss et
al. 2007). We used the InVEST Water Yield Model to determine how much water would be newly
introduced to urban areas by converting Agricultural and Forest Land Cover/Land Use data from 2001.
The subsequent changes in water yield were treated as the run-off expected if those areas were
developed.
The values reported do not include costs of pretreatment units which may be required, design or
engineering fees, permit fees, land costs, contingencies, etc. The costs of storm-water BMPs are usually
reported along with the corresponding watershed size and/or the water quality volume (WQV) for which
the storm-water BMP was designed. Unfortunately, the associated costs are not readily available for the
Upper Neuse Watershed. In their place, we substituted values from a study completed in Mecklenburg
County, NC to determine what some expected costs might be if the same BMPs were implemented (AF
2010).
Nutrient Retention:
Introduction
The InVEST Nutrient Retention is a series of three models. The first, Water Yield, requires 6 “biophysical”
raster datasets: precipitation, potential evapotranspiration, soil depth, plant available water fraction,
land use/land cover, and watersheds. In addition, it requires a biophysical table that references each
land use/land cover category and assigns the corresponding evapotranspiration expected for that type
of land cover, the root depth of any plant life that might be at that location, the amount of nitrogen
loaded to the environment by the land cover/land use type, and lastly the efficiency of the land cover to
remove the nutrient of choice. After gathering the data from the appropriate sources (see Figure 1), the
model was able to produce a new raster, water yield per cell. This is the amount of water available at
each cell where nutrients of interest (NOI) can be loaded.
Source/Process
The first model, Water Yield, required various data points. All cells corresponding to water or urban in
our land cover data were assigned a soil depth of 0. The plant available water content (PAWC) values
were loaded into ArcMap using the Soil Data Viewer. Null data points were determined using
neighborhood geospatial statistics. For root depth, each land cover was determined based on most
dominant species. For the Forest Land Cover, the U.S. Forestry service was referenced to determine the
most abundant species in the area (Oak, Pine, and Hickory.) The documented root depths for each genus
were cumulatively averaged to determine average root depth for Forest Land Cover. For Agriculture, the
NC agricultural census provided the most dominant crops in the Upper Neuse (Sweet potato, potato,
soybean, corn, oat, barley, rye, tobacco, and wheat.) The root depths were averaged and applied to the
Agriculture Land Cover. For Wetlands, two dominant NC wetland species were used (Sweetgum and
Loblolly Pine). Nutrient loading values were obtained from the North Carolina DENR/DWQ WARMF
Report and then divided by the number of hectares provided in the Upper Neuse Watershed. ETK and
EFF_N provided by WWF were used to determine evapotranspiration and nitrogen removal efficiency by
land cover.
The next model, Nutrient Retention, requires 4 raster datasets: digital elevation model (DEM), water
yield per cell, land use/land cover, a sub-watershed raster, and a watershed raster. In addition, two
database files are needed: the Biophysical Table as described above and a Water Purification Table. The
Water Purification Table requires watersheds to be delineated, a calibration unit based on validated
reading at any point of interest, the cost of removing the NOI, the maximum allowable amount of NOI
that can be loaded to the watershed, the time span for the cost of removing the NOI (i.e. how long it will
be at that cost), and a discount rate used in net present value calculations. See Figure 1 for each value
applied. The Nutrient Retention model produces a raster dataset and a database file necessary for the
final model, Valuation. The raster dataset reveals where the NOI is immobilized. Where the NOI is
immobilized is determined based on the relative elevation of the land cover, the NOI loading values at
higher elevations, and the efficiency of the land cover to absorb the NOI. These values were normalized
by dividing all cells by the largest possible cell value using Raster Calculator.
The last model, Valuation, requires 3 raster datasets and 2 database file. The raster datasets include the
DEM, nutrient retained (produced by the second model), and watershed raster file. The cost for the
valuation model was adopted from the NC DENR/DWQ WARMF Report. The report estimates a future
Ecosystem Enhancement Program (EEF) nitrogen offset rate of $44/lb of nitrogen. The rate was
converted to $/kg of nitrogen. Based on proposed 2 stage Nutrient Strategy Rules from the NC
Environmental Management Commission, the cost value is applied through the completion of the first
stage of action. A standardized 5% discount rate was applied. Maximum annual allowable nitrogen
loading for our single point of interest (Falls Lake Dam) was determined from NCAC 15A Rule .0234 and
.0279. Rule .0234 (6) (A) is aimed at those dischargers greater than/equal to .05 MGD. It estimates that
15% of the annual discharge limit in section (6)(A) should be allowed through the dam. 15% of the Stage
1 annual discharge limit provided in proposed rule NCAC 15A .0279 (19,303 lbs/yr) was adopted as the
maximum annual allowable nitrogen loading value at our point of interest. This value was then
converted from lbs/yr to kg/yr.
Figure 1: Data and Associated Citations.
Where used
in Model
Data Name
Maximum
Allowable
Nitrogen
Loading
(Entire
Upper Neuse
Watershed)
Nitrogen
Loading (At
point of
interest –
Falls Dam)
Value
58,370
.99
8,756.
00
Perso
n
who
foun
d
data
Unit
Citation
kg
N/yr
North Carolina Department of Environment and Natural
Resources & Division of Water Quality. (2010) 15A NCAC 2B
.0279 (4)(a); Proposed Falls Water Supply Nutrient Strategy:
Wastewater Discharge. November, 2, 2010, Retrieved at:
http://portal.ncdenr.org/c/document_library/get_file?folderId=
209700&name=DLFE-19076.pdf.
Water
Purification
Table;
Nutrient
Retention
Model Input
CC
kgN/
yr
North Carolina Department of Environment and Natural
Resources & Division of Water Quality. (2010) 15A NCAC 2B
.0234 (6)(a); Neuse River Basin – Nutrient Sensitive Waters
Management Strategies: Wastewater Discharge Requirements.
November, 4, 2010, Retrieved at:
http://h2o.enr.state.nc.us/nos/2b-0234.pdf.
Water
Purification
Table:
Nutrient
Retention
Model Input
CC
Water
Purification
Table;
Nutrient
Retention
Model Input
CC
Cost
97.00
$/kg
/yr
North Carolina Department of Environment and Natural
Resources. (2010) Fiscal Analysis for Proposed Nutrient Strategy
for Falls of Neuse Reservoir. November, 2, 2010, Retrieved at:
http://portal.ncdenr.org/c/document_library/get_file?uuid=2a2
9f5a4-3db1-4c63-bd63-cad51a5ac385&groupId=38364.
(raster
)
mm
Prism Climate Group. (2010). Oregon State University. October,
2010, Retrieved at: http://www.prism.oregonstate.edu/.
Precipitation
; Water Yield
Input Raster
NW
Precipitation
Evapotranspi
ration
(raster
)
Food and Agriculture Organization of the United Nations (2010).
October, 2010, Retrieved at: http://www.fao.org/geonetwork/.
Potential
Evapotranspi
ration
NW
mm
LULC
(raster
)
n/a
???
Water Yield
Model Input
KB
Watershed
DEM
Soil Depth
Plant
Available
Water
Content
Evapotranpir
ation
coefficient
Root_depth
(raster
)
(raster
)
(raster
)
(raster
)
NC OneMap. (2007). Local Watershed Plans –EEP. September,
2010. Retrieved at:
www.lib.unc.edu/reference/gis/datafinder/index.html
Water Yield
Model Input,
Nutrient
Retention
Model Input
ML/S
DS
KB
n/a
???
Nutrient
Retention
Model Input,
Valuation
Data Input
Water yield
Model Input
NW/S
DS
Mm
Soil Survey Staff, Natural Resources Conservation Service, United
States Department of Agriculture. Soil survey of Upper Neuse
Watershed, North Carolina [Online WWW]. Available URL:
“http://soildatamart.nrcs.usda.gov/Survey.aspx?State=NC”
[Accessed September 2010].
Water yield
model input
NW/S
DS
%
Soil Survey Staff, Natural Resources Conservation Service, United
States Department of Agriculture. Soil survey of Upper Neuse
Watershed, North Carolina [Online WWW]. Available URL:
“http://soildatamart.nrcs.usda.gov/Survey.aspx?State=NC”
[Accessed September 2010].
The Nature Conservancy. (2010). InVEST 1.005 Beta User’s
Guide: Integrated Valuation of Ecosystem Services and
Tradeoffs. November, 2010, Retrieved at:
http://www.naturalcapitalproject.org/ConEX/InVEST_1.005beta
_Users_Guide.pdf.
Water yield
model input
SDS
Mayaki, W.C. Teare, I.D., and Stone, L.R. (1976) Top and Root
Growth of Irrigated and Non-irrigated Soybeans.Crop Science,
Vol 16. January- February 1976.
Biophyical
Table Value;
Water Yield
Model Input,
Nutrient
Retention
Model Input.
SDS
n/a
(raster
)
Multip
le
values
mm
Mou, P. Jones, R.H., Mitchell, R.J. (1995) Spatial Distribution of
Above-Ground Biomass and Soil Nutrients. Functional Ecology,
Vol. 9, No. 4, pp. 689-699.
Evans, R., Cassel, D.K., and Sneed, R.E. Soil, Water, and Crop
Characteristics Important to Irrigation Scheduling. (1996) North
Carolina Cooperative Extension Service. Publication Number: AG
452-1. November, 6, 2010, Retrieved at:
http://www.bae.ncsu.edu/programs/extension/evans/ag452-
1.html
Food and Agriculture Organization of the United Nations (2010).
November, 2010, Retrieved at:
http://www.fao.org/nr/water/cropinfo_tobacco.html.
The Nature Conservancy. (2010). InVEST 1.005 Beta User’s
Guide: Integrated Valuation of Ecosystem Services and
Tradeoffs. November, 2010, Retrieved at:
http://www.naturalcapitalproject.org/ConEX/InVEST_1.005beta
_Users_Guide.pdf.
Palta, J.A., Mingtan, L.T., and Fillery, R.P. (2004). Rooting
patterns in wheat differing in vigour are related to the early
uptake of nitrogen in deep sandy soils. Crop science. November,
8, 2010, Retrieved at:
http://www.cropscience.org.au/icsc2004/poster/2/4/1/473_palt
aja.htm.
Nitrogen
Loading
(load_n)
Nitrogen
Removal
Efficiency
Multip
le
values
Multip
le
values
(eff_N)
LIT REVIEW:
North Carolina Department of Environment and Natural
Resources & Division of Water Quality. (2009). Falls Lake
Watershed Analysis Risk Management Framework (WARMF)
Development Draft. November, 2, 2010, Retrieved at:
http://h2o.enr.state.nc.us/tmdl/documents/July09DraftFallsLak
eWatershedModelReport.pdf.
Biophyical
Table Value;
Water Yield
Model Input,
Nutrient
Retention
Model Input.
SDS
CC
Lin, J.P. (2004). Review of Published Export Coefficient and Event
Mean Concentration (EMC) Data. Retrieved at:
http://el.erdc.usace.army.mil/elpubs/pdf/tnwrap04-3.pdf.
Biophyical
Table Value;
Water Yield
Model Input,
Nutrient
Retention
Model Input.
<<Neal is currently gathering the literature for this section.>>
RESULTS:




Again the order of these depends on the graphs we make and how we want to proceed
with the story - probably in two-three sections:
1. General Results - Maps of different services (descriptive
2. ES and Biodiversity
3. Land Values and ES
1. General Results
<<We need to determine the order of things before we start putting maps/etc into this section.>>
2. Significant Natural Heritage Areas
The NC Natural Heritage Program has delineated Significant Natural Heritage Areas that are core
conservation lands in the state and of the highest priority for protection. The boundaries of these areas
are established to ensure long-term persistence of species and natural communities of conservation
interest. Significant Natural Heritage Areas occupy 142 sq-km (7%) of the 1,961 sk-km Upper Neuse
River Basin (Table 1).
Table 1. Distribution of ecosystem Significant Natural Heritage Areas by quantile of ecosystem service

One question commonly about the concordance of ecosystem services with biological diversity
is whether the protection of land providing high levels of ecosystem service would also protect
areas of high value for species and natural communities of conservation concern. We found
that the lands providing the highest biophysical level (top quartile) of ecosystem services in the
Upper Neuse Basin overlapped 22% of the Significant Natural Heritage Areas in the Basin
(Figure 1, Table 1). Lands in the top two quartiles of ecosystem service provision (972 sq-km)
overlapped 62% (87 sq-km) of the Heritage Areas.
Figure 1. Distribution of ecosystem services within Significant Natural Heritage Areas.
A related question is whether conserving areas based on their value for species and natural
communities of conservation concern would include areas of high ecosystem service value.
The Significant Natural Heritage Areas covered 7% of the total area in the top quartile of
ecosystem services, defined biophysically (Table 1).
Element Occurrences
The Natural Heritage Program also maps the locations of species and natural communities of
conservation concern as points called element occurrences, which are available in a
geographic information system format. Each element occurrence has associated information
about its status, including national and state rank, current status (extinct, extant, historic, etc),
and uncertainty in its location. We started with the full element occurrence data for our study
area and removed records that were marked as destroyed, extinct, or historic; that had not
been observed during the past 25 years; or that had an estimated locational accuracy of less
than 20%. There were 563 occurrences of 87 different species that met these criteria (Table 2).
We used these to estimate the proportion of occurrences included in each of our ecosystem
service quartiles (completeness) and the proportion of the 87 different species included at least
once in the quartiles (representation). Because there is uncertainty in the location of each
element occurrence, we counted occurrences as included if they were within a given (in the
database) uncertainty distance of a portion of the landscape in an ecosystem service quartile.
[language still a bit rough]
We found that the land in the top quartile of ecosystem services would likely include 71% of all
species and natural communities of concern at least once (representation), and 39% of all
element occurrences (completeness) (Table 2). A large proportion of species and communities
in wetlands (93% representation, 82% completeness) were included. Representation and,
especially, completeness were less for aquatic and terrestrial species.
Table 2. Cumulative completeness and representation by type of species and community type.
Completeness is the proportion of the total number of occurrences of species or community
included in the quartile and all higher quartiles. Representation is the proportion of unique
species or communities included.
BIODIVERSITY- Species Richness from GAP
Vertebrate species richness per pixel ant 30m resolution. Richness ranges from 5-169 species
with dark areas representing high richness values.
Mean values in species richness are not significantly different across ecosystem service
quartiles (more analyses to come....)
REVISED FIGURE 3 (NCH-11/28/10)
The final metric of biodiversity we used to evaluate concordance with ecosystem service was
vertebrate species richness. This data was
obtained from 229 species distribution models developed by NC GAP project. We found that
geographic locations with the highest
stacked ecosystem services, represented by quartile four, also had the highest average
species richness. Average species
varied significantly across the ecosystem service quartiles and this range differed by a
maximum 12 species between the highest and
lowest quartile. However, the representation of species differ not vary significantly or
cumaltively across the ecosystem service quartiles. For example, all species in the study area
occured in the the lowest ecosystem service quartile; while 98% of the species occurred in the
quartile with the highest stacked ecosystem services.
3. COMPARING PROPERTY VALUES TO PAYMENTS FOR ECOSYSTEM SERVICES
One question of interest is whether payments for ecosystem services can compete with the opportunity
cost of not selling property for development. This question is of particular import in areas near expanding
cities. We examined this issue by comparing property value to payments for ecosystem service in our
study area.
We used the output from InVEST models for carbon, nitrogen retention, and pollination to estimate
payments for ecosystem services. For carbon, we considered both storage and sequestration. The
estimated value ($) of carbon storage is an InVEST model output; we evaluated $<<social cost>> and
$<<middle cost, based on ...>>. For sequestration, we ran the InVEST model using estimates for annual
net primary productivity for each land cover in our study area to provide an estimate of one year of
payments for sequestration at both the social and middle cost. We used this information to estimate the
value ($) of 20 annual payments for sequestration, with a 1% discount rate reflecting current economic
conditions. For nitrogen retention, we used output from the InVEST model assuming a cost of $97 per
kilogram nitrogen removed for a 20-year period with a 1% discount rate. <<For pollination, we estimated
the value of natural pollination services by calculating the replacement cost using managed pollinators.
To do so we assumed managed pollinators cost $300 an acre for the entire growing season (####) and
that all agriculture areas are pollinated by managed pollinators. We normalized the replacement cost
based on the pollination ecosystem service value to distribute the costs proportionally across the
landscape. These values were used to estimate the cost of 20 annual payments for managed pollination
services with a 1 % discount rate. - bigsby>>
We used the tax value of each parcel of land as an estimate of property value. Triangle Land
Conservancy compiled tax value data from the most recent tax appraisal for the six counties in April,
2010, and shared them with us. Tax appraisals are not coordinated among counties, the the appraisal
dates varied: Durham (2008), Franklin (2004), Granville (2010), Orange (2009), Person (2005), Wake
(2008). We adjusted all property values for inflation to 2010 dollars using factors from the Bureau of Labor
Statistics inflation calculator (Table, http://www.bls.gov/data/inflation_calculator.htm 2010 Nov 24). To
bring these data into alignment with the spatial scale of the InVEST model's output, we calculated the
value of property for each 30mx30m pixel in our study area.
Table: Factors to bring property values to 2010 dollars.
County / year of appraisal
Franklin / 2004
Person / 2005
Durham & Wake / 2008
Orange /2009 & Granville / 2010
Inflation factor
1.16
1.12
1.02
1.0
We divided property value of the land by the value ($) of ecosystem services, per 30mx30m pixel. Thus,
values larger than one indicate places where property value exceed the value of ecosystem services;
where values are less than one, the value of the ecosystem services exceeds the land value. The result
was arranged in order-of-magnitude categories, with the exception of the category 0.95 - 1.05 for values
near one (where property value and ecosystem service value are nearly equal).
Category Range
1 0.0 - 0.001
2 0.001 - 0.01
3 0.01 - 0.1
4 0.1 - 0.95
5 0.95 - 1.05
6 1.05 - 10
7 10 - 100
8 100 - 1,000
9 1,000 - 10,000
10 10,000 - 100,000
11 100,000 - 1,000,000
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We compared property value to ecosystem service value ($) for the following combinations:
Nitrogen retention (20 years) - because this is generally the highest value ($) ecosystem service
in our study area and because nutrient retention is an important issue here because of our
drinking water supply and because markets are developing <<rough language>>
Carbon sequestration (20 years) - because there is a market for this, and we can imagine
property owners taking a 20-year option rather than selling their property now IF the option price
we high enough
Carbon storage + sequestration (20 years) - because markets may be developing for storage (is
this true?)
Stacked, high values: N-retention (20 years), C-storage (social cost), C-sequestration (social
cost, 20 years), pollination - to evaluate what paying the highest possible values in a system that
allow stacking reveals.
DISCUSSION:
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Why do each of these results matter? so 1. Our Biodiversity and ES results relate to other studies and either disagree- agree- or
both ;) or we don't know - Include a conclusion on these results - (i.e. we think that these
results show that ES and Biodiversity are definitely related and that if we protected land
for one or the other we would be preserving both functioning ecosystems and biodiversity
that we value <<I made that up>>)
2. Our results show that in some areas ES have a high value and for now can outcompete
land prices - but if the highly urbanized region expands and the prices increase it is
unlikely - <<again making things up>>
3. Other research shows that this is likely to be expected - so ES might work in oregon
with all the hippies next to an undevelopable mountain but the triangle and its never
ending sprawling growth will never survive under this framework
4. Besides the fact that stacking values makes ES values higher - and some people think
that stacking will never work - and this is why (bendor) - if that is the case then we really
have no chance <<remember I am making things up>>
5. Furthermore, what happens when the method for technologically replacing an ES
becomes cheaper than preserving land -NY worked because NY saw that they were
saving money by saving the watershed.
6. Finally - Also interesting to note is that ecosystem services were predicted to be highly
heterogeneous across the landscape - that makes preserving large areas of high service
value a bit difficult. We should also keep in mind that areas of high value are close to
people - as predicted by this model - what does that mean for preserving large open
spaces that actually can still function effectively. Areas on edges of human habitats are
more highly valued - often edge areas are less quality ecosystems - if we try to maximize
ES would we actually create the type of functioning ecosystem that we need?
BIODIVERSITY DISCUSSION
In our study area, area of high ecosystem service did not match areas of high value for biodiversity well.
Protecting areas of high ecosystem service was not an efficient approach if one's goal was to protect
areas of conservation concern for biodiversity. Although protecting areas providing ecosystem services in
the top two quartiles would result in protecting 66% of the Significant Natural Heritage Areas, the SNHAs
are only 9% of the area in the top two quartiles (87 sq-km of 972 sq-km) (Table 1, Figure 1). Conversely,
protecting areas for biodiversity is not sufficient if one's goal is to protect areas of high ecosystem service
value. Protecting all of the Significant Natural Heritage Areas in the study area would conserve only 7% of
the land in the top quartile of ecosystem service value (Table 1).
We noted that the land providing ecosystem services in the top quartile included 82% of the wetland
species and communities of conservation concern (completeness), including 93% of the different species
and communities in the study area (representation). This likely reflects the high level of ecosystem
services, in terms of carbon storage and nutrient retention, afforded by wetlands.
NOTE: We need to explain that water received an ecosystem service value of zero and accounted for 1%
of the area and wound up in the lowest quartile - mainly the lake. We did not remove these because some
of the aquatic species are in the water.
Property Values Discussion
Need to check - some of the property values might reflect being in an use value program (ag, forestry,
horticulture) which artificially lowers property value because the land is being used for ag, forestry, or
horticulture. The values would be less than the market value for development. <<This is an error in the
"right" direction, in that it underestimates the property value so that the full value of the property is even
higher than reflected in this analysis. Using full value would skew the property value / ES comparison
even further toward property value.>
IMAGES are large, so I linked to the JPGs instead of displaying. These are the 20-year 1% discount rate
values.
Property values ($/ha - legend needs cleaning): PropertyValues.jpg
Full Landscape
Carbon Seq (Med): CSeqMed.jpg
Pollination: Pollination.jpg
Nitrogen retention: Nitrogen.jpg
Stacked (the above 3): Stacked.jpg
ES4 Only (top quartile)
Carbon Seq (Med): CSeqMedES4Only.jpg
Pollination:PollinES4Only.jpg
Nitrogen retention:NRetentionES4Only.jpg
Stacked (the above 3): StackedES4Only.jpg
Analysis files are on velocity: https://velocity.ncsu.edu/dl/2Obfl26/23083
Spreadsheet with analyses: PropertyValueAnalyses.xlsx
Figure 1. Comparison of property value to value ecosystem service (20 annual payments with a 1%
discount rate), shown for carbon (at <<$medium value>>), nitrogen retention, pollination, and all three
stacked. Results shown proportion in each category (color / legend) for the full study area (full) and the
area in top quartile of ecosystem services, defined biophysically (top). Property value was divided by
ecosystem service value, so that results around one occur when the values are approximately equal
(yellow outlines in read); results < 1 occur when ecosystem service value are greater than property values
(bottom portion of bars). Areas of NoData occur when tax value is undefined (government-owned land) or
ecosystem service value is zero (dividing by zero is an undefined result).
Results / Discussion
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Payments for carbon sequestration are competitive with property values for a much larger portion
of the landscape than payments for nitrogen retention and pollination (Figure 1, compare carbon
full and top quartile to N full and top quartile). This is because C-sequestration occurs throughout
the landscape, including many places distant from development where property values are
relatively low. Nitrogen retention, in contrast, attains its highest value proximate to development,
where property values are high. As a result, the $value of C-seq is higher than the $value of Nretention for a large portion of the landscape.
Stacking increases the portion of the landscape for which ecosystem services payments are
competitive with property values. From 34% to 42% for full landscape and from 52% to 64% for
the top quartile (Figure 1).
Interesting that pollination is worth more than N retention - do we believe this?
Figure 2. Comparison of property value to ecosystem service value (20 annual payments with
a 1% discount rate), shown for stacked carbon (at <<$medium value>>), nitrogen retention,
and pollination. Results shown proportion in each category (color / legend) for each land use /
cover category; barren and open water are omitted. Property value was divided by ecosystem
service value, so that results around one occur when the values are approximately equal
(yellow outlines in read); results < 1 occur when ecosystem service value are greater than
property values (bottom portion of bars). Areas of NoData occur when tax value is undefined
(government-owned land) or ecosystem service value is zero (dividing by zero is an undefined
result).
Table 1. Proportion and area of land in each cover that is in the top quartile of ecosystem
services, defined biophysically.
Proportion in
Top
Quartile
Agriculture 0%
Developed 1% (3 sq-km)
Forest
38% (1,112 sq-km)
Grass/Shrub 1% (2 sq-km)
Wetlands
99% (43 sq-km)
Cover
Proportion in Top Quartile
and
Prop/ES <= 1.05
0%
0.5%
25%
1%
80%
Results / Discussion
Eighty percent of wetland in the study area have values for ecosystem service (20-year, 1% discount,
stacked Carbon Med, pollination, nitrogen retention) greater than or comparable to property value (Figure
2), and 99% of wetlands are in the highest quartile of ecosystem service value, defined biophysically
(Table 1). Simply put, purchasing wetlands is a good bet in terms of providing high-value ecosystem
services. In contrast, only 10% of developed land has an ecosystem service value greater than or
comparable to property value and only 1% of develop land is in the highest quartile of ecosystem
services.
CONCLUSION:
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In conclusion our results show that we need to rethink the application of ES for
biodiversity protection and for the protection of ES in highly urbanizing regions