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To The University of Wyoming: The members of the Committee approve the thesis of Abigail J. Mellinger presented on July 13, 2012. Benjamin S. Rashford, Chairperson L. Steven Smutko, Chairperson Hall Sawyer, External Department Member Scott N. Lieske APPROVED: Dr. Roger Coupal, Head, Department of Agricultural and Applied Economics Dr. Frank Galey, Dean, College of Agriculture and Natural Resources

Transcript of Abby Thesis

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To The University of Wyoming:

The members of the Committee approve the thesis of Abigail J. Mellinger

presented on July 13, 2012.

Benjamin S. Rashford, Chairperson

L. Steven Smutko, Chairperson

Hall Sawyer, External Department Member

Scott N. Lieske

APPROVED:

Dr. Roger Coupal, Head, Department of Agricultural and Applied Economics

Dr. Frank Galey, Dean, College of Agriculture and Natural Resources

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Mellinger, Abigail, Economic and ecological tradeoffs of targeting conservation easements for habitat protection: A case study of Sublette County, Wyoming, M.S., Department of Agricultural and Applied Economics, August, 2012.

Extensive energy development in Sublette County, Wyoming has prompted land

management agencies to undertake compensatory (off-site) mitigation projects aimed at off-

setting adversely impacted wildlife species, particularly mule deer (Odocoileus hemionus),

pronghorn antelope (Antilocapra americana), and Greater sage grouse (Centrocercus

urophasianus). Agencies have used conservation easements, or purchases of development rights,

as a tool for protecting wildlife habitat on private agricultural lands. To most effectively mitigate

impacts to wildlife from energy development and from expanding rural residential development,

decision-makers must protect lands that offer the most biological value at the least cost. Given

increasing demand for rural, amenity-rich residential properties in Sublette County, I define the

economic value of agricultural lands as the sum of a given parcel’s productive value in

agriculture and its value in residential development.

I use propensity score matching to estimate the unobservable future residential value of

parcels currently in agricultural use and hence, assess each parcel’s economic value. I impute

the median value of residential parcels to their matched agricultural counterparts to calculate an

economic score. Similarly, I calculate a biological score for each parcel based on the parcel’s

acreage of and proximity to critical wildlife habitat. Combined, the economic and biological

scores form a production possibilities frontier that represents economically efficient

arrangements of parcels in either agricultural or residential use across the landscape of Sublette

County. I identify optimal conservation easement purchases according to four different policy

approaches and compare the current Sublette County landscape to my results.

My results indicate that while the economic efficiency of conservation easement

purchases can be improved, opportunities to protect critical biological values are limited by a

lack of key habitat on private agricultural lands. Further, I find that substantial biological values,

including those on already protected lands, are likely to continue in the absence of conservation

easements given my estimate of observing each parcel in a residential rather than agricultural

use. This suggests that resource managers should carefully target conservation easement

purchases based on parcels’ risk of development in addition to increasing efforts to carry out on-

site mitigation on public lands.

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ECONOMIC AND ECOLOGICAL TRADEOFFS OF TARGETING CONSERVATION EASEMENTS FOR HABITAT PROTECTION: A CASE STUDY

OF SUBLETTE COUNTY, WYOMING

by Abigail J. Mellinger

A thesis submitted to the University of Wyoming in partial fulfillment of the requirements

for the degree of

MASTER OF SCIENCE in

AGRICULTURAL AND APPLIED ECONOMICS

Laramie, Wyoming August 2012

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© 2012, Abigail J. Mellinger

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TABLE OF CONTENTS

1 Introduction………..…………………………………………….……………………………………. 1

2 Background…….….……………………………………………………..……………………………. 3

2.1 Oil and Gas Development in Sublette County .......................................................................... 3

2.2 Rural Residential Development and Land Use in Sublette County ........................................... 7

2.3 Impacted Species ...................................................................................................................... 8

Sage grouse and energy development ....................................................................................... 9

Mule deer and energy development ......................................................................................... 12

Pronghorn and energy development ....................................................................................... 13

2.4 The Jonah Interagency Office and Pinedale Anticline Project Office ..................................... 15

2.5 Conservation Easements .......................................................................................................... 17

3 Methods………………………………………………………………..……………………………. 19

3.1 Economic Score ....................................................................................................................... 20

Propensity Score Matching ..................................................................................................... 24

Data ......................................................................................................................................... 27

Calculating the Economic Score ............................................................................................. 32

3.2 Biological Score ...................................................................................................................... 34

3.3 Production Possibilities Frontier ............................................................................................. 39

4 Results……………………………...……………………………………………………………....... 40

4.1 Economic Score ....................................................................................................................... 40

Logit Results ............................................................................................................................ 40

Results of sub-class and caliper matching .............................................................................. 47

4.2 Biological Score Results ......................................................................................................... 50

4.3 Estimated Ecological-Economic Tradeoffs ............................................................................. 54

5 Optimal Targeting of Conservation Easements .................................................................................... 59

Targeting Approaches ............................................................................................................. 62

6 Conclusion………………...……………………………………………………..………………….. 69

7 Literature Cited …………………………………………………………………………………….. 74

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LIST OF FIGURES

Figure 1. Sublette County, Wyoming. Source: www.wy.blm.gov/jio-papo. .............................................. 4

Figure 2. Sample production possibilities frontier of land use patterns across a landscape. ..................... 19

Figure 3. Land cover in Sublette County, Wyoming ................................................................................. 31

Figure 4. Mule deer habitat and privately-owned lands in Sublette County, Wyoming. ........................... 36

Figure 5. Pronghorn habitat and privately-owned agricultural lands in Sublette County, Wyoming. ....... 37

Figure 6. Sage grouse habitat and privately-owned agricultural lands in Sublette County, Wyoming. .... 38

Figure 7. Frequency of predicted probabilities for agricultural parcels. .................................................... 44

Figure 8. Map of predicted propensity scores on agricultural parcels using AGRES model – Sublette

County, Wyoming. .............................................................................................................................. 45

Figure 9. Map of predicted propensity scores on agricultural parcels using AGRESVAC model – Sublette

County, Wyoming. .............................................................................................................................. 46

Figure 10. Map of predicted propensity scores on agricultural parcels using AGRESRESVAC model –

Sublette County, Wyoming. ................................................................................................................ 47

Figure 11. Estimated biological scores for mule deer on agricultural parcels in Sublette County,

Wyoming............................................................................................................................................. 51

Figure 12. Estimated biological scores for pronghorn on agricultural parcels in Sublette County,

Wyoming............................................................................................................................................. 52

Figure 13. Estimated biological scores for sage grouse on agricultural parcels in Sublette County,

Wyoming............................................................................................................................................. 53

Figure 14. Estimated total biological scores on agricultural parcels in Sublette County, Wyoming. ........ 54

Figure 15. Production possibilities frontier from the alternative propensity score models using median

residential values. ................................................................................................................................ 55

Figure 16. Production possibilities frontiers by species-specific biological scores estimated using

AGRESVAC model. ............................................................................................................................. 57

Figure 17. Production possibilities frontier using the expected biological score and economic score

estimated using the AGRESVAC model. ............................................................................................. 58

Figure 18. Production possibilities frontiers by species using expected biological scores and economic

score estimated using AGRESVAC model........................................................................................... 59

Figure 19. Comparison of existing conservation easements to the efficient production possibilities

frontier - AGRESVAC model. .............................................................................................................. 60

Figure 20. Comparison of existing conservation easements to the efficient production possibilities

frontier disaggregated by species - AGRESVAC model. ..................................................................... 62

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Figure 21. Total biological scores produced from alternative targeting strategies with a total budget of

$36 million. ......................................................................................................................................... 64

Figure 22. Landscape-level expected biological scores produced from alternative targeting strategies with

a total budget of $36 million. .............................................................................................................. 65

Figure 23. Relationship between parcel-level biological scores and propensity scores. ........................... 66

Figure 24. Relationship between the biological and residential values of parcels. .................................... 67

Figure 25. Ten "best" currently unconserved parcels for conservation according to each targeting

approach. Existing conservation easements designated by cross-hatch pattern. ................................ 69

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LIST OF TABLES

Table 1. Expected initial and life-of-project well field components' surface disturbance. .......................... 5

Table 2. Explanatory variables used in binary logit models. ..................................................................... 29

Table 3. Summary statistics of agricultural lands' assessed values in Sublette County. ............................ 33

Table 4. Distribution of habitat across private and public lands in Sublette County. ................................ 38

Table 5. Parameter estimates for AGRES, AGRESVAC, and AGRESRESVAC logit models. ............... 40

Table 6. Average marginal effects. ............................................................................................................ 42

Table 7. Average predicted probabilities by observed land use and model. .............................................. 43

Table 8. Average per acre value of matched residential parcels using each matching approach. ............. 48

Table 9. Predicted residential values calculated for AGRESVAC model using median, minimum, and

average values per acre of matched residential parcels. All values are in dollars per acre. ............... 50

Table 10. Summary statistics of biological scores calculated for agricultural parcels in Sublette County,

Wyoming............................................................................................................................................. 50

Table 11. Difference in biological scores for each species between current easement point and efficient

frontier................................................................................................................................................. 61

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1 Introduction

Oil and gas development in Sublette County, Wyoming has proceeded at levels relatively

unprecedented since 2002, when developers began exploration and drilling in the Jonah Infill

Drilling Project Area (JIDPA) and the Pinedale Anticline Project Area (PAPA). The

development of the fields has had substantial impacts. Combined, the two fields have

contributed substantially to the Wyoming state and federal governments in severance and other

tax revenues. Despite staggered, geographically strategic development and the implementation

of seasonal drilling restrictions and other best management practices, development has had

environmental consequences. For example, the mule deer population that relies on winter range

in the PAPA declined by nearly 60 percent during the first 10 years of development (Sawyer and

Nielson 2011).

The Bureau of Land Management’s (BLM) Record of Decision (ROD) for the JIDPA

(BLM 2006) and the PAPA (BLM 2000, 2008) mandated the creation of a multi-agency office to

oversee and coordinate mitigation and monitoring efforts for each field, the Jonah Interagency

Office (JIO) and Pinedale Anticline Project Office (PAPO), respectively. Each office is staffed

by representatives of the BLM, U.S. Forest Service (USFS), Wyoming Game and Fish

Department (WGFD), and Wyoming Department of Environmental Quality (DEQ). Each office

uses limited operator-committed funds to carry out its monitoring and mitigation mandates. Both

the JIO and PAPO have undertaken a variety of mitigation and monitoring projects, but based on

spending practices, have favored conservation easements as a tool to protect wildlife habitat, air

quality, and scenic values. Of the $14 million earmarked by the JIO for wildlife mitigation

projects, nearly $8 million has already been committed to mitigation projects that include a

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conservation easement component. Given limited funding and the substantial need for

mitigation, this thesis research explores the cost minimization problem faced by the JIO and

PAPO: how should agencies best allocate mitigation funds to achieve the most biological benefit

at least cost?

This research examines this cost minimization problem while focusing on three species,

pronghorn, mule deer and sage grouse. Because the limiting habitat (e.g., winter range,

migration routes, lek sites) of these species is directly impacted by oil and gas development,

these species are arguably most in need of off-site (compensatory) mitigation efforts. Given the

habitat requirements of these key species, including functional migration corridors, stopover

sites, and shrub communities in the form of Wyoming big sagebrush, the JIO and PAPO should

seek to place conservation easements on those properties that provide such biological

requirements and mimic habitat lost to development on the JIDPA and PAPA.

The following chapters describe how this research examines conservation easement

purchases using a spatially-explicit propensity score model to estimate optimal placement of

easements, given parcel-specific land characteristics. Economic efficiency is defined as the

optimal placement of conservation easements on those parcels that maximize benefit to mule

deer, pronghorn, and sage grouse for given levels of economic returns to land in non-

conservation uses. This analysis will highlight existing purchases that are economically

efficient, and inform future purchases.

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2 Background

2.1 Oil and Gas Development in Sublette County

Sublette County, Wyoming, situated in southwest Wyoming’s Upper Green River Basin,

holds vast reserves of natural gas. The geologic composition of the region is unique; below the

stretches of sagebrush rangeland south of Pinedale, Wyoming are trillions of cubic feet of natural

gas trapped within tight-gas fluvial reservoirs (Stilwell and Crockett 2006). Advances in

technology in the mid 1990’s made the extraction of gas held within such formations technically

and economically feasible (Pinedale Anticline Working Group 2005).

The natural gas resources in Sublette County are part of an energy sector growing in

national importance. The dry, tight-sands gas found in Sublette County’s formations are

considered an unconventional resource play; unconventional gas has increasingly contributed to

the United States’ domestic production, offsetting declines in conventional gas production

(Stilwell and Crockett 2006). Unconventional gas increased from 32 percent of total domestic

gas production in 2002 to 40 percent by 2004 (Stilwell and Crockett 2006), and was

approximately 30 percent in 2011 (Secretary of Energy Advisory Board 2011). The Energy

Information Administration anticipates that continued advances in technology and continued

exploration of shale resources will result in increases in natural gas’ share of electricity

generation and increases in other types of natural gas consumption in coming decades (U.S.

Energy Information Administration 2012).

Sublette County is largely within the jurisdiction of the Pinedale BLM Field Office. The

U.S. Geological Survey (USGS) has classified 80% of the Field Office’s jurisdictional area as

having “high occurrence potential” – indicating a high likelihood of oil and gas resource

potential (Stilwell and Crockett 2006). The USGS characterizes two of the biggest plays in the

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region, the Jonah Field (JIDPA) and the Pinedale Anticline PAPA as having high development

potential (Stilwell and Crockett 2006). The Jonah Field and the Pinedale Anticline are among

Wyoming’s largest natural gas fields (Figure 1), contributing to the state’s status as having the

second largest proven dry natural gas reserves in the nation. Wyoming accounts for 12 percent

of the nation’s dry natural gas proven reserves (Stilwell and Crockett 2006).

Figure 1. Sublette County, Wyoming. Source: www.wy.blm.gov/jio-papo.

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In 2000, the BLM approved exploration and development on the Pinedale Anticline1

(BLM 2000) and in 2008, the BLM completed a supplemental environmental impact statement

for the PAPA (BLM 2008), which detailed a comprehensive plan for the field’s development.

The PAPA ROD approved a maximum of 4,339 wells and 600 well pads, stipulated the

implementation of a liquids gathering system, and adopted a plan for phased development to

minimize impacts to wildlife, water and air quality, and to minimize surface disturbance (BLM

2008). The PAPA ROD details the expected amount of surface disturbance from the

development of the field (Table 1). Life-of-project estimations included in the PAPA ROD

assume a reclamation rate of 60 percent. The field is estimated to contain 25 trillion cubic feet of

recoverable natural gas (Stilwell and Crockett 2006).

Table 1. Expected initial and life-of-project well field components' surface disturbance.

Infrastructure Initial Disturbance

Life-of-Project Disturbance a

Well pads (600) 8,113 acres 3,245 acres

Gas (100 miles) and liquids (471 miles) gathering pipelines

- - 3,157 acres

Local and resource roads (100 miles) 606 acres 484.8 acres

Pipelines, ancillary facilities, compressor sites, stabilizer sites, and other gathering facilities

1008 acres 282 acres

Total well field components 12,885 acres 4,012 acres

a Surface disturbance information shown in Table 1 is adapted from Bureau of Land Management (2008; pp. 36).

In 2002, EnCana Oil & Gas, BP America, and other operators submitted a proposal to the

BLM that would substantially increase drilling in the Jonah Field, located just south of the

1 See the Record of Decision for the Pinedale Anticline Oil and Gas Exploration and Development Project (2000).

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PAPA. In 2003, the BLM began the public scoping process required under the National

Environmental Policy Act to gather public comment, research, and general involvement. Over

the course of the succeeding three years, the BLM issued draft and final environmental impact

statements, with the BLM’s ROD signed in March of 2006.

The JIDPA covers a surface area of 30,500 acres, of which the ROD stipulates only 46

percent can be disturbed at one time. The 14,030 acres of disturbance allowed under the ROD

can be supplemented with up to 6,304 reclaimed acres, for a cumulative maximum surface

disturbance of 20,334 acres (BLM 2006). The recommended alternative adopted in the ROD

includes the surface disturbance stipulations outlined above, administrative requirements,

operator-committed practices, adaptive management guidelines, and a commitment to limiting

environmental impacts. The Jonah Field is expected to produce more than 8 trillion cubic feet of

natural gas over the next 76 years, enough to heat 4.8 million homes for 20 years (JIO 2009).

Given the significance of the energy resource found within the Jonah Field and the

Pinedale Anticline, the BLM’s decisions are an effort to fulfill its multiple use mandate by

developing energy resources while protecting other valued resources.2 Significant benefits will

accrue to the state of Wyoming from the development of the fields. The Jonah Field alone is

expected to generate up to $6.1 billion in taxes and federal royalties, half of which will go to the

state of Wyoming (JIO 2012). The Pinedale Anticline will also generate significant revenue in

royalties and taxes: it is estimated to generate $232 million in average total federal mineral

royalties alone by 2065 (BLM 2008). Wyoming allocates 25 percent of all severance taxes to the

Permanent Wyoming Mineral Trust Fund (PWMTF), which acts as a savings account for the

state, earning interest and acting as a loan source for other state programs. Natural gas

2 The BLM decision was based on its obligation under the Federal Land Policy and Management Act (FLPMA), the National Energy Policy Act of 2005, the Mineral Leasing Act, and the President Bush National Energy Policy.

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production also accounts for a larger portion of Wyoming’s gross state product than any other

industry (Bureau of Economic Analysis 2012).

2.2 Rural Residential Development and Land Use in Sublette County

Population growth and land use issues in the Rocky Mountain region have increased in

recent years. Colorado, Utah, Idaho, and Montana have seen increases in population up to three

times the national rate (Taylor and Lieske 2002). Wyoming’s population growth between 2000

and 2010 was 14 percent, 45 percent higher than the national average (Census Bureau 2010), and

some counties within the state have seen even higher increasing rates of growth since the 2000

nationwide census. Sublette County experienced a 22.2 percent increase in population from

1990 to 2000 and another 12.4 percent population growth between 2000 and 2004, much of

which is attributable to the county’s oil and gas development (Pinedale Anticline Working Group

2005). Since 2004, Sublette County’s population continued to boom: the county’s growth rate

between 2000 and 2010 was greater than 73 percent – the highest within the state (Census

Bureau 2010).

While much of the population growth in Sublette County is attributable to an influx of

gas field workers, growth is also due to in-migrants seeking rural and outdoor amenities.

“Second home” growth – seasonal or temporary residences – makes up nearly 20 percent of

Wyoming’s housing units (Census Bureau 2010), increasing from only 5.5 percent in the 2000

census (Taylor and Lieske 2002a). Wyoming falls behind only Montana, Arizona, and Idaho for

second-home growth since 2000 (Census Bureau 2010). Sublette County has the highest

percentage of second homes in the state, with 25 percent of residences considered second homes;

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in comparison, Teton County, which is widely known for its large number of second homes, only

contained 22 percent second homes (Census Bureau 2010).

Because many second home owners seek outdoor amenities, much of the growth in such

residences occurs outside of municipal boundaries. Sublette County is one of seven counties in

Wyoming where rural growth exceeds urban growth (Census Bureau 2010). Between 2000 and

2010 Sublette County’s rural population nearly doubled (Census Bureau 2010). Such rural

residential development is driven by demand for amenities like recreational access, scenery,

wildlife, and open space, and is the catalyst for conversion of agricultural lands to residential

uses.

2.3 Impacted Species

The oil and gas reservoirs below the JIDPA and PAPA are thin (i.e., may not be in

communication with surrounding reservoirs) and vary in depth, which necessitates dense well

spacing. Drilling densities in the area may be as high as one well per five acres, or as low as one

well per 40 acres (Stilwell and Crockett 2006). The BLM Reservoir Management Group

predicts well spacing in the PAPA of 10 to 20 acres per well and well spacing in the Jonah Field

of five acres per well in productive areas (Stilwell and Crockett 2006). The density of drilling

and associated surface disturbance combined with traffic and human presence have impacted

wildlife in Sublette County (Lyon and Anderson 2003, Ingelfinger and Anderson 2004, Holloran

2005, Holloran et al. 2010, Sawyer et al. 2006, 2009a,b, Gilbert and Chalfoun 2011, Beckman et

al. 2012).

The predominant land cover on the Pinedale Anticline and Jonah Field is Wyoming big

sagebrush grasslands, which have been significantly altered by gas development (Walston et al.

2009). Sagebrush is a key habitat for mule deer (Odocoileus hemionus), pronghorn antelope

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(Antilocapra americana), and the Greater sage grouse (Centrocercus urophasianus) - hereafter

referred to as “sage grouse”. Among the many sagebrush obligate species affected by oil and gas

development in Sublette County, these three have garnered special attention on both a state and

national scale.

Sage grouse and energy development

Sage grouse populations in Wyoming and in the Rocky Mountain region have declined in

the last century despite management and research efforts that began as early as the 1930’s

(Connelly et al. 2004). Sage grouse range historically covered 296 million acres in the West

(Johnson and Holloran 2010), including Oregon, Wyoming, Montana, Utah, Idaho, Colorado,

Nevada, and Washington (Macsalka 2011). The bird currently occupies only 56 percent of its

historic range (Johnson and Holloran 2010), and its overall decline in population varies from at

least 17 percent to 47 percent throughout its current range (Connelly et al. 2004). Researchers

have identified habitat reduction and fragmentation from rural sprawl, energy development,

agricultural practices, invasive species, and fire as primary causes of the bird’s decline (Johnson

and Holloran 2010).

Researchers believe rural and suburban sprawl is one of the leading causes for the sage

grouse’s regional population decline, as over 60 percent of counties in the Rocky Mountain West

are experiencing some degree of sprawl (Johnson and Holloran 2010). Infrastructure associated

with houses, roads, and utility lines can displace habitat directly or otherwise disturb sage

grouse, effectively limiting or fragmenting habitat (Johnson and Holloran 2010, Walker et al.

2007).

Agriculture has impacted sage grouse habitat and population levels in multiple ways.

Sage grouse have demonstrated a preference for using agricultural lands as habitat for brood-

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rearing, but have also shown sensitivity to pesticide use on agricultural lands (Connelly et al.

2004, USFWS 2008). Livestock grazing can reduce grass heights and shrub cover, leaving

nesting areas exposed to increased predation (Johnson and Holloran 2010). Livestock grazing

can also reduce forb availability, creating direct competition for food (Johnson and Holloran

2010). Agricultural practices – for crop production or livestock grazing – can directly eliminate

sagebrush habitat through mechanical treatment, herbicide use, or controlled burning, impacting

sage grouse populations (USFWS 2008).

Fires set by humans – to clear sagebrush for agriculture or set by accident – are

detrimental to sagebrush habitat. Fire has reduced some sage grouse populations by more than

80 percent (Connelly et al. 2004). After burning, sagebrush communities can take 100 to 200

years to re-establish (Johnson and Holloran 2010). In the meantime, burned areas are susceptible

to invasion from other species, such as cheatgrass (Rowland 2006). Other surface disturbances

can similarly make sagebrush communities vulnerable to invasive species.

Energy development has the potential to create these or similar impacts to sage grouse

and their habitat. The access roads, utility lines and pipelines, fences, well-pads, holding ponds,

seismic surveys, and noise and human activities associated with energy development have all

been shown to impact sage grouse populations (Johnson and Holloran 2005, Walker et al. 2007).

Roads, fences, and pipeline corridors fragment habitat, directly reducing its availability. Well-

pad construction results in the removal of habitat, and collisions with utility lines and vehicles

result in direct sage grouse mortality. The presence of roads has had a demonstrated impact on

sage grouse populations; one study found that male lek attendance declined within less than 2

miles of an access road when the traffic volume on the road exceeded one vehicle per day

(Johnson and Holloran 2010).

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Energy development indirectly reduces sage grouse habitat by creating noise, changing

water and habitat quality, and creating opportunities for predation (Walker et al. 2007). Utility

lines and other energy-related infrastructure give raptors a predatory advantage; they are better

able to prey upon sage grouse with the perching opportunities infrastructure provides. Energy

development indirectly affects sage grouse populations by helping the spread of West Nile virus;

mosquitoes that transmit the virus are more abundant near produced-water holding ponds

(Walker et al. 2007).

Holloran (2005) and Naugle et al. (2009) found that impacts to sage grouse from energy

development activities can be detected at distances of 3-5 kilometers and 3-4 miles (5-6

kilometers), respectively. Walker et al. (2007) cite four individual studies that found declines in

sage grouse populations following the introduction of energy development. In the Sublette

County region, specifically, Holloran (2005) found that numbers of displaying males and

recruitment of juvenile males decreased in proximity to gas fields, and that nesting females and

brooding females avoided producing wells. These findings are consistent with Wyoming Game

and Fish Department (WGFD) reports. WGFD monitoring reports indicate decreases in both

active lek counts in Sublette County and decreases in male lek attendance (WGFD 2011).

Given the widespread declines in population, the sage grouse was considered as a

candidate for listing under the Endangered Species Act (ESA) in 2010. The Greater sage grouse

ESA listing decision of 2010 was “warranted but precluded,” meaning that currently the bird is

not receiving protection under the ESA, but could still be listed at a later time. Wyoming state

government and agencies, businesses, and residents have taken steps towards protecting the bird

and its leks to prevent the broad and potentially negative impacts of listing.

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Mule deer and energy development

Development of the PAPA has negatively impacted Sublette County mule deer

populations, particularly the Mesa herd, through direct and indirect habitat loss. Direct habitat

loss occurs when native habitat is converted to infrastructure (e.g.., well pads, roads, pipelines),

whereas indirect habitat loss occurs when animals avoid infrastructure. Indirect habitat loss has

been shown to be much larger than direct habitat loss, and is concerning because it effectively

reduces the size of the winter range (Sawyer et al. 2006). Although indirect habitat loss can be

reduced by minimizing traffic levels and installing underground liquids gathering systems

(Sawyer et al. 2009b), it cannot be eliminated. Through the first 10 years of development,

monitoring efforts in the PAPA indicate mule deer have declined 56 percent, from 5,228 animals

in 2001 to 2,318 in 2010 (Sawyer and Nielson 2011). This level of decline, when compared with

other herd units near the PAPA, is sufficient to prompt a mitigation response from the BLM,

according to its Wildlife Monitoring and Mitigation Matrix (WMMM): “…changes requiring

mitigation are as follows: 15% population decline in any year, or cumulatively overall years,

compared to the Sublette mule deer herd unit or other mutually agreeable area” (BLM 2012).

Further development outside of the PAPA has the potential to interfere with seasonal

migration, which could further decrease herd numbers. Between 2,500 and 3,500 mule deer

migrate from winter ranges in the PAPA to summer ranges across western Wyoming (Sawyer et

al. 2005). Despite loss of crucial winter range from development of the PAPA, the migration

corridor has remained functional (Sawyer et al. 2009a). Mule deer and other ungulates survive

by using a migration strategy that allows them to maximize their access to forage of the highest

nutritional value. While capable of completing the spring and autumn migration in as little as

one day, mule deer consistently spend up to three weeks completing their migrations, in order to

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take advantage of phenological forage gradients along the migration corridor (Sawyer and

Kauffman 2011). Foraging along the migration route is believed to be critical because it allows

animals to recover body condition earlier in the spring and maintain it later into the autumn

(Sawyer et al. 2005).

Migration corridors serve two functions for migrating ungulates: they serve as both a

movement corridor and a series of stopover sites (Sawyer et al. 2009c). Analysis of the

movements of GPS-collared deer show that 95 percent of migrating animals’ time is spent

foraging in stopover sites, and only five percent of migrating animals’ time is spent in movement

corridors (Sawyer and Kauffman 2011). Mule deer show high fidelity to migrations routes,

seasonal ranges, and stopover sites; as young deer learn migration behavior from their mothers

(Sawyer and Kauffman 2011). Strong fidelity to learned migration routes and seasonal ranges is

an important consideration for resource managers because it emphasizes that habitat

improvement or protection efforts must overlap with existing routes and ranges in order to be

effective. Like crucial winter range, direct and indirect impacts from human activities can lessen

the functionality of stopover sites or can make movement corridors impassable to migrating

animals (Sawyer et al. 2009c).

Pronghorn and energy development

Like mule deer, pronghorn antelope complete annual migrations to maximize their access

to seasonal range and nutritional forage (Berger 2004, Sawyer et al. 2005, Berger et al. 2006).

Each fall and spring, between 1,500 and 2,000 pronghorn antelope migrate over 300 miles

roundtrip between summer range in Grand Teton National Park and crucial winter range in the

PAPA and JIDPA – the longest-known terrestrial migration in the 48 contiguous states (Berger

2004, Sawyer et al. 2005). Expanding residential and energy development threaten pronghorn

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migration corridors in Sublette County. Pronghorn contend with roads, impassable fences, and

human presence and disturbances throughout the migration corridor. Fragmentation of the

migration corridor could have adverse impacts on the area’s pronghorn population (Berger

2004).

Though mule deer and pronghorn share migration behavior, migration corridors, and

winter range, they appear to differ in their response to disturbance association with energy

development (see Beckman et al. 2012). In contrast to mule deer, monitoring efforts in the

PAPA indicate that pronghorn do not avoid well pads or roads (Nielson and Sawyer 2010)

However, roads and highways are particularly troublesome for migrating pronghorn. Roads and

highways pose a risk to migrating pronghorn in two ways: directly, through vehicle-animal

collisions, and indirectly, through the migration corridor fragmentation that right-of-way fences

create (Sawyer and Rudd 2005). Data from GPS-collared pronghorn indicates a bottleneck in the

migration corridor, commonly known as “Trapper’s Point,” which is located just north of

Pinedale, Wyoming (Sawyer et al. 2005, Berger et al. 2006). Trapper’s Point is a natural

bottleneck – a corridor only 1.6 kilometers wide between two plateaus – that is bisected by US

Highway 191. Residential and commercial development near Trapper’s Point has narrowed the

navigable width of the bottleneck from its natural width of 1.6 kilometers to less than 0.8

kilometers (Sawyer et al. 2005). As many as 800 animals have been observed passing through

the area (Riis 2009). Given such high numbers of migrating animals, and increased traffic

related to energy and residential development, Trapper’s Point is one of the leading areas in

Wyoming for frequency of animal-vehicle collisions (WyDOT 2007). In an effort to mitigate

this problem, WYDOT is constructing two over-passes designed specifically for pronghorn.

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Fences present another impediment to pronghorn migration. Though physically capable

of jumping over fences, pronghorn nearly always pass underneath them, requiring at least 16

inches of space between the bottom wire of the fence and the ground (Sawyer et al. 2005).

Fences lining highway and utility right-of-ways and fences associated with rural residential

development fragment pronghorn migration corridors, and will further fragment migration

corridors as rural development expands.

Monitoring efforts have shown that pronghorn, though facing substantial challenges

along their migration corridor, have continued to use winter range on the PAPA (Nielson and

Sawyer 2010). Nielson and Sawyer (2010) found that during the winter of 2009-2010,

pronghorn were not negatively affected by the energy development in the PAPA (see Beckman

et al. 2012).

2.4 The Jonah Interagency Office and Pinedale Anticline Project Office

Recognizing the potential for negative impacts from natural gas extraction, the JIDPA

ROD established the Jonah Interagency Office (JIO), an amalgamation of agency representatives

from the Wyoming Department of Agriculture, WGFD, Wyoming Department of Environmental

Quality, and the BLM. The JIO’s charge is “overall management of field monitoring and

mitigation activities, both on- and off-site,” (BLM 2006). The office also manages the

approximately $21 million and $3 million that EnCana Oil & Gas and BP America, respectively,

have put towards compensatory (off-site) mitigation of impacts from the Jonah Infill Drilling

Project’s development. Of the total $24.5 million, $16.5 million is specifically earmarked for

compensatory mitigation projects for wildlife.

As of December 2010, $12,888,718 of the $16.5 million earmarked for wildlife

mitigation projects was already committed to conservation easements on private lands in the area

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and similar measures on BLM grazing allotments, leaving $3,111,282 for other projects. Most of

these conservation easements have been placed on privately-owned agricultural lands that

contain valuable habitat. To accomplish off-site, or compensatory, mitigation, the JIO and

PAPO have purchased the development rights on private agricultural lands that could provide

habitat that would off-set direct and indirect habitat losses from energy development.

Agricultural operations continue under the conservation easements.

Aside from directing funds towards conservation easements, the JIO has approved

projects to minimize human-wildlife conflicts and improve habitat outside of the JIDPA.

Examples of past projects include:

• construction of snow fences to add moisture to lands under reclamation;

• purchase and installation of Dynamic Message Sign boards placed along Sublette County highways to minimize animal/vehicle collisions;

• prescribed burning of upland plant communities to enhance a mosaic vegetation pattern for the benefit of several species, including sage grouse;

• drilling and improvement of water wells, installation of diversions and improvement of existing springs to provide water sources for migrating pronghorn and mule deer;

• construction and placement of nesting platforms for ferruginous hawks, installation of wildlife escape ramps in all BLM range improvement water tanks, and, contribution to the Green River Valley Land Trust’s (GRVLT) Wildlife Friendly Fencing Initiative.

Like the JIDPA ROD, the PAPA ROD established an agency office to oversee and

administer funds for mitigation and monitoring of the effects of developing the Pinedale

Anticline. The PAPO is charged with the “overall management of on-site monitoring and off-

site mitigation activities,” (PAPO 2012). Like the Jonah Interagency Office, the PAPO is staffed

by representatives of several state and federal agencies. Members of the Wyoming Department

of Agriculture, WGFD, Wyoming Department of Environmental Quality, BLM, and Sublette

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County Commissioners staff the Pinedale Anticline Mitigation Management Board (PAMMB),

an entity that oversees the PAPO’s mitigation and monitoring efforts (BLM 2012).

The PAPO focuses its efforts on the key species discussed previously: mule deer,

pronghorn, and sage grouse. To meet its obligations to monitor impacts from the development

and mitigate those impacts as necessary, the PAPO obtains, collects, stores, and distributes data

intended to inform adaptive management in the PAPA. The PAPO operates on its Monitoring

and Mitigation Fund; the fund is generated through $7,500 contributions from Ultra, Shell, and

Questar for each new well drilled in the PAPA. The fund is currently $16,507,500, and is

expected to reach $36 million during the life of the Pinedale Anticline project (PAPO 2012).

PAPO administrators distribute funds for projects that meet the office’s goals on both federal and

non-federal lands.

Past PAPO-funded projects include annual data collection on mule deer, pronghorn, sage

grouse, pygmy rabbit, raptor, and white-tailed prairie dog populations, and snow and traffic

monitoring. PAPO-funded on-site mitigation projects have included sagebrush fertilization and

mule deer winter habitat improvement on the Anticline. The PAPO has also collaborated with

the JIO to fund conservation easement projects in its off-site mitigation efforts. Therefore, I do

not consider the mitigation funds of the two offices – the JIO and the PAPO – separately in my

analysis; because the JIO and PAPO have similar mitigation goals and similar mitigation needs, I

treat both funds as a single entity in this model.

2.5 Conservation Easements

Conservation easements have become a popular tool for conservation of lands valuable

for wildlife habitat, scenic or recreational amenities, or protection of open space. Conservation

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easements are a legal construct enabling private landowners to forego their right to develop their

property in exchange for direct payments, tax benefits, and for estate planning purposes. In

essence, a conservation easement can be considered a purchase of development rights (PDR)3.

Conservation easements effectively prevent development of the parcel for residential use

or mineral extraction. Generally, before a conservation easement can be placed on a parcel, a

licensed geologist is required to analyze the potential for oil and gas or other extractive

development on the parcel, should the parcel have a split estate (privately-owned surface and

federally-owned mineral rights) and have the potential for development of federally-owned

minerals in the future. Federally-owned mineral rights in some cases would not be prevented

from development under a conservation easement (Perrigo and Iversen 2002). In the case of split

estate on a property under conservation easement, “the…mineral estate owner generally has the

right to reasonable use of the surface estate to access and extract the minerals” (Benson 2005).

3 The terms ‘conservation easement’ and ‘purchase of development rights’ are often used interchangeably. When land is donated as a conservation easement in exchange for tax benefits and for estate planning purposes, it is considered a ‘conservation easement.’ When land is conserved under an easement in exchange for money or is traded in a market, the term ‘purchase of development rights’ is appropriate (Perrigo and Iversen 2002).

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3 Methods

To assess the efficiency of conservation easement purchases, I characterize the trade-off

between biological (mule deer, pronghorn, and sage-grouse) and economic value across Sublette

County. I construct a production possibilities frontier (PPF) to represent the trade-off between

economic development and critical wildlife habitat (Polaskey et al. 2008, Polasky et al. 2005,

Lichtenstein and Montgomery 2003). I construct a biological “score” to represent the expected

number of species that can be sustained on the landscape, and an economic “score” which sums

the net present value (NPV) of economic returns for each land parcel under different land uses

(Polasky et al. 2008). To generate an efficient hypothetical pattern of land use, I maximize the

economic score for a given biological score, or vice versa (Figure 2).

Figure 2. Sample production possibilities frontier of land use patterns across a landscape.

Similarly, I generate an economic score for each parcel in Sublette County – its value in

either agricultural or rural residential use – and a biological score using data on mule deer,

pronghorn, and sage-grouse population distributions, and evaluate which lands should be put

under a conservation easement to maximize the biological score at least cost. Given the intent

and contractual stipulations of conservation easements, I consider conservation and agriculture to

Biological Score

Economic Score

PPF: Efficient patterns of land use

Inefficient patterns of land use

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be the same land use (as opposed to the methods used in Polasky et al. 2008). Because I am

investigating the most efficient placement of conservation easements on private land, where

there is relatively little oil and gas, commercial, or industrial development, I limit parcels to

either agricultural or rural (outside of municipal boundaries) residential use in my model.

3.1 Economic Score

Theoretically, the purchase of a conservation easement, because it prevents development,

compensates the landowner for the profits they could have captured by developing the land – i.e.,

the opportunity cost of future development. Given this concept, I must estimate the value of

future development on parcels currently in agriculture to identify which agricultural parcels have

the least value in future residential development, and therefore the least cost of easement

purchase for a given biological score.

From both a legal and economic perspective, the land value of a parcel can be based on

two components, including: 1) productive use and 2) potential use (Plantinga et al. 2002). I

consider these components for any given agricultural parcel’s value to be the productive

agricultural value and the potential for residential development. Thus, the value of a given

parcel is a function of the net returns to agriculture and the potential returns to future residential

development. To maximize profits, the landowners should convert land from agriculture to

residential development at the optimal time, t*, given market conditions. Assuming conversion

to residential use is irreversible, the current value of a parcel of agricultural land i can be

expressed as:

(1) *

*, ,

0 *

( , ) ( , )t

rt rt rti i ag i i res i

t t

P t z e dt t z e dt Ceπ π∞

− − −

=

= + −∫ ∫ ,

where,

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πi,ag(t,zi) is the net return to agriculture on parcel i with characteristics z;

πi,res(t,zi) is the net returns to residential use on parcel i with characteristics z; and

C is the one-time cost of converting parcel i from agricultural to residential use.

The first term in (1) is the present value of returns from using the parcel for agricultural

production, from the current time to the optimal conversion time, t*. The second term is the

present value of future returns from residential development less conversion costs, from t* to

infinity.

Equation (1) demonstrates the opportunity cost of future development for which the purchase of

a conservation easement should compensate a private landowner. The value of the conservation

easement, or cost of the purchase of development rights, should be equal to the net present value

(NPV) of foregone residential rents less conversion costs (Plantinga and Miller 2001).

Given this definition of the cost of a conservation easement, I must separate the

residential value from the agricultural value of every agricultural parcel in the county. Hedonic

modeling could provide parameter estimates to show how much of the observed price represents

future residential development (Rosen 1974, Bastian et al. 2002). The standard hedonic

approach, however, requires accurate price observations that capture the true economic value of

each agricultural parcel (i.e., recent sales data) and a set of measureable characteristics for each

parcel to regress over the sales data. Parameter estimates from a hedonic model could then be

t = 0 t*

“NPV of Agricultural Rents”

“NPV of Residential Rents”

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used to derive the proportion of residential development value in current land price for any

parcel (Plantinga et al. 2001, Bastian et al. 2002).

Because agricultural lands in Sublette County change ownership infrequently, adequate

transactions data to estimate a hedonic model do not exist. In the absence of actual transactions

data, assessed land values are the most readily available proxy for market values. Assessed

values are estimated by city or county assessors to determine property taxes. In ex-urban or rural

areas where conversion to development is largely driven by amenity values, Ma and Swinton

(2012) found that assessed values underestimate or omit amenity values in determining future

residential development values and, thus underestimate current market value. Spahr and

Sunderman (1998) found that assessed values differed from market value as well; they concluded

that agricultural properties with amenity values are undervalued, as assessors underestimate the

economic value of non-agricultural attributes. Moreover, agricultural lands in Wyoming are

assessed strictly on the basis of their productivity for agricultural purposes. Assessors combine

commodity price data and capitalized net income figures to determine agricultural land value

(Wyoming Department of Revenue 2012). This assessment approach explicitly ignores the value

of future development. Thus, hedonic models applied to assessed agricultural land values will be

biased because parameter estimates are based on assessed values that do not account for future

residential development.

Accordingly, I propose an alternative method for deriving the potential future residential

value of agricultural parcels using observed assessed values. Given that assessors only consider

a parcel’s current use, the assessed value (AV) of an agricultural parcel i and a residential parcel j

can be expressed as:

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(2) , and

(3)

If the characteristics of the two parcels are identical (i.e., zi = zj), then an estimate of the true

economic value of the agricultural parcel i is given by:

(4) .

The estimated value in (4) is a biased approximation of the true value, Pi, because it assumes that

parcel i can receive both agricultural and residential rents in perpetuity – from t to infinity. In

reality, the contribution to rents in perpetuity from agriculture and development depends on the

optimal conversion time (t*), which is unobservable. The bias in is explicitly given by:

(5) .

Since (5) is strictly non-negative, overestimates the true economic value. As t* approaches

infinity (i.e., parcels with little or no foreseeable development pressure), the bias in (5) is given

by:

(6) .

overestimates the true value by giving too much weight to future development rents, which

are highly unlikely. Similarly, as t* approaches zero, the bias in (5) is given by:

(7) .

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Here overestimates the parcel’s true value by giving too much weight to agricultural rents,

which are soon to disappear.

If parcels are matched well according to the propensity score matching approach I use

(see below), the magnitude of bias in (4) should be relatively small. If a very rural agricultural

parcel with low development pressure is matched with a similarly rural residential parcel, then

the assessed value of the residential parcel should reflect the low market demand for such

residential properties. The assessed residential value will be close to the assessed agricultural

value and the bias will be relatively small (as opposed to matching the rural agricultural parcel

with a parcel under high development pressure). Similarly, for parcels facing high development

pressure, the assessed agricultural value will be small relative to the assessed residential value,

making the bias of including agricultural rents relatively small. I therefore use (4) to define the

economic score of each parcel using assessed values.

Propensity Score Matching

Propensity score matching was developed in the health sciences to reduce selection bias

when comparing non-equivalent groups (Rosenbaum and Rubin 1983). The standard approach

to understanding the effects of a health treatment – a vaccination, for example – is to conduct an

experiment where individuals from a population are chosen at random and assigned to treatment

and control groups. Randomly assigning individuals implies that a comparison of outcomes

between the treatment and control group will provide an unbiased estimate of treatment effects.

With observational data, individuals are not assigned to treatment and control groups

randomly. This means that researchers can observe the outcomes for treated and untreated

individuals, but not the counterfactual – the outcomes of treated individuals had they not

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received the treatment and vice versa. Differences in outcomes between the treated and

untreated may then be attributable to the treatment, or underlying differences in the individual

characteristics of the experiment subjects, such as gender, income, education, and race. If these

individual characteristics influence who gets treated or how they respond to treatment, then a

comparison of the outcomes of the two groups will produce a biased estimate of the treatment

effect. Propensity score matching can be used to reduce this bias by matching individuals based

on the similarity in their characteristics, and is widely applicable outside of the health science

disciplines. In short, it assumes that if two individuals share every characteristic other than

receiving or not receiving the treatment, then the difference in their outcomes is the treatment

effect.

I employ the propensity score matching approach because I can only observe agricultural

parcels that have not yet been converted to residential use or residential parcels that have already

converted from agricultural use. In each case, I cannot observe a counterfactual. In keeping

with the example of propensity score applications in the health sciences, I consider conversion to

residential use as a “treatment” to understand how this treatment affects the value of untreated

parcels – i.e., the counterfactual. This tells me what the assessed value of an existing agricultural

parcel would be if it were a residential parcel.

However, the nature of assessors’ data for parcel value does not allow for the standard

application of a propensity score matching approach. Assuming that assessors accurately

capitalized future development values into current agricultural land values, the assessed value of

an agricultural parcel i would be given by:

(8) *

*, , ,

0 *

( , ) ( , ) ,t

rt rt rti ag i ag i i res i

t t

AV t z e dt t z e dt Ceπ π∞

− − −

=

= + −∫ ∫

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and the assessed value of a residential parcel j would be:

(9) *

, ,0

( , ) .rt rtj res j res jAV t z e dt Ceπ

∞− −= −∫

If (8) and (9) were true, I could use propensity scores to match agricultural and residential

parcels, and the residential value of the matched parcel would proxy for the proportion of the

agricultural value that is attributed to future development potential.

Because assessed values do not capitalize future development values into current

agricultural land values, I cannot observe this “treatment.” Therefore, I use a propensity score

matching method to directly estimate (4), by matching agricultural and residential parcels that

share similar physical and geographical characteristics, and use the matched residential value as

a proxy for the future development values of the agricultural parcel to which it is matched.

Specifically, I use the following approach to estimate an economic score using propensity

score matching and assessed values:

i) Estimate propensity scores [p(z)] using a standard binary logit model.

ii) Match agricultural parcels to residential parcels using each parcel’s predicted

propensity score.

iii) Estimate the residential value of agricultural parcels based on the matched

residential parcels:

(10)

where ( ), ( )k res kf AV z is a function (depending on matching method) of the

assessed values of matched residential parcels.

I use a standard binary logit model, which predicts the probability of observing each

parcel in residential use, to estimate propensity scores. Discrete choice regression models are

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often used to estimate propensity scores (Caliendo and Kopeinig 2008)4. The binary logit model

can be expressed as:

(11) 𝑝(𝑌𝑖 = 1|𝑥) = 𝑒𝑥′𝛽

1+𝑒𝑥′𝛽

where Y is an indicator variable equal to 1 if parcel i is in residential use, x is a vector of

characteristics expected to influence the probability of residential use, and β is a vector of

parameters to be estimated.

Data

I use the Sublette County assessors’ data from the Wyoming Department of Revenue

cadastral dataset to define parcels according to ownership and to characterize their current use.

The data classifies parcels according to an account type describing the parcel’s current use as:

agricultural, commercial, commercial vacant, industrial, industrial vacant, residential, residential

vacant, state assessed, exempt, and other. Because I examine conservation easement purchases

on agricultural lands that will preclude residential development, I narrow the cadastral data to

include only those parcels in “agricultural,” “residential,” or “residential vacant” uses. I consider

three different binary logit models that each use a different combination of the agricultural

parcels and the two residential parcel types: agricultural and residential (AGRES), agricultural

and residential vacant (AGRESVAC), and agricultural, residential and residential vacant

combined (AGRESRESVAC). In each data set, I exclude parcels within municipal boundaries.

Residential and residential vacant parcels within the municipalities of Sublette County face

different market conditions, different types of development, and different motivations for

4 There are several other methods to match observations, including matching based directly on the distribution of characteristics themselves (i.e., covariate matching). The propensity score matching approach avoids the ‘curse of dimensionality’ in cases when there are a large number of characteristics that need to be compared (see Caliendo and Kopeinig 2008).

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conversion from one land use to another. They may therefore not be explained using the same

variables I include in the binary logit model for predicting ex-urban and rural residential use.

I run the binary logit model (11) on the AGRES, AGRESVAC, and AGRESRESVAC

datasets using the same set of independent variables. These include variables commonly

included in hedonic models, as they are explaining the probability of observing residential use. I

calculate measurements of independent variables for each parcel using spatial data in ArcMap

10® (ESRI 2010) and the CommunityViz® Scenario 360™ (Placeways, LLC 2012) analysis

software package. A review of the hedonic literature informed my choice of independent

variables (see Table 2).

Bergstrom and Ready (2009) and Plantinga et al. (2002) used time-series demographic

and land development trend data to disaggregate the value of future residential development and

explain the contribution of amenity value to overall farmland value. Because demographics in

Sublette County are generally homogenous and the geographic scope of my study is limited to

one county, thereby limiting the availability of demographic data at a fine scale, I do not include

demographic variables in my study. Additionally, I only observe cross-sectional data for one

year, preventing me from including time-series data, such as development rates, population

growth and price trends, as previous studies have (Plantinga et al. 2002; Bergstrom and Ready

2009; and Ma and Swinton 2012).

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Table 2. Explanatory variables used in binary logit models.

Variable Variable Name Description Distance to town DISTTOWN Distance from parcel boundary to nearest town Distance to road DISTROAD Distance from parcel boundary to nearest road Average standard deviation of slope AVGSTDD_SLOPE Average standard deviation of slope measurements

on parcel to measure roughness of terrain Agricultural land in neighborhood AGNEARVIEW Share of agricultural lands, based on land cover

data, within 1 mile viewshed of parcel Residential land in neighborhood RESNEARVIEW Share of residential lands, based on cadastral data,

within 1 mile viewshed of parcel Commercial land in neighborhood COMMNEARVIEW Share of commercial lands, based on cadastral

data, within 1 mile viewshed of parcel Land cover – wetlands WETLANDCOVER Share of wetlands, based on land cover data, on

parcel Land cover – forest FORESTCOVER Share of forest, based on land cover data, on parcel Land cover – not developable UNDEVCOVER

Share of undevelopable lands – perennial ice/snow, open water, or barren land – based on land cover data, on parcel

Viewshed – mountain peaks MTN_PKS Count of mountain peaks above 13,000 feet

elevation visible from parcel

Distance to town (DISTTOWN) measures the proximity of parcels to services, such as

shopping, schools, and economic activity, and therefore likely influences the value of residential

development (Ma and Swinton 2012; Plantinga et al. 2002; Bastian et al. 2002). Urban spatial

models show that distance from a central business district is negatively related to rents for

developed land (Plantinga et al. 2002). I expect that there will be a negative relationship

between DISTTOWN and the probability of observing residential development in my model, as

the value of future residential development is a decreasing function of a parcel’s total value as its

distance from town increases (Plantinga et al. 2002). To calculate DISTTOWN I use the

“MinDistance” tool in CommunityViz® Scenario 360™ software, which measures the minimum

distance from each parcels’ boundary to the nearest municipality.

Consistent with land value theory, Plantinga et al. (2002) found that a one unit increase in

highway density, as a proxy for increased population density and decreased commuting costs,

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increased the value of agricultural land substantially. Similar to DISTTOWN I include

DISTROAD as an indicator of the likelihood that a parcel will be converted to residential

development (Ma and Swinton 2012). I expect increased ease of access to a parcel to increase

the probability of observing residential use.

I include two independent variables to represent conversion cost from agricultural land to

residential use: AVGSTDD_SLOPE and UNDEVCOVER. Using the National Elevation Dataset

(USGS 2012), I calculate the standard deviation of the slope on each parcel (AVGSTDD_SLOPE)

to indicate roughness of terrain (Ma and Swinton 2012) and buildability. I also create a shapefile

layer to represent land covers that preclude construction of housing by reassigning the following

land covers from the National Land Cover Database (NLCD): barren, perennial ice/snow, and

open water. Using the Scenario 360™ “GridOverlap” tool, I calculate the combined area of

these land cover categories for each parcel. I include this area measurement as a share of each

parcel’s total area (UNDEVCOVER).

I use the same method to calculate other land cover area measurements by parcel. I

combine NLCD land covers “woody wetlands” and “emergent herbaceous wetlands” to create a

single land cover for wetlands. Finally, I combine NLCD land covers “deciduous,” “evergreen,”

and “mixed forest” to create a single land cover for forest. I calculate these area measurements

as a share of each parcel’s total area to construct the WETLANDCOVER and FORESTCOVER

variables. Both of these variables potentially serve as proxies for on-parcel amenities. Figure 3

shows the distribution of land covers according to agricultural parcels’ status as privately or

publicly owned lands. The distribution of wetlands and agricultural lands follow the pattern of

river beds and bottom lands, indicating the likelihood that soil characteristics and water

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availability are shared or similar. Bergstrom and Ready (2009), Bastian et al. (2002), Ma and

Swinton (2012), and Spahr and Sunderman (1998) include similar measures of land productivity.

Figure 3. Land cover in Sublette County, Wyoming

View quality rating and scenic beauty modeling is an emerging field that is relevant for

hedonic modeling (Germino et al. 2001). I construct a variable, MTN_PKS, to capture the

viewshed from each parcel in Sublette County. Using a viewshed model constructed in ArcMap

10® to count the number of mountain peaks that exceed 13,000 feet in elevation that are visible

from each parcel. For parcels near the border of the county, I include a count of peaks within a

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20 mile viewshed of the county line. I expect that the higher the count of mountain peaks visible

from a parcel, the higher the probability of observing residential development.

To capture the nearby scenic values from each parcel, I calculate shares of land use,

according to cadastral data categories (i.e. “commercial,” “industrial,” “residential,” or

“agricultural”) within a one mile viewshed of each parcel. I construct the variable

COMMNEARVIEW by calculating the area of parcels in commercial and industrial in the near

viewshed of each parcel, and combine those areas to calculate the share of commercial and

industrial land within a one mile radius of the center of each parcel. Similarly, I calculate the

share of residential (RESNEARVIEW) and agricultural (AGNEARVIEW) land within a one mile

radius of each parcel. Bergstrom and Ready (2009) found that neighboring land uses influence

amenity values and relative scarcity of agricultural lands influences farmland value.

Calculating the Economic Score

After estimating each parcel’s propensity score (i.e., predicted residential probability)

from the binary logit model, I calculate the economic score for each agricultural parcel by

imputing the value of residential or residential vacant parcel matched to the agricultural parcel. I

consider the assessed value for agricultural land to be its productive value only. The Wyoming

Department of Revenue assesses agricultural land value according to a formula that combines the

Wyoming Agricultural Statistics Service’s commodity price data and capitalized net income

(Wyoming Department of Revenue 2012). Summary statistics (Table 3) of assessed agricultural

values show a wide range of values.

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Table 3. Summary statistics of agricultural lands' assessed values in Sublette County.

Summary Statistic Value (per acre)

Minimum $9.30

Maximum $21,521.74

Mean $535.31

Median $195.71

I match parcels using two alternative approaches: the sub-class matching approach and

the caliper matching approach; after evaluating each, I use the caliper approach. Sub-class

matching is simply dividing parcels into equal groups according to propensity score and

matching residential parcels with a propensity score that falls into the range of those observed in

each sub-class and imputing the average value of matched residential parcels to agricultural

parcels. I perform the sub-class matching approach in two different ways: first by dividing

agricultural parcels into 10 groups (sub-classes) and then into 20 groups. I arbitrarily chose 10

and 20 groups to gain accuracy in matching; the finer the sub-classes (i.e., more groups), the

fewer the number of parcels in each sub-class. I rank agricultural parcels by their estimated

propensity score from lowest to highest and then divide agricultural parcels (n=1,053) into 10

(n=105) or 20 (n=52) sub-classes with an even number of parcels in each. For example, the 105

agricultural parcels with the lowest propensity scores were assigned to sub-class 1 in the 10-sub-

class matching, and the 52 parcels with the lowest propensity scores were assigned to sub-class 1

in the 20-sub-class matching. Next, I match residential parcels’ propensity scores to agricultural

parcels’ propensity scores and assign residential parcels the same sub-class as the agricultural

parcel to which it is matched. For example, a residential parcel with a propensity score of 0.76

was matched to an agricultural parcel with the same propensity score that was assigned to sub-

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class 8, so the residential parcel was also given a sub-class number of 8. I then average the

assessed per acre land value5 of all matched residential parcels within each matched sub-class to

impute the potential residential value of agricultural parcels. The sum of the imputed residential

value and the assessed agricultural value constitute the economic score using the sub-class

matching approach (see Equation 10).

The caliper approach matches parcels based on the stratification of their propensity

scores, like the sub-class approach, but defines strata according to a caliper statistic, ε:

(12) 𝜀 ≤ 0.25𝜎𝑝,

where 𝜎𝑝 is the standard deviation of the predicted propensity scores across all parcels.

Agricultural parcels are therefore matched to a residential parcel if the difference between their

propensity scores falls into caliper strata calculated using (12), or one quarter of the standard

deviation of all parcels’ propensity scores (Guo and Fraser 2010). The caliper approach results

in each agricultural parcel being matched to a group of residential parcels that satisfy (12). I

calculate the economic score using this approach by imputing the per acre residential land value

of parcels to the agricultural parcels to which they are matched. I impute the residential values

using the minimum, maximum, average, and median value per acre of matched residential

parcels.

3.2 Biological Score

I construct a simple index to rank habitat quality for the three key species that the JIO and

PAPO have targeted mitigation activities toward: pronghorn, mule deer, and sage grouse.

5 I separate assessed land values from assessed value of improvements and structures within the cadastral dataset before modeling parcel values.

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I base the biological score for mule deer and pronghorn on three key factors: 1) distance

to crucial winter range and/or area of crucial winter range on parcels, 2) overlap with migratory

stopover sites, and 3) overlap with migratory movement corridors. I weight and sum these

measurements, which I calculated using Scenario 360™ and spatially explicit migration data

(Sawyer and Nielson 2011)6 and winter range data (WGFD 2011):

(13) 𝑀𝐷𝐵𝑖𝑜𝑙𝑆𝑐𝑜𝑟𝑒,𝑖 = ∑ 𝐷𝑖𝑠𝑡𝑘,𝑤,𝑖𝐴𝑐𝑟𝑒𝑠𝑤,𝑖 + 𝐴𝑐𝑟𝑒𝑠𝑠,𝑖𝑘 + (0.5)𝐴𝑐𝑟𝑒𝑠𝑚,𝑖, and

(14) 𝑃𝐻𝐵𝑖𝑜𝑙𝑆𝑐𝑜𝑟𝑒,𝑖 = ∑ 𝐷𝑖𝑠𝑡𝑘,𝑤,𝑖𝐴𝑐𝑟𝑒𝑠𝑤,𝑖 + 𝐴𝑐𝑟𝑒𝑠𝑠,𝑖𝑘 + (0.5)𝐴𝑐𝑟𝑒𝑠𝑚,𝑖,

Where Acresw,i is acres of crucial winter range on parcel i, Acress,i is acres of stopover habitat,

and Acresm,i is acres of movement corridor. Acres of crucial winter range are weighted by the

distance between the parcel and the nearest crucial winter range. Parcels that contain crucial

winter range get a weight of 1 (i.e., Distk,w,i = 1), parcels within 1 km of winter range get a

weight of 0.5 (i.e., Distk,w,i = 0.5), and parcels that are greater than 1 km from crucial winter

range get a weight of zero (i.e., Distk,w,i = 0) (Sawyer, pers. comm. 2012). This metric reflects

the importance of conserving parcels located in habitat already used by mule deer and pronghorn

(Sawyer, pers. comm. 2012). Finally, parcels containing migratory stopover habitat receive a

greater weight than parcels containing movement corridors, due to the ecological importance of

stopover to migrating animals (Sawyer et al. 2011).

To incorporate sage grouse into the biological score, I construct a 5 kilometer buffer

around occupied leks, calculate the area within that buffer, and calculate the number of acres of

resulting sage grouse habitat contained within each parcel:

(15) 𝑆𝐺𝐵𝑖𝑜𝑙𝑆𝑐𝑜𝑟𝑒,𝑖 = 𝐴𝑐𝑟𝑒𝑠𝑆𝐺,𝑖,

6 I only calculate the biological score for parcels north of Hwy 351, because radio-collar data for mule deer and pronghorn migration is limited to that area. The herds impacted by natural gas development in the Jonah Field and Pinedale Anticline are included in the data.

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Holloran (2005) found that over 64 percent of sage grouse nest within 5 kilometers of the lek

site, so I use this buffer to represent both leking and nesting habitat.

I add the scores for each species without assigning any weighting (i.e., weight of 1 per

acre) to generate a total biological score for each agricultural parcel:

(16) 𝑇𝑜𝑡𝑎𝑙 𝐵𝑖𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑆𝑐𝑜𝑟𝑒𝑖 = 𝑀𝐷𝐵𝑖𝑜𝑙𝑆𝑐𝑜𝑟𝑒,𝑖 + 𝑃𝐻𝐵𝑖𝑜𝑙𝑆𝑐𝑜𝑟𝑒,𝑖 + 𝑆𝐺𝐵𝑖𝑜𝑙𝑆𝑐𝑜𝑟𝑒,𝑖

Figure 4 shows mule deer winter range, migratory stopover sites, and movement corridors

overlaid with agricultural parcels and existing conservation easements in Sublette County.

Figure 4. Mule deer habitat and privately-owned lands in Sublette County, Wyoming.

Similarly, Figure 5 shows pronghorn movement, stopover, and crucial winter range in Sublette

County, and is overlaid with privately-owned agricultural parcels and existing conservation

easements.

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Figure 5. Pronghorn habitat and privately-owned agricultural lands in Sublette County, Wyoming.

Finally, Figure 6 shows currently occupied sage grouse leks with a five kilometer buffer

surrounding each, overlaid with agricultural parcels and existing conservation easements in

Sublette County.

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Figure 6. Sage grouse habitat and privately-owned agricultural lands in Sublette County, Wyoming.

Figures 4, 5, and 6 show the distribution of habitat types across existing easements and

agricultural parcels in Sublette County. Importantly, much of the habitat for mule deer,

pronghorn, and sage grouse is located on public lands. Table 4 provides a comparison of the

acreage available to each species on public and private lands, and a maximum biological score

according to my definition.

Table 4. Distribution of habitat across private and public lands in Sublette County.

Acres Sage Grouse

Mule Deer Movement

Mule Deer Stopover

Mule Deer Winter

Pronghorn Movement

Pronghorn Stopover

Pronghorn Winter

Public 926,198 117,244 69,722 395,514 123,682 71,393 167,639 Private 328,127 45,149 17,381 95,868 134,296 97,896 27,959 Percent Private

26.1%

27.8%

19.9%

19.5%

52.0%

57.8%

14.2%

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Table 4 indicates that while nearly half of pronghorn migration habitat is on private land,

only approximately 20 to 28 percent of sage grouse, mule deer winter and mule deer migration

habitat is located on private lands. Pronghorn winter habitat is limited to only 14 percent on

private lands.

3.3 Production Possibilities Frontier

To understand and visualize the economic-ecological tradeoff, I combine the biological

and economic scores to construct a production possibilities frontier (PPF). I rank parcels

according to total biological score (benefit) and total conservation easement cost, which I define

as the estimated foregone value of future residential development (cost). This arrangement of

parcels according to benefit-cost ranking forms the PPF. Additionally, I rank parcels similarly

according to economic score, biological score, and propensity score to assess alternative

strategies for targeting easements on the landscape. This illustrates the potential cost for

achieving different levels of habitat protection, given different conservation objectives. Chapter

5 provides an in-depth analysis of targeting strategies.

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4 Results

4.1 Economic Score

Logit Results

As discussed in the sections above, I conducted a review of hedonic modeling literature

to inform my econometric model. The data generally fit each of the binary logit models –

AGRES, AGRESVAC and AGRESRESVAC - well. Independent variables are generally

significant at the 1% level. The likelihood ratio for each model is also significant at the 1%

level. With few exceptions, the logit model gave the expected sign for the estimated coefficients

(Table 5). Additionally, a comparison of parameter estimates between the AGRES, AGRESVAC

and AGRESRESVAC models shows similar signs and magnitude, with a few exceptions.

Table 5. Parameter estimates for AGRES, AGRESVAC, and AGRESRESVAC logit models.

Variable

AGRES a Coefficient

AGRESVAC b

Coefficient AGRESRESVAC c

Coefficient Intercept 11.9612***

(1.9071) 23.5819*** (2.4281)

19.371*** (2.2756)

DistTown -0.00001*** (0.000003256)

-0.00000778** (0.000003483)

-0.00000868*** (0.000002872)

DistRoad -0.00419*** (0.000429)

-0.00130*** (0.000183)

-0.00195*** (0.000184)

AvgStdD_Slope -0.8480*** (0.0567)

-0.6746*** (0.0555)

-0.7729*** (0.0467)

AgNearView -9.9308*** (1.9045)

-22.3094*** (2.4443)

-17.0809*** (2.2812)

ResNearView -3.4804 (3.5155)

-23.5311*** (3.7738)

-13.3267*** (3.8571)

CommNearView -56.8621*** (11.9696)

-59.3578*** (14.0856)

-62.4819*** (11.6260)

WetlandCover -1.3780*** (0.1933)

-2.0502*** (0.222)

-1.5520*** (0.1679)

ForestCover 3.7394*** (0.4143)

3.4315*** (0.3777)

3.6354*** (0.3570)

UndevCover -14.3355*** (5.3951)

1.1253 (3.0179)

-2.9434 (2.804)

Mtn_Pks -0.0088** (0.00396)

0.00123 (0.00388)

-0.00452 (0.00346)

Likelihood Ratio 1248.1685*** (n=3362)

1091.0318*** (n=2939)

1411.4881*** (n=5259)

Note: *,**,*** denote significance at the 10%, 5%, and 1% level, respectively. Standard errors are in parentheses. a AGRES is a binary logit model where is the data includes agricultural and residential parcels. bAGRESVAC is a binary logit model where data includes agricultural and residential vacant parcels. cAGRESRESVAC is a binary logit model where the data includes agricultural, residential, and residential vacant parcels.

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Coefficients for both AgNearView and ResNearView were much lower in the AGRES

model than the AGRESVAC and AGRESRESVAC models. The estimated sign for WetlandCover

was negative; I hypothesized that it would measure amenity value and/or soil and soil moisture

levels that make it productive land because of its nearness to lands currently used for agricultural

production. If WetlandCover is measuring amenity value, it would have a positive sign –

indicating an increasing probability of observing residential land use. Because WetlandCover is

negative, it is likely a proxy for soil characteristics that make it suitable for agriculture; further, it

could represent high conversion costs of vegetation removal, high water table or flood plain

issues, or other factors that make locations unsuitable for residential development. Therefore, a

negative sign on the parameter estimate for WetlandCover likely is indicating correctly that

parcels containing wetlands are more profitable in agriculture, relatively, than other parcels and

are therefore less likely to be observed in residential use.

Coefficients of UndevCover and Mtn_Pks had different signs in the AGRESVAC model

than the AGRES and AGRESRESVAC models. I expect UndevCover to decrease as the

probability of observing residential use increases. Because Mtn_Pks measures amenity value –

the number of mountain peaks within each parcel’s viewshed – I expect it to be positive.

Though the signs of the parameter estimates from the logit model are informative, given

that the binary logit model is a non-linear function, I cannot directly interpret their magnitude. I

therefore calculate marginal effects of each parameter:

(17) 𝜕𝑃𝑖𝜕𝑋𝑘

= 𝜕𝑒𝑥𝛽

1+𝑒𝑥𝛽

𝜕𝑥𝑘,

where 𝑃𝑖 is the probability given by the logit model for observing residential use on parcel i and

𝑋𝑘 is the value observed for a characteristic k. Equation (17) can be re-written as:

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(18) 𝜕𝑃𝑖𝜕𝑋𝑘

= [𝛽𝑘𝑃�𝑖(1 − 𝑃�𝑖)].

I calculate the marginal effect of each variable on each parcel’s propensity score and then

average them to get an estimated marginal effect (Table 6).

Table 6. Average marginal effects.

Variable AGRES AGRESVAC AGRESRESVAC DistTown -0.000001 -0.000001 -0.000001 DistRoad -0.000594 -0.000203 -0.000225 AvgStdD_Slope -0.120296 -0.105303 -0.088984 AgNearView -1.408769 -3.482419 -1.966519 ResNearView -0.493724 -3.673122 -1.534299 CommNearView -8.066374 -9.265545 -7.193523 WetlandCover -0.195481 -0.320029 -0.178681 ForestCover 0.530466 0.535645 0.418543 UndevCover -2.033613 0.175655 -0.338873 Mtn_Pks -0.001248 0.000192 -0.000520

The marginal effects for the independent variables in Table 6 are interpreted as the

change in the probability of observing residential use on a given parcel for a one unit change in

the explanatory variable. Therefore, a one unit increase (1 foot) in the distance between a given

parcel and the nearest municipality decreases the probability of observing that parcel in

residential use by 0.0001%; DistRoad can be interpreted similarly. The near viewshed variables,

AgNearView, ResNearView, and CommNearView can be interpreted in the following way: an

increase of 1% in the share of the near view in the land use of interest (agricultural, residential,

or commercial, respectively) decreases the probability of observing a parcel in residential use by

1.4%, 0.49%, or 8.06%, respectively, in the AGRES model. Marginal effects for the AGRESVAC

and AGRESRESVAC models can be interpreted in the same way. Marginal effects for the other

variables that measure shares – WetlandCover, ForestCover, and UndevCover – of land cover on

a given parcel can also be interpreted in this way: an increase of 1% in the share of forest land

cover on a given parcel increases the probability of observing a parcel in residential use by

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0.53% in the AGRES model, for instance. Finally, my calculation of marginal effects indicates

that an increase of one mountain peak within a given parcel’s viewshed increases the probability

of observing that parcel in residential use by approximately 0.02% in the AGRESVAC model and

decreases that probability by 0.12% and 0.05% in the AGRES and AGRESRESVAC models,

respectively.

The AGRES model predicts the broadest range of propensity scores; the AGRESRESVAC

model predicts the narrowest range of propensity scores (Table 7). The frequency of predicted

probabilities (propensity scores) shows that predicted probabilities of observing residential use

on agricultural parcels is skewed to the right in both the AGRES and AGRESRESVAC models,

and is more evenly distributed in the AGRESVAC model (Figure 7). Because residential parcels,

as opposed to only residential vacant parcels are included in the former datasets, the AGRES and

AGRESRESVAC predict more high residential probabilities. Figures 8, 9, and 10 display

predicted propensity scores from each model on agricultural parcels in Sublette County.

Table 7. Average predicted probabilities by observed land use and model.

Observed Use AGRES AGRESVAC AGRESRESVAC

Agriculture 0.4472 0.4233 0.5642 Residential/Residential Vacant 0.7961 0.7640 0.8587

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Figure 7. Frequency of predicted probabilities for agricultural parcels.

0

20

40

60

80

100

120

140

160

180

0.05 0.

10.

15 0.2

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95 1

Freq

uenc

y

Predicted Probability

AGRES AGRESVAC AGRESRESVAC

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Figure 8. Map of predicted propensity scores on agricultural parcels using AGRES model – Sublette County, Wyoming.

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Figure 9. Map of predicted propensity scores on agricultural parcels using AGRESVAC model – Sublette County, Wyoming.

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Figure 10. Map of predicted propensity scores on agricultural parcels using AGRESRESVAC model – Sublette County, Wyoming.

Results of sub-class and caliper matching

I evaluate the sub-class and caliper matching procedures by how closely each matches

agricultural parcels to residential parcels according to their estimated propensity scores. The

caliper matching approach generally provides closer matches than the sub-class approach, though

each approach gives similar average predicted values (measured in dollars per acre) between

models (Table 8).

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Table 8. Average per acre value of matched residential parcels using each matching approach.

Model

Sub-class

10 Groups

Sub-class

20 Groups

Caliper

(n=1053)

AGRES $17,984.14 $12,731.15 $15,062.88 AGRESVAC $18,131.59 $12,179.17 $14,945.91 AGRESRESVAC $17,466.79 $12,575.00 $14,969.90

While average predicted values between matching approaches and models are similar, predicted

values for each parcel differ substantially between the matching approaches. The accuracy of

the sub-class matching approach is less consistent than the caliper approach. The range of

matched propensity scores is 0.26 and 0.20 for sub-class 10 and sub-class 20, respectively. Such

a large range implies that an agricultural parcel with a propensity score of 0.99 and a parcel with

a propensity score of 0.73 are matched to the same group of residential parcels. Alternatively,

this can be interpreted as assigning the same future residential value to a parcel with a 99 percent

probability of being observing in residential use and a parcel with only a 73 percent probability

of observing in residential use.

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Conversely, within some of the sub-classes, the match between agricultural and

residential parcels’ propensity scores is closer than the match using the caliper approach. The

caliper approach matches residential and agricultural parcels based on a consistent caliper of

0.068, 0.068, and 0.053 for the AGRES, AGRESVAC, and AGRESRESVAC models, respectively.

Where the range of propensity scores is less than 0.068, in the AGRESVAC model for example,

the sub-class approach results in a closer match. Given these tradeoffs between approaches, I

use the results of the caliper matching approach in my analysis because it more consistently

matches agricultural parcels with residential parcels with a similar propensity score and

generates more heterogeneity in residential parcels matched to agricultural parcels.

Based on the results of the logit model and caliper matching, I continue my analysis using

only the AGRESVAC model. Propensity scores and estimated future residential development

value are most accurately captured using the AGRESVAC model, as including parcels currently

in residential use could over-estimate future residential value for two reasons: parcels already

developed might add additional bias to assessed values, and there may be variables omitted from

the logit model associated with residential parcels that are not associated with residential vacant

parcels. Further, I use the median value per acre of caliper-matched residential parcels to impute

future residential development value for agricultural parcels when calculating the economic

score. The median value is the best representation of the range of values of residential parcels

matched to a given agricultural parcel because it is less affected by outlier residential values of

matched parcels (high or low). Table 9 shows summary statistics of the economic score

calculated using the median residential value of residential parcels matched to agricultural

parcels using the caliper matching approach.

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Table 9. Predicted residential values calculated for AGRESVAC model using median, minimum, and average values per acre of matched residential parcels. All values are in dollars per acre.

Imputed Values

Economic Score Average Minimum Maximum

Median $10,434.77 $7,010.00 $31,972.00

Minimum $1,723.09 $153.47 $22,285.75

Average $17,997.45 $8,035.27 $51,734.28

4.2 Biological Score Results

Conservation easements are limited in their effectiveness as mitigation mechanisms by

the relatively small amount of critical habitat located on private lands in Sublette County (see

Table 4). Table 10 shows summary statistics of the biological score calculations for privately-

owned7 agricultural parcels.

Table 10. Summary statistics of biological scores calculated for agricultural parcels in Sublette County, Wyoming.

Total Biological Score

Mule Deer Biological Score

Pronghorn Biological Score

Sage Grouse Biological Score

Average 549.93 132.46 110.81 306.66

Minimum 0 0 0 0

Maximum 9,492.36 3,975.52 9,132.64 5,925.25

7 Some conservation easement projects funded by the JIO or PAPO include components wherein conservation practices occur on the landowners’ grazing allotments on federally-owned lands. Biological scores are not calculated for these lands.

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These statistics illustrate the heterogeneity among agricultural parcels in their estimated total

biological score, and in the estimated biological scores of individual species. Figures 11 - 13

show the estimated biological scores in total and for each species on private agricultural parcels.

Figure 11. Estimated biological scores for mule deer on agricultural parcels in Sublette County, Wyoming.

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Figure 12. Estimated biological scores for pronghorn on agricultural parcels in Sublette County, Wyoming.

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Figure 13. Estimated biological scores for sage grouse on agricultural parcels in Sublette County, Wyoming.

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Figure 14. Estimated total biological scores on agricultural parcels in Sublette County, Wyoming.

4.3 Estimated Ecological-Economic Tradeoffs

The production possibilities frontiers (PPFs) that I construct using predicted economic

and biological scores depict the economic and ecological tradeoffs associated with protecting

impacted species’ habitat using conservation easements to mitigate for habitat loss on the Jonah

Field and Pinedale Anticline. Regardless of the propensity score model used (AGRES,

AGRESVAC, or AGRESRESVAC), the predicted PPFs show an increasing rate of product

transformations between the economic value of land in Sublette County (i.e., the economic

score) and the biological value of the land for mule deer, pronghorn, and sage grouse (Figure

15). As expected, the highest landscape (total of all parcels) economic score occurs when no

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parcels are placed under a conservation easement and thus no habitat is guaranteed to be

conserved. Parcels efficiently conserved are those with the highest “bang per buck,” or highest

biological score per dollar of foregone economic value (i.e., predicted future residential

development value). By selecting the most efficient parcels first, the biological score increases

rapidly for relatively low cost. As more parcels are conserved, each additional parcel is

successively less efficient (i.e., protects less biological score per dollar), giving the PPF the

traditional concave form.

Figure 15. Production possibilities frontier from the alternative propensity score models using median residential values.

Figure 15 also demonstrates that the alternative propensity score models generate similar

results. In each case, the biological scores of conserved parcels are identical because they are not

calculated used estimated propensity scores. The propensity scores determine which residential

0

1,000

2,000

3,000

4,000

5,000

6,000

0 100 200 300 400 500 600 700

Eco

nom

ic S

core

(mill

ions

)

Total Biological Score (1000s)

AGRESVAC AGRES AGRESRESVAC

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parcels are matched with each agricultural parcel, which generates differences in the assigned

residential values (and thus economic scores) across models. As noted in Section 4.1, the models

that include parcels currently in residential use (AGRES and AGRESRESVAC) systematically

predict higher residential value and hence higher economic scores than the AGRESVAC model,

which only includes residential vacant parcels.

I also construct production possibilities frontiers that separate components of the total

biological score to demonstrate species-specific tradeoffs (Figure 16). While Figure 16 only

shows PPFs generated from using the AGRESVAC model, the other propensity score models

generate the same pattern. Figure 16 shows that the tradeoff between the economic and

biological scores is much steeper for mule deer and pronghorn. This is because most mule deer

and pronghorn habitat does not overlap with private agricultural lands. Accordingly, the potential

to mitigate impacts to mule deer and pronghorn through conservation easements is limited.

Additionally, optimal conservation targeting depends on which species are targeted or weighted

most heavily.

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Figure 16. Production possibilities frontiers by species-specific biological scores estimated using AGRESVAC model.

The PPFs shown in Figure 16 assume that habitat is only protected on parcels with

conservation easements (i.e., the minimum landscape biological score is equal to zero). This

assumption underestimates the biological score of the total landscape since many agricultural

parcels have a low propensity of being observed in residential use and may not convert to

residential use, thereby maintaining their function as habitat. Because even in the absence of a

conservation easement many agricultural parcels will remain in agriculture and continue to

provide mule deer, pronghorn, and sage grouse habitat, I also generate PPFs using an expected

biological score. To calculate the expected biological score, I assume that agricultural parcels

without easements will generate biological scores equal to their biological score multiplied by

their probability of being in agriculture (i.e., 1- propensity score). The total expected biological

score can be interpreted as what the landscape is likely to produce in the long-run as agricultural

parcels convert to residential use according to my predicted propensity scores.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

0 100 200 300 400 500 600 700

Eco

nom

ic S

core

(mill

ions

)

Landscape Biological Score (1000s)

Total Score MD Score PH Score SG Score

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PPFs using the expected biological scores no longer have a minimum value of zero, since

even if no parcels are placed under a conservation easement some parcels will continue to

provide habitat (see Figure 17). The tradeoffs in Figure 17, the total biological score, are similar

to those shown in Figure 15, but the PPF simply begins with a positive biological score,

demonstrating that some biological score is achieved in the absence of conservation easements.

Figure 17. Production possibilities frontier using the expected biological score and economic score estimated using the AGRESVAC model.

Figure 18 shows PPFs by species using the expected biological score, and compared to

Figure 16, suggests some important differences. Mule deer and pronghorn biological scores are

substantially lower in expectation than the biological score for sage grouse since sage grouse

habitat is widely dispersed and thus more sage grouse habitat is present even in the absence of

conservation easements. In contrast, mule deer and pronghorn scores show a very steep tradeoff.

The steep tradeoff and relatively low minimum expected biological scores again indicate that

although parcels with mule deer and pronghorn habitat have relatively low propensity to be

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

0 100 200 300 400 500 600 700

Eco

nom

ic S

core

(mill

ions

)

Landscape Biological Score (1000s)

Total Score

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residential, there are so few of them that even without conservation easements much of the mule

deer and pronghorn biological scores are not lost in expectation.

Figure 18. Production possibilities frontiers by species using expected biological scores and economic score estimated using AGRESVAC model.

5 Optimal Targeting of Conservation Easements

The PPFs derived in the previous section identify the theoretically efficient allocation of

conservation easements for any given biological or economic score. In other words, for every

possible biological score, the associated efficient point on the PPF identifies the highest

economic score (i.e., lowest reduction in potential residential values) that can be achieved.

These efficient allocations may not be achievable in reality for many practical reasons.

Conservation easements are voluntary and thus efficient parcels may not be available. Economic

theory suggests that every landowner should be willing to accept a conservation easement if

offered the full opportunity cost of foregone development; however, landowner preferences and

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uncertainty about the future development rights makes it difficult to determine individual

landowners’ maximum willingness to accept conservation easements and hence difficult to target

precisely on the efficient frontier. Nonetheless, from a social welfare perspective, achieving a

landscape that is represented by a point on the efficient frontier should be the goal. I use GIS

data on currently existing conservation easements to calculate the current landscape’s economic

and biological score and compare it to the PPF (Figure 19).

Figure 19. Comparison of existing conservation easements to the efficient production possibilities frontier - AGRESVAC model.

The current landscape arrangement and conservation easements are less efficient than

what is theoretically possible. The existing easements have an associated economic score of

approximately 3.59 million and a total biological score of 51,361. This biological score is

approximately 125,000 points lower than the efficient point with the same economic score (i.e.,

moving horizontally from the current easement point to the efficient frontier). The same

economic score and a much higher biological score could be achieved by re-allocating the

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easements to parcels that generate a higher total biological score per dollar than those included in

current easements. Alternatively, the same approximate biological score could be achieved with

a higher economic score (3.95 million) through more efficient targeting (i.e., moving vertically

from the current easement point to the efficient frontier).

I also calculate this relative efficiency by species (Table 11). Current conservation

easements are closest to being efficient for sage grouse and furthest from being efficient for

pronghorn. This is likely because of the wider distribution of sage grouse habitat (see Figure 6).

Figure 20 separates the total biological score shown in Figure 19 by individual species.

Table 11. Difference in biological scores for each species between current easement point and efficient frontier.

Mule Deer Pronghorn Sage Grouse Difference in relative efficiency (biological scores) -27,268 -87,287 -10,787

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Figure 20. Comparison of existing conservation easements to the efficient production possibilities frontier disaggregated by species - AGRESVAC model.

Targeting Approaches

The preceding PPFs identify efficient allocations of conservation easements for every

possible economic score by iteratively selecting parcels with the highest benefit-cost ratio, or

highest biological score per dollar of foregone residential value. This approach is only one way

to target conservation easements on the landscape. Newburn et al. (2005) discuss four common

alternative methods for targeting conservation, each of which have different policy implications:

1) Benefit targeting: select parcels to maximize ecological benefits for a given conservation

budget;

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2) Benefit-cost targeting: select parcels with the highest benefit-cost ratio until a given

conservation budget is exhausted;

3) Benefit-loss targeting: select parcels to minimize the expected loss of biological benefits

for a given budget;

4) Benefit-loss-cost targeting: select parcels to minimize the expected loss of biological

benefits per dollar for a given budget.

Applying different targeting strategies (for a fixed budget of $36 million) significantly affects

which parcels are optimally conserved and the resulting biological scores. I identify parcels that

are the “best” candidates for conservation under each targeting strategy by ranking parcels

according to each strategy and then iteratively selecting parcels until the budget of $36 million is

exhausted. For example, to select parcels that should be targeted first according to the benefit

targeting strategy, I ranked parcels according to their biological score and selected them

iteratively, from highest biological score down the ranking, until the budget was exhausted.

Considering only the biological scores of conserved parcels (i.e., assuming un-conserved

parcels produce no biological values), benefit-cost targeting produces the highest biological

score (Figure 21). This result is consistent with theory since benefit-cost targeting maximizes

the “bang per buck” of selected parcels. Benefit targeting performs relatively poorly because it

targets parcels with high biological scores regardless of cost. Considering cost – defined as the

foregone future residential development value of each parcel – in the benefit-cost targeting

strategy gives more biological benefit (captures a greater total biological score) than targeting

only according to biological benefit. This is because given a budget of $36 million, resource

managers could hypothetically purchase more parcels with slightly lower biological value than

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they could high-biological benefit parcels that are much more expensive, resulting in a greater

total of biological benefit captured.

Figure 21. Total biological scores produced from alternative targeting strategies with a total budget of $36 million.

Benefit and benefit-cost targeting do not consider the relative risk of losing parcels,

however, so the expected landscape biological score is lower (i.e., they ignore the expected

biological score of un-conserved parcels). When risk of losing parcels to development is

considered across the entire landscape, benefit-loss-cost targeting produces the best conservation

outcome (Figure 22). This approach performs best because it targets parcels with the highest

expected loss of biological value while also accounting for cost of conservation. It includes

parcels that have relatively low expected biological losses if they are sufficiently inexpensive,

and only includes relatively expensive parcels if they have sufficiently high expected losses. In

contrast, benefit-loss targeting performs poorly because it includes high risk (and often

expensive) parcels even if their biological score is too low to be cost-effective.

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Figure 22. Landscape-level expected biological scores produced from alternative targeting strategies with a total budget of $36 million.

Figure 22 highlights the importance of considering risk of development when targeting

easements. Parcels with low propensity scores (i.e., low probability of being residential) are

likely to continue producing biological value regardless of whether they are placed in a

conservation easement. Benefit-loss-cost targeting performs better than other approaches so long

as there are low propensity (i.e., low expected biological loss) parcels with high biological value.

Accounting for expected loss, these parcels should not be conserved since they are unlikely to be

lost – rather, limited budgets should be targeted to higher risk-higher biological value parcels.

The relationship between propensity scores and biological scores for parcels in Sublette

County shows that there are many relatively low risk parcels with relatively high biological

scores (Figure 23). Figure 23 illustrates that this trend exists for each individual species as well:

for mule deer, pronghorn, and sage grouse, there are parcels that provide high biological value

and have a low probability of being observed in residential use. There is a general positive

relationship between propensity scores and residential value (i.e., opportunity cost of the

easement) and thus a positive relationship between biological scores and residential value

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(Figure 24). As a result, accounting for risk of development, cost of easement purchases, and

level of biological value generates the best conservation outcome given a limited budget.

Figure 23. Relationship between parcel-level biological scores and propensity scores.

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Figure 24. Relationship between the biological and residential values of parcels.

The relationship between risk and biological value has important implications for the

effectiveness of conservation easements as a conservation tool. As shown in Figure 23, there are

many low risk, high biological score parcels, and the highest risk parcels tend to have relatively

low biological value. This suggests that there is little competition between species’ critical

habitat and parcels that are likely candidates for residential development. This also suggests that

the landscape is likely to produce substantial biological value even in the absence of

conservation easements, and suggests that conservation or mitigation efforts may be better

directed at minimizing impacts to species on public lands, where much of species’ critical habitat

is located. Because so many agricultural parcels are likely to remain in agriculture, by placing

them under a conservation easement, resource managers would be paying for a service (provision

of critical habitat) that would otherwise still be provided at no expense.

The expected biological score across all parcels is 377,570, while the total biological

score if all parcels were placed under conservation easement is 587,618. Thus, approximately 64

percent of the biological value on the landscape is likely to remain even if there were no

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conservation easements. Current conservation easement projects are relatively inefficient

because they do not appear to account for risk8. Parcels within current easements have a total

biological score of 51,361, with an expected biological score of 34,953. Thus, nearly 70 percent

of the biological score on currently conserved parcels was likely to remain in the absence of the

easements. In contrast, a similar calculation and comparison of efficient conservation plans

generated with benefit-loss-cost targeting have ratios less than 50 percent – meaning that less

than 50 percent of the conserved biological score is likely to remain without easements. This

illustrates the potential gains in biological value that exist given careful targeting. Species

specific targeting, as discussed above, is another policy consideration given the level of observed

impacts to species like mule deer.

Finally, I identify, according to each of the targeting strategies discussed above, the 10

next best parcels to put under a conservation easement as a hypothetical policy recommendation

(see Figure 25). I identified these parcels by ranking all parcels according to each targeting

strategy and selecting the top ten in each ranking (regardless of the budget constraint). Parcels

that I target as next-best purchases are relatively small and widely distributed. Though smaller

than already-purchased easements, they provide high biological value.

As discussed previously, there are many reasons for theoretically optimal purchases to

differ from purchases that are practically feasible. Landowners and resource managers have

differing information regarding the other’s willingness to pay and willingness to accept for a

purchase of development rights. Landowners face uncertainty surrounding future residential

markets, costs associated with converting agricultural land to residential properties, and when the

optimal conversion time (t*) might be. These dynamics are among many reasons for what I

8 Easement buyers may have additional, parcel-specific information on relative risk, such as landowner plans to retire, that are not captured in my model.

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identify as optimal and what is practically feasible to differ; however, identifying the

theoretically next best candidate parcels for conservation provides a starting point for resource

managers. Importantly, identifying next best candidate parcels, as shown in Figure 25, illustrates

the limitations to mitigating on private lands the loss of habitat on public lands.

Figure 25. Ten "best" currently unconserved parcels for conservation according to each targeting approach. Existing conservation easements designated by cross-hatch pattern.

6 Conclusion

This study examines the tradeoff between biological value and economic value on

agricultural lands in Sublette County, Wyoming. Extensive natural gas production on mule deer

and pronghorn crucial winter range, increasing fragmentation of mule deer and pronghorn

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migration corridors, and sage grouse habitat degradation attributable to many changes on the

landscape have prompted compensatory mitigation efforts to benefit these impacted species. I

explore ways in which agencies charged with overseeing these mitigation efforts can best

allocate their limited funds to achieve the most biological benefit at least cost, which I define as

the opportunity cost of future residential development on privately-owned agricultural lands. My

key findings are: opportunities for conservation are limited by a lack of critical habitat on private

agricultural lands, and the risk of development for a given parcel must be a consideration when

targeting conservation easements.

To explore which parcels provide the most biological value at least cost, I use a method

for estimating the opportunity cost of future residential development that enables me to correct

for limitations of assessors’ data. Specifically, I estimate a binary logit model using parcel-level

physical and geographical characteristics commonly included in hedonic models to generate a

propensity score – the probability of observing a given parcel in residential use – for each parcel

in the county. Using a matching procedure, I then impute the potential residential value of each

agricultural parcel. Hence, I am able to consider this disaggregated future use value as the

amount of compensation needed to purchase a conservation easement on a given agricultural

parcel. The sum of the assessed agricultural value and the median residential value resulting

from matching is the economic “score” (i.e., total value) of each parcel.

I combine economic scores with a simple biological score that I estimate by weighting

parcels’ distance to crucial mule deer and pronghorn winter range and their acreage of winter

range, stopover range, and movement corridors. I combine these individual species scores with

the acreage of sage grouse habitat (a five kilometer buffer around currently occupied leks) to

form a total biological score for each parcel. Constructing production possibilities frontiers from

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these total and disaggregated biological scores and economic scores shows that the economic-

biological tradeoff is particularly steep for mule deer and pronghorn. This is likely because

much of these species’ critical habitat is on public land rather than privately-owned agricultural

lands, which limits the amount of mitigation that can be achieved using private land conservation

easements.

To avoid underestimating the total biological score available across the entire landscape,

I calculate an expected biological score for each parcel that accounts for both the parcel’s

biological value and the likelihood of observing that parcel in a residential use. This identifies

parcels that currently provide habitat and that are unlikely to become residential in the future,

and thus will continue to provide biological value regardless of whether they are placed in a

conservation easement. Even incorporating expected biological values, the tradeoff between

economic and biological value remains steep for mule deer and pronghorn.

I identify multiple approaches for targeting conservation easement purchases: benefit,

benefit-cost, benefit-loss, and benefit-loss-cost targeting. I find that benefit-loss-cost targeting

performs the best of each strategy for conserving the most biological value at the least cost.

Two key observations can be made from this analysis: cost of conservation easements and risk of

future development should inform conservation easement purchases. In most cases, targeting

conservation easements towards large, expensive parcels does not generate as much biological

value as targeting smaller, less expensive parcels with high biological value (see Figure 25).

However, a limitation of my study is that I do not consider the possibility of dividing parcels for

either conservation use (placing a conservation easement on only part of the agricultural parcel)

or for residential use (subdividing only part of the agricultural parcel). It may be possible for

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resource managers to conserve only key portions of larger parcels, thereby capturing more

biological value at a lower cost when compared to the cost of purchasing the entire parcel.

It is critical that policy makers consider the likelihood of losing agricultural lands to

development when making conservation easement purchases; I found that many agricultural

parcels have both a low probability of converting to residential use and a high biological value.

Conservation easements would be better placed on parcels that offer a high biological value and

are at higher risk for residential development (accounting for conservation costs).

Finally, I compare what is optimal according to my analysis with conservation easement

purchases that have already been made. While these purchases are not efficient according to my

model, I cannot account for many factors influencing purchase decision. I am unable, for

example, to observe landowners’ individual preferences and therefore their minimum willingness

to accept. There is likely a self-selection bias in the application process to obtain conservation

easements through the available funding mechanisms. Landowners with strong preferences for

preserving agricultural production or the environment are the most likely to seek out easement

agreements and may be willing to accept less than the opportunity cost. Because conservation

easements are strictly voluntary, I cannot draw any certain conclusions regarding the actual cost

of the easement (I can only estimate what the cost should be, according to land value theory) and

I cannot predict which lands are actually likely to be placed under an easement. Each of these

may substantially affect the efficient frontier and the optimal targeting strategy. Though less

efficient than what is theoretically possible, considering these challenges of targeting

conservation easement purchases, existing easements appear to be relatively good purchases.

Future research could resolve several such limitations of my study, such as correcting for

spatial autocorrelation in the econometric model, and calculating a more sophisticated biological

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score. A more sophisticated biological score should reflect contiguity between parcels’ locations

and reward habitat agglomeration (Parkhurst et al. 2002). Additional econometric analysis of

highly-valued parcels that appear to be statistical outliers should also be considered. Some

parcels that had extremely high assessed values could be indicating a structural change in the

market for land in Sublette County or could indicate an omitted variable problem.

My research suggests that while some compensatory mitigation is achievable, it is limited

by the lack of critical habitat on private agricultural lands – only approximately 20 – 30 percent

of sage grouse and mule deer habitat exists on private lands, and while nearly half of pronghorn

migration habitat exists on private lands, all but 14 percent of pronghorn winter range exists on

public lands. Thus, limiting the impacts to species will require some level of habitat protection

on public lands. In conclusion, my results suggest it may be more effective for land managers to

invest less in conservation easement purchases – except where carefully targeted to consider

parcels’ risk of development – and instead focus efforts and funding on habitat reclamation and

minimizing on-site impacts.

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