BLM Technical Note 433

An Assessment of the Effects of Oil and Gas

Field Activities on Nesting Raptors in the 

Rawlins, Wyoming and Price, Utah Field Offices 

of the Bureau of Land Management

BLM
BLM/WY/ST-10/008+1110
Jeff P. Smith, Steven J. Slater1

, and Mike C. Neal

HawkWatch International, Inc.

2240 South 900 East

Salt Lake City, Utah 84106

1 Phone: (801) 484-6808 x108 

 Email: sslater@hawkwatch.org

Prepared for:

U.S. Department of Interior, 

Bureau of Land Management

Utah State Office, Salt Lake City,

Wyoming State Office, Cheyenne, and

Colorado State Office, Lakewood

BLM Technical Note 433

An Assessment of the Effects of Oil and Gas

Field Activities on Nesting Raptors in the

Rawlins, Wyoming and Price, Utah Field Offices

of the Bureau of Land Management

iii Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Executive Summary . . . . . . . . . . . . . . . . . . . . . . xi

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Results and Discussion . . . . . . . . . . . . . . . . . . . . . xiii

Price, Utah Study Area . . . . . . . . . . . . . . . . . . xiii

Rawlins, Wyoming Study Area . . . . . . . . . . . . xiv

Overall Conclusions . . . . . . . . . . . . . . . . . . . . xvi

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . xvii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Oil and Gas Development and Raptors . . . . . . . . . . . 1

The Raptor Radii Research Project . . . . . . . . . . . . . . 2

Goals and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . 3

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Nest Cluster Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Study Areas and Background Information . . . . . . . . 5

Price, Utah Study Area . . . . . . . . . . . . . . . . . . . . 5

Rawlins, Wyoming Study Area . . . . . . . . . . . . . . 6

Description of Datasets and Data Preparation . . . . . 8

Raptor Nest Data . . . . . . . . . . . . . . . . . . . . . . . . 8

Oil and Gas Well Data . . . . . . . . . . . . . . . . . . . . 9

Road Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Landcover/Vegetation Data . . . . . . . . . . . . . . . . 9

Climate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Other Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Calculation of GIS-based Metrics . . . . . . . . . . 11

Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Objective 1: Describe temporal

trends in development near focal-species 

nest clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Objective 2: Describe distributional patterns 

of nest cluster status in relation to 

development hotspots. . . . . . . . . . . . . . . . . . . . . 11

Objective 3: Assess relationships between 

nest-cluster status and development. . . . . . . . . 12

Objective 4: Assess Ferruginous Hawk nest 

success and productivity in relation 

to development. . . . . . . . . . . . . . . . . . . . . . . . . . 14

Objective 5: Assess the response of 

Ferruginous Hawks to artificial 

nest structures. . . . . . . . . . . . . . . . . . . . . . . . . . 15

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Price, Utah Study Area . . . . . . . . . . . . . . . . . . . . . . . 17

Objective 1: Describe temporal trends 

in development near focal-species 

nest clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Objective 2: Describe distributional patterns 

of nest cluster status in relation to 

development hotspots. . . . . . . . . . . . . . . . . . . . . 18

Objective 3: Assess relationships between 

nest cluster status and development. . . . . . . . . 19

Rawlins, Wyoming Study Area . . . . . . . . . . . . . . . . . 27

Objective 1. Describe temporal trends 

in development near focal-species 

nest clusters. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Objective 2: Describe distributional patterns 

of nest cluster status in relation to 

development hotspots. . . . . . . . . . . . . . . . . . . . . 28

Objective 3: Assess relationships between 

nest cluster status and development. . . . . . . . . 29

Objective 4: Assess Ferruginous Hawk nest 

success and productivity in relation 

to development. . . . . . . . . . . . . . . . . . . . . . . . . . 39

Objective 5: Assess the response of 

Ferruginous Hawks to artificial 

nest structures. . . . . . . . . . . . . . . . . . . . . . . . . . 43

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Price, Utah Study Area . . . . . . . . . . . . . . . . . . . . . . . 45

Golden Eagles . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Red-tailed Hawks . . . . . . . . . . . . . . . . . . . . . . . 46

Prairie Falcons . . . . . . . . . . . . . . . . . . . . . . . . . 47

Common Species Patterns . . . . . . . . . . . . . . . . 47

Rawlins, Wyoming Study Area . . . . . . . . . . . . . . . . . 48

Golden Eagles . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Ferruginous Hawks . . . . . . . . . . . . . . . . . . . . . 49

Red-tailed Hawks . . . . . . . . . . . . . . . . . . . . . . . 53

Prairie Falcons . . . . . . . . . . . . . . . . . . . . . . . . . 53

Common Species Patterns . . . . . . . . . . . . . . . . 54

Comparison of Price and Rawlins Results . . . . . . . 54

Conclusions and Management Implications . . . . . . 57

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Appendix A. Glossary. . . . . . . . . . . . . . . . . . . . . 63

Table of Contents

v Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Table 9.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Prairie Falcon nest cluster being “used” in a given year 

and oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Price, Utah study area. . . . . . . . . . . . . . . . . . . . . 24

Table 10.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability of a 

Prairie Falcon nest cluster being “active” in a given year 

and oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Price, Utah study area. . . . . . . . . . . . . . . . . . . . 25

Table 11.	 Top multiple regression models (∆AICc <4.0) describing

relationships between proportional “use” of Golden 

Eagle nest clusters (i.e., proportion of years in which

evidence of nest tending or breeding was obtained) and

oil and gas development factors (DFs; cluster averages 

and variances) and average landcover/vegetation

factors (VFs) in the Price, Utah study area. . . . . . . . 25

Table 12.	 Top multiple regression models (∆AICc <4.0) describing

relationships between proportional “activity” of Golden 

Eagle nest clusters (i.e., proportion of years in which a

breeding attempt occurred) and oil and gas

development factors (DFs; cluster averages and 

variances) and average landcover/vegetation factors

(VFs) in the Price, Utah study area. . . . . . . . . . . . 26

Table 13.	 Comparison (univariate t-tests) of 1978 and 2006 well

and road metrics near 1,097 raptor nest clusters

(including Golden Eagles, Ferruginous Hawks, Redtailed Hawks, and Prairie Falcons) in the Rawlins,

Wyoming study area. . . . . . . . . . . . . . . . . . . . . . 27

Table 14.	 Percentages of 1,097 raptor nest clusters (including

Golden Eagles, Ferruginous Hawks, Red-tailed Hawks,

and Prairie Falcons) with oil and gas wells within 0.8

and 2.0 km of cluster centroids in 1978 and 2006

in the Rawlins, Wyoming study area. . . . . . . . . . . 28

Table 15.	 Percentages by land owner/manager of 1,097 raptor

nest clusters (including Golden Eagles, Ferruginous

Hawks, Red-tailed Hawks, and Prairie Falcons) with no

oil and gas wells within 0.8 and 2.0 km of cluster

centroids in 1978 and 2006 in the Rawlins,

Wyoming study area. . . . . . . . . . . . . . . . . . . . . . 28

Table 1.	 Comparison (univariate t-tests) of 1998

and 2006 well and road metrics near 264 raptor

nest clusters (including Golden Eagles, Red-tailed

Hawks, and Prairie Falcons) in the Price,

Utah study area. . . . . . . . . . . . . . . . . . . . . . . . . 17

Table 2.	 Percentages of 264 raptor nest clusters (including

Golden Eagles, Red-tailed Hawks, and Prairie Falcons)

with oil and gas wells within 0.8 and 2.0 km of cluster

centroids in the Price, Utah study area in

1998 and 2006. . . . . . . . . . . . . . . . . . . . . . . . . 18

Table 3.	 Percentages by land owner/manager of 264 raptor nest

clusters (including Golden Eagles, Red-tailed Hawks,

and Prairie Falcons) with no gas or oil wells within 0.8

and 2.0 km of cluster centroids in 1998 and 2006 in the

Price, Utah study area. . . . . . . . . . . . . . . . . . . . 18

Table 4.	 Summary of Principal Components Analysis describing

seven primary landcover/vegetation factors with

eigenvalues >1.0 derived from 22 original landcover/

vegetation variables representing proportional coverage 

within 2 km of 264 raptor nest clusters in the Price,

Utah study area (asterisks indicate high loadings that

comprise the primary variables involved 

in each factor). . . . . . . . . . . . . . . . . . . . . . . . . . 19

Table 5.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability of a 

Golden Eagle nest cluster being “used” in a given year 

and various oil and gas development factors (DFs),

landcover/vegetation factors (VFs), and climate

variables in the Price, Utah study area. . . . . . . . . . 22

Table 6.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Golden Eagle nest cluster being “active” in a given year 

and oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Price, Utah

study area. . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Table 7.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability 

of a Red-tailed Hawk nest cluster being “used” in a

given year and oil and gas development factors (DFs),

landcover/vegetation factors (VFs), and climate

variables in the Price, Utah study area. . . . . . . . . . 23

Table 8.	 Top multiple logistic regression model (∆AICc <4.0)

describing the relationships between the probability of a 

Red-tailed Hawk nest cluster being “active” in a given

year and oil and gas development factors (DFs),

landcover/vegetation factors (VFs), and climate

variables in the Price, Utah study area. . . . . . . . . . 23

List of Tables
vi BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

Table 16.	 Summary of Principal Components Analysis describing

six primary landcover/vegetation factors with

eigenvalues >1.0 derived from 19 original landcover/

vegetation variables representing proportional coverage 

within 2 km of 1,109 raptor nest clusters in the Rawlins,

Wyoming study area (* indicates high loadings that 

comprise the primary variables involved in 

each factor). . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Table 17.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Golden Eagle nest cluster being “used” in a given year 

and oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Rawlins, Wyoming study area. . . . . . . . . . . . . . . . 32

Table 18.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Golden Eagle nest cluster being “active” in a given year 

and oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Rawlins, Wyoming study area. . . . . . . . . . . . . . . . 32

Table 19.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

non-ANS Ferruginous Hawk nest cluster being “used”

in a given year and oil and gas development factors 

(DFs), landcover/vegetation factors (VFs), and climate

variables in the Rawlins, Wyoming study area. . . . . 33

Table 20.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability of a 

non-ANS Ferruginous Hawk nest cluster being “active”

in a given year and various oil and gas development 

factors (DFs), landcover/vegetation factors (VFs),

climatic variables, and interactions in the Rawlins,

Wyoming study area. . . . . . . . . . . . . . . . . . . . . . . . . . 33

Table 21.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Ferruginous Hawk ANS nest cluster being “used” in a

given year and oil and gas development factors (DFs),

landcover/vegetation factors (VFs), and climate

variables in the Rawlins, Wyoming study area. . . . . 34

Table 22.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability of a 

Ferruginous Hawk ANS nest cluster being “active” in a

given year and various oil and gas development factors 

(DFs), landcover/vegetation factors (VFs), climatic

variables, and interactions and in the Rawlins,

Wyoming study area. . . . . . . . . . . . . . . . . . . . . . 35

Table 23.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability of a 

Red-tailed Hawk nest cluster being “used” in a given

year and various oil and gas development factors 

(DFs), landcover/vegetation factors (VFs), climatic

variables, and interactions in the Rawlins, Wyoming

study area. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Table 24.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Red-tailed Hawk cluster being “active” in a given year

and oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climatic variables in the

Rawlins, Wyoming study area. . . . . . . . . . . . . . . . 36

Table 25.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Prairie Falcon cluster being “used” in a given year and 

oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Rawlins, Wyoming study area. . . . . . . . . . . . . . . . 36

Table 26.	 Top multiple logistic regression models (∆AICc <4.0)

describing relationships between the probability of a 

Prairie Falcon cluster being “active” in a given year and 

oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Rawlins, Wyoming study area. . . . . . . . . . . . . . . . 37

Table 27.	 Top multiple regression models (∆AICc <4.0) describing

relationships between proportional “use” of Golden 

Eagle nest clusters (i.e., proportion of years in which

evidence of nest tending or breeding was obtained)

and oil and gas development factors (DFs; cluster 

averages and variances) and average landcover/

vegetation factors (VFs) in the Rawlins,

Wyoming study area. . . . . . . . . . . . . . . . . . . . . . 38

Table 28.	 Top multiple regression models (∆AICc <4.0) describing

relationships between proportional “activity” of Golden 

Eagle nest clusters (i.e., proportion of years in which a

breeding attempt was confirmed) and oil and gas

development factors (DFs; cluster averages and 

variances) and average landcover/vegetation factors

(VFs) in the Rawlins, Wyoming study area. . . . . . . 39

Table 29.	 Top multiple regression models (∆AICc <4.0) describing

relationships between proportional use of non-ANS

Ferruginous Hawk nest clusters (i.e., proportion of

years in which evidence of nest tending or breeding 

was obtained) and various oil and gas development

factors (DFs) and landcover/vegetation factors (VFs)

in the Rawlins, Wyoming study area. . . . . . . . . . . 39
vii Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Table 30.	 Top multiple regression models (∆AICc <4.0) describing

relationships between proportional activity of non-ANS

Ferruginous Hawk nest clusters (i.e., proportion of

years in which a breeding attempt was confirmed) and

various oil and gas development factors (DFs) and

landcover/vegetation factors (VFs) in the Rawlins,

Wyoming study area. . . . . . . . . . . . . . . . . . . . . . 39

Table 31.	 Summary of confirmed Ferruginous Hawk breeding

events in the Rawlins, Wyoming study area between

1976–2006 classified by nest substrate and the

number of nestlings raised to ≥80% of average

fledging age. . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Table 32.	 Summary of confirmed Ferruginous Hawk breeding

events in the Rawlins, Wyoming study area between

1976–2006 classified by nest substrate/accessibility

categories and the number of nestlings produced to 

≥80% of average fledging age. . . . . . . . . . . . . . . 40

Table 33.	 All significant multiple logistic regression models

describing relationships between nesting success of 

accessible Ferruginous Hawk nests in a given year and

oil and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables in the

Rawlins, Wyoming study area. Top models are those

with ∆AICc values <4.0. . . . . . . . . . . . . . . . . . . . 41

Table 34.	 All significant multiple linear regression models

describing relationships between productivity per nest 

start at accessible Ferruginous Hawk nests and oil and

gas development factors (DFs), landcover/vegetation

factors (VFs), and climate variables in the Rawlins,

Wyoming study area. Top models are those with

∆AICc values <4.0. . . . . . . . . . . . . . . . . . . . . . . 41

Table 35.	 Top multiple logistic regression models (∆AICc <4.0)

describing the relationships between the probability of 

an inaccessible Ferruginous Hawk nest cluster being

successful in a given year and various oil and gas 

development factors (DFs), landcover/vegetation

factors (VFs), climatic variables, and interactions

and in the Rawlins, Wyoming study area. . . . . . . . . 42

Table 36. Top multiple linear regression models 

(∆AICc <4.0) describing relationships between

productivity per nest attempt at inaccessible 

Ferruginous Hawk nests in a given year and oil

and gas development factors (DFs), landcover/

vegetation factors (VFs), and climate variables and

in the Rawlins, Wyoming study area. . . . . . . . . . . 42

Table 37.	 Total minutes of nest attendance, total prey deliveries,

hunting attempts by location, and number of times

adults flushed at inaccessible (n = 35 nests) and

accessible (n = 15) Ferruginous Hawk nests in the

Rawlins, Wyoming study area from 2000–2004 (from

Neal 2007). All values are calculated on a per-nest

basis and reflect consistent observation effort 

(6 hours total) at all nests. . . . . . . . . . . . . . . . . . 43

Table 38. Summary comparison of logistic regression results for 

top development-only and development-plus (including

climate and vegetation covariates) models of annual

nest-cluster “use” and “activity” in the Price, Utah, and

Rawlins, Wyoming, study areas. . . . . . . . . . . . . . 56

ix Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Figure 9. Total number of new and established oil and gas wells 

found in the Rawlins, Wyoming study area between

1978 and 2006. . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 10.	 Average numbers of active oil and gas wells within 0.8

and 2.0 km of 1,097 raptor nest-cluster centroids in the

Rawlins, Wyoming study area between 1978

and 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Figure 11. Map depicting the distribution of raptor nests from nest 

clusters with ≥5 years of survey history between 1998

and 2006 within three oil and gas development

“hotspots” in the Rawlins, Wyoming study area. . . . 28

Figure 12.	 Average standardized distances between development

“hotspot” centers and “active” and “other” Ferruginous 

Hawk nest clusters in the Rawlins, Wyoming

study area from 1998 to 2006. . . . . . . . . . . . . . . . 29

Figure 13.	 Kernel density (within a 5-km radius) of active

Ferruginous Hawk (FEHA) nests in the Rawlins,

Wyoming study area relative to 105 artificial nest

structure (ANS) locations before (1970–1987, upper

panel) and after (1990–2006, lower panel)

their installation. . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 14.	 Graphical display of individual development-variable

loadings associated with two primary development 

factors identified by Principal Components Analysis of

characteristics associated with 1,097 raptor nest

clusters at the 0.8-km and 2.0-km radius spatial

scales in the Rawlins, Wyoming

study area. . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 15.	 Comparison of annual trends in species-specific

proportional nest activity, the Palmer Drought Severity

Index (PDSI; higher values = less drought severity),

PDSI in the previous year (PDSI_lyr), and winter

precipitation (WintPPT) in the Rawlins, Wyoming

study area from 1998–2006 (period of greatest raptor

data quality). All measures have been standardized

using the entire 1978–2006 dataset for direct

comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Figure 1. Map showing the locations of active oil and gas wells in 

1998 and 2006 in relation to 264 raptor nest clusters in

the Price, Utah study area. Raptor clusters are

represented by 2-km buffers extending around the

individual Golden Eagle, Red-tailed Hawk, and Prairie

Falcon nests included in each cluster; we included only 

clusters used at least once during the 1998–2006

raptor survey period to create the 

buffers shown. . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 2. Map showing the locations of active oil and gas wells in 

1970 and 2006 in relation to 1,109 raptor nest clusters

in the Rawlins, Wyoming study area. Raptor clusters

are represented by 2-km buffers extending around the

individual Golden Eagle, Ferruginous Hawk, Red-tailed

Hawk, and Prairie Falcon nests included in each

cluster; only clusters used at least once during the 

1970-2006 raptor survey period were

used to create the buffers shown. . . . . . . . . . . . . . . 7

Figure 3. Total number of new and established oil and gas 

wells found in the Price, Utah study area between

1998 and 2006. . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 4.	 Average numbers of active oil and gas wells within 0.8

and 2.0 km of 264 raptor nest-cluster centroids in the

Price, Utah study area from 1998 to 2006. . . . . . . . 17

Figure 5. Map depicting the distribution of raptor nests from nest 

clusters with ≥5 years of survey history between 1998

and 2006 within three oil and gas development

“hotspots” in the Price, Utah study area. . . . . . . . . 18

Figure 6.	 Average standardized distance between development

“hotspot” centers and used and unused Golden Eagle 

nest clusters in the Price, Utah study area from

1998 to 2006. . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 7.	 Graphical display of individual development-variable

loadings associated with two primary development 

factors identified by Principal Components Analysis

of characteristics associated with 264 raptor nest

clusters at the 0.8-km and 2.0-km radius spatial

scales in the Price, Utah study area. . . . . . . . . . . . 19

Figure 8.	 Comparison of trends in annual, species-specific,

proportional nest-cluster activity, the Palmer Drought

Severity Index (PDSI; higher values = less drought

severity), PDSI in the previous year (PDSI_lyr), and

winter precipitation (WintPPT) in the Price, Utah study

area from 1998–2006. All measures have been

standardized for direct comparison. . . . . . . . . . . . 21

List of Figures

xi Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

The major goals of Phase I of the Raptor Radii Research 

Project were:

Goal 1. Compile historic data to evaluate the 

relationship between past OG development 

activities and nesting raptors.

Goal 2. Where suited, use historic data to evaluate 

the effectiveness of previously applied 

raptor nest-protection buffers.

Goal 3. Assess strengths and weaknesses of historic 

data and provide recommendations for 

improvement.

We address the first two goals in this technical note and 

third goal in a separate document (Smith et al. 2010). To 

achieve the first two goals, we established the following 

objectives:

Objective 1. Describe temporal trends in 

development near focal-species nest 

clusters.

Objective 2. Describe distributional patterns of nestcluster status in relation to development 

hotspots.

Objective 3. Assess relationships between nestcluster status and development.

Objective 4. Assess Ferruginous Hawk (Buteo 

regalis) nest success and productivity 

in relation to development in the 

Rawlins, Wyoming study area.

Objective 5. Assess the response of Ferruginous 

Hawks to artificial nest structures 

(ANSs) in the Rawlins, Wyoming study 

area.

Methods

We adopted a “nest-cluster” approach for Phase I 

assessments of data collected in the Price, Utah, and 

Rawlins, Wyoming, study areas. The Price study area 

topography was dominated by canyons and cliffs and 

primarily contained pinyon-juniper (Pinus edulisJuniperus spp.) vegetation, interspersed with sagebrush 

(Artemisia spp.) and grass openings. The Utah Division 

of Wildlife Resources (UDWR) began conducting 

Introduction

Approximately 15 million ha of land managed by the 

Bureau of Land Management (BLM) in the western 

United States are leased for oil, gas, or geothermal 

energy production, but few studies have specifically 

investigated the potential disturbance effects energy 

development may have on nesting raptors. The potential 

for disturbance is suggested by studies of nesting raptors 

and other human activities, such as construction, hiking, 

camping, and rock climbing. The potential for nest 

disturbance is influenced by factors such as disturbance 

proximity and type, as well as previous exposure to 

disturbance. 

Federal legislation, specifically the Migratory Bird 

Treaty Act and Bald and Golden Eagle Protection Act 

(BGEPA), prohibit the “taking” of raptors, their young, 

or nests. Additionally, the BGEPA protects nesting 

eagles from human disturbance. As a result, federal land 

management plans commonly include spatial and/or 

temporal nest-protection stipulations that restrict human 

activities near raptor nests to ensure compliance with 

federal legislation. Specific stipulations applied by land 

managers typically are based on limited data on nest 

disturbance collected for some of the more abundant or 

visible raptor species found within the relevant area of 

responsibility. Because of these limited and often 

sporadic datasets, proactive stipulations applied to 

nesting raptors often vary between and within federal 

agencies.

In 2006, the BLM entered into an assistance agreement 

(JSA065003) with HawkWatch International, Inc. to 

conduct a two-phase “Raptor Radii Research Project.” 

The underlying goal of this research was to assess 

existing protective spatial and temporal buffers and 

provide guidance to minimize potential disturbance to 

nesting raptors exposed to oil and gas (OG) development 

activities. Phase I of the project was intended to be a 

retrospective evaluation of past raptor nesting activity in 

relation to concurrent OG development activity on lands 

managed primarily by the Price, Utah, and Rawlins, 

Wyoming, Field Offices of the BLM. Phase II was 

intended to be a two-year field study designed to 

experimentally test raptor nest-disturbance thresholds in 

Colorado, Utah, and Wyoming study areas selected based 

on current and expected OG development activities and 

available raptor nesting data. This report provides the 

results of the Phase I portion of the Raptor Radii 

Research Project.

Executive Summary
xii BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

annual helicopter surveys in the Price study area in 

1998, following completion of an Environmental Impact 

Statement undertaken by the Price BLM in response to 

proposed coalbed natural-gas development in the area. 

More than 1,100 OG wells were present in the study area 

in 2006. Active nests on federal lands in the Price study 

area typically were protected from surface disturbing 

activities within 0.8 km during the nesting season, 

although exceptions were occasionally granted based 

on topography and other considerations. Helicopter 

surveys conducted once per year during May from 

1998–2006 aided in identification of 264 Golden Eagle 

(Aquila chrysaetos), Red-tailed Hawk (B. jamaicensis), 

and Prairie Falcon (Falco mexicanus) nest clusters in the 

study area.

The Rawlins study area consisted of rolling topography 

interspersed with rocky escarpments. The vegetation 

was predominantly sagebrush (Artemisia spp.)-grassland 

and desert shrubs. The Rawlins BLM identified 13 

Key Raptor Areas within the Rawlins Field Office 

(FO) and the study area supports one of the largest 

known breeding populations of Ferruginous Hawks. 

Rapid expansion of OG development activities began 

in the study area during the 1980s. As of 2006, there 

were more than 4,200 OG wells in the study area. As 

in the Price area, active nests in the Rawlins study 

area on federal lands were typically protected from 

surface-disturbing activities within 0.8 km, although 

exceptions were occasionally granted for similar 

reasons. Raptor nests were surveyed opportunistically 

in the area with some degree of regularity beginning in 

1978. During the 1980s OG expansion, Ferruginous 

Hawks began attempting to nest on OG structures and 

commonly failed. In response to this situation, the 

BLM erected 105 ANSs between 1987 and 2002, and 

began intensively monitoring nesting activities on these 

structures in 1988. Additionally, more intensive surveys 

(primarily from the ground) of all raptor species nesting 

in the area began in 1998. Nest surveys conducted 

between 1978 and 2006 aided in the identification of 

1,109 Golden Eagle, Ferruginous Hawk, Red-tailed 

Hawk, and Prairie Falcon nest clusters in the study area.

We compiled existing data on Golden Eagle, Red-tailed 

Hawk, and Prairie Falcon nests and OG development 

activities in the Price study area for 1998–2006. We 

compiled existing data on Golden Eagles, Ferruginous 

Hawks, Red-tailed Hawks, and Prairie Falcons and 

OG development activities in the Rawlins study area 

for 1978–2006. We grouped individual nests into 

nest clusters (analogous to the concept of nesting 

territories maintained by individual pairs, but unrelated 

to evaluation of territorial defense behaviors) based on 

spatial relationships and individual nest histories. For 

analytical purposes, we classified all nest clusters on an 

annual basis in two ways: “used” (i.e., evidence of nest 

attendance or actual nesting obtained) versus “unused” 

or “active” (i.e., egg laying confirmed) versus “other.” 

We classified OG wells as new or established in relation 

to each survey-year nesting season. We compiled 

road layers from existing data files and augmented 

them through digitization of additional roads within a 

geographic information system (GIS) based on 2006 

aerial photographs (1-m resolution). We classified 

roads as primary, secondary, or OG; additionally, we 

assigned establishment years for wellhead roads (i.e., 

road segments dead-ending at a single well) based on 

the associated well. We merged and reduced available 

high-resolution vegetation layers for each study area 

to individual layers containing 22 and 19 vegetation 

variables for the Price and Rawlins study areas, 

respectively. We obtained climatic data on pre-nestingseason winter precipitation, the Palmer Drought Severity 

Index (PDSI), and previous-year PDSI for each study 

area and relevant survey year. We acknowledge that 

other factors, such as prey availability, distribution of 

power lines, and livestock grazing, also may influence 

nesting raptors, but we were unable to retrospectively 

characterize these factors to our satisfaction.

From the collected and compiled datasets, we 

calculated individual nest-cluster development and 

vegetation metrics for each survey year. We calculated 

development metrics within 0.8-km and 2.0-km radii of 

nests, and vegetation metrics at the 2.0-km scale. We 

reduced suites of vegetation and development variables 

down to more manageable and orthogonal datasets using 

principal components analysis (PCA). We associated 

climate variables with nest clusters by study area and 

year. We used descriptive statistics, analysis of variance 

(ANOVA), and GIS software to describe changes in 

development near nest clusters, and spatial relationships 

between development and nest clusters. We used modelbuilding techniques, specifically variable selection and 

Akaike’s information criterion (AIC), to assess annual 

and multi-year relationships between the status of nest 

clusters and development, vegetation, and climate 

metrics. We also modeled Ferruginous Hawk nest 

success and productivity in the Rawlins study area in 

relation to these variables and nest accessibility. Finally, 

we assessed the ability of ANSs to alter the response of 

Ferruginous Hawks to OG development and the species’ 

general nesting ecology and behavior.
xiii Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Results and Discussion

Price, Utah Study Area

In the Price study area, the number of OG wells increased 

from 451 to 1,177 between 1998 and 2006. The most 

dramatic increase in wells near nest clusters occurred 

from 1999–2002, corresponding to the period of greatest 

OG development. Over the study period, the distance 

between nest clusters and the nearest OG well decreased 

significantly and the number of wells near clusters 

increased significantly. The percentage of nest clusters 

with no wells nearby decreased by ~19% at both the 0.8-

km (90% to 71%) and 2.0-km (73% to 54%) scales.

We identified three OG-development “hotspots” (i.e., 

areas with significant increases in well density between 

1998 and 2006) in the Price study area and associated, 

proximate nest clusters with at least 5 years of survey 

history. Only Golden Eagle nest clusters had sufficient 

sample sizes (n = 43) for analysis. We found no 

significant difference between the distance from “used” or 

“unused” nest clusters and development hotspot centers, 

but visual inspection of the data did suggest a potential 

temporary shift of used nests away from development 

during the period of most intensive OG development. 

We speculate that this shift may have been temporary 

due to an eventual habituation of Golden Eagles to OG 

development in the area.

PCA reduced the original Price development dataset to 

two development factors (DFs); DF1 reflected changes 

in OG development (i.e., cover and proximity of OG 

roads and wells), while DF2 reflected changes in nonOG roads (i.e., cover and proximity of non-OG roads). 

Investigation of relationships between annual nestcluster status and development patterns alone (called 

“development-only” models) revealed that Golden Eagle 

cluster “use” and “activity” related negatively to OG 

development (DF1), but positively to non-OG roads 

(DF2). Red-tailed Hawk cluster use and activity related 

positively to DF1 at the 0.8-km scale but negatively to 

DF1 at the 2.0-km scale, and cluster use related positively 

to DF2 within 0.8 km. Prairie Falcon nest-cluster status 

did not relate significantly to development factors alone.

When vegetation and climate variables were considered 

in conjunction with development patterns (called 

“development-plus” models), annual Golden Eagle cluster 

status in the Price area was still negatively related to OG 

development, but positively related to non-OG roads. 

Variable interactions suggested that negative relationships 

to OG development were enhanced in human landscapes, 

habitats dominated by rabbitbrush (Chrysothamnus

spp.), and riparian forests. In contrast, the relationship 

to OG development became positive in open habitats 

featuring black sagebrush (A. nova). We speculate that 

OG structures may provide beneficial perches for Golden 

Eagles in low-stature black-sagebrush habitats.

Investigation of development-plus models of annual Redtailed Hawk cluster use and activity in the Price study 

area revealed positive relationships with OG development 

at the 0.8-km scale, but negative relationships at the 2.0-

km scale. Additionally, a few models suggested a positive 

relationship between cluster use and non-OG roads within 

0.8 km. These are the same relationships revealed by the 

development-only models; however, variable interactions 

suggested that positive cluster-activity relationships 

with development were muted in riparian forest, grass, 

and forb habitats. We suggest that any “benefits” of 

development accrued to nesting Red-tailed Hawks in the 

dominant pinyon-juniper (Pinus edulis or monophyllaJuniperus spp.) habitats of the Price study area from 

creation of open hunting areas and/or changes in prey 

abundance may not carry over to riparian and grass/forb 

habitats, because the latter habitats likely already contain 

adequate perches, hunting areas, and prey resources for 

Red-tailed Hawks.

When we examined development-plus models of annual 

Prairie Falcon nest-cluster status in the Price study 

area, we found that cluster use related positively to OG 

development at both spatial scales when we considered 

each DF1 scale variable alone, but related negatively 

to DF1 at the 2.0-km scale when we considered both 

DF1 variables simultaneously. Additionally, Prairie 

Falcon cluster use and activity both related negatively to 

non-OG roads, perhaps reflecting sensitivity to closely 

intruding recreational roads. Note that the developmentonly models revealed no significant relationships with 

development for Prairie Falcons. We also caution that 

Price-area Prairie Falcons were represented by the 

smallest sample sizes, and model results therefore should 

be interpreted with particular caution.

We also attempted to investigate longer-term 

relationships by assessing proportional cluster use and 

activity (i.e., the proportion of years surveyed during 

which a particular cluster was used or active) relative to 

development and vegetation. Sample sizes limited our 

ability to assess such relationships to Golden Eagles, 

and top models of proportional use and activity for 

this species accounted for only 13–15% of explainable 

variation in cluster status. Top models suggested 

that proportional use and activity related positively 

to the variance in OG development and non-OG road 

levels. We speculate that the indicated relationships 
xiv BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

may reflect more consistent use and activity of nest 

clusters containing greater diversity of either type of 

development (i.e., higher habitat diversity and therefore 

more nesting options). We caution, however, that 

clusters selected for the proportional analyses were more 

likely to be in development hotspots, given the focus of 

monitoring efforts in such areas.

Considering the dynamics of all three species in the 

Price study area revealed mixed relationships with 

development. Only Golden Eagles exhibited consistent, 

negative relationships with OG development and at 

both spatial scales. In contrast, both Red-tailed Hawks 

and Prairie Falcons apparently benefited from proximate 

OG development (within 0.8 km), but this relationship 

reversed or diminished at the broader 2.0-km scale. 

Golden Eagles and Red-tailed Hawks exhibited 

some positive relationships with non-OG roads, 

whereas Prairie Falcons exhibited fairly consistent 

negative relationships with this factor. Vegetation and 

climate variables were influential in top models of use 

and/or activity for all three species. Specifically, the 

cluster status of all three species related positively to 

wetter current year conditions, but drier pre-nesting 

winters.

Rawlins, Wyoming Study Area

In the Rawlins study area, the number of wells increased 

from 1,438 to 4,258 between 1978 and 2006. The 

number of wells near nest clusters increased most 

dramatically from 1978–1985 and 1994–2006, two 

periods of intensified development. The total length 

of roads within 2.0 km of nest clusters increased 

significantly between 1978 and 2006. Additionally (and 

similar to Price), the distance between nest clusters and 

OG wells decreased significantly and the number of 

nearby wells increased significantly across this period. 

The percentage of nest clusters with no wells nearby 

declined by 9% at the 0.8-km (95% to 86%) and 12% at 

the 2.0-km (86% to 74%) scale.

We identified three OG-development hotspots in the 

Rawlins study area and associated nest clusters with a 

minimum of 5 years of survey history from 1998–2006. 

Cluster sample sizes were sufficient for analysis only 

for Ferruginous Hawks (n = 54). Used and active 

Ferruginous Hawk clusters were closer to development 

centers than unused or other clusters. This relationship 

likely was driven by a shift to nesting on OG structures 

and ANSs beginning in the late 1980s, as supported by 

a graphical assessment of the densities of active nest 

clusters pre- and post-ANS installation.

PCA reduced the original Rawlins development dataset 

to two development factors, DF1 and DF2, with 

interpretations comparable to those for the Price study 

area. Development-only models revealed that the 

annual nest-cluster status of Golden Eagles and Redtailed Hawks related negatively to OG development 

and positively to non-OG roads at both spatial scales. 

Ferruginous Hawk non-ANS cluster status relationships 

were similar, but slightly more complex. Use and 

activity generally related negatively to OG development, 

but positively to non-OG roads. However, when 

DF1 or DF2 entered a model from both spatial scales 

simultaneously, these relationships reversed at the 

2.0-km scale. In contrast, Ferruginous Hawk ANS 

cluster use and activity both increased with OG 

development measured at either scale, and non-OG 

roads within 2 km, but decreased with non-OG roads at 

the smaller scale. Annual Prairie Falcon cluster status 

related negatively to both OG development and non-OG 

roads at both scales.

Development-plus models of annual status suggested that 

development did not influence Golden Eagle nest-cluster 

use in the Rawlins area. Instead, vegetation appeared 

to influence both use and activity similarly and both 

were apparently greater during drought recovery years 

(i.e., wet years on the heels of dry years). In contrast, 

development-plus cluster activity models revealed a 

negative relationship with OG development at the 2.0-

km scale.

Top development-plus models of Rawlins area non-ANS 

Ferruginous Hawks suffered from poor fit. However, 

in general annual models suggested that cluster use and 

activity related positively to non-OG roads within 0.8 

km, but that this relationship reversed at the larger spatial 

scale. Top models also suggested that cluster activity 

related negatively to OG development within 0.8 km. 

Variable interactions suggested that the positive influence 

of non-OG roads at the smaller scale was enhanced in 

areas with greater cover of evergreen forest, but less 

Wyoming big sagebrush (A. tridentata wyomingensis) 

and agriculture. We speculate that this relationship may 

have resulted from either roads providing beneficial 

openings in forested habitats or, perhaps, a spurious 

effect of survey bias associated with surveying from 

roads in such habitats.

Top models of Rawlins ANS Ferruginous Hawk cluster 

status also suffered from poor fit. However, the models 

suggested that relationships between cluster status 

and development were the opposite of those identified 

for non-ANS clusters. That is, ANS cluster use and 

activity related negatively to non-OG roads within 

0.8 km, but this relationship reversed at the larger 
xv Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

scale. Additionally, cluster use and activity related 

positively to OG development at the 0.8-km scale. 

We suggest that the positive relationship with OG 

development at ANS clusters may be related to changes 

in prey abundance and availability associated with OG 

development in combination with the security provided 

by these inaccessible nests (and hence a tolerance for 

development activities). Variable interactions suggested 

that the positive influence of OG development at the 

smaller spatial scale was enhanced in areas with more 

riparian forest, grass, mountain mahogany (Cercocarpus 

spp.), and agriculture. The effect appeared driven by a 

few ANS clusters with relatively more OG development, 

grass cover, and high annual use and activity. We 

speculate that OG-mediated changes to prey abundance 

and accessibility, access to prime natural habitat (grass), 

and secure ANS nest sites may have caused the observed 

interaction.

Top development-plus models of annual Red-tailed 

Hawk cluster status in the Rawlins study area suggested 

that cluster use related negatively to OG development 

at the 0.8-km scale, but positively related to OG 

development at the 2.0-km scale. This suggests 

that Red-tailed Hawks may be relatively tolerant of 

OG activities in the larger landscape, but not in the 

immediate vicinity of the nest. This pattern is the 

opposite of the relationships detected for Price-area 

Red-tailed Hawks. Variable interactions suggested 

that the positive association with OG development 

within 2.0 km was enhanced in grass, forested, or 

agriculture landscapes, but muted in habitats dominated 

by Wyoming big sagebrush. Additionally, cluster 

use related negatively to non-OG roads within 2.0 

km. In contrast to these cluster use relationships, top 

models of Red-tailed Hawk cluster activity revealed no 

relationships with development factors.

Annual development-plus models of Prairie Falcon 

cluster use and activity suggested negative relationships 

with both OG development and non-OG roads. The 

development-plus model results were similar to those 

produced when we considered development factors 

alone. Variable interactions suggested that the negative 

relationships to OG development were enhanced in 

barren landscapes, but diminished in Wyoming big 

sagebrush. We speculate that increased prey accessibility 

associated with OG activities in sagebrush habitats, but 

no clear benefit in open, barren landscapes, may drive 

this relationship. Our results appear to suggest that 

Rawlins-area Prairie Falcons were the most sensitive to 

both types of development (i.e., OG and non-OG roads), 

but these results must be interpreted with caution given 

the smaller sample sizes available for this species.

As in Price, sample sizes limited our comparison 

of longer-term trends in the Rawlins study area. 

However, we were able to assess proportional clusterstatus relationships for Golden Eagles and non-ANS 

Ferruginous Hawks. The top models of Golden Eagle 

proportional cluster status performed well, accounting 

for 44–54% of the explainable variance. Golden Eagle 

cluster use and activity models suggested a positive 

association with areas containing more OG development, 

but with less change in development (i.e., variance) over 

time. Additionally, proportional cluster use and activity 

were positively associated with areas of greater and 

more variable non-OG road metrics. This suggests that 

Rawlins Golden Eagles were tolerant of OG activity, 

as long as development did not occur too rapidly. 

Additionally, use and activity were apparently greater 

in clusters with a greater diversity in levels of nonOG roads (perhaps reflecting more diverse foraging or 

nesting options).

Rawlins area non-ANS Ferruginous Hawk proportional 

use and activity models revealed a negative relationship 

with OG development at the 0.8-km scale, but the 

opposite relationship at the 2.0-km scale. Proportional 

use and activity also related positively to non-OG 

roads within 0.8 km, but also the amount of change 

in non-OG road levels over time. Similar to Rawlins 

Golden Eagles, this association with greater variance 

in non-OG roads may reflect an attraction to greater 

development heterogeneity. As with Golden Eagles, the 

top Ferruginous Hawk proportional cluster status models 

performed well, explaining 35–38% of the variance. 

The general agreement between the poor-fitting annual 

models and better-performing proportional models 

bolsters our confidence in the non-ANS Ferruginous 

Hawk relationships. That is, both analyses suggested 

that, at the 0.8-km scale, cluster use and activity related 

negatively to OG development, but positively to 

non-OG roads.

We attempted to model Rawlins Ferruginous Hawk 

nest success and productivity at accessible (mostly 

natural) and inaccessible (mostly manmade, including 

ANSs) nests, but produced few, mostly poor-fitting 

models. Nevertheless, the models generally suggested 

that success and productivity at accessible nests were 

largely independent of development, while success and 

productivity at inaccessible nests related positively 

to OG development, but negatively to non-OG roads. 

Overall, accessible nests had lower success (63 vs. 

94%) and productivity (1.6 vs. 2.7 large [≥80% of 

fledging age] chicks produced per nesting attempt) than 

inaccessible nests. We suggest that accessibility was 

the overriding driver of success and productivity, and 
xvi BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

therefore, other variables (i.e., development, vegetation, 

and climate) were of less importance to Ferruginous 

Hawk reproductive output, resulting in poor model fit.

A comparison of the relationships identified in the 

Rawlins study area revealed some common trends among 

species. Overall, cluster use and activity of all four 

species (excluding ANS Ferruginous Hawks, a special 

case) in the Rawlins study area related negatively to 

OG development at both the 0.8 and 2.0-km scales. 

Additionally, non-ANS Ferruginous Hawks, Redtailed Hawks, and Prairie Falcons exhibited negative 

relationships with non-OG roads at the 2.0-km scale. 

Only the status of non-ANS Ferruginous Hawk clusters 

related positively to non-OG roads, and this occurred at 

the smaller spatial scale. Vegetation and climate factors 

were influential in at least some top models of cluster 

use and activity for each species. In particular, the 

proportion of forest, grassland, and agriculture relative 

to sagebrush in the landscape appeared to be important 

to cluster status. The nesting activity of all four species 

also responded positively to a “drought recovery” pattern; 

i.e., improved during wetter years with more winter 

precipitation when such conditions followed a drier year.

Overall Conclusions

Both the Price and Rawlins study areas experienced 

significant OG development during the study periods 

with apparent effects on raptor breeding activity. We 

found more consistent evidence of negative impacts of 

OG development in the Rawlins study area, with all four 

focal species exhibiting negative impacts at either the 

0.8 or 2.0-km scale. Nesting raptors in both study areas 

also exhibited negative relationships with current-year 

drought severity. Our results suggest that the inclusion 

of vegetation and climate variables consistently modified 

relationships between nesting status and development. 

Additionally, Rawlins nesting activity appeared to 

increase in drought recovery years (i.e., wetter years and 

pre-season winters following drier years). Additional 

climatic detail and data on prey abundance would 

likely have improved our ability to model nest-status 

relationships.

Given the retrospective nature of this study, we stress that 

our results suggest correlations only; clear cause-effect 

inferences cannot be assumed. We have made every 

effort to acknowledge the limitations and weaknesses 

of the datasets used and results produced, but also point 

out that better-suited datasets do not currently exist 

and are unlikely to be forthcoming in the near future. 

That said, our results suggested that OG development 

produced negative effects for species with the largest 

sample sizes in both the Price (i.e., Golden Eagles) and 

Rawlins (i.e., Golden Eagles and non-ANS Ferruginous 

Hawks) study areas. Our results suggest that current 

management stipulations applied in the two study areas 

(i.e., 0.8-km radius buffers during the nesting season) 

should not be reduced, as Price-area Golden Eagles and 

Rawlins-area Ferruginous Hawks, Red-tailed Hawks, 

and Prairie Falcons exhibited negative relationships 

with OG development that occurred within 0.8 km of 

nest clusters. Additionally, current stipulations may be 

insufficient to avoid negative impacts to nesting raptors, 

as we also detected negative relationships between OG 

development and Price-area Golden Eagles and Rawlinsarea Golden Eagles and Prairie Falcons at the broader 

2.0-km spatial scale. Unfortunately, we were unable to 

evaluate effectively potential population-level impacts 

of development due to limitations in the existing raptor 

datasets.
xvii Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Survey, Idaho), and Ed Hollowed and Brett Smithers 

(BLM White River Field Office, Colorado). Independent 

technical reviews by Karen Steenhof (U.S. Geological 

Survey, Idaho), Tom Thurow (University of Wyoming), 

and Jim Watson (Washington Department of Fish and 

Wildlife) further improved the study plan.

This final technical note benefited from initial reviews by 

members of the BLM Steering Committee, Chris Colt, 

and Mark Fuller, as well as independent reviews by Fred 

Adler (Department of Mathematics, University of Utah), 

Michael Kochert (U.S. Geological Survey, Idaho), Gwyn 

McKee (Thunderbird-Jones & Stokes, Inc., Wyoming), 

and Jim Watson (Washington Department of Fish and 

Wildlife).

Funding for this research project was provided by the 

U.S. Department of Energy through the BLM Utah State 

Office (BLM Assistance Agreement JSA065003).

The initial study plan for this project benefited from 

reviews provided by members of the Raptor Radii Project 

BLM Steering Committee, which included Dave Mills 

(BLM Utah, Richfield Field Office), Steve Madsen (BLM 

Utah State Office), Dave Roberts (BLM Wyoming State 

Office), and Wes Anderson (BLM Colorado State Office). 

Additional semi-independent reviews were provided by 

members of a more extensive core “working group” that 

helped to oversee the project and other vested individuals, 

including Chris Colt (Fishlake National Forest, formerly 

UDWR Price Field Office), Mark Fuller (U.S. Geological 

Acknowledgements

1 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

attempting to understand relationships between raptors 

and development. Most recently, a study in northeastern 

Utah on BLM lands revealed that nests of breeding 

Ferruginous Hawks were associated with high numbers 

of active (i.e., currently operational) OG wells; however, 

greater reproductive success was associated with larger 

distances to the closest active well. Thus, although 

development of OG wells did not appear to have a 

negative influence on the suitability of breeding habitat, 

it appeared that development had a negative influence on 

reproductive success if active wells occurred too close to 

nest sites (Keough 2006).

Other raptor “disturbance” studies also may shed light 

on potential effects of OG development. For example, 

development activities associated with dam construction 

(i.e., road construction, vehicle traffic, heavy equipment 

operation, and blasting) did not appear to influence 

negatively the behavior or productivity of Prairie Falcons 

in the Snake River Birds of Prey National Conservation 

Area (Holthuijzen 1989). In this situation, the position 

of aeries at least 50 m above development activities 

may have served to buffer nests from disturbance. The 

response of raptors to roads can vary by species and 

traffic volume (Bautista et al. 2004). Unfortunately, 

studies investigating the more general relationship 

between raptors and human-altered (primarily by roads 

and habitations) landscapes have commonly failed to 

separate the potential influence of disturbance from 

those of habitat or other environmental changes (e.g., 

Craighead and Mindell 1981, Bechard et al. 1990, 

Bosakowski et al. 1993, Berry et al. 1998).

Regardless, several studies have clearly demonstrated 

the potential disturbance effects of human activities 

on nesting raptors. White and Thurow (1985) 

experimentally demonstrated negative effects of human 

disturbance (i.e., walk-in and drive-up approaches and 

operation of a motor near nests) on nesting Ferruginous 

Hawks. A variety of recreational activities also may 

influence raptors. For example, hikers and pedestrian 

foot traffic can change the behavior of both nesting 

(Swenson 1979, Swarthout and Steidl 2003, Watson 

2004) and wintering raptors (Fletcher et al. 1999). 

Similarly, camping activities altered the behavior of 

nesting Bald Eagles (Haliaeetus leucocephalus; Steidl 

and Anthony 2000) and reproductive output of Ospreys 

(Pandion haliaetus; Swenson 1979). Rock climbing also 

can be deleterious to cliff-nesting raptors, such as the 

Peregrine Falcon (F. peregrinus; Brambilla et al. 2004).

The effects of human disturbance on nesting raptors 

Oil and Gas Development

and Raptors

Currently ~15 million ha of western lands managed by 

the Bureau of Land Management (BLM) are leased for 

gas, oil, or geothermal energy production (BLM 2005). 

Although recent large-scale expansions in domestic oil 

and gas (OG) extraction activities are likely to continue 

in the United States (see the Energy Policy Act of 2005), 

very few studies have attempted to investigate the 

potential implications for nesting raptors (e.g., Harmata 

1991, Squires et al. 1993). OG development may affect 

raptors through direct habitat loss or disturbance (we 

have italicized key terms on first usage to draw the 

reader’s attention to definitions provided in the glossary; 

please note that scientific names also are italicized per 

usual practice). While the footprint of individual OG 

wells is minimal relative to other energy developments 

(e.g., mining), the total habitat lost to the network of 

wells and connecting roads can be considerable in areas 

undergoing full-field development (Postovit and Postovit 

1989). The potential for OG-related disturbance of 

nesting, foraging or roosting raptors arises not only from 

new well installation activities, including road and pad 

construction, drilling, and equipment installation over 

the course of several weeks to months, but also from 

continual servicing and maintenance of wells over their 

productive lifetime. Additional potential for disturbance 

arises from increased public access to areas under OG 

development (Postovit and Postovit 1989).

Past studies of direct relevance have focused primarily 

on the relationship between nesting raptors and OG 

development. Ritchie (1991) reported unusual Gyrfalcon 

(Falco rusticolus) and Rough-legged Hawk (Buteo 

lagopus) nest placements on or near development 

infrastructure in Alaska (e.g., pipelines or roads). In 

regards to actual nest productivity, studies focusing 

on Ferruginous Hawks (B. regalis) have reported both 

no clear effect (Van Horn 1993, Zelenak and Rotella 

1997) and higher nest productivity further from 

development (Harmata 1991). Squires et al. (1993) 

found a positive correlation between foraging Prairie 

Falcons (F. mexicanus) and density of oil wells in 

northeastern Wyoming. Note, however, that distance 

from aeries and amount of grassland habitat were the 

best predictors of foraging areas, and that wells were 

coincidentally concentrated in grasslands near the aeries. 

This example reveals the need for awareness of potential 

confounding correlations that may exist between 

development and other habitat characteristics when 

Introduction
2 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

may be influenced by a number of factors, including 

proximity, disturbance type, nest exposure, and resource 

availability (Suter and Joness 1981, White and Thurow 

1985, Grubb and King 1991, Grubb et al. 1992). Studies 

of breeding Bald Eagles suggest that proximity is the 

most influential characteristic of disturbance, with 

both horizontal (Grubb and King 1991, Grubb et al. 

1992) and vertical (Watson 2004) distance identified as 

important. A study of Ferruginous Hawk nests in North 

Dakota illustrated the potential importance of exposure; 

relatively exposed ground nests were located farther 

from human development than tree nests (Lokemoen and 

Duebbert 1976). Disturbance sensitivity also may be 

greater in years of reduced prey availability (White and 

Thurow 1985).

In addition to disturbance characteristics, habituation 

also appears to play a significant role in determining 

disturbance responses. Observations of raptors exposed 

to repeated weapons testing or aircraft flyovers suggest 

raptors may habituate rapidly to disturbances (Andersen 

et al. 1986, 1989; Brown et al. 1999). For example, 

Andersen et al. (1989) found that low-level helicopter 

approaches at Red-tailed Hawk (B. jamaicensis) nests 

in areas with and without previous helicopter activity 

resulted in flushing of adults at 8% and 53% of nests, 

respectively. Similarly, Ferruginous Hawks apparently 

habituated to disturbances present when the adults first 

arrived on the breeding grounds (White and Thurow 

1985).

Recognizing that OG development activities can result in 

disturbance of nesting raptors has important implications 

for federal land managers. The Migratory Bird Treaty 

Act (MBTA) and Bald and Golden Eagle Protection Act 

(BGEPA) require federal land management agencies 

to prevent the “take” of raptors, their young, and nests. 

Additionally, the BGEPA prohibits the disturbance of 

nesting eagles. In order to comply with MBTA and 

BGEPA regulations, federal land managers commonly 

employ spatial and/or temporal nest protections to 

minimize the potential negative effects of human 

activities on nesting raptors.

Recommended spatial buffers may vary depending on 

the species of concern or be comparatively standardized 

across species. For example, Richardson and Miller 

(1997) suggest buffers of 200–1,600 m for Golden 

Eagles (Aquila chrysaetos), 200–800 km for Ferruginous 

Hawks, and 50–800 m for Prairie Falcons. In contrast, 

Romin and Muck (2002) recommend buffers of 0.8 km 

(0.5 mile) for Golden Eagles and Ferruginous Hawks, 

and 0.4 km (0.25 mile) buffers for Prairie Falcons. 

Suter and Joness (1981) have suggested that these three 

species are the most disturbance-sensitive raptors likely 

to encounter energy development in the West. Previous 

recommendations concerning temporal buffers suggest 

that nesting areas should be protected from the time 

of adult arrival through at least the first few weeks 

after hatch (Suter and Joness 1981, Romin and Muck 

2002). Evidence suggests that nesting raptors may be 

less sensitive to disturbance after hatching (White and 

Thurow 1985).

Currently, raptor nest-protection standards vary 

considerably across the interior West and generally are 

based on limited information about the actual effects of 

different types of potential disturbances on specific raptor 

species. One recent attempt to develop and promote 

standardized spatial and temporal disturbance-buffer 

guidelines was compiled by the Utah Field Office of the 

U.S. Fish and Wildlife Service (Romin and Muck 2002, 

first edition produced in 1999). These guidelines were 

based on a thorough review of published information 

on disturbance responses and thresholds from a variety 

of previous studies. The recommendations contained 

therein generally are applied now throughout Utah, 

and have received some attention by federal and state 

agencies in surrounding states. However, although these 

guidelines were based on the best available disturbancerelated data, it is clear that a number of species and 

relevant disturbance types (e.g., OG development) are 

underrepresented. The general lack of data in support 

of specific raptor protection guidelines for use on BLMmanaged lands (e.g., through implementation of Best 

Management Practices) was partially responsible for the 

establishment of this research project.

The Raptor Radii Research Project

In June 2006, HawkWatch International (HWI) was 

selected by the BLM to serve as the Principal Investigator 

(BLM Assistance Agreement JSA065003) of what was 

intended to be a three-year research project initially 

funded by the U.S. Department of Energy through the 

BLM (Intragovernmental Order DE-A126-06NT15467) 

to investigate the potential disturbance effects of OG 

development activities on nesting raptors in Utah, 

Wyoming, and Colorado. This “Raptor Radii Research 

Project” was originally envisioned to encompass two 

phases. The objective of Phase I was to compile and 

evaluate historical data relevant to relationships between 

natural-gas extraction activities and the distribution, 

abundance, and nest-site occupancy of raptors in the 

BLM Price Field Office (FO) in southeastern Utah 

(focus on coalbed natural gas [CBNG] extraction) and 

the BLM Rawlins FO in south-central Wyoming (focus 
3 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

on conventional natural-gas extraction). Phase II was 

envisioned to comprise a 1–2 year field experiment 

across study areas in Utah, Wyoming, and Colorado to 

quantify the distances and seasonal times at which OG 

development activities have undesirable effects on nesting 

raptors; however, for various complicated reasons, Phase 

II did not proceed as planned. The ultimate goal of this 

research was to provide guidance for applying protective 

spatial and temporal buffers to minimize unwanted 

disturbance to nesting raptors exposed to OG activities. 

This document presents the results of our Phase I 

investigations.

Goals and Objectives

Phase I of the Raptor Radii Project was designed to 

assemble and use historical data available from the 

BLM Price and Rawlins FOs in Wyoming and Utah, 

respectively, to learn about relationships between various 

aspects of the development and operation of oil and 

natural-gas extraction activities and the abundance, 

distribution, nest-site occupancy, and, where possible, 

nesting success and productivity of selected raptor 

species. Because this effort relied solely on historical 

datasets, another major goal of the project was to 

identify the limitations of existing datasets and past 

survey strategies and provide recommendations for their 

improvement.

The specific goals of this Phase I research were to:

Goal 1. Compile existing historical data to 

evaluate their usefulness for quantifying 

and describing the relationship between 

coincident natural-gas field development, 

production, and maintenance operations 

and raptor nesting activities.

Goal 2. Where suited to the task, use available 

raptor monitoring data and documentation 

of the type, time, and extent of 

development activities to evaluate the 

effectiveness of raptor spatial and temporal 

buffer restrictions that have been applied on 

the study sites.

Goal 3. Based on these assessments, evaluate 

whether these historical datasets were 

useful for quantifying the relationship 

between OG development activities and 

nesting raptors, including identifying those 

data that were not useful for this purpose, 

explaining why they were not useful, 

recommending necessary improvements 

or standards that would provide stronger 

inference, and developing objectives 

for use of these data in guiding future 

development.

We address the first two goals outlined above in this 

document and address the third goal in a separate 

document (Smith et al. 2010). In regards to the first 

two goals, an investigation into the relevant data 

available from each study area (outlined below) and the 

inherent limits associated with these datasets guided our 

formulation of specific research objectives, which were as 

follows:

Objective 1. Describe temporal trends in 

development near focal-species nest 

clusters in the Price and Rawlins study 

areas.

Objective 2. Described distributional patterns 

of nest-cluster status in relation to 

development in the Price and Rawlins 

study areas.

Objective 3. Assess relationships between nestcluster status and development in the 

Price and Rawlins study areas. 

Objective 4. Assess Ferruginous Hawk nest success 

and productivity in the Rawlins study 

area.

Objective 5. Assess the response of Ferruginous 

Hawks to artificial nest structures

(ANSs) in the Rawlins study area.

5 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

interest in developing the natural-gas resources of the 

area began to emerge (Bates and Moretti 1994, Parrish 

1995). Raptor data were collected only opportunistically 

prior to this period. Following completion of an areawide Environmental Impact Statement (EIS) in 1997 

related to proposed expansion of CBNG activities in the 

area west of Price, UDWR began implementing annual 

helicopter surveys in 1998 focused on documenting the 

nesting activities of primary nesting raptors in the area 

(BLM 1997). This survey was a requirement of the 

EIS, for which Conoco-Phillips, the primary industry 

corporation now involved in developing the natural-gas 

resources in the area, has been the primary financier.

As of 2006, there were more than 1,100 wells in the 

Price study area, with a nearly uniform distribution 

of well pads at a density of one per quarter section 

(160-acre spacing). In a few cases, the uniformity of 

well-pad distribution was modified intentionally from 

the start to accommodate topographic constraints and 

maintain an EIS-mandated 0.8 km (0.5 mile) nesting 

season (1 February – 15 July) buffer around nest sites on 

Nest Cluster Focus

It is critical for the reader to understand that throughout 

the majority of this report we focus on nest clusters, 

rather than individual nests, as the unit of research 

interest. Among most raptor species, individual breeding 

pairs often maintain variable numbers of clustered, 

alternative nest sites (Steenhof and Newton 2007). 

Although more than one nest may be maintained (i.e., 

tended) in a particular year by a breeding pair, actual 

nesting and laying of eggs will occur in only one nest. 

This suggests that the appropriate sample unit for multiyear assessments of raptor nesting activity is what we 

term the nest cluster (analogous to the “nesting territory” 

definition used by Steenhof and Newton [2007] but 

avoids potential confusion related to the ethologists’ 

concept of territoriality). The nest-cluster approach 

reduces alternate nests associated with individual 

breeding pairs to a single sample unit and avoids issues 

of pattern dilution that may arise from the retention of 

every individual nest. We outline below the procedures 

we used to identify species-specific nest clusters.

Study Areas and 

Background Information

The two study areas included in this project were selected 

based on extensive multi-year raptor nest monitoring 

that occurred in each area coincident with extensive OG 

development.

Price, Utah Study Area

The Price study area was delineated based on the 

extent of nesting data collected by the Utah Division of 

Wildlife Resources (UDWR) and past OG development 

activities on lands primarily managed by the BLM Price 

FO in southeastern Utah (Figure 1). The study area is 

approximately 21,200 km2 (including both public and 

private lands) in size and ranges from 1,193–3,521 m in 

elevation. Canyons and cliffs are common in the study 

area and pinyon-juniper (Pinus edulis-Juniperus spp.) 

woodlands predominate, interspersed with sagebrush 

(Artemisia spp.) and grass openings. The area received an 

annual average of 24 cm of precipitation from 1998–2006 

(NOAA 2007).

The first relatively comprehensive raptor inventory was 

conducted in the area in the mid-1990s when heightened 

Figure 1. Map showing the locations of active oil and gas wells in 

1998 and 2006 in relation to 264 raptor nest clusters in the Price,

Utah study area. Raptor clusters are represented by 2-km buffers

extending around the individual Golden Eagle, Red-tailed Hawk, and

Prairie Falcon nests included in each cluster; we included only clusters 

used at least once during the 1998–2006 raptor survey period to

create the buffers shown.

Methods
6 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

federal lands occupied during the previous three years 

(BLM 1997). In some cases, the BLM granted industryrequested variances to allow development of new well 

pads within 0.8 km of known nest sites when landscape 

screening visually separated well pads from nest sites. 

In other cases, development occurred unintentionally in 

closer proximity to existing nest sites because the nest 

sites were not discovered until after the development had 

proceeded. In addition, a number of land sections within 

the Conoco-Phillips domain are administered by the State 

of Utah, wherein development generally has proceeded 

with few constraints. These areas therefore provide other 

cases where wells have been installed in closer proximity 

to nests and sometimes within the nesting season.

Since 1998, UDWR and Conoco-Phillips personnel 

have conducted helicopter surveys in the study area 

once per year during May (survey dates 8–30 May 

1998–2006). They selected this survey period to allow 

for determination of nest activity while minimizing 

potential early-season disturbance to nesting Golden 

Eagles, the most abundant nesting raptor in the area, and 

other common area nesters, such as Red-tailed Hawks 

and Prairie Falcons. The available data included records 

for 860 nests of these three species distributed across 

the core study area (i.e., in and around the primary 

development area within the Price FO). Many of the 

associated nests had only limited survey histories, 

however, due to their recent addition to the survey effort 

in areas of recent expansion of energy development. 

Other nests received only one-time or infrequent 

surveys in relation to energy speculation, seismic work, 

exploratory drilling, and mining operations. While nests 

were monitored much more consistently within the Price 

CBNG EIS area, a number of nests were still missed 

each year, partly due to the difficulty and high cost of 

helicopter surveys. Of 264 identifiable nest clusters with 

at least one year of confirmed nesting activity identified 

across the core study area for the three focal species, 129 

Golden Eagle, 23 Red-tailed Hawk, and 9 Prairie Falcon 

nest clusters had at least five years of survey history 

between 1998 and 2006.

The primary information available for nests in this survey 

area was the presence/absence of breeding pairs that had 

succeeded to the late-incubation or early brood-rearing 

stage. Although limited brood-size data were collected 

opportunistically, reliable annual productivity estimates 

were not possible because of the timing and limited 

number (once annually) of surveys and the variable 

stages of surveyed nests.

Rawlins, Wyoming Study Area

The Rawlins study area was selected based on the 

extent of nesting data collected by the BLM Rawlins 

FO and associated nearby OG development activities 

that occurred predominantly on lands managed by this 

FO in south-central Wyoming (Figure 2). The study 

area is approximately 44,400 km2 (including both public 

and private lands) in size and ranges from 1,502–3,663 

m in elevation. Rolling hills interspersed with rocky 

escarpments are common topographic features, while 

sagebrush-grasslands and other desert shrubs dominate 

the vegetation. Annual average precipitation was 29 cm 

from 1978–2006 (NOAA 2007). Thirteen Key Raptor 

Areas have been identified within the Rawlins FO, and 

the area contains important nesting habitat for a variety 

raptors.

In particular, the Rawlins FO encompasses important 

habitat for the Ferruginous Hawk; south-central Wyoming 

supports one of the largest known breeding populations 

of this species (Olendorff 1993). The species is currently 

listed as a state sensitive species in Wyoming and in 

most other states where it occurs, and garners similar 

recognition in several state-level and bird conservation 

region plans developed by the overarching Partners in 

Flight avian conservation program (Rich et al. 2004). 

Rapid expansion of OG development activities in the 

Rawlins FO in the 1980s stimulated interest among 

BLM biologists in monitoring and assessing the potential 

impacts of such development on the Ferruginous Hawks 

nesting in the area. Several industry companies have been 

involved in natural-gas development in the Rawlins FO; 

primary players include BP-Amoco, Williams, Forest Oil, 

and Anadarko Petroleum Corporation. Wyoming began 

to experience a boom in OG exploration and development 

in the late 1970s. By 2006, more than 4,200 OG wells 

were in the study area. Compared to the Price study area, 

wells are much less regularly distributed throughout the 

Rawlins study area, reaching high densities of one well 

per quarter section (160-acre spacing) in some areas, but 

with lower densities in others. However, much of the 

past development activity was concentrated in the open 

sagebrush and grassland dominated landscapes commonly 

used as nesting habitat by the Ferruginous Hawk, as well 

as species such as the Golden Eagle and Prairie Falcon. 

Similar to the Price study area, Rawlins area nests on 

federal lands were generally protected by a 0.8-km 

nesting season (1 February – 31 July) buffer, although 

exceptions were occasionally granted after consideration 

of topographic screening or other factors (BLM 1990).

Initial raptor nest monitoring in the FO was sporadic 

and largely opportunistic, but at least some minimal 
7 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

monitoring effort was made each year beginning in 

1978. However, as natural-gas development expanded 

in the Rawlins FO, Ferruginous Hawks quickly began 

attempting to exploit natural-gas structures (primarily 

condensation tanks) as nesting substrates. Equally 

quickly, it became apparent that this was a recipe for 

disaster, as such nests regularly failed due to human 

disturbance related to maintenance of the structures 

(Tigner and Call 1996). Accordingly, in an attempt 

to improve nesting habitat for the species and offset 

the effects of natural-gas development, between 1987 

and 2002 the BLM and certain natural-gas companies 

cooperated in erecting 100 ANSs for Ferruginous Hawks 

and five other ANSs in the FO. The Ferruginous Hawk 

ANSs extended 2.5-m above the ground and supported 

a 1-m2 nest platform (see Tigner and Call [1996] for an 

illustration). ANSs were installed near (i.e., within 1 km) 

natural-gas extraction facilities to provide alternative 

substrates for birds that otherwise attempted to nest 

on associated structures, but also in areas away from 

development activities to provide alternatives for birds 

that previously nested on collapsed or otherwise poorquality natural nests.

Coincident with this effort, in 1988 the BLM initiated 

a program to monitor nesting activities associated 

with all of the installed ANSs. Largely opportunistic 

monitoring of various natural nests and other species 

continued to occur through 1997 in conjunction with the 

ANS monitoring. Then in 1998, the BLM initiated a 

more intensive, initially largely volunteer driven effort 

to expand their annual monitoring to cover most known 

Ferruginous Hawk nests in the FO with a recent history 

of use (i.e., within the past 5–10 years), as well as attempt 

to collect at least presence–absence data for other raptor 

species nesting in the area. Each year nest searches 

included some areas not known to have been used 

previously by Ferruginous Hawks, but no standardized 

sampling design was applied. After 1999, paid seasonal 

technicians conducted the survey effort. At this time, 

Mike Neal (now an HWI employee) took over as the 

BLM seasonal field supervisor of this effort, a capacity in 

which he continued to serve annually through 2004. He 

also developed his Master’s thesis based on this project 

(Neal 2007).

The surveys conducted beginning in 1998 focused on 

assessing the breeding status (i.e., breeding or nonbreeding), nesting success (i.e., successful or failed), 

and productivity (i.e., number of nestlings produced 

to at least 80% of fledging age) of all ANS and many 

natural Ferruginous Hawk nests in the study area with 

Figure 2. Map showing the locations of active oil and gas wells in 1970 and 2006 in relation to 1,109 raptor nest clusters in the Rawlins, Wyoming

study area. Raptor clusters are represented by 2-km buffers extending around the individual Golden Eagle, Ferruginous Hawk, Red-tailed Hawk,

and Prairie Falcon nests included in each cluster; only clusters used at least once during the 1970-2006 raptor survey period were used to create

the buffers shown.
8 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

a documented history of use. Monitoring data for other 

species also were collected opportunistically during this 

period, as before. All surveys were conducted on the 

ground. Surveys to document initial nesting activity 

were conducted during April and May, with follow-up 

surveys in June and July to document nesting success and 

productivity. A total of 3,475 individual nest locations of 

Ferruginous Hawks, Golden Eagles, Red-tailed Hawks, 

and Prairie Falcons were recorded effectively in the 

BLM dataset we were given to work with. These nests 

comprised 1,109 identifiably distinct, species-specific 

nest clusters for which at least one year of nesting activity 

was confirmed. Of these, 317 Ferruginous Hawk clusters, 

116 Golden Eagle clusters, 44 Red-tailed Hawk clusters, 

and 33 Prairie Falcon clusters had at least 5 years of 

survey history since the late 1970s. When constrained to 

the period 1998–2006 (period of best survey coverage), 

however, these numbers dropped to 240 Ferruginous 

Hawk, 50 Golden Eagle, 18 Red-tailed Hawk, and 9 

Prairie Falcon clusters. The information of value for 

the latter three species was largely limited to breeding 

status, as success and productivity were collected much 

too inconsistently for species other than the Ferruginous 

Hawk.

Description of Datasets

and Data Preparation

We gathered historical datasets of nests monitored in the 

Price and Rawlins FOs, available OG well information, 

and other relevant Geographic Information System (GIS) 

datasets and information available for each study area.

Raptor Nest Data

a. Nest-site coordinates for Golden Eagles, Red-tailed 

Hawks, and Prairie Falcons monitored in the Price FO 

from 1998–2006 provided by the UDWR.

b. Nest-site coordinates for Golden Eagles, Ferruginous 

Hawks, Red-tailed Hawks, and Prairie Falcons 

monitored in the Rawlins FO from 1970–2006 

provided by the BLM.

c. Activity status (i.e., breeding, tended or nonbreeding) for surveyed nests in both study areas, 

though often intermittent for individual nests from 

year to year.

d. Nesting success and productivity data for a subset of 

Ferruginous Hawk nests in the Rawlins FO. Limited 

nestling production data for Golden Eagle nests, but 

accurate nestling ages rarely determined.

e. All Price FO nests were characterized by substrate 

type, with the vast majority of surveyed nests located 

on cliffs.

f. All Rawlins FO nests were characterized by substrate 

type and height above ground level. All Ferruginous 

Hawk nests with a history of success also were 

classified as either accessible or inaccessible to 

humans (unaided by equipment) and mammalian 

predators (Neal 2007).

We omitted nests for which no breeding event had been 

recorded during the monitoring period of interest to 

remove the influence of nests with incongruent histories. 

We also omitted some Golden Eagle nest records from the 

Rawlins study area that lacked adequate information for 

nest activity status and survey dates.

For both datasets, we classified all nests with known 

species associations into distinct nest clusters, and 

then identified subsets of clusters with adequate survey 

histories for inclusion in longer-term analyses. We 

identified nest clusters using a combination of GIS 

nearest-neighbor analysis, consideration of topographic 

features, and inspection of individual nest histories. 

We used GIS to identify potential clusters of nests 

with inter-nest distances less than likely maximum 

spacing of alternate nests associated with distinct 

breeding pairs. Due to a scarcity of information on 

spacing of alternate nests, we selected 3 km to serve as 

a conservative cut-off search distance, although there is 

evidence that Golden Eagles may maintain more widely 

spaced alternates (McGahan 1968). After we identified 

potential nest-clusters using GIS-based nearest-neighbor 

analyses, we further refined the cluster classifications 

by examining spatial relationships of nests relative to 

distinct topographic features such as ridgelines. That 

is, an intervening ridgeline may allow for closer-thanusual spacing of nest clusters if they lie on opposite 

sides of the ridgeline and their placement affords access 

to independent foraging domains extending away from 

each side of the ridgeline. Finally, further modification 

of cluster classifications occurred following inspection of 

individual nest histories to ensure that all defined clusters 

contained only nests with non-overlapping use histories. 

That is, in a given year, breeding by the focal species will 

have been confirmed in only one nest within each cluster, 

the only possible exception being uncommon cases where 

conclusive evidence indicated that the same breeding pair 

renested in a second nest within their cluster following an 

early failure.
9 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Oil and Gas Well Data

a. Well surface location coordinates and dates of well 

spudding (i.e., initiation of drilling) and abandonment 

(where relevant) from the Utah Division of Oil, Gas 

and Mining (UDOGM) for 1,245 wells that were 

active in the Price study area after 1997 and were 

spudded before 14 June 2006.

b. Well surface location coordinates and dates of well 

spudding and abandonment (where relevant) from 

the Wyoming Oil and Gas Conservation Commission 

(WOGCC) for 6,011 wells that were active in the 

Rawlins study area after 1969 and spudded before 14 

June 2006.

The Price FO well layer required additional work to 

add drilling dates for ~400 wells drilled prior to 2000, 

for which no integrated electronic data were currently 

available. A similar number of wells in the Rawlins FO 

well layer also were missing temporal attributes. All such 

data were obtainable, however, by retrieving individual 

well records accessible from the UDOGM and WOGCC 

websites.

For each corresponding year of nest-monitoring data, we 

classified wells as either new (i.e., drilled after 1 June 

of the previous nesting season in the Price study area 

[no surveys were conducted after May] or after 1 July in 

Rawlins study area [to reflect the longer survey window 

in this area]), established (drilled during or prior to the 

previous nesting season), or abandoned (ceased operation 

prior to 15 February of the current nesting year). We 

omitted from consideration any wells that became nonoperational prior to the study period of interest. Our 

initial goal was to further classify new wells as pre-season 

(drilled since the previous nesting season, but prior to the 

nesting season of interest) or in-season (drilled during 

the nesting season of interest); however, preliminary 

data inspections suggested that for both study areas the 

number of raptor nests within even 2 km, let alone 0.8 

km (typical 0.5-mile buffer), of in-season wells was 

insufficient to support robust analyses.

Road Data

We obtained GIS data layers of road networks in each 

study area from various sources, but then substantially 

augmented those layers through digitizing of 1-mresolution aerial photographs (U.S. Department of 

Agriculture National Agriculture Imagery Program) taken 

in 2006. All digitizing occurred at a scale of 1:30,000, 

focused primarily on augmenting roads related to OG 

activities (i.e., we created a road to each well location 

active during the study period), but also added other 

easily visible (primary) roads. We added 1,063 km of 

roads to the Price FO layer, representing 1,527 individual 

road segments and increasing the total length of roads 

by 5.3%. For the Rawlins FO, we added 2,910 km of 

roads, representing 4,352 individual road segments 

and increasing the total length of roads by 6.6%. The 

pre-existing road layers classified roads to type, but 

classifications were not consistent in the two study areas. 

To suit our particular needs, we classified roads based on 

the following conventions:

Primary: major paved roads and other well 

maintained connecting state or county roads.

Secondary: lesser maintained roads, town roads, 

unimproved dirt and gravel roads, etc.

Oil & Gas: wellhead roads (roads dead-ending at a 

single well or well pad), connecting OG roads (roads 

connecting ≥2 proximate wellhead roads), and haul 

roads (roads servicing larger fields of development).

Because individual wellhead roads serviced only a single 

well, we assumed wellhead roads were created during 

the year of well establishment (i.e., we assigned to these 

roads establishment years that were used to add wellhead 

roads to the road layer on an annual basis).

Landcover/Vegetation Data

Price FO, Utah.—We obtained custom vegetation/

landcover layers prepared by the UDWR for the 

East Manti, South Manti, and West Tavaputs areas 

(delineated at 1:10,000 scale, using 2002 or newer aerial 

photographs) and by the U.S. Forest Service for the 

Manti-La Sal National Forest (1997 version, delineated 

at 1:16,000 scale). We then merged the two datasets 

into a 30-m-resolution raster layer and incorporated 

supplemental information derived from the Southwest 

ReGAP raster INFO file (USGS National Gap Analysis 

Program 2004). We reduced and matched landcover 

categories from the two original layers, initially 

producing 31 landcover types, which we further collapsed 

based on high correlations and reduction of rare classes 

(i.e., combined aspen (Populus tremuloides) and aspen/

conifer; alpine and other forbs; spruce-fir (Picea spp.-

Abies spp.), ponderosa pine (Pinus ponderosa), and 

limber pine (P. flexilis)/bristlecone pine (P. aristata); 

desert and high desert shrub; grass and desert grass; 

mountain shrub and mountain mahogany (Cercocarpus

spp.); wetland and riparian shrub; and deleted silver 

sagebrush (A. cana). The final Price vegetation dataset 

contained the following 22 variables: grass, forb, 
10 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

sagebrush general, Wyoming big sagebrush (A. tridentata 

wyomingensis), mountain big sagebrush (A. t. vaseyana), 

basin big sagebrush (A. t. tridentata), black sagebrush (A. 

nova), rabbitbrush (Chrysothamnus spp.), desert shrub, 

riparian shrub, riparian forest, mountain shrub, oak-maple 

(Quercus spp.-Acer spp.), aspen mix, conifer, pinyonjuniper, pinyon, juniper, barren, water, agriculture, and 

human habitation.

Rawlins FO, Wyoming.—We obtained custom 30-m 

resolution vegetation/landcover layers prepared at the 

Wyoming Geographic Information Science Center at 

the University of Wyoming (Driese and Nibbelink 2005, 

Rodemaker and Driese 2006). We reclassified landcover 

categories from two separate maps to match categories 

in the less detailed of the two, and merged those into 

a single vegetation layer with 26 initial categories 

that covered the entire area of interest. We further 

collapsed these categories based on high correlations 

between categories and reduction of rare classes (i.e., we 

combined evergreen, evergreen/deciduous, and evergreen/

mixed; dryland and irrigated agriculture; riparian 

and upland grassland; mountain big sagebrush and 

mountain big sagebrush/bitterbrush (Purshia tridentata); 

greasewood (Sarcobatus vermiculatus) and desert shrub; 

and Wyoming big sagebrush and Wyoming big sagebrush/

black sagebrush). The final Rawlins vegetation dataset 

contained the following 19 variables: grass, sagebrush/

grass, Wyoming big sagebrush, mountain big sagebrush, 

Wyoming/mountain big sagebrush, basin big sagebrush, 

desert shrub, mountain mahogany, mesic shrub, riparian 

shrub, riparian forest, deciduous forest, evergreen forest 

mix, juniper, barren, disturbed land, water, agriculture, 

and human habitation.

Climate Data

We obtained monthly precipitation, temperature, and 

Palmer Drought Severity Index (PDSI) data for state 

climate divisions encompassing the Wyoming and Price 

FO regions from the National Climatic Data Center 

(NOAA 2007). We used data for Climate Division 7 

(Southeast) in Utah and for Wyoming Divisions 3 (Green 

and Bear [River] Drainages) and 10 (Upper Platte [River 

Drainage]). We then used the monthly data to calculate 

average temperatures and total precipitation for each 

pre-nesting-season winter (November–February), and 

total precipitation and average temperatures and PDSIs 

for each division and year of interest. After initial 

evaluation of these data, we determined that average 

winter temperatures simply did not vary enough on an 

annual basis to represent a useful index of interannual 

climatic variation. Not surprisingly, we also found that 

annual total precipitation and average PDSI values were 

highly correlated (Pearson r = 0.84 for Price data from 

1998–2006; 0.75 for Rawlins data from 1978–2006) 

and therefore could not be included together in the 

same analytical models; accordingly, we opted to 

focus on the more informative PDSI as the variable 

of interest. Our climatic variables of interest became 

winter precipitation (WINT_ppt), PDSI, and PDSI of the 

previous year (PDSI_lyr). We entered these variables 

into our multivariate models to account for the potential 

influence of climatic conditions on annual variation in 

raptor nesting activity and productivity. For example, 

Steenhof et al. (1997) found that the percentage of Golden 

Eagle pairs that laid eggs in a given year was influenced 

negatively by the severity of the preceding winter during 

periods of low prey abundance.

Other Data

Prey availability can strongly influence raptor nesting 

activity (e.g., jackrabbit [Lepus spp.] abundance for 

desert-nesting Golden Eagles; Smith and Murphy 1979, 

Steenhof et al. 1997). In the context of a study such as 

this, ideally data on prey population abundances and 

trends should be modeled along with other environmental 

factors to account for annual and spatial variability in 

ecosystem and raptor productivity that is unrelated to 

potential OG development effects. Unfortunately, no 

useful information on prey populations was available 

for the two study areas. Hence, our investigations were 

constrained to using landcover/vegetation and climatic 

variables as surrogate indicators of ecosystem variability 

and condition. We acknowledge the potential critical 

importance of prey availability to nesting raptors and our 

limited ability to portray it accurately in a retrospective 

manner due to the absence of reliable prey-monitoring 

information.

Power-line structures may attract nesting and perching 

raptors in relatively treeless areas (APLIC 2006). 

Consequently, the presence of power lines may 

influence raptor distribution in the relatively shortstature vegetation dominant in the two study areas. 

Unfortunately, the power-line layers that are currently 

readily available for the study areas primarily depict 

high voltage, long-distance transmission lines and do 

not accurately portray the distribution of shorter-length 

lines found in both study areas. Power lines and poles 

also are not easily detectable on currently available aerial 

photographs.

One other facet of human-related disturbance that 

may have a distinct impact on habitat quality for 

nesting raptors in both study areas is livestock grazing. 

Livestock grazing is purported to have the potential to 
11 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

increase shrub densities and decrease the availability of 

herbaceous vegetation (Blaisdell et al. 1982, Miller et al. 

1994). Such changes can alter raptor prey abundances 

and vulnerability (see review in Kochert 1999). Again, 

however, available stocking and grazing activity 

information for the two study areas was inadequate 

to account for the potential influence of grazing at the 

appropriate spatial and temporal scales.

Calculation of GIS-based Metrics

We used ArcGIS 9.2 Spatial Analyst to generate metrics 

to help describe relationships between raptor nest 

clusters, OG activities, and the vegetation characteristics 

surrounding each nest cluster:

1) For each nest cluster, we calculated the distance to 

the nearest well and road on an annual basis (i.e., 

the nearest well or road may change across years). 

For a given year, we designated the currently active

nest (confirmed breeding attempt) or cluster centroid 

(when a cluster was inactive or occupied but no 

breeding attempt was confirmed) as the point of 

reference for distance calculations. We adopted this 

protocol for all cluster-based spatial calculations (see 

below).

2) For each nest cluster, we calculated well and road 

densities (i.e., total linear length of roads) within 0.8-

km (based on the heretofore standard recommended 

spatial buffer) and 2-km (selected to represent larger 

landscape characteristics and capture the majority 

of the home range of the study species) radii on an 

annual basis.

3) To capture general patterns of vegetation variation 

near relevant raptor nest clusters, we calculated the 

coverage of individual vegetation classes within 

a 2-km radius (i.e., within larger landscape and 

probable core foraging areas) of all relevant nestcluster centroids. A centroid-based approach allowed 

us to initially characterize the vegetation near each 

nest cluster from a single reference point (i.e., without 

considering the potentially shifting location of active/

occupied nests within a cluster on an annual basis). 

Additionally, we also used individual nest-cluster 

survey histories to calculate year-specific coverage 

of the original vegetation variables (i.e., we used the 

active nest as the point of reference during years of 

nest-cluster activity but the cluster centroid during all 

other years).

Analytical Methods

Below we outline the primary analytical methods we used 

to address the five research objectives outlined above. 

Prior to undertaking formal data analyses, we inspected 

variable distributions to identify potential outliers and 

determine the need for data transformations. We removed 

two nest clusters in the Bairoil area of the Rawlins study 

area due to extremely high well densities (e.g., during 

their peak OG development years, these clusters were 

within 2 km of 164 and 262 wells; the next highest well 

count within 2 km of a nest cluster was 31). We used the 

square-root transformation to improve the distribution 

of the distance-to-well and road and total-length-ofroad metrics. We applied an arc-sine transformation 

to all individual vegetation coverage variables before 

performing the analyses outlined in the previous section

Objective 1: Describe temporal trends 

in development near focal-species nest 

clusters.

We calculated means and variances summarizing 

distances to, and densities of, roads and wells 

(distinguishing established and new wells) in relation to 

known raptor nest clusters for each year of the relevant 

monitoring periods. We calculated and examined both 

species-specific and combined-species summary statistics, 

and used simple analysis of variance (ANOVA) or 

regression analyses to discern patterns of variation across 

years. We designed this basic approach to demonstrate 

how the proximity and density of wells changed at raptor 

nest clusters over the monitoring period. In addition to 

providing a thumbnail sketch of overall development 

patterns, this information also shed light on any 

relationships (or lack thereof) between development and 

nest-cluster activity revealed by subsequent analyses. We 

also prepared and examined GIS-based maps displaying 

the distributions of wells and roads by year to investigate 

spatial patterns of development over time.

Objective 2: Describe distributional 

patterns of nest cluster status in relation to 

development hotspots.

We used GIS to compare the spatial arrangement of 

species-specific nest clusters in relation to breeding 

status and development activities over time. We focused 

our assessment on the period 1998–2006 in both study 

areas. This period encompassed all primary survey years 

in Price and the most suitable survey period in Rawlins. 

Data inspection revealed limited survey efforts in Rawlins 
12 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

between 1993 and 1997, especially for species other than 

the Ferruginous Hawk (e.g., only 2–9 Golden Eagle and 

0–3 Prairie Falcon nests were surveyed each year during 

this period). The period prior to this gap also was largely 

unsuited to this analysis due to initial lower survey efforts 

and fewer known nests. We identified development 

“hotspots” as areas with 2006 well densities >0.6 per km2

(or the equivalent of >1.2 wells within a 0.8-km radius), 

but also continual additional OG development since 1998. 

From these identified hotspots, we selected all areas 

containing at least five identifiable raptor nest clusters 

with five or more years of survey history between 1998 

and 2006. We selected this criterion to meet minimum 

sample size needs and survey consistency needs to detect 

long-term patterns in species’ activity histories. However, 

five years may well be an inadequate period to ensure 

effective representation of Golden Eagle nesting histories, 

given that individual pairs may not breed every year 

(Kochert et al. 2002).

Next, we used ArcGIS Spatial Analyst to quantify 

interannual distances between clusters of differing 

breeding status (see definitions below) and centers of 

development activity. We derived development center 

points in the GIS from the mean center location of 

1998 active wells within each selected hotspot (i.e., to 

serve as a static “initial conditions” reference point). 

In classifying the breeding status of nests and nest 

clusters for purposes of the above analyses, we used two 

overlapping classification scenarios to account for statusclassification uncertainties inherent in both study-area 

datasets, and conducted separate analyses to evaluate 

whether or not the two classification scenarios yielded 

different results:

(1) “Used” = evidence of recent nest tending, 

occupation, or actual breeding attempt obtained

“Unused” = no such evidence obtained, despite 

nest check

(2) “Active” = breeding attempt confirmed

“Other” = all other cases, including some used 

(i.e., evidence of tending, but not actual breeding) 

and all unused classifications 

It is important to note that the used vs. unused 

classification scheme was relatively loose in regards to 

“positive” status classifications (i.e., used or active), while 

the active versus other classification scheme was more 

restrictive, comparing only clusters with verified breeding 

attempts against all other clusters. For example, evidence 

of tending was much more subjective than that of actual 

breeding attempts. We adopted this dual classification 

approach in an attempt to strike a balance between 

maximum “positive” sample sizes (used vs. unused 

scheme) and minimum “false positive” classifications 

(active vs. other scheme).

Because distances between individual nest clusters and 

hotspot centers varied in relation to the availability of 

nesting sites within each hotspot, we chose to standardize 

distances by individual hotspot (i.e., by the mean and 

standard deviation of distances obtained in each hotspot). 

We then compared the average standardized distance to 

used versus unused and active versus other nest clusters 

relative to hotspot centers on an annual basis. We used 

the output to describe relative spatial distribution patterns 

of nest-cluster status over time in areas subjected to 

significant and expanding OG development activities.

Here it is important to note that evaluating the 

spatial arrangement of all nest clusters over time was 

problematic, because the numbers of previously unknown 

nests detected during each additional survey year by itself 

produced an apparent increasing density of nest clusters. 

For example, in 2005, 42 previously unknown (at least 

in the context of the 1998–2006 database provided by 

the UDWR) Golden Eagle nests were added to the Price 

FO raptor database, with few if any of these representing 

newly constructed nests. In addition, note that the output 

produced during the pursuit of Objectives 1 and 2 did not 

provide insight into actual nest-cluster densities, because 

the distribution of all nest clusters was not known.

For Wyoming Ferruginous Hawks, we also compared 

the overall distribution of active nests before 1988 and 

after 1989 to illustrate distributional shifts related to 

availability of 105 ANSs beginning in 1988. To do this, 

we used the Kernel Density Estimator tool in ArcGIS 

Spatial Analyst to calculate kernel densities of active 

Ferruginous Hawk nests, using a 5-km-radius spatial 

window, for the periods 1970–1987 and 1990–2006, 

and developed graphical comparisons of the indicated 

distributional patterns.

Objective 3: Assess relationships between 

nest-cluster status and development.

a) Describe development patterns near nest clusters.—

We used principal components analysis (PCA; Jongman 

et al. 1987) to describe general patterns in human-related 

development near all focal-species nest clusters at the 

0.8 and 2.0-km-radius spatial scales. We performed each 

scale-dependent PCA on seven original development 

variables calculated in a GIS for all surveyed nest clusters 

for each year of the study period. The original variables 
13 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

were distance to nearest road, distance to nearest OG 

well, total length of primary, secondary, and OG roads 

within 0.8 and 2.0 km, and total count of established and 

new wells within 0.8 and 2.0 km. PCA reduced the suite 

of development variables down to a limited number of 

orthogonal (i.e., uncorrelated) principal components or 

“development factors” (DFs) to be used in subsequent 

analyses incorporating a larger suite of potential variables 

(i.e., development, vegetation, and climate variables). 

Such data reduction serves to reduce the potential risk 

of model overfitting (i.e., entering too many parameters 

relative to available sample sizes) and simplifies 

interpretation of model results. Additionally, because we 

expected a priori that correlations would exist between 

selected individual development variables (e.g., distance 

to well and density of wells), the production of simplified, 

orthogonal development factors was appropriate prior 

to proceeding with more complicated analyses. We 

described the individual DFs produced for each study 

area and spatial scale based on original development 

variable loadings on each factor (Jongman et al. 1987). 

Additionally, we entered the original development 

variables calculated annually for each surveyed nest 

cluster into the PCA equations to produce year-specific, 

individual nest-cluster DF scores.

b) Annual nest cluster activity.—Once we calculated 

the DF scores, we began with relatively simple speciesspecific logistic regression analyses (Hosmer and 

Lemeshow 1989, Agresti 1990) to identify associations 

between the breeding status of nest clusters and DFs 

(“development-only” models). For Rawlins Ferruginous 

Hawks, we conducted separate analyses for non-ANS 

and ANS nest clusters, so that in the first case we derived 

results comparable to those for other species and that 

provided insight unencumbered by the confounding 

influence of ANSs, and in the second case so we could 

compare results for the two suites of nests. We used 

this initial analysis as a first cut to determine if strong 

relationships appeared to exist between the breeding 

status of species-specific nest clusters and the level of 

surrounding development, independent of considering 

other possible confounding factors (i.e., vegetation and 

climatic variation).

Next, we used PCA to simplify the original suites of 

vegetation variables calculated within 2.0 km of each 

Price and Rawlins cluster centroid into a lesser series of 

orthogonal “vegetation factors” (VFs). Although each 

cluster was represented only once in the initial PCA, we 

entered the original vegetation variables calculated for 

each year-specific cluster activity or centroid location 

into the resulting PCA equations to produce year-specific 

VF scores for each nest cluster. In this way, we reduced 

the original, large (19–22 variables) vegetation datasets 

to 6 or 7 more manageable vegetation factors to be used 

in subsequent data analyses (again, serving to reduce 

the danger of model overfitting and simplify model 

interpretation).

We then proceeded to more complex multiple logistic 

regression analyses to further discern species-specific 

relationships between the annual breeding status of 

nest clusters (i.e., a binomial response variable defined 

in various ways [see below]) and OG development 

levels, while simultaneously accounting for the potential 

influence of vegetation and climate. These “developmentplus” models took the following form:

Annual Nest Cluster Breeding Status = Development 

(DF) PC Scores + Vegetation (VF) PC Scores + 

Annual Climate Indexes + Interaction Terms + Error

Note that this approach required us to assume that 

the activity status of individual nest clusters could be 

considered independent from year to year, as the same 

nest cluster could be entered into the model more than 

once. Although we recognize the weakness of this 

assumption (i.e., among the raptor species of interest here, 

the same breeding pair may use the same nest cluster in 

multiple years and therefore a given year’s outcome may 

depend on the pair’s previous experiences at that site), 

we felt that the inconsistent surveying of individual nests 

on a yearly basis minimized the influence of potential 

autocorrelation among nest clusters across years. The fact 

that development levels changed annually in each study 

area lent further credence to this approach. Regardless, 

we assessed the independence of the errors produced 

by various permutations of the model to determine the 

degree to which autocorrelation was an issue. As in 

Objective 2, we used two overlapping classification 

scenarios to account for status-classification uncertainties 

inherent in both study-area datasets (i.e., used/unused and 

active/other), and conducted separate analyses to evaluate 

whether or not the two classification scenarios yielded 

different results. We considered modeling of annual 

climate variables a substitute for including “year” as a 

main effect or blocking factor in the analyses.

We used stepwise variable selection procedures and 

consideration of Akaike’s information criterion (AICc) 

scores to identify the most robust and parsimonious 

model(s) (Burnham and Anderson 2002). Using 

iterative forward and backward addition of variables 

with P = 0.15 used as the selection criteria to enter or 

remove specific variables, we first identified all potential 

significant models in the following model groups: 1) 

development only, 2) vegetation only, 3) climate only, 4) 
14 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

and vegetation–climate only. We next inspected the top 

performing models from these model groups to guide 

the creation of more complex models of development 

in combination with vegetation and climate variables 

(“development-plus” models). We used automatic 

forward and backward selection procedures to identify 

additional significant models not suggested by the 

component group models. At each step of model building 

in this more complex framework, and where sample sizes 

allowed for it, we inspected the significance of DF x VF 

and DF x climate-variable interactions in the presence of 

relevant main effects.

We then evaluated all significant group and combination 

models with AICc. We chose to use AICc because we 

were evaluating some models (e.g., Price Prairie Falcons) 

with small sample sizes. AICc is recommended for small 

sample sizes, but produces nearly identical results when 

sample sizes are large (Burnham and Anderson 2002). 

For each species and study area, we considered the top 

models (i.e., the most likely models, given the data 

under consideration) to be those with values of ΔAICc 

< 4 (Burnham and Anderson 2002). We also calculated 

Hosmer–Lemeshow goodness-of-fit tests (Hosmer and 

Lemeshow 1989) to evaluate whether or not the topranked model represented a significant fit to the data, and 

specifically draw attention in the results section to those 

cases where poor fit was suggested. For each species, 

we present the top models and log-likelihood values, 

ΔAICc values, and associated Akaike weights (wi; i.e., the 

probability of a model given the data) for each model.

Although model averaging is a popular method for 

summarizing the top models identified by AIC (e.g., 

see Burnham and Anderson 2002), our model building 

procedures precluded this approach. We allowed our 

models to contain development variables measured 

at either or both of the 0.8 and 2.0-km spatial scales, 

and occasionally found that the relationship of cluster 

status to a particular development factor measured at 

the 2-km scale changed sign in the presence of the same 

development factor measured at the 0.8-km scale. This 

was because the 2-km scale encompassed the information 

captured at the 0.8-km scale, but also additional 

information. Given this reality, model averaging had the 

potential to obscure the actual relationships revealed by 

the individual top models.

c) Proportional nest-cluster use and activity.—We used 

multiple regression (Sokal and Rohlf 1995) to analyze 

relationships between species-specific, proportional nestcluster “use” and “activity” rates (i.e., the proportion 

of survey years in which a nest cluster was “used” or 

“active” [as defined above]) and OG development levels, 

while accounting for other potentially confounding 

factors. For this analysis, we limited consideration to 

those clusters surveyed for a minimum of five consecutive 

years during the survey period of interest (1998–2006). 

For Rawlins Ferruginous Hawks, we limited these 

analyses to consideration of only non-ANS nest clusters. 

Because the response variables were derived from 

multiple years of data gathered from the same nest 

clusters, it was necessary to account for the potential 

variation in development and vegetation metrics that may 

have occurred during the survey years. For example, 

development metrics associated with a particular nest 

cluster could change from year to year due to actual 

changes in development levels or from shifting use of 

different nests within the cluster. In contrast, variation in 

vegetation metrics from one year to the next could only 

result from shifting nest use within a cluster (i.e., because 

the vegetation layer used in our analyses was static). In 

order to more accurately reflect the development and 

vegetation patterns at each nest cluster, we chose to 

average the DF and VF PCA scores obtained from each 

year a nest cluster was surveyed between 1998–2006. 

Additionally, we calculated the variance associated with 

the DF scores from each nest cluster to reflect the amount 

of change in development that occurred during the years 

of survey. Therefore, the models took the form:

Proportional Nest Cluster Use or Activity = Average 

DF Scores + Variance of DF Scores + Average VF 

Scores + Interaction Terms + Error

Note that because the datum of interest in this analysis 

was calculated across a standardized period for all nest 

clusters, we did not account for annual environmentalcondition variables as in (b) above. For these speciesspecific analyses, we adopted similar variable and model 

selection and diagnostic procedures as outlined in (b) 

above.

This was the only tool at our disposal to investigate 

whether development activities affected the long-term 

activity status of individual nest clusters.

Objective 4: Assess Ferruginous Hawk 

nest success and productivity in relation to 

development.

Only Wyoming Ferruginous Hawks were surveyed 

adequately to provide robust nest-cluster sample 

sizes for which nesting success and productivity were 

determined. Similar to the approach outlined for 

Objective 3 above, we began by formulating relatively 

simple logistic regression (nesting success) or linear 
15 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

regression (productivity) analyses that incorporated only 

data from non-ANS nest clusters and used DF scores as 

the predictor variables. We then proceeded to analyze 

more complex multiple logistic and linear regression 

models that included both non-ANS and ANS nests and 

incorporated other environmental covariates similar 

to those modeled under Objective 3b, plus additional 

substrate/accessibility predictors. Note that the same 

issues outlined above concerning possible problems with 

lack of independence between repeat measures of activity 

at the same nest cluster applied here.

Objective 5: Assess the response of 

Ferruginous Hawks to artificial nest 

structures.

Accomplishing this objective entailed summarizing 

additional insight derived from Neal (2007) concerning 

how Ferruginous Hawks in the Wyoming study area 

responded over time to the availability of naturalgas condensation tanks as nesting substrate and their 

subsequent responses to installation of ANSs to reduce 

such occurrences. Preliminary evidence suggested 

that installation of natural-gas condensation tanks 

initially provided Ferruginous Hawks with attractive 

nesting opportunities in an area largely lacking other 

vertically raised nesting structures. Although hard data 

testifying to this fact were not collected, we presumed 

that novel, secure nest sites located in areas rich in prey 

resources were the primary attractor in this context. We 

designed the analyses outlined above to help us discern 

if use of ANSs translated to different responses to OG 

development compared to use of natural nest substrates. 

Preliminary investigations further suggested that use of 

ANSs resulted in noticeable alterations of the species’ 

ecology relative to sensitivity to human disturbance, use 

and maintenance of alternate nest sites, allocation of adult 

effort toward nest protection/vigilance versus foraging, 

and susceptibility of nests to mammalian predation. As 

part of his thesis work, Neal collected additional data to 

help highlight the nature of these factors and responses. 

For example, he compared the average number of 

alternate nests in nest clusters pre- and post-ANS 

availability, with the evidence suggesting that availability 

of ANSs resulted in a substantial reduction in the use of 

alternate nests over time. Second, he collected 299 hours 

of observational data at 25 natural nests and 25 ANSs to 

quantify differences in nest attendance and prey-delivery 

rates. Third, he amassed a few dozen observations 

testifying to occurrence of mammalian predation as 

a significant cause of adult and nestling mortality at 

accessible natural nests, with predation of any kind rarely 

occurring at inaccessible ANSs or natural nests.

17 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

increased significantly between 1998 and 2006 (Table 1). 

Between 1998 and 2006, the average distance to a well 

decreased by 1.2 km, while the average numbers of wells 

within 0.8 and 2.0 km of relevant nest clusters increased 

three and four fold, respectively (Table 1). Additionally, 

the maximum number of wells proximate to nest clusters 

increased from four to six at the 0.8-km-radius scale 

and from 17 to 27 at the 2.0-km-radius scale. Over this 

same period, the overall percentage of nest clusters with 

no wells nearby declined ~19% at both the 0.8-km and 

2.0-km scales (Table 2); however, further investigation 

revealed substantial variation in this reduction factor 

Price, Utah Study Area

Objective 1: Describe temporal trends 

in development near focal-species nest 

clusters.

The total length of all roads within the Price study area 

increased only slightly from 14,210 km in 1998 to 

14,375 km in 2006. The change was due entirely to an 

increase in the total length of OG roads added annually 

in association with newly developed wells (i.e., 1,432 km 

of OG roads in 1998 vs. 1,597 km in 2006). The total 

number of OG wells increased much more dramatically 

from 451 to 1,177 between 1998 and 2006. The greatest 

addition of new wells occurred between 1999 and 2002 

(Figure 3). Similarly, the average number of wells within 

0.8 and 2.0 km of Golden Eagle, Red-tailed Hawk, and 

Prairie Falcon nest-cluster centroids (n = 264) increased 

most dramatically from 1999 to 2002 (Figure 4).

The average distance to the nearest road relative to 

264 identifiable raptor nest clusters (including Golden 

Eagles, Red-tailed Hawks, and Prairie Falcons), as well 

as the total length of roads within 0.8 km and 2.0 km of 

nest clusters did not change significantly between 1998 

and 2006 (Table 1). It is important to note, however, 

that these results likely are confounded by incomplete 

representation of road-development histories, in that the 

road layer we worked with did not reflect changes in 

non-OG roads between 1998 and 2006 (i.e., classification 

of non-OG roads reflected only the 2006 scene, not yearspecific scenes, and once assigned the classification by 

type of individual road segments remained static across 

all years). In contrast to the above results, the average 

distance to the nearest well decreased significantly and 

the average number of wells within 0.8 and 2.0 km 

Figure 4. Average numbers of active oil and gas wells within 0.8 and

2.0 km of 264 raptor nest-cluster centroids in the Price, Utah study

area from 1998 to 2006.

Figure 3. Total number of new and established oil and gas wells 

found in the Price, Utah study area between 1998 and 2006.

Table 1. Comparison (univariate t-tests) of 1998 and 2006 well and road metrics near 264 raptor nest clusters (including Golden Eagles, Redtailed Hawks, and Prairie Falcons) in the Price, Utah study area.

1998 2006

Variable Mean SE Mean SE P

Distance to nearest road (km) 0.58 0.029 0.57 0.028 0.718

Length of road within 0.8 km (km) 1.43 0.084 1.52 0.088 0.467

Length of road within 2.0 km (km) 11.66 0.413 12.48 0.457 0.186

Distance to nearest well (km) 5.30 0.273 4.08 0.274 0.002

Number of wells within 0.8 km 0.16 0.034 0.63 0.072 <0.001

Number of wells within 2.0 km 1.25 0.192 4.08 0.427 <0.001

Results
18 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

depending on land ownership. The percentages of 

nest clusters with no wells within 0.8 km and 2.0 km 

declined by roughly 25% at both scales on BLM lands, 

and declined the most on state lands (42% decline within 

0.8 km; 33% decline with 2.0 km; Table 3). Due to the 

concentrated nature of OG development in the Price study 

area, most nest clusters in areas of energy development 

were proximate to multiple wells. For example, by 2006, 

61% of all identifiable nest clusters near wells had two or 

more wells within 0.8 km of cluster centroids, and 70% 

had five or more wells within 2.0 km of cluster centroids

Objective 2: Describe distributional 

patterns of nest cluster status in relation to 

development hotspots.

We identified three OG development hotspots in the 

Price study area that met our screening criteria (Figure 

5). Within these hotspots, we identified 43 Golden Eagle, 

14 Red-tailed Hawk, and 3 Prairie Falcon nest clusters 

that met our screening criteria. To be considered, we 

required a species to have at least five nest clusters per 

year represented in both the “used” and “unused” or 

“active” and “other” status categories (see the Glossary 

for a description of each category), which limited the 

comparison to Golden Eagles with clusters classified 

as used and unused. Relative to the hotspot center 

locations (with distances standardized based on withinhotspot averages), the average standardized distance 

Table 2. Percentages of 264 raptor nest clusters (including Golden Eagles, Red-tailed Hawks, and Prairie Falcons) with oil and gas wells within 0.8

and 2.0 km of cluster centroids in the Price, Utah study area in 1998 and 2006.

Within 0.8 km Within 2.0 km

# Wells 1998 2006 # Wells 1998 2006

0 90.2% 70.8% 0 73.1% 54.2%

1 5.3% 11.4% 1 8.7% 8.3%

2 3.0% 6.1% 2-5 9.5% 14.0%

3 1.1% 8.3% 6-10 4.9% 6.8%

4 0.4% 2.7% 11-15 2.7% 3.0%

5 0.0% 0.4% 16-20 1.1% 8.0%

6 0.0% 0.4% >20 0.0% 5.7%

Table 3. Percentages by land owner/manager of 264 raptor nest clusters (including Golden Eagles, Red-tailed Hawks, and Prairie Falcons) with no

gas or oil wells within 0.8 and 2.0 km of cluster centroids in 1998 and 2006 in the Price, Utah study area.

Within 0.8 km Within 2.0 km

Land Manager n 1998 2006 1998 2006

BLM 107 89% 66% 74% 49%

USFS 48 98% 98% 85% 83%

State 46 83% 41% 59% 26%

Private 63 92% 79% 73% 62%

Figure 5. Map depicting the distribution of raptor nests from nest 

clusters with ≥5 years of survey history between 1998 and 2006 within

three oil and gas development “hotspots” in the Price, Utah study area.

to used Golden Eagle nests (n = 204, mean = 0.03 ± 

0.07 standardized units) was greater than the average 

distance to unused nests (n = 209, mean = -0.02 ± 0.07 

standardized units), but the difference was not significant 
19 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

(t = -0.53, df = 411, P = 0.60). A plot of average 

standardized distances to used versus unused nests by 

year further suggested, however, that used nests were 

consistently farther from development centers from 2000 

through 2003, the period of most intensive development 

(Figure 6).

2.0-km dataset, and the relevant component loadings 

yielded similar interpretations at both scales (Figure 7). 

The first PC, or development factor 1 (DF1), contrasts 

OG development levels; i.e., moving right along DF1 is 

primarily associated with increasing total length of OG 

roads, increasing densities of established and new wells, 

and decreasing distances to the nearest wells and roads. 

The second PC (DF2) contrasts non-OG roads levels; 

i.e., moving up along DF2 is primarily associated with 

increasing total length of secondary and primary roads, 

and decreasing distance to roads. The fact that distance 

to road appears in both DF1 and DF2 suggests that 

decreasing distance to the nearest road is a characteristic 

common to both high levels of OG development and 

non-OG roads near raptor nest clusters. Given the 

straightforward interpretation and high congruence 

between PCs produced at each scale, we chose to use 

DF1 and DF2 scores in place of individual development 

variables in subsequent analyses.

b) Annual nest-cluster activity.—Initial species-specific 

analyses of nest-cluster status (with the dependent 

variable as “used/unused” or “active/other”) versus DF1 

and DF2 (i.e., logistic regression without inclusion of 

vegetation and climatic variables) revealed significant 

relationships between development and the activity 

status of nest clusters. For Golden Eagles, the analyses 

indicated negative relationships between DF1 and cluster 

“use” at both scales and cluster “activity” at the 0.8-km 

Figure 6. Average standardized distance between development

“hotspot” centers and used and unused Golden Eagle nest clusters in 

the Price, Utah study area from 1998 to 2006.

Objective 3: Assess relationships between 

nest cluster status and development.

a) Describe development patterns near nest clusters.—

The PCAs identified two principal components with 

eigenvalues >1.0 at both the 0.8 and 2.0-km spatial 

scales. The first two PCs explained 57% of the variance 

in the 0.8-km dataset and 58% of the variance in the 

Figure 7. Graphical display of individual development-variable loadings associated with two primary development factors identified by Principal

Components Analysis of characteristics associated with 264 raptor nest clusters at the 0.8-km and 2.0-km radius spatial scales in the Price, Utah

study area.
20 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

scale (i.e., use or activity was greater in areas with less 

OG development). In contrast DF2 related positively 

to cluster use at the 2-km scale and cluster activity at 

both scales (i.e., use and activity were greater in areas 

with more non-OG roads). For Red-tailed Hawks, the 

probability of cluster use or activity related positively 

to OG development at the 0.8-km scale, but related 

negatively to it at the 2.0-km scale. Cluster use also 

related positively to non-OG roads at the 0.8 km scale, 

but not at the larger scale. No significant relationships 

were indicated for Prairie Falcons.

PCA of the proportional coverage of 22 original 

landcover/vegetation variables within a 2-km radius of 

nest clusters produced seven PCs with eigenvalues >1.0, 

which collectively explained 68% of the variation in the 

dataset (Table 4). It is important to note that we created 

these vegetation factors to account for the potential 

confounding influence vegetation may have on nestcluster status in subsequent models and to assess how 

vegetation modified relationships with development. 

Therefore, it is not our intention to provide detailed 

interpretations of cluster status/vegetation relationships. 

That said, the first PC, labeled VF1, accounted for 19.6% 

of the total variance and involved a contrast between 

primarily clusters dominated by mixes of pinyonjuniper, desert shrub, and Wyoming big sagebrush, 

and those dominated by mixes of conifer, aspen-mix 

forest, mountain big sagebrush, and mountain shrub 

cover (i.e., for the most part reflecting an elevational 

gradient). VF2 accounted for an additional 13.6% of 

the total variance and further distinguished between 

primarily clusters dominated by pinyon, juniper, and 

mixed-species sagebrush cover, and those dominated 

by mountain and basin big sagebrush. VF3 accounted 

for an additional 9.7% of the total variance and further 

distinguished primarily clusters dominated by riparian 

forest, grass, forbs, and black sagebrush. VF4 accounted 

for an additional 8.0% of the total variance and further 

distinguished primarily clusters dominated by pinyon, 

Table 4. Summary of Principal Components Analysis describing seven primary landcover/vegetation factors with eigenvalues >1.0 derived from

22 original landcover/vegetation variables representing proportional coverage within 2 km of 264 raptor nest clusters in the Price, Utah study area

(asterisks indicate high loadings that comprise the primary variables involved in each factor).

Land Cover Factors Produced by Principal Components Analysis

Land cover variables 1 2 3 4 5 6 7

Grass 0.151 -0.431 0.508* 0.051 -0.107 -0.277 0.099

Forb 0.449 0.104 0.567* 0.189 -0.166 0.069 -0.294

Sagebrush general 0.153 -0.723* -0.371 -0.164 0.106 -0.026 -0.049

Wyoming big sagebrush -0.677* 0.416 0.019 0.119 -0.013 0.002 -0.315*

Mountain big sagebrush 0.538* 0.552* -0.041 -0.193 -0.163 0.127 0.183

Basin big sagebrush -0.322 0.516* -0.146 0.121 -0.153 0.219 -0.238

Black sagebrush 0.213 0.139 0.595* 0.002 -0.517* 0.044 0.125

Rabbitbrush -0.220 0.288 0.169 0.204 0.479* 0.233 0.296

Desert shrub -0.572* -0.242 0.345 -0.505* -0.044 -0.098 0.004

Riparian shrub 0.372 0.184 0.002 -0.305 0.391 -0.368* -0.383*

Riparian forest -0.027 0.000 0.556* 0.062 0.462* 0.123 0.072

Mountain shrub 0.744* 0.309 0.139 0.058 -0.099 0.313 0.070

Oak-maple 0.395 0.440 -0.444 -0.215 0.058 0.089 0.396*

Aspen mix 0.673* 0.271 0.000 -0.239 0.226 -0.272 -0.242

Conifer 0.778* -0.040 -0.152 0.303 0.222 -0.043 0.050

Pinyon-juniper -0.673* 0.066 -0.177 0.512* -0.115 -0.089 0.084

Pinyon 0.289 -0.513* -0.280 0.429* 0.044 0.090 -0.033

Juniper 0.023 -0.662* -0.105 -0.273 -0.002 0.457* -0.012

Barren 0.488 -0.442 0.224 0.460* -0.057 -0.101 0.071

Water 0.125 -0.069 0.026 0.144 0.153 0.546* -0.515*

Agriculture -0.090 -0.208 0.270 -0.486* 0.121 0.346* 0.116

Human habitation -0.314 0.113 0.317 0.211 0.582* -0.025 0.177

Cumulative variance explained 19.6% 33.2% 42.9% 50.9% 57.6% 63.1% 68.0%
21 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

pinyon-juniper mix, and barren lands from those 

dominated by desert shrub and agricultural habitats. VF5 

accounted for an additional 6.3% of the total variance 

and further distinguished primarily clusters dominated by 

black sagebrush from those dominated by rabbitbrush, 

riparian forest, and human habitations. VF6 accounted 

for an additional 5.5% of the total variance and further 

distinguished primarily clusters dominated by juniper, 

water, and agricultural habitats, with a weak contrast 

against clusters with relatively high representation 

of riparian shrub cover. Finally, VF7 accounted for 

an additional 4.9% of the total variance and further 

distinguished primarily clusters associated with a 

relatively high degree of open water and to lesser degrees 

riparian shrub and Wyoming big sagebrush, with a weak 

contrast against clusters with relatively high oak-maple 

cover.

An initial graphical examination of relationships 

between the probability of clusters being active in May 

and climatic variables suggested possible correlations 

(Figure 8). In particular, activity levels appeared to 

track PDSI during the previous year (PDSI_lyr) fairly 

well, with relatively high correlations between these 

parameters for Prairie Falcons (0.69) and Golden Eagles 

(0.67), but a weaker relationship for Red-tailed Hawks 

(0.36). Additionally, the probability of nests being active 

declined markedly for all three focal species during 

the worst drought years of 2002 and 2003, but quickly 

improved again as moisture conditions began to rebound 

in the following years.

Adding landcover/vegetation and climatic variables to 

the models revealed additional complexity, with all bestfitting models including one or more landcover/vegetation 

factors and, with one exception, one or more annual 

climatic variables (Tables 5–12).

With the seven VFs and climate variables incorporated 

as potential covariates along with the four DFs as 

predictors, we identified six top models that helped 

explain differences between “used” (n = 534 events) and 

“unused” (n = 569) Golden Eagle nest clusters (Table 

5). Top models (i.e., the most likely models, given the 

data under consideration) were identified as those with 

∆AICc values <4.0. Although the top models varied 

slightly, they revealed underlying vegetation, climate, 

and development relationships. Inspection of actual 

model coefficients suggested that nest cluster use related 

positively to DF2_2K, VF2, PDSI, and PDSI_lyr, and 

related negatively to DF1_0.8K, DF1_2K, VF5, and 

VF7. In addition, five of the six top models contained 

an interaction between VF5 and DF1. These results 

suggest that the probability of a Golden Eagle nest 

Figure 8. Comparison of trends in annual, species-specific, proportional nest-cluster activity, the Palmer Drought Severity Index (PDSI; higher

values = less drought severity), PDSI in the previous year (PDSI_lyr), and winter precipitation (WintPPT) in the Price, Utah study area from 1998–

2006. All measures have been standardized for direct comparison.
22 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

cluster being used in a given year generally increased as 

overall development activity decreased and where the 

prevalence of non-OG roads was relatively high at the 

2-km scale. However, a significant interaction between 

DF1 and VF5 in five of the six top models also indicated 

that the negative influence of expanding development was 

particularly enhanced in areas with human habitations, 

cover of rabbitbrush, and/or riparian forest, but shifted 

to a positive relationship in habitats with high cover 

of black sagebrush. Otherwise, the results for VF2 

suggested that the probability of cluster use increased 

as the proportion of mountain and basin big sagebrush 

cover within 2 km of the cluster increased and declined 

as the proportion of pinyon, juniper, and mixed-species 

sagebrush cover increased. The significance of VF7 

further suggested that the probability of cluster use also 

increased in habitats with a relatively high degree of 

open water (i.e., in clusters located near the prominent 

reservoirs in the study area: Joe’s Valley Reservoir and 

those in Huntington Canyon). Drought severity during 

the current year (PDSI) and previous year (PDSI_lyr) also 

significantly influenced the probability of cluster use, with 

use declining with increasing drought severity.

We identified four top models that helped explain 

differences between “active” (n = 312 events) and 

“other” (n = 791) Golden Eagle nest clusters (Table 

6). These models suggested that nest-cluster activity 

related positively to DF2_2K, VF2, and PDSI, and 

related negatively to DF1_0.8K, DF1_2K, VF4, VF5, and 

WintPPT (Table 6). Similar to the “use” models above, 

an interaction between DF1 (0.8 or 2.0-km scale) and 

VF5 also was identified in the top “activity” models. The 

top activity and use models revealed similar relationships 

with both DF1 and DF2. Overall, both the use and 

activity models suggested that the probability of a Golden 

Eagle cluster being used or active in May increased as 

overall OG development activity decreased proximate 

Table 5. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of a Golden Eagle nest cluster

being “used” in a given year and various oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the

Price, Utah study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K, DF2_2K VF2, VF5, VF6, VF7 PDSI, PDSI_lyr DF1_0.8K*VF5, 

DF2_2K*VF6 11 -727.93 0.00 0.28

DF1_0.8K, DF2_2K VF2, VF5, VF6, VF7 PDSI, PDSI_lyr DF2_2K*VF6 10 -729.30 0.70 0.20

DF1_2K VF2, VF5, VF7 PDSI, PDSI_lyr D1_2K*VF5 8 -731.89 1.80 0.11

DF1_2K VF2, VF5, VF7 PDSI, WintPPT DF1_2K*VF5 8 -732.28 2.58 0.08

DF1_0.8K, DF2_2K VF2, VF5, VF7 PDSI, PDSI_lyr DF1_0.8K*VF5, 

DF2_2K*PDSI 11 -729.76 3.67 0.04

DF1_0.8K VF2, VF5, VF7 PDSI, PDSI_lyr

DF1_0.8K*F5, 

DF1_0.8K*VF7, 

D1_0.8K*PDSI_lyr

10 -730.86 3.82 0.04

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 6. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Golden Eagle nest cluster

being “active” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Price,

Utah study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_2K VF1, VF2, VF4, VF5 PDSI, WintPPT DF1_2K*VF5 9 -606.74 0.00 0.43

DF1_0.8K VF1, VF2, VF4, VF5 PDSI, WintPPT DF1_0.8K*VF5 10 -606.94 2.44 0.13

DF1_2K, DF2_2K VF2, VF4, VF5 PDSI, WintPPT DF1_2K*VF5 9 -608.04 2.60 0.12

DF1_2K VF2, VF4, VF5 PDSI, WintPPT DF1_2K*VF5 8 -609.25 2.99 0.10

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
23 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

to clusters, as the prevalence of non-OG roads increased 

at the 2.0-km scale, as current-year drought severity 

decreased, as cover of mountain and basin big sagebrush 

increased (and pinyon and juniper decreased [VF2]), 

and as cover of miscellaneous human infrastructure and 

rabbitbrush decreased and cover of black sagebrush 

increased (VF5).

A comparison of used (n = 98 events) and unused (n

= 119) Red-tailed Hawk nest clusters produced 18 top 

models with considerable model variation (Table 7). 

However, some common patterns were evident. The 

top models commonly revealed that cluster use related 

positively to DF1_0.8K, DF2_0.8K, VF2, PDSI, and 

PDSI_lyr, but related negatively to DF1_2K, VF4, and 

WintPPT. These results suggest that the probability 

of Red-tailed Hawk nest clusters being used increased 

as all types of development increased close to the nest 

(i.e., within 0.8 km), but decreased as OG development 

increased in the larger landscape (i.e., within 2.0 km). 

Additionally, use increased in habitats dominated by 

a mix of mountain and basin big sagebrush cover, but 

decreased in habitats dominated by a mix of pinyon, 

juniper, and mixed-species sagebrush cover. Use also 

increased as current or prior-year drought severity 

declined.

We identified only a single top model that helped 

explain differences between active (n = 72 events) and 

other (n = 145) Red-tailed Hawk nest clusters (Table 

8). The coefficients of this model suggest that cluster 

activity related positively to DF1_0.8K and VF2, and 

Table 7. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of a Red-tailed Hawk nest cluster

being “used” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Price, Utah

study area.

Development Factors Vegetation Factors Climate Factors K Log(L) ∆AICc wi

DF1_0.8K, DF1_2K VF2 PDSI_lyr 5 -141.54 0.00 0.13

DF1_0.8K, DF1_2K VF4 PDSI_lyr 5 -141.72 0.35 0.11

DF1_0.8K, DF1_2K VF4 None 4 -143.07 0.93 0.08

DF1_0.8K, DF1_2K VF2 PDSI, WintPPT 6 -141.21 1.46 0.06

DF1_0.8K, DF1_2K VF4 PDSI, WintPPT 6 -141.32 1.69 0.05

DF1_0.8K, DF1_2K None None 3 -144.71 2.11 0.04

None VF4 PDSI_lyr 3 -144.74 2.17 0.04

DF2_0.8K VF4 PDSI_lyr 4 -143.75 2.29 0.04

DF1_2K VF4 PDSI_lyr 4 -143.88 2.56 0.04

DF1_0.8K, DF1_2K None PDSI, WintPPT 5 -143.07 3.05 0.03

DF1_0.8K, DF2_0.8K, DF1_2K None None 4 -144.17 3.13 0.03

DF2_0.8K VF4 None 3 -145.26 3.21 0.03

DF1_2K VF4 None 3 -145.27 3.25 0.02

None VF4 PDSI, WintPPT 4 -144.24 3.27 0.02

None VF4 None 2 -146.38 3.38 0.02

DF2_0.8K VF4 PDSI, WintPPT 5 -143.28 3.48 0.02

None VF4, VF6 None 3 -145.55 3.79 0.02

None VF1, VF4 PDSI_lyr 4 -144.51 3.82 0.02

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 8. Top multiple logistic regression model (∆AICc <4.0) describing the relationships between the probability of a Red-tailed Hawk nest cluster

being “active” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Price, Utah

study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K, DF1_2K VF2, VF3 None D1_0.8K*VF3 6 -123.42 0.00 0.85

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
24 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

negatively related to DF1_2K and VF3. The model also 

contains a significant interaction between DF1_0.8K and 

VF3. These results are similar to those derived from 

the used/unused analysis in suggesting the same mixed 

relationships with overall development activity at the 0.8 

and 2.0-km spatial scales, and relative to the vegetation 

contrast represented by VF2. These results differ from 

the used/unused analysis in not representing a significant 

effect of drought severity and in including a significant 

interaction between overall development activity at the 

0.8-km scale and VF3. The interaction term suggests that 

the positive effect of increasing, proximate development 

activity was muted in habitats dominated by mixes of 

riparian forest, grass, forbs, and oak-maple. The lack 

of a significant climate term in the active/other model 

compared to the used/unused model suggests that drought 

did not affect actual breeding attempts to the same degree.

We identified 13 top models that helped explain 

differences between used (n = 34 events) and unused (n

= 39) Prairie Falcon nest clusters (Table 9). Due to small 

sample sizes for this species, we only considered main 

effects and excluded potential interactions to avoid model 

overfitting. Overall, these models indicated that Prairie 

Falcon cluster use related positively to PDSI_lyr or PDSI, 

and related negatively to DF2_0.8K, DF2_2K, VF3, 

VF4 (and less commonly VF1 or VF5), and WintPPT. 

Cluster use related positively to DF1 at the 0.8 or 2.0-km 

scales when either scale entered a model individually; 

however, use had a mixed relationship with DF1 when 

both scales were included in the same model (i.e., a 

positive relationship at the 0.8-km scale and negative at 

2.0-km). These results suggest that the probability of a 

Prairie Falcon nest cluster being used increased as OG 

development increased, but that this effect diminished 

at the larger landscape scale. In contrast, cluster use 

increased as prevalence and proximity of non-OG roads 

decreased at both scales. Cluster use also increased 

as proportional cover of riparian forest, grass, forbs, 

and black sagebrush decreased (VF3); increased as the 

proportional cover of pinyon, pinyon-juniper mix, and 

barren lands decreased and the proportional cover of 

desert shrub and agricultural habitats increased (VF4); 

and increased as prior or current year drought severity 

declined. Note that we detected no relationships between 

cluster status and development in prior development-only 

models.

We identified three top models that helped explain 

differences between active (n = 28 events) and other (n = 

45) Prairie Falcon nest clusters (Table 10). These models 

suggested that cluster activity related positively to VF2 

and PDSI, and negatively related to DF2_0.8K, VF3, 

VF4, VF5, VF6, and WintPPT. These results agree with 

those of the used/unused analysis in suggesting similar 

relationships with trends in the density and proximity of 

non-OG roads (although the active models only identified 

the 0.8-km scale as important). However, the top models 

of Prairie Falcon cluster activity identified no significant 

relationships with OG development. The negative 

relationships between cluster use or activity and VF3 and 

VF4 also are apparent in all top models (Tables 9 and 

Table 9. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Prairie Falcon nest cluster

being “used” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Price,

Utah study area.

Development Factors Vegetation Factors Climate Factors K Log(L) ∆AICc wi

DF1_0.8K, DF2_2K VF3, VF4 PDSI_lyr 6 -34.80 0.00 0.17

DF2_0.8K VF3, VF4, VF5 PDSI_lyr 6 -34.91 0.20 0.16

DF2_0.8K VF3, VF4 PDSI_lyr 5 -36.33 0.60 0.13

DF2_2K VF3, VF4 PDSI_lyr 5 -36.47 0.89 0.11

DF1_0.8K, DF1_2K, DF2_0.8K, VF2, VF3, VF4, VF5, VF7 PDSI_lyr 10 -30.46 1.91 0.07

DF2_2K VF1, VF3, VF4 PDSI, WintPPT 7 -34.98 2.88 0.04

DF1_0.8K, DF2_2K VF3, VF4 PDSI, WintPPT 7 -35.02 2.96 0.04

None VF3, VF4, VF5 PDSI_lyr 5 -37.51 2.97 0.04

DF2_0.8K VF3, VF4, VF5 PDSI, WintPPT 7 -35.03 2.97 0.04

DF2_0.8K VF3, VF4 PDSI, WintPPT 6 -36.35 3.09 0.04

None VF1, VF3, VF4 PDSI_lyr 5 -37.70 3.35 0.03

DF1_2K, DF2_2K VF3, VF4 PDSI, WintPPT 7 -35.33 3.58 0.03

DF2_2K VF3, VF4 PDSI, WintPPT 6 -36.79 3.97 0.02

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
25 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

10). Again, these relationships suggest that cluster use or 

activity increased as proportional cover of riparian forest, 

grass, forbs, and black sagebrush decreased (VF3), and 

as the cover of pinyon, pinyon-juniper mix, and barren 

lands decreased and cover of desert shrub and agricultural 

habitats increased (VF4). Additionally, all use and 

activity models suggested a positive response to wetter 

conditions during either the previous year (PDSI_lyr) or 

current year (PDSI) as long as winter precipitation was 

not too high (WintPPT).

c) Proportional nest-cluster activity.—We identified 

129 Golden Eagle, 23 Red-tailed Hawk, and 9 Prairie 

Falcon nest clusters with at least five years of survey 

history between 1998 and 2006. Limited sample 

sizes precluded investigating relationships between 

proportional cluster use or activity and development 

metrics for other than Golden Eagles. We also modeled 

only main effects to avoid model overfitting.

We identified three top models that helped explain 

differences in proportional “use” of Golden Eagle nest 

clusters (i.e., with annual events classified as “used” 

or “unused”; Table 11). The best model accounted for 

only 14.7% (adjusted R2 value) of the total variance 

in the dependent variable, suggesting that although 

our modeling identified several important contributors 

to variation in proportional cluster use, much of 

the variation remained unexplained. Generally, the 

models suggested that cluster use related positively 

to VarDF1_0.8K, VarDF2_0.8K, VF1, and VF2 and 

negatively related to VF5 and VF7. This suggests that 

proportional use increased in clusters with high variation 

in OG levels at the 0.8 km spatial scale. Greater variance 

in OG development levels could occur either at clusters 

with large interannual changes in the amount of nearby 

development or at clusters with multiple, widely spaced 

alternate nests (i.e., shifting center points from which 

development metrics were calculated). On the other 

hand, proportional cluster use increased with variation in 

the representation of non-OG roads, perhaps reflecting the 

benefit of large breeding territories with high diversity in 

non-OG road patterns (perhaps reflecting more nesting 

options). Note that greater variance in non-OG roads 

could be driven only by shifting nest use within a cluster 

across years, because our representation of non-OG roads 

was static across years. Proportional cluster use also 

increased in areas of mountain or basin big sagebrush, 

mountain shrub, conifer, and aspen, and decreased in 

areas of high pinyon-juniper and Wyoming or mixedspecies sagebrush cover (VF1 and VF2); decreased 

in areas of high human habitation, rabbitbrush, and 

riparian forest cover and increased in areas of high black 

sagebrush cover (VF5); and increased in areas with a 

relatively high proportion of open water (VF7).

Table 10. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of a Prairie Falcon nest cluster

being “active” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Price, Utah

study area.

Development Factors Vegetation Factors Climate Factors K Log(L) ∆AICc wi

DF2_0.8K VF2, VF3, VF4, VF5 PDSI, WintPPT 8 -23.37 0.00 0.32

DF2_0.8K VF2, VF3, VF4, VF5, VF6 PDSI, WintPPT 9 -22.04 0.04 0.31

DF2_0.8K VF1, VF3, VF4, VF6 PDSI, WintPPT 8 -24.64 2.55 0.09

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 11. Top multiple regression models (∆AICc <4.0) describing relationships between proportional “use” of Golden Eagle nest clusters (i.e.,

proportion of years in which evidence of nest tending or breeding was obtained) and oil and gas development factors (DFs; cluster averages and

variances) and average landcover/vegetation factors (VFs) in the Price, Utah study area.

Development Factors Vegetation Factors K RSS ∆AICc wi

VarDF1_0.8K, VarDF2_0.8K VF1, VF5, VF7 6 7.15 0.00 0.54

VarDF2_0.8K VF1, VF5, VF7 5 7.39 2.00 0.20

VarDF2_0.8K VF2 3 7.70 2.87 0.13

Note: Top model total R2 = 18.1%, adjusted R2 = 14.7%. K = number of parameters, including constant and error terms; 

RSS = residual sum of squares; wi = Akaike’s weight, indicating probability of model given the data (Burnham and 

Anderson 2002).
26 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

The top two models of proportional nest cluster “activity” 

(i.e., with annual events classified as “active” or “other”; 

Table 12) also performed marginally, with the best 

model accounting for a relatively small proportion of 

the variance in the dependent variable (adjusted R2 = 

13.1%). The models suggested proportional cluster 

activity related positively to VarDF1_0.8K or VarDF1_2K 

and VarDF2_0.8K, and related negatively to VF4. 

Relationships with development identified by models 

of use and activity generally were similar. Proportional 

activity was greater in areas where a high degree of 

change in overall OG development activity occurred 

between 1998 and 2006 and/or the diversity of OG 

development levels across alternate nests within clusters 

was high at the 0.8 or 2.0-km scales (positive association 

with VarDF1). In addition, proportional activity was 

greater in areas where the level of non-OG roads was 

more diverse across alternate nests within clusters 

at the 0.8-km scale (i.e., more options?). However, 

only a single vegetation variable was identified in the 

proportional activity models, suggesting that activity 

increased where cover of pinyon-juniper and barren 

lands within 2 km was low and cover of desert shrub and 

agricultural habitats was relatively high (VF4 contrast).

Table 12. Top multiple regression models (∆AICc <4.0) describing relationships between proportional “activity” of Golden Eagle nest clusters (i.e.,

proportion of years in which a breeding attempt occurred) and oil and gas development factors (DFs; cluster averages and variances) and average

landcover/vegetation factors (VFs) in the Price, Utah study area.

Development Factors Vegetation Factors K RSS ∆AICc wi

VarDF1_0.8K, VarDF2_0.8K VF4 4 4.31 0.00 0.52

VarDF1_2K, VarDF2_0.8K VF4 4 4.40 2.84 0.13

Note: Top model total R2 = 15.2%, adjusted R2 = 13.1%. K = number of parameters, including constant and error terms; 

RSS = residual sum of squares; wi = Akaike’s weight, indicating probability of model given the data (Burnham and 

Anderson 2002).
27 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Rawlins, Wyoming Study Area

Objective 1. Describe temporal trends 

in development near focal-species nest 

clusters.

The total length of all roads within the Rawlins study 

area increased slightly from 45,788 km in 1978 to 47,041 

km in 2006. The 1,253-km increase solely reflected 

the addition of OG roads associated with new wells 

established during this period (4,099 km in 1978 vs. 

5,352 km in 2006). The actual change in length of roads 

during this period was likely larger than these reported 

values, but our ability to characterize road additions was 

limited to roads associated with new OG activity. The 

total number of OG wells in the study area increased 

from 1,438 to 4,258 between 1978 and 2006. Within 

this time frame, two distinct periods of increased OG 

activity occurred: 1978–1985 and 1994–2006 (Figure 

9). Similarly, increases in the average number of wells 

within 0.8 and 2.0 km of Golden Eagle, Ferruginous 

Hawk, Red-tailed Hawk, and Prairie Falcon nest-cluster 

centroids (n = 1,097) corresponded roughly to these 

periods (Figure 10).

A comparison of the average distance to the nearest 

road and total length of roads proximate to Rawlins nest 

clusters suggested that only the total length of roads 

within 2.0 km of cluster centroids differed significantly 

between 1978 and 2006, increasing by 0.6 km (Table 

13). In contrast, all well-related measures changed 

significantly over this period. The average distance to 

the nearest well decreased by 1.3 km, while the average 

number of wells within both 0.8 and 2.0 km increased 

more than five-fold (Table 13). The maximum number of 

wells near relevant nest clusters increased from 4 to 13 at 

the 0.8-km scale and from 20 to 40 at the 2.0-km scale, 

and the overall proportion of clusters with no wells within 

0.8 and 2.0 km decreased by 9% and 12%, respectively 

(Table 14). Further investigation by land owner/manager 

revealed slightly larger reductions on BLM-managed 

lands (12% and 17%) and lower reductions on other 

lands, with the percentage of clusters with no wells within 

2.0 km actually increasing between 1978 and 2006 on 

state lands (Table 15).

Figure 9. Total number of new and established oil and gas wells 

found in the Rawlins, Wyoming study area between 1978 and 2006.

Figure 10. Average numbers of active oil and gas wells within 0.8 and

2.0 km of 1,097 raptor nest-cluster centroids in the Rawlins, Wyoming

study area between 1978 and 2006.

Table 13. Comparison (univariate t-tests) of 1978 and 2006 well and road metrics near 1,097 raptor nest clusters (including Golden Eagles,

Ferruginous Hawks, Red-tailed Hawks, and Prairie Falcons) in the Rawlins, Wyoming study area.

1978 2006

Variable Mean SE Mean SE P

Distance to nearest road (km) 0.39 0.011 0.37 0.010 0.258

Length of road within 0.8 km (km) 2.28 0.051 2.36 0.052 0.225

Length of road within 2.0 km (km) 13.98 0.205 14.61 0.213 0.032

Distance to nearest well (km) 6.93 0.177 5.57 0.174 <0.001

Number of wells within 0.8 km 0.07 0.011 0.35 0.041 <0.001

Number of wells within 2.0 km 0.31 0.032 1.77 0.144 <0.001
28 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

Objective 2: Describe distributional 

patterns of nest cluster status in relation to 

development hotspots.

We identified three energy development hotspots in the 

Rawlins study area that encompassed at least five raptor 

nest clusters with ≥5 years of survey history between 

1998 and 2006 (Figure 11). Within the three development 

hotspots, we identified 14 Golden Eagle, 54 Ferruginous 

Hawk, 1 Red-tailed Hawk, and 2 Prairie Falcon nest 

clusters that met our initial screening criteria. However, 

only the Ferruginous Hawk had sufficient representation 

of nest clusters in both the “used” and “unused” or 

“active” and “other” categories to support analyses 

(i.e., at least five nests per year in each category). A 

comparison of used versus unused and active versus other 

nest clusters produced nearly identical results (i.e., similar 

annual trends, group means, and P-values from group 

mean comparisons), so we report here only the results 

from the nest-cluster activity analysis. Active clusters 

(n = 194) averaged significantly closer to developmenthotspot centers than other clusters (n = 205; active mean: 

-0.38 ± SE of 0.06 standardized units; unused mean: 

0.35±0.07 units; t = 7.94, P < 0.001, df = 396; Figure 

12). This may be related to the fact that 46% of the 

analyzed nest clusters included ANSs installed primarily 

to mitigate for use of natural-gas condensation tanks by 

Ferruginous Hawks (sample sizes were not sufficient to 

exclude clusters containing ANSs).

Table 14. Percentages of 1,097 raptor nest clusters (including Golden Eagles, Ferruginous Hawks, Red-tailed Hawks, and Prairie Falcons) with oil

and gas wells within 0.8 and 2.0 km of cluster centroids in 1978 and 2006 in the Rawlins, Wyoming study area.

Within 0.8 km Within 2.0 km

# Wells 1978 2006 # Wells 1978 2006

0 95.1 85.9 0 85.9 73.5

1 3.7 6.6 1 7.1 7.3

2 0.5 2.9 2-5 5.7 8.5

3 0.5 1.7 6-10 0.9 3.8

4 0.2 1.4 11-15 0.3 2.1

5 0.0 0.6 16-20 0.1 2.4

>5 0.0 0.9 >20 0.0 2.5

Table 15. Percentages by land owner/manager of 1,097 raptor nest clusters (including Golden Eagles, Ferruginous Hawks, Red-tailed Hawks, and

Prairie Falcons) with no oil and gas wells within 0.8 and 2.0 km of cluster centroids in 1978 and 2006 in the Rawlins, Wyoming study area.

Within 0.8 km Within 2.0 km

Land Manager n 1978 2006 1978 2006

BLM 609 96 83 86 69

State 68 97 94 84 87

Private 397 94 88 86 78

Other1 23 100 96 100 87

1

 Includes Bureau of Reclamation, Water Reserve, and U.S. Forest Service lands.

Figure 11. Map depicting the distribution of raptor nests from nest 

clusters with ≥5 years of survey history between 1998 and 2006 within

three oil and gas development “hotspots” in the Rawlins, Wyoming

study area.
29 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

The comparison of pre-1988 and post-1990 kernel 

densities of active Ferruginous Hawk nests confirmed 

a distinct distributional shift related to the post-1990 

availability of ANSs (Figure 13). Although pockets 

of relatively limited nesting activity remained in other 

scattered locations after the ANSs were installed, the 

highest-density activity centers clearly shifted to locations 

of ANSs once they were available.

Figure 12. Average standardized distances between development

“hotspot” centers and “active” and “other” Ferruginous Hawk nest

clusters in the Rawlins, Wyoming study area from 1998 to 2006.

Figure 13. Kernel density (within a 5-km radius) of active Ferruginous

Hawk (FEHA) nests in the Rawlins, Wyoming study area relative to

105 artificial nest structure (ANS) locations before (1970–1987, upper

panel) and after (1990–2006, lower panel) their installation.

Objective 3: Assess relationships between 

nest cluster status and development.

a) Describe development patterns near nest clusters.—

The PCAs identified two PCs, or DFs, with eigenvalues 

>1.0 at both the 0.8 and 2.0-km spatial scales. The two 

primary PCs were able to explain 56% and 57% of the 

variation in the 0.8 and 2.0-km scale datasets. The nature 

of contrasts represented in the primary PCs was similar 

for both spatial scales (Figure 14) and closely analogous 

Figure 14. Graphical display of individual development-variable

loadings associated with two primary development factors identified

by Principal Components Analysis of characteristics associated with

1,097 raptor nest clusters at the 0.8-km and 2.0-km radius spatial

scales in the Rawlins, Wyoming study area.
30 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

to DF1 and DF2 derived for the Price study area. That 

is, at both spatial scales, DF1 could be interpreted as 

representing the overall degree of OG development 

activity (i.e., increasing total length of OG roads, 

increasing well density, and decreasing distance to wells), 

while DF2 could be interpreted as representing the degree 

of primarily non-OG roads (i.e., increasing total lengths 

of secondary and primary roads, decreasing distance 

to roads). Given the straightforward interpretation and 

high congruence between PCs produced at each scale, 

we again chose to use DF1 and DF2 scores in place of 

individual development variables in subsequent analyses.

b) Annual nest-cluster activity.—Initial species-specific 

analyses of nest-cluster status (with the dependent 

variable classified as used/unused or active/other) 

versus DF1 and DF2 (i.e., logistic regression without 

inclusion of vegetation and climatic variables) revealed 

significant relationships between development and the 

annual status of nest clusters. Golden Eagle nest-cluster 

use and activity were both negatively related to DF1 at 

both the 0.8-km and 2.0-km spatial scales (i.e., use and 

activity declined with increasing OG development), 

and positively related to DF2 at the 2.0-km scale (use 

and activity increased with non-OG roads at the larger 

scale). A slightly more complicated scenario emerged 

for Ferruginous Hawk non-ANS nest clusters. NonANS nest-cluster use and activity related negatively to 

OG development at both spatial scales if only one DF1 

variable was included in the model, but with both 0.8 and 

2.0-km variables in the model, a positive relationship was 

indicated at the 2.0-km scale. A similar scenario applied 

to non-OG roads: positive relationships at both spatial 

scales with only one DF2 variable included; negative 

relationship at the 2.0-km scale with both variables 

modeled together. An initial inspection of relationships 

between development variables and Ferruginous Hawk 

ANS cluster use and activity suggested that both 

related positively to OG development at both scales 

and non-OG roads at the 2.0-km scale. In contrast, a 

negative relationship with non-OG roads within 0.8 km 

was indicated. Note that opposite relationships with 

development were observed at non-ANS and ANS nest 

clusters. Red-tailed Hawk nest-cluster use and activity 

related negatively to OG development at either spatial 

scale, while cluster activity related positively to non-OG 

roads at either scale, suggesting patterns similar to those 

shown for Golden Eagles. In contrast, Prairie Falcon 

nest-cluster use and activity related negatively to both 

OG development and non-OG roads at both spatial scales. 

This suggests a more pervasive negative relationship with 

development in general, whether OG-related or not.

PCA of the proportional coverage of 19 original 

landcover/vegetation classes within a 2-km radius of 

1,110 raptor nest clusters (including Golden Eagles, 

Ferruginous Hawks, Red-tailed Hawks, and Prairie 

Falcons) produced six VFs with eigenvalues >1.0, which 

collectively explained 66% of the variation in the Rawlins 

dataset (Table 16). VF1 accounted for 21% of the total 

variance and distinguished clusters dominated by mixes 

of mountain and Wyoming big sagebrush, grass, mesic 

shrubs, and deciduous forest from those dominated by 

mixed desert shrubs. VF2 accounted for another 17% of 

the total variation and distinguished clusters dominated 

by mixes of grass, mountain mahogany, riparian and 

evergreen forest, and agriculture from those dominated by 

sagebrush mixes. VF3 accounted for another 8% of the 

variation and distinguished clusters containing relatively 

high cover of barren land and low cover of Wyoming 

big sagebrush. VF4 accounted for another 8% of the 

variation and distinguished clusters with relatively higher 

cover of basin big sagebrush and juniper. VF5 accounted 

for another 6% of the variation and distinguished clusters 

featuring mixes of basin big sagebrush, riparian shrubs, 

human habitations, and miscellaneous disturbed lands, but 

little juniper cover. VF6 accounted for another 6% of the 

variation and distinguished clusters with relatively high 

evergreen forest cover from those with relatively high 

cover of Wyoming big sagebrush, human habitations, 

and agriculture. Again, note that the primary impetus for 

producing vegetation PCs was to account for the potential 

confounding influence of vegetation on cluster status 

and to determine how vegetation modified relationships 

between status and development; it is not our intention 

to provide detailed interpretations of cluster status/

vegetation relationships.

An initial graphical examination of relationships 

between the probability of a cluster being active and 

climatic variables for the Rawlins study area during the 

period of highest raptor data quality (i.e., 1998–2006) 

suggested less distinct relationships between individual 

parameters (Figure 15) than found in the Price study 

area. Correlation coefficients suggested that the 

strongest relationships were between winter precipitation 

(WintPPT) and cluster activity for Golden Eagles (0.70) 

and Ferruginous Hawks (0.63) during this period. 

Additionally, a general depression of activity occurred 

during 2002, the year of severest drought, similar to 

trends observed in the Price study area. However, over 

the entire study period (i.e., 1978–2006), all climate/

activity correlations were less than 0.33, suggesting 

generally weak relationships between individual climate 

variables and cluster activity over this longer time frame.

Adding landcover/vegetation and climatic variables to 

the models of cluster use and activity revealed additional 

complexity, with all final best-fitting models including 
31 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Table 16. Summary of Principal Components Analysis describing six primary landcover/vegetation factors with eigenvalues >1.0 derived from 19

original landcover/vegetation variables representing proportional coverage within 2 km of 1,109 raptor nest clusters in the Rawlins, Wyoming study

area (* indicates high loadings that comprise the primary variables involved in each factor).

Vegetation/landcover variables Vegetation Factors produced by PCA

1 2 3 4 5 6

Grass -0.054 0.726* -0.222 -0.309 0.191 0.057

Sagebrush/grass 0.841* -0.282 -0.083 0.085 -0.051 -0.075

Wyoming big sagebrush -0.262 -0.478* 0.527* -0.328 0.010 0.398*

Mountain big sagebrush 0.856* 0.023 0.190 -0.059 0.109 -0.134

Wyoming/mountain big sagebrush 0.762* -0.425* -0.021 0.195 0.130 0.018

Basin big sagebrush -0.078 -0.429* 0.097 0.502* 0.421* -0.188

Desert shrub -0.637* -0.296 -0.399* 0.364 -0.190 -0.190

Mountain mahogany 0.029 0.614* -0.356 0.049 0.228 0.080

Mesic shrub 0.840* -0.232 -0.175 -0.152 -0.082 0.085

Riparian shrub 0.034 0.415 -0.252 0.063 0.350 0.296

Riparian forest 0.283 0.727* 0.309 0.164 -0.061 -0.068

Deciduous forest 0.800* 0.034 -0.162 -0.041 -0.075 0.091

Evergreen forest mix 0.167 0.513 0.364 0.047 0.079 -0.568*

Juniper 0.093 0.051 0.307 0.564* -0.363 0.279

Barren 0.153 -0.021 -0.706* 0.185 -0.231 0.072

Disturbed land -0.037 -0.094 0.064 0.250 0.470* -0.019

Water -0.024 0.323 0.061 0.342 -0.270 -0.115

Agriculture 0.143 0.566* 0.235 0.295 -0.183 0.394*

Human habitation -0.010 -0.044 0.020 0.313 0.446* 0.308

Cumulative variance explained 21% 38% 46% 54% 60% 66%

Figure 15. Comparison of annual trends in species-specific proportional nest activity, the Palmer Drought Severity Index (PDSI; higher values =

less drought severity), PDSI in the previous year (PDSI_lyr), and winter precipitation (WintPPT) in the Rawlins, Wyoming study area from 1998–

2006 (period of greatest raptor data quality). All measures have been standardized using the entire 1978–2006 dataset for direct comparison.
32 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

one or more landcover/vegetation factors and, with one 

exception, one or more annual climatic variables (Tables 

17–24).

With the six VFs and climate variables incorporated as 

potential covariates along with the 4 DFs, we identified 

four top models that helped differentiate between used 

(n = 721 events) and unused (n = 592) Golden Eagle 

clusters (Table 17). Top models reflected only slight 

variations on a common theme and suggested cluster 

use related positively to VF1, VF2, PDSI, and WintPPT, 

and negatively related to PDSI_lyr. No development 

factors were included in the top models, suggesting that 

OG or other development patterns did not greatly affect 

the probability of a Golden Eagle nest cluster being 

used in a given year. Instead, primarily habitat and 

especially climatic variation drove patterns of Golden 

Eagle nest-cluster use. Cluster use apparently increased 

with cover of grass, mountain mahogany, and agriculture, 

and declined with Wyoming big sagebrush (VF2); and 

increased with cover of general sagebrush, mountain and 

mixed mountain/Wyoming big sagebrush, mesic shrub, 

and deciduous forest, and declined with cover of desert 

shrub (VF1). The climate variables suggested that cluster 

use was greater in wetter years (PDSI) and following 

wetter winters (WintPPT), particularly when the previous 

year was drier (PDSI_lyr); in other words, use was greater 

in “drought recovery” years.

The four top models comparing active (n = 671 events) 

and other (n = 642) Golden Eagle nest clusters suggested 

that the probability of a cluster being active related 

positively to VF2, PDSI, and WintPPT, and negatively 

related to DF1_2K and PDSI_lyr (Table 18). Thus, 

increasing OG development with 2.0 km of clusters 

influenced only actual breeding attempts negatively. Both 

top use and activity models suggested similar positive 

relationships to increased cover of grass, mountain 

mahogany, and agriculture, but declining Wyoming big 

sagebrush cover (VF2), and positive relationships with 

drought recovery years (combination of relationships to 

all three climate variables).

We identified six top models that helped explain 

differences between used (n = 730 events) and unused 

(n = 1,539) non-ANS Ferruginous Hawk nest clusters 

(Table 19). However, even the best model among the 

top six failed the Hosmer-Lemeshow goodness-of-fit 

test (Χ = 17.31, df = 8, P = 0.027; note that a significant 

result implies a significant mismatch between predicted 

and actual nest-cluster classifications given the model), 

indicating that the models provided very little effective 

discrimination ability. Overall, the top models suggested 

that cluster use related positively to DF2_0.8K, VF2, 

VF3, VF4, VF6, PDSI, and WintPPT, and negatively 

to DF2_2K, VF5, and PDSI_lyr. This suggests that the 

probability of a Ferruginous Hawk nest cluster being 

Table 17. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Golden Eagle nest cluster

being “used” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Rawlins,

Wyoming study area.

Development Factors Vegetation Factors Climate Factors K Log(L) ∆AICc wi

None VF1, VF2 PDSI, PDSI_lyr, WintPPT 6 -862.30 0.00 0.35

None VF1, VF2 PDSI, PDSI_lyr 5 -863.56 0.49 0.27

None VF2 PDSI, PDSI_lyr, WintPPT 5 -863.84 1.05 0.21

None VF2 PDSI, PDSI_lyr 4 -865.15 1.65 0.15

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 18. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Golden Eagle nest cluster

being “active” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Rawlins,

Wyoming study area.

Development Factors Vegetation Factors Climate Factors K Log(L) ∆AICc wi

None VF2 PDSI, PDSI_lyr, WintPPT 6 -851.07 0.00 0.45

None VF2 PDSI, PDSI_lyr, WintPPT 5 -852.52 0.88 0.29

DF1_2K VF2 PDSI, PDSI_lyr 5 -853.19 2.22 0.15

None VF2 PDSI, PDSI_lyr 4 -854.93 3.67 0.07

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
33 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

used tended to increase as the relative abundance of 

non-OG roads increased and the proximity of the nearest 

road decreased within 0.8 km of the cluster centroid, 

but the strength of those associations diminished with 

distance. Interactions between DF2_0.8K and VF4 or 

VF6 also occurred in all top models, suggesting that the 

positive influence of relatively abundant non-OG roads 

in proximity to nest clusters was enhanced in habitats 

featuring relatively high cover of juniper, basin big 

sagebrush, or evergreen forest mix, and/or relatively low 

cover of Wyoming big sagebrush or agriculture.

The top six models comparing active (n = 637 events) 

and other (n = 1,632) non-ANS Ferruginous Hawk nest 

clusters were similar to the top use models (Table 20). 

Again the top-ranking activity model failed the Hosmer–

Lemeshow goodness-of-fit test (Χ= 37.48, df = 8, P < 

0.001). Generally, cluster activity related positively to 

Table 19. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a non-ANS Ferruginous Hawk

nest cluster being “used” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in

the Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF5, 

VF6

PDSI, PDSI_lyr, 

WintPPT DF2_0.8K*VF4 12 -1325.67 0.00 0.28

DF_0.8K, DF2_2K VF2, VF3, VF4, VF6 PDSI, PDSI_lyr, 

WintPPT DF2_0.8K*VF4 11 -1326.89 0.42 0.23

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF6 PDSI, PDSI_lyr, 

WintPPT DF2_0.8K*VF6 11 -1327.23 1.08 0.16

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF6 PDSI_lyr, WintPPT DF2_0.8K*VF4 10 -1328.80 2.22 0.09

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF6 PDSI_lyr, WintPPT DF2_0.8K*VF6 10 -1329.25 3.11 0.06

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF5, 

VF6 WintPPT DF2_0.8K*VF4 10 -1329.58 3.77 0.04

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 20. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of a non-ANS Ferruginous

Hawk nest cluster being “active” in a given year and various oil and gas development factors (DFs), landcover/vegetation factors (VFs), climatic

variables, and interactions in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate 

Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF3, VF4, VF5, VF6 WintPPT DF2_0.8K*VF3 11 -1246.77 0.00 0.22

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF3, VF4, VF6 WintPPT DF2_0.8K*VF6 10 -1247.79 0.02 0.22

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF3, VF4, VF6 WintPPT DF2_0.8K*VF3 10 -1247.90 0.23 0.20

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF4, VF6 WintPPT DF2_0.8K*VF6 9 -1249.38 1.18 0.12

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF6 WintPPT DF2_0.8K*VF6 9 -1249.81 2.05 0.08

DF2_0.8K, DF2_2K VF2, VF3, VF4, VF6 WintPPT DF2_0.8K*VF3 9 -1249.87 2.16 0.08

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
34 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

DF2_0.8K, VF2, VF3, VF4, VF6, and WintPPT, and 

negatively to DF1_0.8K, DF2_2K, and VF5, and all 

top models included a negative interaction between 

DF2_0.8K and VF6 or VF3. Similar to cluster use, 

cluster activity related positively to proximate (within 0.8 

km) non-OG roads, but this relationship diminished or 

reversed at the broader spatial scale (2.0 km).

The activity models differed from the use models in 

suggesting a negative relationship with OG development 

at the 0.8-km scale, and in not including relationships 

with PDSI and PDSI_lyr. This suggests that the 

probability of an actual breeding attempt occurring was 

more sensitive to changes in OG development levels 

within 0.8 km of the cluster, but less sensitive to drought 

conditions than the probability of simple cluster use. 

The interactions suggested that the positive effect of 

DF2_0.8K again was enhanced in habitats featuring 

more evergreen forest and less Wyoming big sagebrush 

or agriculture (VF6), and was enhanced in habitats with 

relatively high cover of barren land and low cover of 

Wyoming big sagebrush and desert shrubs (VF3).

We identified four top models that helped explain 

differences between used (n = 522 events) and unused 

(n = 935) Ferruginous Hawk ANS nest clusters (Table 

21). As with the top models of non-ANS cluster use and 

activity, the best model of ANS cluster use failed the 

Hosmer-Lemeshow goodness-of-fit test (Χ = 19.03, df = 

8, P = 0.015), suggesting poor model performance. In 

general, these models suggested that ANS cluster use 

related positively to DF1_0.8K, DF2_2K, VF2, VF6, and 

WintPPT, and negatively to DF2_0.8K, VF5, and PDSI, 

and interactions between DF1_0.8K and VF2 or VF6 

were common. The development results suggest that 

cluster use was highest in areas with relatively high OG 

development and correspondingly low density of non-OG 

roads proximate to nests (0.8-km scale; as expected given 

the close association of ANSs and OG infrastructure), 

but relatively high cover of non-OG roads at the larger 

2.0-km spatial scale. Again, note that these development 

relationships at ANS clusters were opposite those 

detected at non-ANS clusters. ANS cluster use also was 

greater in areas with increased cover of riparian forest, 

grass, mountain mahogany, and/or agriculture, but less 

evergreen forest, disturbed land, human habitation, and/or 

basin big sagebrush. The variable interactions indicated 

that the positive effects of proximate OG development 

were enhanced in habitats featuring relatively high cover 

of riparian forest, grass, mountain mahogany, agriculture, 

and/or areas with less evergreen forest mix (all relatively 

rare components around Ferruginous Hawk ANS nest 

clusters). Also note that the positive association with 

winter precipitation is the same as in the non-ANS 

models, but that the negative association with PDSI (i.e., 

suggesting that cluster use was greater in drier years) is 

opposite the relationship seen in the non-ANS models.

We identified three top models from the comparison of 

active (n = 596) versus other (n = 861) Ferruginous Hawk 

ANS nest clusters (Table 22). The top models suggested 

similar relationships as the use models; however, the 

best activity model did not fail the goodness-of-fit test. 

As with use, ANS cluster activity related positively to 

DF1_0.8K, DF2_2K, VF2, VF6, and WintPPT, negatively 

to DF2_0.8K, VF5, and PDSI, and an interaction between 

DF1_0.8K and VF2 was included in all top models. This 

suggests that both ANS cluster use and activity were 

highest in areas with relatively high OG development and 

low non-OG roads proximate to nests (0.8-km scale), 

but relatively high cover of non-OG roads at the larger 

Table 21. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Ferruginous Hawk ANS nest

cluster being “used” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the

Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF5, VF6 PDSI, WintPPT DF1_0.8K*VF2, DF1_0.8K*VF6 11 -908.52 0.00 0.50

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF5, VF6 PDSI, WintPPT DF1_0.8K*VF2, DF1_0.8K*VF5 11 -909.52 1.99 0.18

DF1_0.8K VF2, VF5, VF6 PDSI, WintPPT DF1_0.8K*VF2, 

DF1_0.8K*VF6 9 -911.78 2.45 0.15

DF1_0.8K VF2, VF6 PDSI, WintPPT DF1_0.8K*VF2, 

DF1_0.8K*VF6 8 -913.05 2.96 0.11

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
35 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

2.0-km spatial scale. Both cluster use and activity also 

were greater in areas with increased cover of riparian 

forest, grass, mountain mahogany, and/or agriculture, but 

less evergreen forest, disturbed land, human habitation, 

and/or basin big sagebrush. The consistent interaction 

in all top use and activity models suggests the positive 

effects of proximate OG development were enhanced in 

habitats featuring relatively high cover of riparian forest, 

grass, mountain mahogany, and/or agriculture. Cluster 

use and activity were both positively associated with prenesting season winter precipitation, but negatively related 

to overall wetter conditions during the year (PDSI).

We identified 14 top models that helped explain 

differences between used (n = 384 events) and unused 

(n = 200) Red-tailed Hawk nest clusters (Table 23). The 

top models generally suggested that cluster use related 

positively to DF1_2K, VF1, VF2, VF3, VF4, and PDSI, 

and negatively to DF1_0.8K and DF2_2K. A few models 

also suggested that use related positively to VF6 and 

Table 22. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of a Ferruginous Hawk

ANS nest cluster being “active” in a given year and various oil and gas development factors (DFs), landcover/vegetation factors (VFs), climatic

variables, and interactions and in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF5, VF6 PDSI, WintPPT DF1_0.8K*VF2, DF1_0.8K*VF5 11 -946.14 0.00 0.47

DF1_0.8K, DF2_0.8K, 

DF2_2K VF2, VF5, VF6 PDSI, WintPPT DF1_0.8K*VF2, DF1_0.8K*VF6 11 -947.03 1.79 0.19

DF1_0.8K VF2, VF5, VF6 PDSI, WintPPT

DF1_0.8K*VF2, 

DF1_0.8K*VF5, 

DF1_0.8K*PDSI

10 -948.60 2.88 0.11

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 23. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of a Red-tailed Hawk nest

cluster being “used” in a given year and various oil and gas development factors (DFs), landcover/vegetation factors (VFs), climatic variables, and

interactions in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K, DF1_2K VF1, VF2, VF3, VF4 PDSI DF1_2K*VF2 9 -313.21 0.00 0.20

DF2_2K VF1, VF2, VF3, VF4 PDSI DF2_2K*VF1 8 -315.13 1.79 0.08

DF1_0.8K, DF1_2K VF1, VF3, VF5 PDSI DF1_2K*VF2 9 -314.33 2.26 0.06

DF2_2K VF1, VF2, VF3, VF4 PDSI None 7 -316.71 2.88 0.05

DF1_0.8K, DF1_2K VF1, VF2, VF3, VF5 PDSI DF1_2K*VF2 9 -314.67 2.92 0.05

DF1_0.8K, DF1_2K, 

DF2_2K VF1, VF2, VF3, VF4 PDSI None 9 -314.95 3.49 0.04

DF1_0.8K, DF1_2K VF1, VF2 PDSI DF1_0.8K*PDSI, 

DF1_2K*VF2 8 -316.01 3.55 0.03

DF2_2K VF1, VF2, VF4, VF6 PDSI None 7 -317.07 3.60 0.03

None VF1, VF2, VF3, VF5 PDSI None 6 -318.10 3.60 0.03

DF1_0.8K, DF1_2K VF1, VF2, VF4, VF6 PDSI DF1_2K*VF2 9 -315.03 3.64 0.03

DF1_0.8K, DF1_2K VF1, VF2, VF3, VF4 PDSI None 8 -316.14 3.80 0.03

DF1_0.8K, DF1_2K VF1, VF2, VF3, VF5 PDSI None 8 -316.15 3.82 0.03

None VF1, VF2, VF3, VF5 PDSI, WintPPT None 7 -317.22 3.89 0.03

DF2_2K VF1, VF2 PDSI DF2_2K*VF1 6 -318.26 3.92 0.03

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
36 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

WintPPT and negatively to VF5, while an interaction 

between DF1_2K and VF2 was present in 5 of the 14 

top models. Overall, the models suggested a mixed 

relationship with OG development, with a negative effect 

at the smaller 0.8-km scale but a positive effect at the 

larger 2.0-km spatial scale. Note that this is the opposite 

pattern shown for Red-tailed Hawks in the Price study 

area. The significant interaction with VF2 suggested that 

the apparent positive effect of increasing development at 

the 2.0-km scale was enhanced in habitats with relatively 

high cover of riparian forest, grass, mountain mahogany, 

and/or agriculture, but was diminished in habitats with 

high cover of Wyoming big sagebrush. Finally, as with 

most other species and situations, Red-tailed Hawks 

appeared to benefit from reduced drought severity (PDSI; 

i.e., wetter conditions) and their nesting activity also 

varied across habitats.

In contrast to the large number of top Red-tailed Hawk 

use models, and similar to the case in the Price study 

area, relatively few (four) top models were selected to 

distinguish between active (n = 359 events) and other 

(n = 225) Red-tailed Hawk nest clusters in the Rawlins 

study area (Table 24). In contrast to the Red-tailed 

Hawk use models, no DFs were included in the top 

activity models. Otherwise, both the use and activity 

models suggested similar positive relationships with 

VF1, VF2, VF4, VF6, PDSI, and WintPPT. These results 

suggest that the probability of an actual breeding attempt 

occurring in a Red-tailed Hawk nest cluster was largely 

independent of OG development activity, but did vary 

significantly across certain habitat types and in relation 

to drought severity. Increased cluster use and activity 

apparently occurred in wetter years and, to a lesser extent, 

following wetter pre-nesting season winters (WintPPT).

We identified 10 top models that helped differentiate 

between used (n = 307 events) and unused (n = 148) 

Prairie Falcon nest clusters (Table 25). The top models 

indicated that use related positively to VF1, VF2, VF3, 

Table 24. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Red-tailed Hawk cluster

being “active” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climatic variables in the Rawlins,

Wyoming study area.

Development Factors Vegetation Factors Climate Factors K Log(L) ∆AICc wi

None VF1, VF2, VF4, VF6 PDSI 6 -321.89 0.00 0.39

None VF1, VF2, VF4, VF6 PDSI, WintPPT 7 -321.21 0.69 0.28

None VF1, VF2 PDSI 4 -324.58 1.28 0.21

None VF1, VF2, VF4, VF6 PDSI_lyr, WintPPT 7 -322.75 3.76 0.06

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 25. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Prairie Falcon cluster being

“used” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Rawlins,

Wyoming study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_0.8K VF1, VF2, VF3, VF5 WintPPT DF1_0.8K*VF3 8 -264.52 0.00 0.19

DF1_0.8K, DF2_0.8K VF1, VF5 WintPPT DF2_0.8K*VF1 8 -265.27 1.50 0.09

DF1_0.8K, DF2_0.8K VF1, VF5 WintPPT DF2_0.8K*VF1 7 -266.35 1.58 0.09

DF1_0.8K VF1, VF2, VF3, VF5 PDSI, PDSI_lyr DF1_0.8K*VF3 9 -264.49 2.03 0.07

DF1_0.8K VF1, VF2, VF3, VF5 WintPPT None 7 -266.70 2.28 0.06

DF2_0.8K VF1 PDSI, WintPPT DF2_0.8K*VF1 6 -267.83 2.46 0.06

DF1_0.8K VF1, VF2, VF3, VF5 PDSI DF1_0.8K*VF3 8 -265.89 2.73 0.05

DF2_0.8K VF1 WintPPT DF2_0.8K*VF1 5 -269.02 2.79 0.05

None VF1, VF2, VF3, VF5 WintPPT None 6 -268.02 2.84 0.05

DF1_0.8K, DF2_0.8K VF1 WintPPT D2_0.8K*VF1 6 -268.31 3.42 0.04

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
37 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

PDSI, and WintPPT, and negatively to DF1_0.8K, 

DF2_0.8K, VF5, and PDSI_lyr. These results suggest 

that the probability of a Prairie Falcon nest cluster being 

used declined as OG development increased and in areas 

where the prevalence of non-OG roads was high within 

0.8 km. Further, interactions between DF1_0.8K and 

VF3 and between DF2_0.8K and VF1 were present in 

three and five of the top models, respectively. The first 

interaction suggests that the negative influence of OG 

development within 0.8-km of clusters was enhanced in 

habitats featuring relatively high cover of barren land, 

but diminished in habitats with relatively high cover of 

Wyoming big sagebrush. The second interaction suggests 

that the negative influence of non-OG roads within 

0.8-km of clusters was enhanced in habitats featuring 

mixes of mountain and Wyoming big sagebrush, mesic 

shrubs, and deciduous forest, and diminished or reversed 

in habitats dominated by mixed desert shrubs. Winter 

precipitation was in every model, indicating use was 

positively associated with wetter winters prior to the 

nesting season.

We identified four top models that helped distinguish 

between active (n = 279 events) and other (n = 176) 

Prairie Falcon nest clusters (Table 26). These models 

indicated that the probability of a cluster being active 

related positively to VF1, VF2, VF3, PDSI, and WintPPT, 

and negatively to DF1_2K, DF2_2K, VF5, and PDSI_lyr. 

These results were similar to those derived from the 

use models, except that the activity models suggested 

relationships with development factors were best modeled 

at the 2.0-km scale, rather than the 0.8-km scale. The 

activity models also identified a similar interaction 

between DF1 (but at the 2.0-km scale) and VF3. 

Again, this suggests that the negative influence of OG 

development was enhanced in habitats featuring relatively 

high cover of barren land, but diminished in habitats with 

relatively high cover of Wyoming big sagebrush. Cluster 

activity was also consistently and positively associated 

with reduced drought severity.

c) Proportional nest-cluster activity.—We identified 

151 non-ANS Ferruginous Hawk, 42 Golden Eagle, 17 

Red-tailed Hawk, and 9 Prairie Falcon nest clusters with 

at least five years of survey history between 1998 and 

2006. Sample-size limitations precluded investigation of 

proportional nest-cluster use and activity for Red-tailed 

Hawks and Prairie Falcons.

The top eight proportional-use models for Golden 

Eagles performed relatively well, with the best model 

accounting for 43.8% (adjusted R2 value) of the variance 

in proportional use. Overall, the top models suggested 

that proportional cluster use related positively to 

DF1_0.8K or DF1_2K, DF2_2K, and VarDF2_0.8K, and 

negatively to VarDF1_2K or VarDF1_0.8K (only one 

top model), VF1, VF3, VF4, and VF6 (Table 27). These 

results suggest that the proportion of years in which 

Golden Eagle clusters were used increased in areas with 

relatively high OG development, but low variance in 

OG development levels at the broader 2.0-km spatial 

scale, and a relatively high prevalence of non-OG roads 

and high variance in the representation of non-OG roads 

within 0.8 km. The variance detected in non-OG roads 

could be driven only by shifting nest use within a cluster 

across years, because non-OG road representation in the 

landscape was static. As a result, the positive association 

between proportional use and VarDF2_0.8K must reflect 

greater use of clusters with more variable non-OG 

conditions across alternate nests. We also detected this 

relationship for Golden Eagles in the Price area.

The top 11 activity models for Golden Eagles also 

appeared to perform well, with the best model 

accounting for 53.5% of the variance in proportional 

Table 26. Top multiple logistic regression models (∆AICc <4.0) describing relationships between the probability of a Prairie Falcon cluster being

“active” in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Rawlins,

Wyoming study area.

Development Factors Vegetation Factors Climate Factors Interactions K Log(L) ∆AICc wi

DF1_2K, DF2_2K VF1, VF2, VF3, VF5 PDSI, WintPPT DF1_2K*VF3 10 -256.50 0.00 0.36

DF1_2K VF1, VF2, VF3, VF5 PDSI, WintPPT DF1_2K*VF3 9 -257.70 0.32 0.31

DF1_2K VF1, VF2, VF3, VF5 PDSI, PDSI_lyr DF1_2K*VF3 9 -258.20 1.32 0.19

DF1_2K VF1, VF2, VF3, VF5 PDSI DF1_2K*VF3 8 -259.61 2.03 0.13

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).
38 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

activity. The top models suggested that the proportion 

of years in which clusters supported a breeding attempt 

related positively to DF1_0.8K or DF1_2K, DF2_2K, 

VarDF2_0.8K or VarDF2_2K, and VF2, and negatively to 

VarDF1_2K, VF1, VF3, VF4, and VF6 (Table 28). The 

relationships between proportional activity or use and 

OG development were overall very similar, suggesting 

that proportional use and activity were higher in areas 

with more but less variable OG development, and in 

areas with both more and more variable non-OG road 

networks.

The best model of proportional use at non-ANS 

Ferruginous Hawk nest clusters explained 37.8% of the 

variance in use. Overall, the top seven proportional-use 

models revealed positive relationships with DF1_2K, 

Table 27. Top multiple regression models (∆AICc <4.0) describing relationships between proportional “use” of Golden Eagle nest clusters (i.e.,

proportion of years in which evidence of nest tending or breeding was obtained) and oil and gas development factors (DFs; cluster averages and

variances) and average landcover/vegetation factors (VFs) in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors K RSS ∆AICc wi

DF1_0.8K, DF2_2K, VarDF1_0.8K, VarDF2_0.8K VF3, VF4 7 1.27 0.00 0.22

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF3, VF4, VF6 8 1.19 0.49 0.17

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF1, VF4, VF6 8 1.21 1.15 0.12

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF1, VF4, VF6 8 1.24 2.29 0.07

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF3, VF4 7 1.34 2.29 0.07

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF3, VF4, VF6 8 1.25 2.56 0.06

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF2 6 1.45 2.60 0.06

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF2 6 1.46 2.86 0.053

Note: Top model total R2 = 55.8%, adjusted R2 = 43.8%. K = number of parameters, including constant and error terms; 

RSS = residual sum of squares; wi = Akaike’s weight, indicating probability of model given the data (Burnham and 

Anderson 2002).

Table 28. Top multiple regression models (∆AICc <4.0) describing relationships between proportional “activity” of Golden Eagle nest clusters (i.e.,

proportion of years in which a breeding attempt was confirmed) and oil and gas development factors (DFs; cluster averages and variances) and

average landcover/vegetation factors (VFs) in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors K RSS ∆AICc wi

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF2, VF4, VF6 8 0.83 0.00 0.18

DF1_0.8K, DF2_2K, VarDF1_2K, VarDF2_2K VF2 6 0.99 0.71 0.12

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF3, VF4, VF6 8 0.87 1.58 0.08

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF2, VF4, VF6 8 0.87 1.63 0.08

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF4, VF6 7 0.95 2.13 0.06

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF2 6 1.03 2.42 0.05

DF1_0.8K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF1, VF4, VF6 8 0.88 2.45 0.05

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF2 6 1.05 3.35 0.03

DF2_2K, VarDF2_0.8K VF2, VF4, VF6 6 1.06 3.59 0.03

DF1_2K, DF2_2K, VarDF2_0.8K, VarDF1_2K VF4, VF6 7 0.99 3.78 0.03

DF1_0.8K, DF2_2K, VarDF2_0.8K VF2, VF4, VF6 7 0.99 3.82 0.03

Note: Top model total R2 = 61.4%, adjusted R2 = 53.5%. K = number of parameters, including constant and error terms; 

RSS = residual sum of squares; wi = Akaike’s weight indicating probability of model given the data (Burnham and 

Anderson 2002).
39 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

DF2_0.8K, VarDF2_0.8K, and all vegetation factors 

(but not simultaneously), and a negative relationship 

with DF1_0.8K (Table 29). These results suggest that 

proportional use of non-ANS nest clusters was highest 

in areas with relatively low OG development and high 

variance in non-OG roads at within 0.8 km, and that the 

negative relationship with OG development diminished 

or reversed at the 2.0 km scale. As with both Price 

and Rawlins Golden Eagles, the association between 

Ferruginous Hawk proportional cluster use and variance 

in non-OG roads likely reflects greater use of clusters 

with more variable non-OG conditions at alternate nests. 

All top models also revealed positive relationships with 

VF2, VF4, and VF6.

The best model of proportional activity at nonANS Ferruginous Hawk nest clusters had a similar 

explanatory ability as the best use model, accounting 

for 35.4% of the variance in proportional activity. The 

top five proportional activity models indicated positive 

relationships with DF1_2K, DF2_0.8K, VarDF2_0.8K, 

and all vegetation factors (but not simultaneously), and a 

negative relationship with DF1_0.8K (Table 30). These 

results suggest similar relationships for both proportional 

activity and use of non-ANS nest clusters in relation 

to OG development. All use and activity models also 

revealed positive relationships with VF2. This suggests 

that use and activity were greater at clusters with 

relatively high cover of riparian forest, grass, mountain 

mahogany, and/or agriculture.

Objective 4: Assess Ferruginous Hawk 

nest success and productivity in relation to 

development.

It was our intention to investigate differences in nest 

success and productivity at a subset of Ferruginous Hawk 

nest clusters in relation to development factors as well 

as nest substrate (including ANSs) and accessibility. 

Table 29. Top multiple regression models (∆AICc <4.0) describing relationships between proportional use of non-ANS Ferruginous Hawk nest

clusters (i.e., proportion of years in which evidence of nest tending or breeding was obtained) and various oil and gas development factors (DFs)

and landcover/vegetation factors (VFs) in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors K RSS ∆AICc wi

DF1_0.8K, DF2_0.8K, DF1_2K, VarDF2_0.8K VF2, VF4, VF5, VF6 9 4.85 0.00 0.20

DF1_0.8K, DF1_2K, VarDF2_0.8K VF2, VF4, VF5, VF6 8 4.93 0.22 0.18

DF1_2K, VarDF2_0.8K VF2, VF4, VF5, VF6 7 5.02 0.64 0.14

DF1_0.8K, DF1_2K, VarDF2_0.8K VF2, VF3, VF4, VF6 8 4.96 1.22 0.11

DF1_0.8K, DF2_0.8K, DF1_2K, VarDF2_0.8K VF2, VF3, VF4, VF6 9 4.89 1.23 0.11

DF1_2K, VarDF2_0.8K VF2, VF3, VF4, VF6 7 5.06 2.01 0.07

DF1_0.8K, DF2_0.8K, DF1_2K, VarDF2_0.8K VF1, VF2, VF4, VF6 9 4.97 3.73 0.03

Note: Top model total R2 = 41.2%, adjusted R2 = 37.8%. K = number of parameters, including constant and error terms; 

RSS = residual sum of squares; wi = Akaike’s weight, indicating probability of model given the data (Burnham and 

Anderson 2002).

Table 30. Top multiple regression models (∆AICc <4.0) describing relationships between proportional activity of non-ANS Ferruginous Hawk nest

clusters (i.e., proportion of years in which a breeding attempt was confirmed) and various oil and gas development factors (DFs) and landcover/

vegetation factors (VFs) in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors K RSS ∆AICc wi

DF1_0.8K, DF2_0.8K, DF1_2K, VarDF2_0.8K VF2, VF4, VF5, VF6 9 4.47 0.00 0.35

DF1_0.8K, DF2_0.8K, DF1_2K, VarDF2_0.8K VF1, VF2, VF4, VF6 9 4.53 2.07 0.12

DF1_0.8K, DF1_2K, VarDF2_0.8K VF2, VF4, VF5, VF6 8 4.62 2.79 0.09

DF1_0.8K , DF2_0.8K, DF1_2K, VarDF2_0.8K VF2 6 4.79 3.49 0.06

DF1_0.8K, DF2_0.8K, DF1_2K, VarDF2_0.8K VF2, VF3, VF4, VF6 9 4.59 3.88 0.05

Note: Top model total R2 = 38.9%, adjusted R2 = 35.4%. K = number of parameters, including constant and error terms; 

RSS = residual sum of squares; wi = Akaike’s weight, indicating probability of model given the data (Burnham and 

Anderson 2002).
40 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

Inspection of breeding-event classifications by success 

and productivity groupings indicated unacceptably 

inequitable sample sizes across the original 10 substrate 

and four collapsed substrate/accessibility categories, 

but an acceptable array when collapsed to just two 

categories: accessible and inaccessible (Tables 31 and 

32). After additional inspection of breeding-event 

cross-classifications, we chose to limit our assessment 

of success and productivity to these two categories. Of 

283 breeding events ultimately classified as accessible, 

84% were on natural substrates and none was on an 

ANSs. Of 951 breeding events classified as inaccessible, 

87% were located on manmade substrates, with 79% 

on ANSs.

We first compared successful (n = 177) and failed 

(n = 106) accessible nests, but identified only nine 

significant models (Table 33) and the best model failed 

the Homer-Lemeshow goodness-of-fit test (Χ= 19.862, 

df = 8, P = 0.011). Although the poor fit prohibits strict 

interpretation, for consistency we outline the general 

relationships suggested by the models. Development 

factors did not enter any top models, but variation across 

certain habitat types and in relation to PDSI was evident. 

Nest success at accessible nests related positively to VF2, 

VF5, and PDSI, and negatively related to VF4 and VF6.

Similar to nest success, a comparison of productivity at 

accessible nests (n = 283) produced only nine significant 

Table 31. Summary of confirmed Ferruginous Hawk breeding events in the Rawlins, Wyoming study area between 1976–2006 classified by nest

substrate and the number of nestlings raised to ≥80% of average fledging age.

Nest Number of nestlings produced

Substrate1 0 1 2 3 4 5 Total

ANS 53 54 160 307 162 14 750

CLF 4 6 5 8 5 1 29

DCT 2 2 8 2 14

EGT 2 3 9 12 5 1 32

ERR 6 2 4 8 6 26

GHS 25 3 6 6 3 1 44

MMS 8 5 20 34 21 88

NGS 7 1 7 12 4 31

SHB 3 1 6 2 12

ROF 59 10 42 60 30 7 208

Total 167 86 256 461 240 24 1234

1

 ANS = artificial nest structure; CLF = cliff; DCT = deciduous tree; EGT = evergreen tree; ERR = erosional feature; 

GHS = ground/hillside; MMS = other manmade structure; NGS = natural gas structure; SHB = shrub; ROF = other rock 

feature.

Table 32. Summary of confirmed Ferruginous Hawk breeding events in the Rawlins, Wyoming study area between 1976–2006 classified by nest

substrate/accessibility categories and the number of nestlings produced to ≥80% of average fledging age.

Substrate / Accessibility Number of nestlings produced

Category1 0 1 2 3 4 5 Total

MM-ACC 9 2 14 15 5 0 45

NAT-ACC 97 16 51 55 17 2 238

Total accessible 106 18 65 70 22 2 283

MM-INACC 59 58 173 338 182 14 824

NAT-INACC 2 10 18 53 36 8 127

Total inaccessible 61 68 191 391 218 22 951

Grand Total 167 86 256 461 240 24 1234

1

 MM-ACC = manmade accessible; MM-INACC = manmade inaccessible; NAT-ACC = natural accessible; NATINACC = natural inaccessible.
41 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

models (Table 34) and the best model explained little 

of the variance (adjusted R2 = 3.2%). Ignoring their 

obvious weakness, the models appeared to suggest that 

productivity at accessible nests related positively to 

DF1_0.8K, VF2, and PDSI, and negatively related to 

VF4 and VF6. Thus, productivity but not nesting success 

appeared to show a weak positive influence of increasing 

OG development activity within 0.8 km of nest clusters. 

Otherwise, both nest success and productivity varied 

across certain habitat types and benefited from decreasing 

drought severity.

A comparison of nest successes (n = 890) and failures (n

= 61) at inaccessible nests produced 20 top models with 

considerable variation (Table 35). As found at accessible 

nests, the top model of nest success at inaccessible 

nests failed the Hosmer-Lemeshow goodness-of-fit test 

(Χ = 15.653, df = 8, P = 0.048). Again, for the sake of 

consistency, we summarize the apparent trends despite the 

poor model fit. In general, nest success at inaccessible 

nest related positively to DF1 (at either spatial scale) and 

negatively to VF3, VF4, or VF6, and PDSI or WintPPT. 

Fewer models suggested that nest success related 

positively to VF2 or negatively to DF2_0.8K. Thus, 

these models suggested a general positive association 

with increasing OG development and the same negative 

relationship with drought severity (i.e., higher success 

with increasing drought severity) as indicated in previous 

ANS-only models. In this case, however, a negative 

relationship with winter precipitation often substituted for 

Table 33. All significant multiple logistic regression models describing relationships between nesting success of accessible Ferruginous Hawk

nests in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Rawlins,

Wyoming study area. Top models are those with ∆AICc values <4.0.

Development Factors Vegetation Factors Climate Variables K Log(L) ∆AICc wi

None VF2 PDSI 3 -182.86 0.00 0.32

None VF2 None 2 -184.66 1.54 0.15

None VF3, VF4 PDSI 4 -182.70 1.76 0.13

None VF4, VF6 PDSI 4 -182.86 2.07 0.11

None VF3, VF5 PDSI 4 -183.21 2.78 0.08

None None PDSI 2 -185.49 3.19 0.06

None VF3, VF4 None 3 -184.53 3.33 0.06

None V4, VF6 None 3 -184.67 3.62 0.05

None VF3, VF5 None 3 -185.09 4.46 0.03

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 34. All significant multiple linear regression models describing relationships between productivity per nest start at accessible Ferruginous

Hawk nests and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables in the Rawlins, Wyoming study

area. Top models are those with ∆AICc values <4.0.

Development Factors Vegetation Factors Climate Variables K RSS ∆AICc wi

DF1_0.8K VF2 PDSI 4 550.82 0.00 0.31

None VF2 PDSI 3 555.82 0.49 0.24

None None PDSI 2 561.32 1.21 0.17

DF1_0.8K VF4, VF6 PDSI 5 550.36 1.85 0.12

None VF4, VF6 PDSI 4 555.14 2.21 0.10

DF1_0.8K VF2 None 3 565.89 5.56 0.02

None VF2 None 2 570.25 5.68 0.02

DF1_0.8K VF4, VF6 None 4 565.35 7.37 0.01

None VF4, VF6 None 3 569.75 7.49 0.01

Note: K = number of parameters, including constant and error terms; RSS = residual sum of squares; wi = Akaike’s 

weight, indicating probability of model given the data (Burnham and Anderson 2002).
42 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

the negative relationship with PDSI, whereas in previous 

models both variables entered the models together and 

indicated the same negative association with PDSI but a 

positive association with WintPPT.

The top six models of productivity at inaccessible nests 

(n = 951) suggested less model variability (Table 36) 

relative to the previous success and productivity models; 

however, the top model still provided only very weak 

explanatory power (adjusted R2 = 7.3%). The top 

models suggested that productivity at inaccessible nests 

related positively to DF1_0.8K, VF1, and PDSI_lyr, and 

negatively to DF2 (at either scale), VF3, and WintPPT. 

The models of success and productivity at inaccessible 

nests differed considerably, although both showed a 

positive relationship with OG development within 0.8 

Table 35. Top multiple logistic regression models (∆AICc <4.0) describing the relationships between the probability of an inaccessible Ferruginous

Hawk nest cluster being successful in a given year and various oil and gas development factors (DFs), landcover/vegetation factors (VFs), climatic

variables, and interactions and in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate Variables Interactions K Log(L) ∆AICc wi

DF1_2K VF3 PDSI DF1_2K*PDSI 5 -216.47 0.00 0.12

DF1_0.8K VF3 PDSI None 4 -217.74 0.51 0.09

DF1_0.8K VF3 WintPPT None 4 -217.87 0.78 0.08

DF1_2K VF6 PDSI DF1_2K*PDSI 5 -217.17 1.40 0.06

DF1_2K VF3 PDSI None 4 -218.39 1.82 0.05

DF1_0.8K VF6 PDSI None 4 -218.42 1.88 0.05

DF1_2K VF3 WintPPT None 4 -218.53 2.10 0.04

DF1_0.8K VF2, VF4 PDSI None 5 -217.86 2.79 0.03

DF1_2K None PDSI DF1_2K*PDSI 4 -218.92 2.88 0.03

DF1_0.8K VF2, VF4 WintPPT None 5 -218.01 3.08 0.03

DF2_0.8K VF3 WintPPT None 4 -219.03 3.10 0.03

DF1_2K VF6 PDSI None 4 -219.04 3.12 0.03

DF2_0.8K VF3 PDSI None 4 -219.13 3.30 0.02

DF1_2K VF6 WintPPT None 4 -219.16 3.36 0.02

DF1_0.8K VF3 None None 3 -220.21 3.44 0.02

DF1_0.8K VF4 PDSI None 4 -219.21 3.45 0.02

None VF3 WintPPT None 3 -220.23 3.48 0.02

None VF3 PDSI None 3 -220.29 3.59 0.02

DF1_0.8K None PDSI None 3 -220.33 3.68 0.02

DF1_0.8K VF4 WintPPT None 4 -219.33 3.70 0.02

Note: K = number of parameters, including constant and error terms; Log(L) = log likelihood; wi = Akaike’s weight, 

indicating probability of model given the data (Burnham and Anderson 2002).

Table 36. Top multiple linear regression models (∆AICc <4.0) describing relationships between productivity per nest attempt at inaccessible

Ferruginous Hawk nests in a given year and oil and gas development factors (DFs), landcover/vegetation factors (VFs), and climate variables and

in the Rawlins, Wyoming study area.

Development Factors Vegetation Factors Climate Variables Interactions K RSS ∆AICc wi

DF1_0.8K, DF2_0.8K VF1, VF3 PDSI_lyr, WintPPT DF2_0.8K*VF1 8 1153.55 0.00 0.29

DF1_0.8K, DF2_0.8K VF1, VF3 WintPPT DF2_0.8K*VF1 7 1157.41 1.14 0.16

DF1_0.8K, DF2_0.2K VF1, VF3 PDSI_lyr, WintPPT DF2_2K*VF1 8 1155.05 1.24 0.15

DF1_0.8K, DF2_0.8K VF1, VF3 PDSI_lyr, WintPPT None 7 1158.36 1.92 0.11

DF1_0.8K, DF2_2K VF1, VF3 WintPPT DF2_2K*VF1 7 1158.93 2.39 0.09

DF1_0.8K, DF2_0.8K VF1, VF3 WintPPT None 6 1162.36 3.16 0.06

Note: K = number of parameters, including constant and error terms; RSS = residual sum of squares; wi = Akaike’s 

weight, indicating probability of model given the data (Burnham and Anderson 2002).
43 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

km, and negative relationships with non-OG roads within 

0.8 km, VF3, and WintPPT.

Across all years, nest success was significantly lower at 

accessible nests (62.5 ± SE of 2.9%) than at inaccessible 

nests (93.6 ± 0.8%; t = -10.4, df = 326, P < 0.001). 

Similarly, average productivity across all years was 

significantly lower at accessible nests (1.61 ± 0.08 

nestlings produced per nesting attempt) than inaccessible 

nests (2.74 ± 0.04; t = -12.2, df =397, P <0.001). These 

pronounced differences between success and productivity 

at accessible and inaccessible nests may have swamped 

any potential relationships with development, vegetation, 

or climate variables. In other words, accessibility alone 

appears to have had an overriding influence on success 

and productivity.

Objective 5: Assess the response of 

Ferruginous Hawks to artificial nest 

structures.

Neal (2007) determined that the average number of 

alternative nests found in Ferruginous Hawk nest clusters 

was significantly higher before ANS mitigation began 

(3.3 ± SE of 0.10 nests per cluster pre-ANS mitigation, 

2.1 ± 0.12 nests per cluster post ANS mitigation; t 

= -7.66, df = 463, P < 0.001). He also found that 

Ferruginous Hawks at inaccessible nest sites spent more 

minutes attending, delivered more prey items, made more 

proximate hunting attempts, and flushed from the nest less 

often than pairs nesting at sites that were accessible to 

mammalian predators (Table 37). Additionally, although 

the data were limited in scope, over the years 21 adult or 

nestling mammalian-depredation events were recorded 

in association with natural, accessible nests in the study 

area, whereas only two such events were recorded at 

inaccessible nests (in both cases involving dead fledglings 

found near but not in the nests).

Table 37. Total minutes of nest attendance, total prey deliveries, hunting attempts by location, and number of times adults flushed at inaccessible

(n = 35 nests) and accessible (n = 15) Ferruginous Hawk nests in the Rawlins, Wyoming study area from 2000–2004 (from Neal 2007). All values

are calculated on a per-nest basis and reflect consistent observation effort (6 hours total) at all nests.

Accessibility 

Category

Minutes of Nest 

Attendance

Prey 

Deliveries

Hunting Attempts by Location Total 

Hunting 

Attempts

# Flushes Nest Perch Air Ground

Inaccessible 194.5 3.1 2.5 0.4 1.0 0.1 4.1 0.1

Accessible 129.5 1.3 0.2 0.5 0.4 0.3 1.5 0.7

45 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

reproductive output (Tjernberg 1983), or conversely, 

severe winters can serve to further reduce reproductive 

output during periods of reduced prey abundance 

(Steenhof et al. 1997).

Development-plus model assessments also suggested that 

the influence of development activities on Golden Eagles 

varied somewhat depending on habitat. In particular, 

it appeared that the negative effects of expanding OG 

development were enhanced in habitats featuring human 

habitations and increased cover of rabbitbrush and 

riparian forest (perhaps already marginal nesting and 

foraging habitats for Price Golden Eagles), whereas 

expanding development may have had a positive effect in 

relatively open habitats featuring significant cover of lowgrowing black sagebrush. Why the latter effect might 

have occurred is uncertain, but may reflect factors such 

as development infrastructure providing more hunting 

perches in otherwise open habitat, possibly development 

activities leading to a reduction in prevalence of 

competing mammalian predators in such habitats, or 

some other positive benefit of development with regard 

to prey abundance or accessibility in such areas. Because 

of the potentially large influence of prey on Golden Eagle 

nesting ecology (e.g., Smith and Murphy 1979, Bates 

and Moretti 1994, Steenhof et al. 1997), data on prey 

abundance or habitat associations may have clarified this 

relationship.

Although again compromised by limited sample sizes 

due to inconsistent annual monitoring of specific nest 

clusters, analysis of proportional cluster use and activity 

histories revealed additional potential insight. The 

results suggested that the relationship between roads/

development and the longer-term activity histories 

of individual Golden Eagle nest clusters within the 

study area depended primarily on the diversity of 

development levels across alternate nests within clusters 

(i.e., more options) rather than the overall, average 

levels of development nearby. That is, the development 

variables that contributed to the models were exclusively 

“variance” factors, primarily reflecting variation at the 

more proximate 0.8-km spatial scale in the levels of 

both OG development and non-OG roads, and always 

related positively to the probability of eagle nesting 

activity. In interpreting these results, it is important to 

recognize that the majority of eagle nest clusters suited 

to these analyses were located in areas of relatively 

high development activity, because those were the areas 

surveyed most regularly and hence a reasonable degree 

of annual monitoring consistency was achieved. In other 

words, the available dataset did not effectively represent 

Price, Utah Study Area

The results of Objective 1 investigations revealed that 

the average density of OG wells in proximity to raptor 

nest clusters increased three to four-fold at the 0.8 and 

2.0-km spatial scales during the study period, that the 

average distance to the nearest well decreased by roughly 

20%, and that by 2006 roughly 20% fewer nest clusters 

had no OG wells within 0.8 and 2.0 km of cluster 

centroids. That said, as of 2006 still 71% of nest clusters 

had no wells within 0.8 km and 54% had no wells within 

2.0 km.

Golden Eagles

Although compromised by very limited effective 

sample sizes, the results of Objective 2 investigations 

suggested that, at least during the period of greatest 

OG development expansion (1999–2002), used Golden 

Eagle nest clusters consistently tended to be farther 

from centers of OG development hotspots than unused 

clusters; however, this response appeared to fade as the 

pace of further new development in the hotspot areas 

subsided after 2002 (Figure 6). This suggests that a 

high volume of active new well development at least 

temporarily deterred some Golden Eagles from nesting, 

but that the presence of established wells and associated 

maintenance activities did not. Previous studies also 

suggested that nesting raptors might habituate to repeated 

disturbance (Andersen et al. 1989, Brown et al. 1999, 

Steidl and Anthony 2000).

The Objective 3 investigations revealed a distinct 

negative relationship between overall OG development 

levels and the probability of Golden Eagle nest clusters 

being used or active in a given year, whether evaluated 

at the 0.8 or 2.0-km spatial scale. Further, integrated 

modeling of vegetation and climate variables illustrated 

the importance of drought severity in driving activity 

trends, and that activity patterns varied by habitat. 

All top use and activity models suggested that Price 

Golden Eagles responded positively to reduced drought 

severity. In contrast, previous research in Utah and 

Idaho suggested that Golden Eagle nest activity was 

most closely aligned with jackrabbit abundance and 

distribution (Smith and Murphy 1979, Bates and Moretti 

1994, Steenhof et al. 1997), which appear to vary 

independent of drought conditions (Steenhof et al. 1997). 

Our results also suggested that cluster activity was higher 

on the heels of lower-than-average winter precipitation. 

Mild winter conditions can increase Golden Eagle 

Discussion
46 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

nest clusters that otherwise would have reflected low 

development/road variance due to the relative absence of 

OG development and roads. This may be the reason why 

the proportional use/activity models did not incorporate 

average DF1 and DF2 variables and reflect a negative 

response to overall development levels, as was the case 

in the analyses of annual cluster use/activity. Had lowdevelopment clusters been effectively encompassed, 

low DF variance could have reflected either uniformly 

low or high road/development levels, and may not have 

emerged as a significant predictor of cluster activity in 

the absence of DF variables reflecting variation in overall 

road/development levels. Regardless, we reiterate that 

the discrimination ability of the proportional use/activity 

models was low, and hence these results, in particular, 

must be considered with great caution.

Red-tailed Hawks

Compared to Golden Eagles, analyses of relationships 

for Red-tailed Hawks were much more severely 

compromised by limited nest-cluster sample sizes. We 

anticipated this, because we knew from the outset that 

Golden Eagles were the most common nesters within 

the study area and that sample sizes for other nesting 

species would be limited. As with Golden Eagles, 

inconsistent monitoring histories further limited sample 

sizes and, because of this, hotspot and proportional use/

activity analyses were not possible for Red-tailed Hawks. 

Moreover, the fact that the annual-use analyses revealed 

a bewildering array of competing top models (Table 6) 

compared to the annual activity models (Table 7) may 

reflect greater difficulty and subjectivity associated 

with identifying signs of occupation or tending (use), as 

opposed to actual breeding (active), based on a one-time 

aerial survey during May. That is, classifying a cluster as 

active required greater and more concrete evidence than 

classification as used (see definitions), and as a result, 

models of cluster use likely contained greater noise. 

Nevertheless, the two analyses did reveal some consistent 

patterns and novel, apparent relationships between Redtailed Hawk nesting activity and development.

The initial development-only analyses of annual cluster 

use and activity related positively to OG development at 

the 0.8-km scale, but related negatively to it at the larger 

scale. Cluster use also related positively to non-OG roads 

at the smaller scale. That is, these data suggested that the 

probability of a cluster being used or active was highest 

in areas with both high OG development and where nonOG roads were relatively prevalent and proximate to nest 

clusters at the smaller scale, but that these relationships 

may not hold in the larger landscape and may even 

reverse for OG development.

Adding vegetation and climate variables suggested 

additional complexities. Similar to Golden Eagles, 

climate variables appeared as significant covariates in 

most of the use models (Table 6) and indicated positive 

responses to declining drought severity, but a negative 

response to excessive winter precipitation when the 

overall drought response was already accounted for. 

However, no climate variables emerged as significant 

covariates in the activity models (Table 7), suggesting 

that actual breeding attempts were not strongly influenced 

by moisture conditions. As was the case for Golden 

Eagles, both the use and activity models also typically 

included one or more vegetation factors, suggesting that 

the probability of cluster activity varied by habitat. In 

particular, VF2 figured prominently in models for both 

species and consistently indicated higher activity levels 

in habitats featuring relatively high cover of mountain 

and basin big sagebrush, and lower activity in habitats 

dominated by mixes of pinyon, juniper, and mixedspecies sagebrush.

In the presence of VF and climate variables, the top use 

and activity models also revealed a mixed relationship 

with DF1; i.e., a positive relationship at the 0.8-km scale 

but a negative relationship at the 2.0-km scale. The 

apparently conflicting results for the two DF1 variables 

appear to suggest that Red-tailed Hawks benefited from 

a degree of proximate development activity, perhaps 

resulting from creation of more open habitat, increasing 

prey availability, or providing new hunting perches that 

render prey more accessible. Janes (1984) found that 

Red-tailed Hawk reproductive success correlated more 

strongly with the distribution and density of hunting 

perches than with actual prey abundance. That is, 

prey accessibility may be more important than actual 

abundance. The full use models also suggested a positive 

influence of non-OG roads within 0.8 km. Therefore, 

both the development-only and development-plus use 

and activity models indicated positive associations with 

proximate OG development and non-OG roads. The 

Red-tailed Hawk is an abundant, widespread raptor 

species (Preston and Beane 1993) and apparently 

tolerant of human activity (Andersen et al. 1989). For 

example, Red-tailed Hawk productivity in Wisconsin was 

positively associated with urban density and area of roads 

(Stout et al. 2006). In contrast to these findings, however, 

our results suggested that OG development had a negative 

influence on cluster use and activity at the larger spatial 

scale (2.0 km), perhaps reflecting a saturation threshold 

at this broader spatial extent. The activity model also 

uniquely included a significant interaction between 

DF1_0.8K and VF3, suggesting that the positive effect 

of increasing, proximate development activity on the 

probability of a nest being active in May was muted 
47 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

in habitats dominated by mixes of riparian forest, 

grass, forbs, and/or oak-maple. The latter may provide 

further support for the notion that the positive response 

to proximate development occurs in part because OG 

infrastructure provides additional hunting perches 

and access to prey in the predominant pinyon-juniper 

habitats of the Price study area. Riparian-forest habitats 

interspersed with open grass/forb patches may already 

provide ideal combinations of suitable hunting perches, 

open hunting areas, and accessible prey for Red-tailed 

Hawks, such that further development may compromise 

rather than enhance the value of such habitats.

Prairie Falcons

Even more so than for Red-tailed Hawks, small sample 

sizes due to limited nesting habitat and inconsistent 

monitoring histories, as well difficulties associated with 

monitoring cavity nests from the air, greatly limited the 

utility of available data for Prairie Falcons. An additional 

limitation that applied to this species was that we did 

not evaluate potential interactions in the annual use 

and activity models due to the real danger of spurious 

results from model overfitting when the ratio of potential 

predictors to sample units is excessive.

Unlike for both Golden Eagles and Red-tailed Hawks, 

the initial development-only analyses of annual cluster 

use and activity revealed no significant relationships for 

Prairie Falcons. Similar to both other species, however, 

once vegetation and climate variables were included, 

significant models and development relationships 

emerged. Similar to Red-tailed Hawks, and despite 

a limited difference in the numbers of nesting events 

classified as used (n = 34) and active (n = 28), the 

Prairie Falcon annual-use analysis revealed a much more 

complex array of top models than the annual-activity 

analysis (Tables 8 and 9). Nevertheless, again some 

common overarching patterns were evident. Similar to 

the other species, various VFs figured into most models, 

indicating variable responses depending on habitat. In 

this case, negative relationships between cluster use or 

activity and VF3 and VF4 were apparent in all top use 

and activity models, indicating negative responses to 

habitats featuring relatively high cover of riparian forest, 

grass, forbs, and black sagebrush or pinyon, pinyonjuniper mix, and barren lands, and positive responses 

to desert shrub and agricultural habitats. All use and 

activity models also suggested a positive response to 

decreasing drought severity, but a negative response to 

excessive winter precipitation with the overall drought 

effect already accounted for. In southern Idaho, drought 

severity influenced ground squirrel (Spermophilus spp.) 

population fluctuations, which in turn influenced Prairie 

Falcon reproductive rates (Steenhof et al. 1999). In 

contrast, excessive winter precipitation may limit prey 

availability prior to the start of the nesting season. For 

example, a spring snowstorm in northern Wyoming 

correlated with reduced abundance of avian prey and 

nesting cessation in Prairie Falcons (Squires et al. 1991) 

and high precipitation levels before and during the 

breeding season reduced productivity per nest start in 

Idaho (Steenhof et al. 1999).

Top models suggested that Prairie Falcon cluster use had 

a mixed relationship with DF1 when both scales were 

included in the same model (i.e., use related positively 

to OG development at both scales, when considered 

individually, but related negatively to OG development at 

the 2.0-km scales when considered simultaneously). This 

may suggest that a degree of proximate OG development 

may have been beneficial to Prairie Falcons, but not if the 

level of OG activity was too great at the broader 2-km 

scale. In the Price study area, dominated by pinyonjuniper vegetation, OG development may benefit Prairie 

Falcons by creating open foraging areas required by 

breeding birds (Squires et al. 1993). Prairie Falcons in 

Wyoming (n = 6) foraged in areas with a greater density 

of wells; however, these areas were also relatively 

close to the aerie and had greater cover of grasslands, 

revealing the potential for confounding between well 

density and optimal foraging habitat (Squires et al. 1993). 

Cluster use in Price also related negatively to non-OG 

development at both spatial scales. Although cluster 

activity also related negatively to non-OG development 

within 0.8 km, the top activity models identified no other 

significant relationships with development. This suggests 

that the probability of a Prairie Falcon nest being active 

in May was most sensitive to the proximity of nonOG roads, perhaps reflecting the fact that many of the 

miscellaneous non-OG roads in the area are most likely 

to intrude closely into the low cliff and outcrop areas 

used by nesting Prairie Falcons. Boyce (1988) found that 

Prairie Falcon productivity in the Mojave Desert related 

negatively to the number of nearby roads, as well as 

reduced foot-travel times to nests.

Common Species Patterns

With respect to development, only Price Golden 

Eagles exhibited consistent negative relationships with 

OG development at both the 0.8 and 2.0-km spatial 

scales. However, hotspot analyses suggested that the 

negative influence of OG development may have been 

most pronounced during peak development periods 

and declined once the birds acclimated to existing 

development levels. In contrast, Red-tailed Hawks and 

Prairie Falcons commonly exhibited mixed relationships 
48 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

with OG development; i.e., both species appeared to 

benefit from a degree of OG development within 0.8-km 

of nest clusters, but this relationship diminished, or even 

reversed, when OG development was assessed at the 

broader 2.0-km spatial scale. Both the top Golden Eagle 

and Red-tailed Hawk models also suggested positive 

associations with non-OG roads, although the scales 

at which we detected these relationships differed. In 

contrast, non-OG roads influenced Price Prairie Falcons 

negatively. Top models for both Golden Eagles and 

Red-tailed Hawks also suggested significant interactions 

between development and vegetation, but the nature of 

the relationships differed for each species.

The annual use and activity models for all three species 

also commonly included one or more significant VFs, 

indicating that the probability of cluster use and activity 

varied by habitat. The Golden Eagle and Red-tailed 

Hawk models most commonly included relationships with 

VF2, which indicated higher activity levels in habitats 

featuring relatively high cover of mountain and basin big 

sagebrush, and lower activity in habitats dominated by 

mixes of pinyon, juniper, and mixed-species sagebrush. 

In contrast, the Prairie Falcon models most commonly 

included relationships with VF3 and VF4, indicating 

negative responses to habitats featuring relatively high 

cover of riparian forest, grass, forbs, and black sagebrush 

or pinyon, pinyon-juniper mix, and barren lands, and 

positive responses to desert shrub and agricultural 

habitats.

All three species also exhibited positive relationships 

to wetter current-year conditions (i.e., reduced drought 

severity), but negative relationships to pre-nesting season 

winter precipitation when both variables were considered 

together. This suggests a general positive effect of wetter 

conditions, as long as pre-nesting conditions were not 

overly wet or extreme. The potential negative influences 

of drought on Prairie Falcons (Steenhof et al. 1999) and 

winter severity or precipitation on both Golden Eagles 

(Tjernberg 1983, Steenhof et al. 1997) and Prairie 

Falcons (Steenhof et al. 1999) have been discussed 

previously. However, the absence of climate variables as 

significant covariates in the activity models for Red-tailed 

Hawks suggested that actual breeding attempts of this 

species were not affected to the same degree by climatic 

variability. The dearth of previous research documenting 

such climatic relationships for Red-tailed-Hawks appears 

to this conclusion.

Rawlins, Wyoming Study Area

Objective 1 investigations revealed that a substantial 

increase in OG development occurred in the Rawlins 

Study area between 1978 and 2006, with the number 

of active wells nearly tripling during this period. The 

number of wells within 0.8 and 2.0 km of surveyed nest 

clusters increased more than five-fold, while the average 

distance from nest-cluster centroids to the nearest well 

decreased approximately 20% (Table 12). However, in 

2006, 86% of nest clusters still had no wells within 0.8 

km and 74% had no wells within 2.0 km (Table 13).

Golden Eagles

Due to limited sample sizes, we were unable to assess 

effectively changes in the distribution of Golden Eagle 

nest clusters by status in relation to development 

(Objective 2). However, initial Objective 3 investigations 

of development-only relationships suggested that Golden 

Eagle nesting activity was negatively affected by OG 

development at both the 0.8 and 2.0-km scales, with 

a positive relationship with non-OG roads at the 2.0-

km scale further suggesting that they benefited from a 

relatively high prevalence of non-OG roads (typically 

corresponding to a relatively low prevalence of OG roads 

and overall development) across the broader landscape 

surrounding nest clusters.

Integrated modeling of development, vegetation, and 

climate further suggested that vegetation and climate 

factors influenced use and activity similarly. Climate 

appeared to be a particularly important driver of 

Golden Eagle nesting activity, with all three climate 

variables present in all top use and activity models. 

The relationships with climate variables suggested 

that use and activity were greater during periods of 

“drought recovery”; i.e., in wetter years, with wetter 

previous winters (November–February), particularly 

when following drier years. Jackrabbit numbers may 

be a major driver of Golden Eagle reproductive output 

in Utah and Idaho (Smith and Murphy 1979, Bates 

and Moretti 1994, Steenhof et al. 1997) and appear to 

cycle independently of climate conditions (Steenhof et 

al. 1997). However, prairie dogs (Cynomys spp.) and 

ground squirrels may contribute more to the breedingseason diet of Golden Eagles in southern Wyoming 

(Schmalzried 1976, MacLaren et al. 1988). Cluster use 

and activity also appeared to relate positively to greater 

cover of riparian forest, grass, mountain mahogany, and/

or agriculture, and lower cover of relatively high-growing 

sagebrush species. Ignoring mountain mahogany, this 

may reflect greater use of and activity in productive 
49 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

bottomland drainage areas (in the arid West, agriculture is 

commonly associated with these areas).

Although cluster use did not appear to be affected by 

development after accounting for vegetation and climate 

influences, there was some indication (i.e., in one of four 

top models) that cluster activity (i.e., the probability of 

an actual breeding attempt occurring) was negatively 

influenced by increasing OG development within 2.0 

km. Again, the use models may have contained greater 

noise due to their reliance on a less rigorous classification 

scheme. This may have precluded the detection of 

development relationships in our comparison of annual 

Golden Eagle nest-cluster use in the Rawlins study area.

Investigation of proportional Golden Eagle nest use 

and activity between 1998 and 2006 revealed more 

complicated relationships with development. The results 

suggested that the proportion of years in which Golden 

Eagle clusters were used or active generally increased 

in areas with relatively high OG development but low 

variance in OG development levels, and in areas with 

high prevalence and variance of non-OG roads. This 

suggests that proportional use/activity may have been 

greater in areas of a relatively high OG development and 

non-OG roads, but only if the degree of change in overall 

OG development levels was relatively low in the areas 

around nest clusters (i.e., better if not in areas of rapid 

development). A similar theme emerged in the Price 

study-area “hotspot” analysis, suggesting that active nests 

there tended to be farther from development hotspots 

during the period of most rapid development. While 

previous research suggests nesting raptors can habituate 

to human disturbance (Andersen et al. 1989, Brown et 

al. 1999, Steidl and Anthony 2000), responses to initial 

exposure may still be significant. For example, Redtailed Hawks flushed in response to helicopter over-flights 

at 53% of previously unexposed nests, compared to only 

8% of nests exposed to such activities during the previous 

year (Andersen et al. 1989).

Proportional Golden Eagle cluster use and activity 

generally increased with non-OG road coverage and 

at territories with greater variability in the coverage 

of non-OG roads. Because the location and cover of 

non-OG roads was static across years (see methods 

detailing road classification and addition procedures), 

the variance detected in this development factor was 

actually driven entirely by use of alternate nests within 

clusters (i.e., two widely spaced alternate nests within a 

given cluster may have been surrounded by substantially 

different road networks). In this scenario, the positive 

association between proportional use and activity and 

variance in non-OG roads may actually reflect greater 

use and activity within territories containing alternate 

nests situated in diverse non-OG road-network settings 

(i.e., territories providing greater flexibility to Golden 

Eagles in response to the non-OG road network). The 

vegetation relationships were somewhat variable between 

the best proportional use and activity models, but all 

top models identified a positive association with greater 

cover of riparian forest, grass, mountain mahogany, and/

or agriculture, and lower cover of relatively high-growing 

sagebrush species.

Ferruginous Hawks

The potential response of Ferruginous Hawks to 

OG development was complicated by documented 

Ferruginous Hawk use of OG structures (mostly 

condensation tanks) for nesting and subsequent 

mitigation with ANSs. In an attempt to control the 

potential confounding this situation presented, we 

first assessed the response of Ferruginous Hawks to 

OG development by limiting our consideration to nest 

clusters that did not contain ANSs. However, due to the 

common use of ANSs for nesting in later years in the 

Rawlins study area, it was not possible to assess changes 

in the distribution of nest clusters by breeding status in 

relation to development (Objective 2) without inclusion 

of ANS nest clusters. With this caveat in mind, we found 

that used and active nests were significantly closer to 

centers of development hotspots relative to unused or 

other nests. This likely reflected a shift to the use of ANS 

structures situated within and near development hotspots 

(i.e., installation of ANS began in 1987 and our hotspot 

assessment covered the period 1998-2006). A graphical 

assessment of the density of active Ferruginous Hawk 

nest clusters pre- and post-ANS installation supported 

this conclusion (Figure 13).

Non-ANS Relationships.—Considering relationships 

between non-ANS nest clusters and development alone, 

Ferruginous Hawk annual cluster use and activity 

exhibited a mixed relationship with development factors 

at the 0.8 and 2.0 km scales when the influence of both 

scales was considered simultaneously. These mixed 

relationships suggested that nest-cluster use and activity 

were greater in areas with less OG development and 

proportionately more non-OG roads nearby (0.8-km 

scale), but that these relationships diminished with 

greater distance (2.0-km scale). After accounting 

for the potential influence of vegetation and climate 

on annual cluster use and activity, only nest-cluster 

activity models still revealed a negative relationship 

with OG development at the 0.8 km scale. The loss of 

this relationship in models of cluster use may suggest 

that cluster use (i.e., occupancy, tending, etc.) was 

less sensitive to OG development compared to actual 
50 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

breeding events, but more likely reflected the effects 

of greater noise in the use models from a less rigorous 

classification scheme. Previous research on Ferruginous 

Hawks and OG development produced mixed results 

(Harmata 1991, Van Horn 1993, Zelenak and Rotella 

1997, Keough 2006). Most strikingly, nest activity in 

eastern Utah was reportedly greater in association with 

more OG wells and smaller distances to the nearest well, 

but productivity was greater farther from wells (Keough 

2006). It is important to note that the majority of nests 

in this study were located in relatively secure junipers, 

because nest security may decrease the sensitivity of 

Ferruginous Hawks to human disturbance (Lokemoen 

and Duebbert 1976, Neal 2007).

Both full use and activity models suggested a positive 

relationship with non-OG roads at the 0.8-km scale, but 

a negative relationship with this factor at the 2.0-km 

scales. Again, this mixed relationship may reflect an 

initial benefit of roads to nesting Ferruginous Hawks 

that diminishes or reverses with over-saturation in the 

broader landscape. A positive association with roads was 

reported previously for Ferruginous Hawks in Montana, 

where the density of ground-squirrel burrows was greater 

along roadsides compared to adjacent grasslands (Zelenak 

and Rotella 1997). The authors speculated that ground 

squirrels might have been attracted to roadsides due 

to the availability of forage vegetation and disturbed 

soils. Similarly, higher densities of Stephens’ kangaroo 

rats (Dipodomys stephensi) on dirt roads in California 

may have been related to easy access to burrows, forage 

vegetation, dust bathing, and movement corridors (Brock 

and Kelt 2004). Zelenak and Rotella (1997) further 

speculated that increased prey abundance associated 

with extensive OG road networks might have benefited 

Ferruginous Hawks in their study.

Variable interactions suggested that the positive 

relationships between proximate non-OG roads and 

both non-ANS cluster use and activity were enhanced 

in habitats featuring more evergreen forest mix, but 

less Wyoming big sagebrush or agriculture. One might 

speculate that roads provide openings in forested habitats 

for this open-country raptor, but this relationship may 

also reflect a survey bias in such areas (i.e., the very few 

nests located in forested habitats may be more regularly 

visited, and activity more easily detected, in areas with a 

well-developed road network). In contrast, the reduced 

positive effect of roads in agricultural habitats may 

reflect the fact that these areas may already contain high 

densities of Ferruginous Hawk prey, such as ground 

squirrels (Schmutz 1989, Zelenak and Rotella 1997), and 

the previously discussed prey-related road benefits may 

be less important in such areas.

Overall, the models of annual non-ANS Ferruginous 

Hawk cluster use and activity suggested similar 

vegetation and climate relationships. As with Golden 

Eagles, non-ANS Ferruginous Hawk nesting activity 

was greater in years of drought recovery. Similar to the 

discussion for Price-area Prairie Falcons, correlations 

between prey species, such as ground squirrels, and 

drought may be partially responsible for this relationship. 

In Idaho, Ferruginous Hawk reproductive output also 

varied in response to the abundance of ground squirrels 

(Steenhof and Kochert 1985), which may be a similarly 

important dietary component for the species in southern 

Wyoming (MacLaren et al. 1988). The association 

of Ferruginous Hawk cluster use and activity with a 

complex of vegetation factors (i.e., five vegetation factors 

in the best use and activity models) precluded detailed 

interpretation of the vegetation relationships. This reality 

may have been partially responsible for the very poor fit 

of our Ferruginous Hawk top models (i.e., these models 

may have been overfit due the inclusion of all but one of 

the available vegetation factors).

Our assessment of proportional non-ANS cluster use 

or activity between 1998 and 2006 revealed some 

commonalities with the relationships gleaned from 

the annual assessments of use and activity. First, 

proportional use and activity decreased as the amount 

of OG development increased within 0.8 km, but this 

relationship reversed at the 2.0 km scale. Similarly, 

we also detected the previously discussed positive 

relationship with non-OG roads at the 0.8-km scale, 

but not the diminishing or reversing relationship at 

the 2.0-km scale suggested by the annual assessments. 

Therefore, both the top annual and proportional use 

and activity models suggested a negative effect of OG 

development, but a positive effect of non-OG roads on 

Ferruginous Hawk cluster status at the 0.8-km scale. This 

agreement between the poor-fitting annual models and 

better-performing proportional models strengthens our 

confidence in the identified non-ANS Ferruginous Hawk 

cluster status and development relationships.

Similar to Rawlins Golden Eagles, Ferruginous Hawk 

proportional cluster use and activity related positively 

to increased variance of non-OG roads. Again, this 

suggests that proportional use and activity were greater 

at clusters containing alternate nests in differing road 

matrices (i.e., more nesting options relative to roads). 

All Ferruginous Hawk proportional use and activity 

models revealed a positive association with greater 

cover of riparian forest, grass, mountain mahogany, and/

or agriculture. All but one top model of Ferruginous 

Hawk annual and proportional use and activity also 

suggested positive relationships with greater cover of 
51 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

juniper, basin big sagebrush, Wyoming big sagebrush, 

and/or agriculture, but less cover of evergreen forest. 

We suggest these relationships were most likely driven 

by the cover of grass and sagebrush, as Ferruginous 

Hawks are known to preferentially select these habitats 

for nesting and foraging (e.g., see reviews in Olendorff 

1993, Bechard and Schmutz 1995). In contrast, 

previous studies suggested mixed relationships between 

agriculture and nesting Ferruginous Hawks (Howard 

and Wolfe 1976, Schmutz 1989, Bechard et al. 1990). 

These conflicting results may be due to a “threshold” 

relationship with agriculture. For example, Schmutz 

(1989) found that Ferruginous Hawk nesting densities 

in Alberta increased as the proportion of cultivation 

in the landscape surrounding nests increased to 30%, 

but decreased thereafter. In this light, no Ferruginous 

Hawk nest cluster in the Rawlins study area had >12% 

agricultural cover within 2 km, suggesting the potential 

for nesting birds to benefit from agriculture at this 

relatively low level.

ANS Relationships.—Most ANSs present in the Rawlins 

study area were erected to mitigate Ferruginous Hawk 

use of OG structures for nesting. Nesting attempts on 

OG structures, typically condensation tanks, often failed 

following repeated human visitation to the structure. 

Nests on such structures were commonly relocated to 

ANSs erected nearby (i.e., within 1 km) and, as a result, 

the majority of ANSs were located in areas of relatively 

high OG development. As discussed in the previous 

section, Ferruginous Hawk nest use and activity shifted 

markedly after 1989 into development hotspots and 

areas of ANS availability. To determine if Ferruginous 

Hawks nesting on ANSs responded differentially to 

OG development, we separately assessed relationships 

between ANS nest-cluster activity and development, 

vegetation, and climate variables.

Assessing relationships between Ferruginous Hawk ANS 

cluster use/activity and development alone revealed 

striking contrasts with non-ANS cluster patterns. ANS 

nest-cluster use and activity related positively to OG 

development at both spatial scales, whereas nonANS cluster use and activity related negatively to OG 

development, with the effect diminishing at greater 

distance. Moreover, ANS cluster use and activity related 

negatively to non-OG roads at the 0.8-km scale, but 

related positively to non-OG roads at the 2.0-km scale 

when both scales were considered simultaneously, which 

is the exact opposite of suggested non-ANS cluster/

development relationships. This strongly suggests that 

the nesting ecology of Ferruginous Hawks afforded 

access to ANSs for nesting differs markedly from that of 

breeding pairs nesting on primarily natural substrates.

Similar to the non-ANS development-plus models of 

cluster use and activity, incorporating vegetation and 

climate variables into ANS models also produced top 

use models that performed poorly; however, the top 

ANS model of cluster activity was nearly identical to 

the top use models and did not fail the goodness-of-fit 

test. Overall, the full development-plus annual models 

suggested that Ferruginous Hawk ANS cluster use and 

activity related positively to OG development at the 0.8-

km scale and that the previously outlined relationship 

with non-OG roads was unchanged. This relationship 

with development appears to suggest that ANS cluster 

use and activity were greater in areas with considerable 

OG development in the immediate vicinity, but with 

more non-OG roads in the larger surrounding landscape. 

Again, comparing top models of ANS and non-ANS 

nest-cluster status suggested opposite relationships with 

development factors. Potential benefits of proximate 

OG development at ANS clusters may be associated 

with access to previously unavailable prey resources 

(i.e., through the creation of artificial nest opportunities 

and hunting perches where few existed historically), 

increased prey abundance associated with OG roads (see 

the previous discussion of burrowing mammal abundance 

associated with roads; Zelenak and Rotella 1997, Brock 

and Kelt 2004), increased road kill due to higher traffic 

levels, and/or other potential benefits accrued to prey 

species associated with OG infrastructure and activities 

(e.g., road culverts may provide shelter for small 

mammals, disturbed earth associated with well pads may 

attract burrowing mammals, etc.). Although the potential 

benefits of OG development that we suggest are intuitive, 

previous research corroborates only the relationship 

between burrowing-mammal abundance and roads; 

hence, further study is needed to clarify other speculative 

relationships.

Variable interactions suggested that positive relationships 

between proximate OG development and ANS cluster use 

and activity were enhanced in habitats with more riparian 

forest, grass, mountain mahogany, and/or agriculture. 

Such habitats were relatively rare near ANS clusters, but 

data inspection suggested that a few clusters with both 

high OG development and grass cover also experienced 

very high use and activity. This may reflect the combined 

benefits of the previously discussed OG prey effects, 

prime natural foraging habitat (grass-dominated habitats), 

and secure nest sites. Perhaps most importantly, 

compared to many natural-substrate nests, all ANSs were 

inaccessible from the ground and likely provided nesting 

birds with a sense of security in the face of development 

activities (Neal 2007). Previous research suggested that 

Ferruginous Hawks using relatively secure nest sites 

were more tolerant of human activities and a wider range 
52 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

of land-use practices surrounding nests (Lokemoen 

and Duebbert 1976, Gilmer and Stewart 1983, Zelenak 

and Rotella 1997). Nest security may serve to insulate 

Ferruginous Hawks from potential disturbance and 

changes in predator abundance that may be associated 

with OG development. See Neal et al. (2010) for a more 

detailed discussion of Ferruginous Hawk use of ANSs in 

the Rawlins study area and the ability of these structures 

to mitigate potential OG impacts.

Similar to non-ANS clusters, ANS cluster use and activity 

related positively to greater cover of riparian forest, grass, 

mountain mahogany, and/or agriculture, as well as greater 

pre-nesting winter precipitation. In stark contrast with 

non-ANSs, however, ANS cluster use and activity were 

greater when current-year drought conditions were more 

severe. We speculate that drought severity may have 

benefited Ferruginous Hawks utilizing ANSs through the 

concentration of potential prey near OG water sources 

(e.g., evaporation pits). Although we were unable to find 

published research to support this speculation, it is clear 

that wildlife commonly become concentrated near water 

sources in the arid West.

Nesting Success and Productivity.—Our initial 

objective was to assess differences in Ferruginous 

Hawk nest success and productivity at a subset of nest 

clusters to determine the influence of development 

on reproductive output. Because previous evidence 

suggested that success and productivity might differ 

between non-ANS and ANS nests (e.g., Tigner and 

Call 1996) and we suspected that nest substrate and 

accessibility also might influence these reproductive 

parameters, we hoped to describe patterns of nest success 

and productivity in relation to these nest classifications. 

However, as discussed in our results, sample sizes 

limited our comparison to simple categorization of nests 

as accessible or inaccessible (i.e., to potential groundbased humans and nest predators). The majority of 

nests classified as accessible were located on natural 

substrates and included no ANSs, whereas inaccessible 

nests typically were located on manmade substrates 

and especially ANSs (i.e., our reduced classification 

of nests as accessible or inaccessible largely reflected 

the contrasts between natural and. manmade nesting 

substrates and non-ANS versus ANS nests).

Overall, assessments of fit indicated very poor 

performance for models of nesting success and 

productivity for both accessible and inaccessible nests. 

This was likely because accessibility alone had the 

greatest influence on success and productivity, and little 

additional information could be modeled after separating 

nests into these groupings. The fact that both nest success 

and productivity were significantly lower and more 

variable at accessible nests supports this conclusion. It 

may be that success and productivity at accessible nests is 

more chance driven, due to the vulnerability of such nests 

to predation. In contrast, inaccessible nests were nearly 

always successful, and therefore nearly all such nests 

were at least minimally productive.

Neal’s (2007) work evaluating comparative use of 

alternate nests and nest attendance and foraging 

relationships at accessible and inaccessible nests 

provided further insight about how use of ANSs and nest 

inaccessibility yielded greater success and productivity 

for Ferruginous Hawks. A significant reduction in 

the number of alternate nests used within ANS-based 

nesting territories suggested that birds using ANSs 

were less dependent on maintaining large suites of 

alternate nests. Several factors may be involved in this 

dynamic. For example, increased long-term stability 

of ANS substrates may allow for greater perennial use 

of individual nests. In fact, Neal (2007) found that 

although natural, inaccessible nests were overall the most 

successful of all nests, and generally were also the most 

productive, their productivity tended to decline over 

time, suggesting that nest and substrate deterioration 

may have been an issue. Another possibility, though 

purely speculative at present, is that the wire bases of 

the ANSs may allow for greater annual “flushing” of 

the nest substrate by rains, thereby reducing the need to 

rotate nest use to help control build-up of nest parasites 

(Philips and Dindal 1977). Perhaps most importantly, 

however, in light of Neal’s findings, reduced sensitivity 

to disturbance from ground-based activities and predators 

may be the most likely explanation for why reliance on 

multiple, alternative nests is reduced for breeding pairs 

using ANSs. Mammalian predators such as coyotes 

(Canis latrans), badgers (Taxidea taxus), bobcats (Lynx 

rufus), and foxes (Vulpes spp.) are thought to be serious 

threats to ground-nesting Ferruginous Hawks and 

recently fledged young (Bechard and Schmutz 1995). 

Human intrusions also likely function directly and 

indirectly as predation events through direct persecution 

of hawks, nest destruction, and the provision of scent 

trails or roads for other ground predators (L. Apple and 

J. Tigner personal communication; M. Neal personal 

observation). The apparent significant benefit of nesting 

on inaccessible substrates makes perfect sense in light 

of this evidence. Lokemoen and Duebbert (1976) also 

suggested that Ferruginous Hawks nesting on relatively 

secure substrates (i.e., trees) might be less sensitive to 

human activity compared to ground nesters. Suter and 

Joness (1981) also suggested that nest security could be 

an important determinant of raptor responses to human 

disturbance.
53 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Neal’s (2007) nest-attendance and prey-delivery results 

further clarified the fact that the apparent reduction in 

sensitivity to ground-based disturbance and depredation 

potential translated to breeding adults at ANSs and other 

inaccessible nests being able to dedicate significantly 

more time to ensuring efficient provisioning and care of 

their young, which in turn translated to greater overall 

success and productivity for such nests compared to 

accessible nests. Schmutz et al. (1984) also found that 

ANSs were more successful than natural nests (95% vs. 

67% of nests reaching near fledging age) for Ferruginous 

Hawks nesting in Alberta, and concluded that ANSs 

can be a useful tool for augmenting Ferruginous Hawk 

nesting in areas where prey are available but natural nest 

sites are sparse.

Red-tailed Hawks

As with Golden Eagles, small sample sizes precluded 

Objective 2 assessments for Red-tailed Hawks. 

Investigation of relationships between development alone 

and annual cluster use and activity suggested, however, 

that Red-tailed Hawks responded negatively to overall 

OG development levels at both the 0.8 and 2.0 km spatial 

scales. In contrast, cluster activity related positively 

to non-OG roads at both scales. This relationship with 

development was similar to that shown for Golden 

Eagles, and for non-ANS Ferruginous Hawk nest clusters 

when the influence of either scale was considered alone.

Modeling annual Red-tailed Hawk cluster use and activity 

in relation to development, vegetation, and climate also 

revealed that development levels influenced use, but 

not cluster activity. Red-tailed Hawk cluster use related 

negatively to OG development at the 0.8-km scale, but 

related positively at the 2.0-km scale. This suggests that 

the negative influence of OG development diminished 

with distance. Additionally, cluster use related negatively 

to non-OG roads within 2.0 km. The apparent lack of 

development effects relative to cluster activity, when 

relationships were detected for cluster use, suggests that 

actual breeding attempts may have been less sensitive 

to the effects of development. As previously discussed 

for Price Red-tailed Hawks, previous research suggested 

that Red-tailed Hawks are relatively tolerant to human 

activities and human-dominated landscapes (Andersen et 

al. 1989, Stout et al. 2006).

The top models of Red-tailed Hawk use and activity 

suggested that habitat characteristics also were influential 

(the top models of use and activity each contained four 

vegetation variables). Similar to Rawlins Golden Eagles 

and Ferruginous Hawks, Red-tailed Hawks exhibited 

a positive response to greater cover of riparian forest, 

grass, mountain mahogany, and/or agriculture. Riparian 

forest and agricultural areas (both are commonly found 

in bottomland areas in of the arid West), in particular, are 

known to be important nesting and foraging habitats for 

Red-tailed Hawks in the West (Platt 1971, Bosakowski et 

al. 1996). Red-tailed Hawks in Rawlins also responded 

positively to reduced drought severity, and to a lesser 

extent, wetter pre-nesting winter conditions and drier 

conditions during the previous year. Again, this suggests 

an association with drought recovery, a pattern also 

detected for Rawlins Golden Eagles and non-ANS 

Ferruginous Hawks.

Prairie Falcons

Similar to the situation for the Price study area, Prairie 

Falcon sample sizes were most limited among the species 

evaluated in the Rawlins study area, and precluded 

Objective 2 “hotspot” assessments. Initial assessments 

of relationships between Prairie Falcon nest cluster use 

and activity suggested, however, that both increased OG 

development levels and high prevalence and proximity 

of non-OG roads influenced Prairie Falcon nesting 

negatively. This suggests that Prairie Falcons may have 

been the most sensitive of the four species assessed in the 

Rawlins study area to general human activities.

After consideration of the potential influence of 

vegetation and climate variables, models of annual Prairie 

Falcon cluster use and activity still suggested a negative 

relationship with both OG development and non-OG 

roads, although the scale at which the influence was 

detected shifted from 0.8 km for use models to 2.0 km for 

activity models. Again, this suggests that Rawlins Prairie 

Falcons were the most sensitive to all types of human 

development. As was discussed for Price Prairie Falcons, 

roads and accessibility for humans influenced Prairie 

Falcon productivity in the Mojave Desert negatively 

(Boyce 1988). In contrast to these results, however, 

Holthuijzen (1989) did not detect a negative response 

of nesting Prairie Falcons to road and dam construction 

within 1.0 km of aeries in Idaho. It is important to note, 

however, that aeries in the Idaho study were located 

on cliffs at least 50 m above the sources of potential 

disturbance, which may have served to insulate them from 

disturbance. In comparison, most eyries in the Rawlins 

study area were located on low cliffs and outcrops, much 

more proximate to ground-based activities. The ability 

of nest height to influence disturbance responses has 

been demonstrated for nesting Bald Eagles in relation to 

pedestrian traffic (Watson 2004).

A consistent development x vegetation interaction in 

the use and activity models further suggested that the 
54 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

negative influence of OG development was enhanced in 

habitats featuring relatively high cover of barren land, 

but diminished in habitats with relatively high cover 

of Wyoming big sagebrush. Prairie Falcons foraging 

during the breeding season may prefer open grassy 

and barren habitats, and given their inherent openness, 

OG development may be unlikely to confer any further 

advantages to Prairie Falcons in such habitats. In 

contrast, openings created in sagebrush habitats by roads, 

well pads, and pipelines may provide potential benefits 

(e.g., see our previous discussions about burrowing 

mammal associations with roads) that serve to diminish 

the general negative impacts of OG activities for nesting 

Prairie Falcons in the Rawlins study area.

In regards to vegetation and climate, both the use and 

activity models suggested similar relationships. Prairie 

Falcon cluster use and activity related positively to VF2, 

similar to the other Rawlins raptor species, and to wetter 

winter conditions. To a lesser extent, use and activity also 

related positively to wetter current-year conditions but 

drier previous-year conditions (i.e., a drought-recovery 

pattern). As previously discussed in Price, reduced 

drought conditions may influence Prairie Falcon nesting 

indirectly through its influence on prey populations and 

especially ground squirrels (Steenhof et al. 1999).

Common Species Patterns

Except for ANS Ferruginous Hawks, top use and/or 

activity models suggested negative relationships with 

OG development for all four species, although the scale 

of detection varied. Our results suggest that, among 

the four species, Prairie Falcons may have been the 

most sensitive to human activity, with both the use and 

activity models suggesting negative relationships with 

both OG development and non-OG roads. Top use and 

activity models also suggested that Rawlins-area nonANS Ferruginous Hawks, Red-tailed Hawks, and Prairie 

Falcons commonly exhibited negative relationships with 

non-OG roads at the 2.0-km scale. Only the status of 

non-ANS Ferruginous Hawk clusters related positively 

to non-OG roads, and this occurred at the smaller spatial 

scale. Interactions with vegetation appeared to influence 

the effects of development on cluster use or activity for all 

but Golden Eagles. Proportional use and activity of both 

Golden Eagle and non-ANS Ferruginous Hawks related 

positively to the variance in non-OG roads, suggesting 

greater utilization of clusters with more variable nonOG conditions at alternate nests (i.e., clusters with more 

nesting options in relation to non-OG road networks).

Although the vegetation relationships were varied and 

complex for individual species, all exhibited positive 

relationships with VF2, suggesting that all benefited to 

some degree from availability of riparian forest, grass 

cover, mountain mahogany, and/or agricultural habitats in 

the surrounding landscape. We speculate that this likely 

reflects positive associations with productive bottomland 

or drainage areas and open grassland/hayfield/pasture 

habitats.

Golden Eagle, non-ANS Ferruginous Hawk, Red-tailed 

Hawk, and Prairie Falcon use and activity models also 

suggested common positive relationships with wetter 

conditions during the corresponding nesting season and 

greater winter precipitation prior to the nesting season. 

In addition, for each species either use or activity models 

(or both) suggested positive relationships with drier 

conditions the previous year, when the benefit of higher 

current year and preceding winter moisture also was 

accounted for. Considered together, these data suggest 

that raptor nesting activity generally increased during 

periods of drought recovery. As we have discussed 

previously, drought conditions may influence fluctuations 

in ground squirrel populations (Steenhof et al. 1999). 

Ground squirrels are an important prey item for Prairie 

Falcons, Ferruginous Hawks, and Red-tailed Hawks in 

southern Wyoming (MacLaren et al. 1988).

Comparison of Price and 

Rawlins Results

Both the Price and Rawlins study areas experienced 

considerable OG development during the study 

period. The study areas differ some in terms of general 

landscape features (i.e., primarily pinyon-juniper 

dominated canyonlands in Price versus sagebrush 

and grassland-dominated shrubsteppe in Rawlins), 

histories of development (more protracted in Rawlins), 

and general patterns of OG development (generally 

expanded from distinct centers of activity in Price, but 

was more dispersed in Rawlins during the early years 

with subsequent activity generally occurring within 

the existing development landscape). Despite these 

differences, we were able to explain much of the variance 

(i.e., 55–58%) associated with patterns of development in 

the two areas (as measured by a number of road and well 

type, distance, and density metrics) around relevant raptor 

nest clusters with relatively simple PCA ordinations that 

were highly comparable for the two study areas at both 

the 0.8 and 2.0-km radius scales. This greatly facilitated 

comparative evaluations of potential development effects 

across the two study areas.

A comparison of the distribution of used and unused 

nest clusters relative to centers of significant OG 
55 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

development activity suggested that used Golden Eagle 

clusters in the Price study area were found farther from 

development centers during years of peak OG activity. 

In contrast, Rawlins Ferruginous Hawk cluster use and 

activity apparently shifted closer to development centers. 

However, as illustrated in Figure 13, the latter relationship 

was driven by the shift of nesting activity onto ANSs 

erected within development areas. Unfortunately, the 

limited number of nest clusters with adequate survey 

histories to assess true changes in the distribution of 

cluster activity prohibited our assessment of other raptor 

species in this manner.

Modeling annual use and activity patterns of nesting 

raptors commonly suggested that OG and other human 

development patterns (i.e., non-OG roads) significantly 

influenced breeding activity in both study areas. 

Relationships were still evident after incorporating 

the influence of vegetation and climate variables. A 

comparison of the results obtained in the Price and 

Rawlins study areas suggests a number of common 

trends, but also reveals some distinct differences. Table 

38 provides a comprehensive summary of the results 

suggested by the top development-only and developmentplus models of annual cluster use and activity for both the 

Price and Rawlins study areas.

Overall, we detected more consistent evidence of 

potential negative impacts of OG development in the 

Rawlins study area. Our assessment of nest-cluster use 

and activity across all years (1978–2006) suggested 

that all four species of interest (non-ANS Ferruginous 

Hawks only) exhibited negative relationships with OG 

development based on top models of use, activity, or both. 

In contrast, only Golden Eagles demonstrated a consistent 

negative relationship between annual cluster use or 

activity and OG development in the Price study area. 

That said, although Price Red-tailed Hawks exhibited a 

positive relationship with OG development at the most 

proximate spatial scale (0.8-km), a negative influence was 

exhibited at the larger 2.0-km scale, suggesting that too 

much development in the broader landscape did have a 

negative influence on this species as well. In comparison, 

Red-tailed Hawks in the Rawlins study area appeared to 

show the opposite relationship (i.e., a negative influence 

at the smaller scale, but that reversed at the larger scale). 

Our results also appeared to suggest that Rawlins Prairie 

Falcons demonstrated the greatest sensitivity to both 

OG development and non-OG road levels surrounding 

clusters. In contrast, in the Price study area, similar 

to Red-tailed Hawks, the Prairie Falcon use models 

suggested a positive relationship to OG development 

activity proximate to nest clusters (0.8-km scale); 

however, this relationship was not evident in the models 

pertaining to confirmed breeding attempts. Moreover, 

as was the case in Rawlins, the Price data also indicated 

a negative relationship between Prairie Falcon nesting 

activity and the prevalence of non-OG roads, potentially 

suggesting a consistently higher likelihood of disturbance 

for this species when exposed to backcountry roads that 

closely intrude within the low cliff and rock-outcrop 

nesting habitats preferred by this species. That said, we 

caution that Price Prairie Falcons were represented in the 

annual use and activity models by much smaller sample 

sizes relative to Rawlins Prairie Falcons (n = 73 vs. 455), 

which may have compromised the Price results and led 

to a higher probability of spurious results. Additionally, 

assessing nest use (i.e., occupancy or tending) for a 

pothole nesting species such as the Prairie Falcon may 

be particularly subjective, especially when derived from 

limited aerial surveys, further warranting for greater 

caution in interpreting results for this species.

An assessment of proportional use and activity at specific 

nest clusters over multiple years also revealed consistent 

relationships with development. All top proportional 

use and activity models from both study areas (i.e., Price 

Golden Eagles and Rawlins Golden Eagles and non-ANS 

Ferruginous Hawks) suggested positive associations with 

diversity (i.e., variance) of non-OG roads within 0.8 km 

of nest clusters. This suggests that at least Golden Eagles 

and non-ANS Ferruginous Hawks benefit from having a 

higher diversity of nesting options within their territories 

in relation to the proximity and density of non-OG roads.

Another distinct similarity in the results from the two 

study areas is consistent indications of benefit from 

at least current-year reductions in drought severity. 

Virtually all models of annual use and activity indicated 

clear negative responses to increasing current-year 

drought severity for all species, with the conspicuous 

exception of Ferruginous Hawks nesting on ANSs in 

the Rawlins study area. That said, there were other 

subtler species- and study-area-specific differences. For 

example, in the Price study area, the negative responses 

of all species to increased drought severity applied to 

both current and previous year conditions; however, 

in many cases, with the PDSI relationships accounted 

for, a negative relationship with winter precipitation 

was indicated. In contrast, in the Rawlins study area, 

in most cases the results suggested a “preference” for 

wetter current-year conditions, including good winter 

precipitation, but drier previous-year conditions. 

However, closer examination of long-term patterns of 

nesting activity in the Rawlins area in relation to annual 

moisture conditions suggested that the dominant pattern 

there was significant drops in activity following very 

wet years, rather than significant increases in activity 
56 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors Summary comparison of logistic regression results for top development-only and development-plus (including climate and vegetation covariates) models of annual nest-cluster “use” and “activity” in the Price, Utah, and Rawlins, Wyoming, study areas. Development-Only Models3 Development-Plus Models3 Variable(s) In Model3 Study # Nesting Dependent # Top 0.8 km 2.0 km 0.8 km 2.0 km Area Species Events Variable1 Models2 DF1 DF2 DF1 DF2 DF1 DF2 DF1 DF2 V C I Price Golden Eagle 1103 Used/Unused 6 – – + – – + X X X Active/Other 4 – + + – – + X X X Red-tailed Hawk 217 Used/Unused 18 + + – + – X X Active/Other 1 + – + – X X Prairie Falcon 73 Used/Unused 13 + – +/– – X X na Active/Other 3 – X X na

Rawlins

Golden Eagle 1313

Used/Unused 4 – – + X X

Active/Other 4 – + – + – X X

Ferruginous Hawk(non-ANS) 2269

Used/Unused 6 – + –/+ +/– + – X X X

Active/Other 6 – + –/+ +/– – + – X X X

Ferruginous Hawk(ANS)

1457

Used/Unused 4 + – + + + – + X X X

Active/Other 3 + – + + + – + X X X

Red-tailed Hawk 584

Used/Unused 14 – – – + – X X X

Active/Other 4 – + – + X X

Prairie Falcon 455

Used/Unused 10 – – – – – – X X X

Active/Other 4 – – – – – – X X X

1 Used = evidence of recent nest tending, occupation, or actual breeding attempt obtained; Unused = no such evidence obtained, despite nest check. Active = breeding attempt confirmed; Other = all other cases, including some used (i.e., evidence of tending, but not actual breeding) and all unused classifications.2 As suggested by AIC model selection; i.e., ∆AICc <4.0.3 DF1 represents component one derived from Principal Components Analysis (PCA) and represents overall oil and gas (OG) development levels evaluated within 0.8 km or 2.0 km radii of raptor nest clusters. DF2 represents component two from the PCA and represents the extent and proximity of non-OG roads evaluated at the two spatial scales. +/– indicates a significant (P ≤ 0.15) contribution to the model and either a positive or negative association with the dependent variable.4 “X” denotes that vegetation (V), climate (C), and/or interaction (I) terms were included in at least one top model. “na” = not applicable due to modeling limitations associated with small samples sizes.
57 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

following the return of high moisture levels after dry 

years. Although the reasons for these relationships 

and differences across the two study areas are unclear, 

it probably has to do with differences in the overall 

landscape characteristics of the two study areas. For 

example, topographic diversity is generally higher in 

the Price survey area, and portions of the landscape 

occupied by nesting raptors extend into higher elevations 

where winter snow cover is more prevalent, but it is also 

farther south and therefore a somewhat drier landscape 

overall than in the Rawlins area. These factors may 

make the Price landscape and its inhabitants generally 

more sensitive to drought, and may also provide a greater 

diversity of foraging opportunities under diverse snowcover conditions, but also may render raptors that nest in 

the higher elevation areas more sensitive to the vagaries 

of heavy snow cover. Conversely, due to the generally 

flatter terrain in the Rawlins area and generally harsher 

winter conditions, heavy snow cover that lingers well 

into spring may impede early nesting activities more than 

in the Price area by both rendering a marginal thermal 

environment and impeding access to prey necessary 

for maintaining sufficient early-season body condition 

for egg laying. That said, one other possibility is that 

the evaluation of climate relationships in Rawlins 

encompassed a period of nearly three decades, whereas 

the Price models encompassed less than one decade. 

Hence, it may well be that the increased nesting-event 

samples sizes and modeling of longer-term dynamics 

in Rawlins simply produced more robust and accurate 

inference.

Conclusions and Management 

Implications

A number of past studies investigating the relationship 

between raptors and human landscapes have failed to 

account for the potentially confounding influence of 

vegetation and climate (e.g., Craighead and Mindell 

1981, Bechard et al. 1990, Bosakowski et al. 1993, Berry 

et al. 1998). We found that apparent relationships with 

development were often modified by modeling of climate 

and landcover/vegetation variables, further pointing 

to the importance of considering such in evaluating 

disturbance effects. That said, our analyses may well 

have pointed to even greater influences of climatic 

variation had we been able to model, for example, 

precipitation patterns at finer spatial and perhaps 

temporal scales. Additionally, we must reiterate again 

that our analyses may have suffered markedly from not 

being able to model information about prey distribution 

and abundance, known to be another major driver of 

raptor population dynamics.

Given the retrospective nature of this study, significant 

limitations of the available raptor monitoring data 

(discussed further in Smith et al. [2010]), and our limited 

ability to reflect other potentially important sources of 

environmental variation (e.g., prey availability), it must 

be noted that all results derived from this investigation 

serve only to reveal potential correlations between OG 

development and raptor nesting activities; i.e., causeeffect inferences cannot be assumed. We have made 

every effort to clearly identify and address the weaknesses 

associated with the data available to us. For example, 

throughout the analyses we adopted a dual cluster-status 

classification scheme (i.e., used vs. unused and active 

vs. other). Although this scheme does not conform to 

recommended standard nest-status terminology, we 

adopted this approach in an effort to strike a balance 

between maximum “positive” (i.e., used or active) 

classification sample sizes and minimum “false positive” 

classifications. Additionally, we regularly highlight 

when there was or was not agreement between the results 

produced by the two different classification schemes, 

as well as when results suggested poor model fit or low 

explanatory ability. However, we believe the scope and 

pace of OG development in the western U.S. clearly 

points to the critical need for the type of assessment we 

have performed and point out that datasets better suited 

to this task do not currently exist and are unlikely to be 

forthcoming in the near future.

With that said, our results consistently pointed to negative 

effects of OG development in both study areas for the 

species with the largest available sample sizes. That is, 

Price Golden Eagles and Rawlins Golden Eagles and nonANS Ferruginous Hawks exhibited negative relationships 

with OG development, even after accounting for the 

potential confounding influence of vegetation and climate. 

Only ANS-associated Ferruginous Hawks exhibited 

consistent positive relationships between cluster status 

and OG development (Ferruginous Hawk use of ANSs 

is a special situation; i.e., ANSs were erected to mitigate 

their attempts to nest on OG structures and is discussed 

more extensively in Neal et al. [2010]). Additionally, our 

results suggest that the currently applied nesting-season 

protection buffers of 0.8-km radius in the Price and 

Rawlins study areas should not be reduced, as Price-area 

Golden Eagles and Rawlins-area Ferruginous Hawks, 

Red-tailed Hawks, and Prairie Falcons exhibited negative 

relationships with OG development that occurred within 

0.8 km of nest clusters. Further, our results suggest that 

current protections may have been insufficient to preclude 

negative impacts for all species, as we detected negative 

relationships between OG development and Price-area 

Golden Eagles and Rawlins-area Golden Eagles and 

Prairie Falcons at the broader 2.0-km spatial scale. 
58 BLM Technical Note 433 • Oil and Gas Development and Nesting Raptors

An important caveat to consider, however, is that we 

generally had only insufficient or incomplete monitoring 

data to work with, primarily because of different initial 

monitoring objectives and inconsistent surveys, which 

precluded rigorous evaluation of the long-term trends 

in nesting densities, nest success, and productivity. 

Therefore, we were unable to effectively evaluate overall 

long-term trends in nesting activity in relation to changing 

development levels, and thus could not determine whether 

or not the apparent negative effects of development 

caused significant population-level effects.

To overcome the difficulties HWI encountered during 

this research and to gain greater insight into appropriate 

nest-protection guidelines would require that federal land 

managers employ more rigorous and standardized nestmonitoring protocols in the future. Toward this end, HWI 

prepared a separate document entitled “Recommendations 

for Improved Raptor Nest Monitoring in Association 

with Oil and Gas Development Activities” (Smith et 

al. 2010). This document outlines the data limitations 

HWI encountered and, even more importantly, provides 

detailed recommendations for land managers to consider 

to improve raptor-monitoring efforts in areas experiencing 

or expected to experience OG development. Adoption 

of the methods detailed by HWI would not only serve to 

immediately aid land managers in the protection of raptor 

nests and nesting activities, but would also contribute to 

a greater understanding of the disturbance effects of OG 

development and generate additional data that could be 

used to more rigorously assess the efficacy of particular 

spatial and temporal nest protections in the future.
59 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

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63 Oil and Gas Development and Nesting Raptors • BLM Technical Note 433

Appendix A. Glossary.

artificial nest structure (ANS): a manmade structure 

specifically designed to accommodate a raptor nest; 

in the context of this study, comprising a sturdy 4-m 

pole sunk ~1m into the ground, with roughly a 1-m2

hardware cloth and wood-framed platform affixed 

to the top of the pole on which the nest is built, and 

generally with a lateral crossarm extending to the side 

of the platform to serve as a perch for the adult birds 

next to the nest (see Tigner and Call 1996).

breeding attempt: laying of at least one egg, regardless of 

nest fate 

disturbance: any human-caused activity interfering 

with normal wildlife behavior; in the context of 

this document, disturbance particularly refers to 

interference with raptor nesting ecology.

nest cluster: a variable number of clustered, alternative 

nest sites maintained by an individual breeding pair, 

which they may tend each year and use in different 

years for actual nesting. Note that a species-specific 

nest cluster may include some nests that are variably 

used by other species 

nest or cluster status: dual classification schemes 

adopted for the purposes of statistical analyses. The 

overlapping classification schemes allowed the 

comparison of “used vs. unused” and “active vs. 

other” nests/clusters. We defined the categories as 

follows:

(1) “Used” = evidence of recent nest tending, 

occupation, or actual breeding attempt obtained

“Unused” = no such evidence obtained, despite 

nest check

(2) “Active” = breeding attempt confirmed

“Other” = all other cases, including some used 

(i.e., evidence of tending, but not actual breeding) 

and all unused classifications 

nesting success: classification of a nest as either 

successful or failed

productivity: the number of nestlings produced to at least 

80% of the average fledging age for a given species

successful nest: a nest in which at least one nestling 

survived to at least 80% of the average fledging age 

for the relevant species

tended nest: a nest that shows evidence of recent activity, 

maintenance, or sprucing; i.e., fresh sticks, greenery, 

bowl-lining materials, or whitewash.



The mention of trade names or commercial products does not constitute endorsement

or recommendation for use by the Federal Government.
BLM Technical Note 432 Raptor Nesting Near Oil and Gas Development:

An Overview of Key Findings and Implications

for Management Based on Four Reports by

Hawk Watch International 

BLM Technical Note 433 An Assessment of the Effects of Oil and Gas Field 

Activities on Nesting Raptors in the Rawlins, 

Wyoming and Price, Utah Field Offices of the 

Bureau of Land Management

BLM Technical Note 434 Artificial Nest Structures as Mitigation for NaturalGas Development Impacts to Ferruginous Hawks 

(Buteo regalis) in South-Central Wyoming

BLM Technical Note 435 Accipiter Use of Pinyon–Juniper Habitats for 

Nesting in Northwestern Colorado

BLM Technical Note 436 Recommendations for Improved Raptor Nest 

Monitoring in Association with Oil and Gas 

Development Activities

These five documents are an integrated series.