SPR 626 Wildlife Accident Reduction Study and Monitoring: Arizona State Route 64

Wildlife Accident Reduction

Study and Monitoring:

Arizona State Route 64

Final Report 626

November 2012

Arizona Department of TransportaƟon

Research Center

WildlifeAccidentReductionStudy

andMonitoring:

ArizonaStateRoute64



FinalReport626

November2012



Preparedby:

NorrisL.Dodd,Jeffre yW.Gagnon,ScottSprague,

SusanBoe,andRaymondE.Schweinsburg

ArizonaGameandFishDepartment

5000W.CarefreeHwy.

Phoenix,AZ85068



Preparedfor:

ArizonaDepartmentofTransportation

incooperationwith

U.S.DepartmentofTransportation 

FederalHighwayAdministration

ThisreportwasfundedinpartthroughgrantsfromtheFederalHighwayAdministration,

U.S.DepartmentofTransportation.Thecontentsofthisreportreflecttheviewsofthe

authors,whoareresponsibleforthefactsandtheaccuracyofthedata,andfortheuse

oradaptationofpreviouslypublishedmaterial,presentedherein.Thecontentsdonot

necessarilyreflecttheofficialviewsorpoliciesoftheArizonaDepartmentof

TransportationortheFederalHighwayAdministration,U.S.Departmentof

Transportation.Thisreportdoesnotconstituteastandard,specification,orregulation.

Tradeormanufacturers’namesthatmayappearhereinarecitedonlybecausetheyare

consideredessentialtotheobjectivesofthereport.TheU.S.governmentandtheState

ofArizonadonotendorseproductsormanufacturers.

Technical Report Documentation Page

1. Report No.

FHWA-AZ-12-626

2. Government Accession No.

3. Recipient’s Catalog No.

4. Title and Subtitle

Wildlife Accident Reduction Study and Monitoring:

Arizona State Route 64

5. Report Date

November 2012

6. Performing Organization Code

7. Authors

Norris L. Dodd, Jeffrey W. Gagnon, Scott C. Sprague, Susan Boe,

and Raymond E. Schweinsburg

8. Performing Organization Report No.

9. Performing Organization Name and Address

Arizona Game and Fish Department

Research Branch

5000 W. Carefree Hwy.

Phoenix, AZ 85068

10. Work Unit No.

11. Contract or Grant No.

SPR-000-1(171)626

12. Sponsoring Agency Name and Address

Arizona Department of Transportation

Research Center, 206 S. 17th Ave., MD075R

Phoenix, AZ 85007

ADOT Project Manager: Estomih Kombe, Ph.D., PE

13.Type of Report & Period Covered

Final Report

14. Sponsoring Agency Code

15. Supplementary Notes

Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration

16. Abstract

The research team assessed elk (Cervus elaphus), mule deer (Odocoileus hemionus), and pronghorn (Antilocapra

americana) movements and vehicle collision patterns from 2007 through 2009 along a 57 mi stretch of State Route

(SR) 64 to develop strategies to improve highway safety and wildlife permeability. This study followed the SR 64 2006

Final Wildlife Accident Reduction Study that recommended nine wildlife passage structures and further monitoring to

determine the best locations for passage structures and fencing. Research objectives were to:

 Assess wildlife movements, highway crossing patterns, and permeability across SR 64.

 Assess relationships of wildlife crossings and distribution to vehicular traffic volume.

 Investigate wildlife-vehicle collision spatial and temporal incidence and patterns.

 Determine use of Cataract Canyon Bridge by wildlife for below-grade passage.

 Develop recommendations to enhance highway safety and wildlife permeability.

The team tracked 23 elk, 11 deer, and 15 pronghorn with Global Positioning System (GPS) receiver collars, yielding

mean passage rates of 0.44, 0.54, and 0.004 crossings/approach, respectively. In total, 167 wildlife-vehicle collisions

were analyzed. Traffic volume influenced permeability and wildlife-vehicle collision patterns. The team recommended

11 passage structures, including Cataract Canyon Bridge, which had modest current wildlife use, along with wildlife

fencing to reduce collisions and promote permeability for elk, deer, and pronghorn.

17. Key Words

Elk, GPS telemetry, fencing, highway impact, mule deer,

permeability, pronghorn, traffic volume, wildlife passage

structures, wildlife accident reduction, wildlife-vehicle

collisions

18. Distribution Statement

This document is available to the U.S.

public through the National Technical

Information Service, Springfield,

Virginia 22161

23. Registrant’s Seal

19. Security Classification

Unclassified

20. Security Classification

Unclassified

21. No. of Pages

111

22. Price

TABLE OF CONTENTS

1.0 INTRODUCTION ................................................................................................... 3

2.0 LITERATURE REVIEW ........................................................................................ 9

2.1 Background .....................................................................................................9

2.2 Research Justification ....................................................................................12

2.3 Research Objectives ......................................................................................16

3.0 Study Area ............................................................................................................. 17

3.1 Natural Setting ...............................................................................................17

3.1.1 Climate ............................................................................................ 17

3.1.2 Vegetation ....................................................................................... 19

3.1.3 Wildlife Species .............................................................................. 19

3.1.4 Cataract Canyon Bridge .................................................................. 21

3.2 Traffic Volume ..............................................................................................22

4.0 Methods.................................................................................................................. 25

4.1 Wildlife Capture, GPS Telemetry, and Data Analysis ..................................25

4.1.1 Elk Capture ..................................................................................... 25

4.1.2 Mule Deer Capture .......................................................................... 25

4.1.3 Pronghorn Capture .......................................................................... 25

4.1.4 GPS Analysis of Animal Movements ............................................. 27

4.1.5 Calculation of Crossing and Passage Rates .................................... 27

4.1.6 Calculation of Pronghorn Approaches ............................................ 29

4.1.7 Calculation of Weighted Crossings and Approaches...................... 30

4.2 Traffic Volume and Animal Distribution Relationships ...............................30

4.3 Wildlife-Vehicle Collision Relationships .....................................................31

4.4 Wildlife Use of Cataract Canyon Bridge ......................................................31

4.5 Identification of Passage Structure Sites .......................................................33

5.0 Results .................................................................................................................... 37

5.1 Wildlife Capture, GPS Telemetry, and Data Analysis ..................................37

5.1.1 Elk Capture, Movements, and Highway Permeability .................... 37

5.1.2 Mule Deer Capture, Movements, and Highway Permeability ........ 41

5.1.3 Pronghorn Capture, Movements, and Highway Approaches .......... 42

5.2 Traffic Relationships .....................................................................................46

5.2.1 Elk-Traffic Relationships ................................................................ 46

5.2.2. Mule Deer–Traffic Relationships ................................................... 46

5.2.3. Pronghorn-Traffic Relationships .................................................... 51

5.3. Wildlife-Vehicle Collision Relationships .....................................................51

5.4 Wildlife Use of Cataract Canyon Bridge ......................................................58

5.5 Identification of Passage Structure Sites .......................................................59

5.5.1 Passage Structure Recommendations by Highway Section ............ 64

6.0 Discussion .............................................................................................................. 69

6.1 Wildlife Permeability ....................................................................................69

6.2 Wildlife Distribution and Traffic Relationships ............................................71

6.3 Wildlife-Vehicle Collision Relationships .....................................................72

6.4 Cataract Canyon Bridge Wildlife Use ...........................................................74

6.5 Identification of Passage Structure Sites .......................................................75

6.5.1 Passage Structure Design Considerations ....................................... 75

6.5.2 Role of Passage Structure Spacing ................................................. 79

6.5.3 Role of Fencing ............................................................................... 79

7.0 Conclusions and Recommendations ...................................................................... 83

7.1 Final Wildlife Accident Reduction Study Role .............................................83

7.2 Wildlife Permeability and Passage Structures ..............................................84

7.2.1 Elk and Mule Deer Permeability ..................................................... 84

7.2.2 Pronghorn Permeability .................................................................. 85

7.3 Impact of Traffic and Noise ..........................................................................86

7.4 Passage Structure Design and Placement ......................................................86

7.4.1 Role of Passage Structure Spacing ................................................. 87

7.4.2 Role of Fencing ............................................................................... 87

7.5 Highway Safety and Wildlife-Vehicle Collisions .........................................88

7.6 Monitoring .....................................................................................................89

References ......................................................................................................................... 91

LIST OF FIGURES

Figure 1. Landownership, Mileposts, 0.1 mi Segments, Highway Sections

A–E, and Preliminary Wildlife Passage Structures in the SR 64

Study Area Identified by the Final Wildlife Accident Reduction

Study (ADOT 2006). ................................................................................ 15

Figure 2. Great Basin Conifer Woodland Adjacent to SR 64 (Top) with

Open to Dense Stands of Pinyon and Juniper and Cliffrose, Apache

Plume, and Other Shrubs, and Plains and Great Basin Grasslands

(Bottom) Dominated by Blue and Black Grama, Galleta, and

Needle-and-Thread Grasses. ..................................................................... 18

Figure 3. Density Distributions for the Three Target Species of Research

along SR 64: Elk (Left), Mule Deer (Center), and Pronghorn

(Right). ...................................................................................................... 20

Figure 4. Cataract Canyon Bridge on SR 64. ........................................................... 21

Figure 5. Hourly Traffic Volume (Vehicles per Hour) along SR 64, Arizona,

from 2007 through 2009. Note the Low Volume of Traffic during

Nighttime Hours (00:00–04:00). ............................................................... 23

Figure 6. Photographs of Capture Techniques Used for Elk, Mule Deer and

Pronghorn along SR 64 and GPS-Collared Animals: Elk Captured

with Net-Covered Clover Trap (Top), Darting to Immobilize Mule

Deer (Middle), and Net-Gunning of Pronghorn from a Helicopter

(Bottom). ................................................................................................... 26

Figure 7. GPS Locations and Lines between Successive Fixes to Determine

Highway Approaches and Crossings in 0.10 mi Segments. The

Expanded Section Shows GPS Locations and Lines between

Successive Fixes to Determine Approaches to the Highway

(Shaded Band) and Crossings. Example A Denotes an Approach

and Crossing; Example B Denotes an Approach without a

Crossing. ................................................................................................... 29



Figure 8. Reconyx

™

Camera Mounted on a Wood Strip Glued to the

Concrete Surface of Each SR 64 Cataract Canyon Bridge Culvert

Cell to Monitor Wildlife Use. ................................................................... 32



Figure 9. Images of a Mule Deer Doe (Left) and Spike Bull Elk (Right)

Recorded by Reconyx

™

Cameras Mounted in the SR 64 Cataract

Canyon Bridge Culvert Cells. ................................................................... 32



Figure 10. SR 64 Crossings by GPS-Collared Elk along the Entire Study Area

(Top) and Sections A through E of the 2006 Final Wildlife

Accident Reduction Study and Enlarged to Show Crossings along

Sections D and E (Bottom). ...................................................................... 39



Figure 11. SR 64 Weighted Crossings by GPS-Collared Elk along the Entire

Study Area (Top) and Sections A through E of the 2006 Final

Wildlife Accident Reduction Study and Enlarged to Show Crossings

along Sections D and E (Bottom). ............................................................ 40

Figure 12. Mule Deer GPS Fixes along the SR 64 Study Area, as well as

Fixes for Two Deer Captured North of Flagstaff (Numbers 43 and

172). .......................................................................................................... 42



Figure 13. SR 64 Highway Crossings (Top) and Weighted Crossings

(Bottom) by GPS-Collared Mule Deer along Highway Section E

by 0.1 mi Segment. ................................................................................... 43

Figure 14. Highway Approaches (Top) and Weighted Approaches (Bottom)

Made to within 0.3 mi of SR 64 by GPS-Collared Pronghorn and

Sections A through E of the 2006 Final Wildlife Accident

Reduction Study. ....................................................................................... 45

Figure 15. Mean Probability That GPS-Collared Elk Occurred within 330 ft

Distance Bands along SR 64 at Varying Traffic Volumes. ...................... 48

Figure 16. Mean SR 64 Passage Rates by Two-Hour Time Blocks (Reflected

by the Midpoint of the Blocks) and Corresponding Mean Traffic

Volumes during Each Time Block for Elk (Bottom) and Mule Deer

(Top). ........................................................................................................ 49

Figure 17. Mean Probability That GPS-Collared Mule Deer Occurred within

330 ft Distance Bands along SR 64 at Varying Traffic Volumes. ............ 50

Figure 18. Mean Probability That GPS-Collared Pronghorn Occurred within

330 ft Distance Bands along SR 64 at Varying Traffic Volumes. ............ 52



Figure 19. Frequency of Elk and Mule Deer Collisions with Vehicles by

SR 64 Milepost from 2007 through 2009. ................................................ 53

Figure 20. Proportion of SR 64 Single-Vehicle Accidents by Milepost from

1998 through 2008 that Involved Wildlife. ............................................... 54



Figure 21. SR 64 Elk and Mule Deer Collisions with Vehicles by Time of

Day and Associated Traffic Volume. ........................................................ 55



Figure 22. SR 64 Elk and Mule Deer Collisions with Vehicles by Day and

Associated Traffic Volume. ...................................................................... 56



Figure 23. SR 64 Elk and Mule Deer Collisions with Vehicles by Month and

the Mean Traffic Volume. ......................................................................... 58

Figure 24. Ratings for 95 SR 64 0.6 mi Segments Using Wildlife Movement,

WVC Data, and Other Criteria to Determine the Location of

Potential Wildlife Passage Structures. Red Bars Denote Segments

Where Underpasses Were Recommended in the 2006 Final

Wildlife Accident Reduction Study and Orange Where Overpasses

Were Recommended (Table 9). The Green Bars Represent

Segments Where Additional Structures Are Recommended as a

Result of This Study.................................................................................. 61

Figure 25. Recommendations for SR 64 Wildlife Passage Structures and

Wildlife Fencing for Highway Section A (Left) and Section B

(Right). ...................................................................................................... 65

Figure 26. Recommendations for SR 64 Wildlife Passage Structures and

Wildlife Fencing for Highway Sections D and E. .................................... 66

Figure 27. Various Wildlife Passage Structure Options for SR 64, Including

CON/SPAN

Pre-Cast Concrete Arches for Overpasses, with a

Rendering of a Pronghorn Overpass on US 89 Integrated into Cut

Slopes (Top Left) and a Stand Alone Overpass on US 93 in

Montana (Top Right), Single-Span Bridged Underpasses Similar to

Those Used on SR 260 (Center), and Corrugated Multi-Plate Arch

Underpasses Used along US 93 in Montana (Bottom). ............................ 78

Figure 28. An Electrified Barrier Installed in the Pavement to Prevent

Wildlife from Breaching the Fenced Corridor at the Fencing

Terminus. This Mat was Installed on I-40 Off-Ramps in New

Mexico. ..................................................................................................... 81

LIST OF TABLES

Table 1. Vehicle Accidents Involving Collisions with Elk and Mule Deer

along SR 64 from 1991 through 2003, Including the Mean Number

of Collisions (per Year and per Mile). ...................................................... 13

Table 2. SR 64 Sections and Mileposts with Proposed Wildlife Mitigation

Measures for Focal Wildlife Species Identified in the 2006 Final

Wildlife Accident Reduction Study. ........................................................... 14

Table 3. Comparative Mean Values for GPS-Collared Animals by Species

Determined from GPS Telemetry along SR 64. ....................................... 38

Table 4. Mean Probabilities that GPS-Collared Elk, Mule Deer, and

Pronghorn Occurred within Distance Bands from SR 64 at Varying

Traffic Volumes. Documented from 2007 through 2009. ........................ 47

Table 5. WVCs Involving Elk and Mule Deer on SR 64 Sections from 2007

through 2009, including the Total Number and Mean Collisions

(per Mile). ................................................................................................. 51

Table 6. Frequency of Elk and Deer Collisions with Vehicles along SR 64

by Time Period. ......................................................................................... 57

Table 7. Frequency of Elk and Deer Collisions with Vehicles along SR 64

by Season. ................................................................................................. 57

Table 8. Number of Animals by Species that Entered and Successfully

Crossed through Cataract Canyon Bridge on SR 64, and Success

Rates. ......................................................................................................... 59

Table 9. Wildlife Passage Structure Locations along SR 64 by Milepost and

Highway Section and Types Recommended in the Various 2006

Final Wildlife Accident Reduction Study Alternatives and Those

Recommended as a Result of the Current Wildlife Movements

Study. ........................................................................................................ 63

ACRONYMS AND ABBREVIATIONS

AADT average annual daily traffic

ADOT Arizona Department of Transportation

AGFD Arizona Game and Fish Department

ATR automatic traffic recorder

BLM Bureau of Land Management

DPS Department of Public Service

ft foot or feet

GCNP Grand Canyon National Park

GMU Game Management Unit

GPS Global Positioning System

hr hour(s)

I-40 Interstate 40

MCP minimum convex polygon

mi mile(s)

MP milepost

mph miles per hour

NF National Forest

ROW right(s)-of-way

SDI Shannon diversity index

SE standard error

SR State Route

U.S. United States

US 89 U.S. Route 89

US 93 U.S. Route 93

US 180 U.S. Route 180

VHF very high frequency

WVC wildlife-vehicle collision

LIST OF SPECIES

Animals

Badger Taxidea taxus

Black bear Ursus americanus

Black-tailed jackrabbit Lepus californicus

Caribou Rangifer tarandus

Coyote Canis latrans

Desert cottontail rabbit Sylvilagus audubonii

Elk Cervus elaphus

Gray fox Urocyon cinereoargenteus

Grizzly bear Ursus arctos horribilis

Moose Alces alces

Mountain lion Puma concolor

Mule deer Odocoileus hemionus

Pronghorn Antilocapra americana

Raccoon Procyon lotor

Squirrel Spermophilus variegatus

Striped skunk Mephitis mephitis

White-tailed deer Odocoileus virginianus couesi

Wolf Canis lupus

Plants

Apache plume Fallugia paradoxa

Big sagebrush Artemisia spp.

Black grama Bouteloua eriopoda

Blue grama Bouteloua gracilis

Cliffrose Cowania mexicana

Galleta Pleuraphis jamesii

Gambel oak Quercus gambelii

Needle-and-thread grass Hesperostipa comata

One-seed juniper Juniperus monosperma

Pinyon Pinus edulis

Ponderosa pine Pinus ponderosa

Rabbitbrush Ericameria nauseosa

Winterfat Ceratoides lanata

ACKNOWLEDGMENTS

This project was funded by the Arizona Department of Transportation (ADOT) Research

Center and the Federal Aid Wildlife in Restoration Act, Project W-78-R, supporting

Arizona Game and Fish Department (AGFD) research. The research team commends

ADOT for its proactive commitment to promoting wildlife connectivity. The support of

the Federal Highway Administration, including former Environmental Project Manager

Steve Thomas, was instrumental to the funding and conduct of the project.

Many individuals at ADOT provided support and guidance in this project. The research

team commends Roadway Predesign Section for its commitment to developing

preliminary strategies for resolution of wildlife-highway conflicts. Estomih Kombe of the

ADOT Research Center provided project oversight and coordination. The team thanks

John Harper and Chuck Howe of the Flagstaff District for their tremendous support and

innovative management. Doug Eberline and Jennifer Toth of the Multimodal Planning

Division provided traffic data support. The team also thanks Todd Williams and Justin

White, of the Office of Environmental Services as well as Bruce Eilerts and Siobhan

Nordhaugen, formerly with the same office, for their commitment to the project and

overall efforts to address wildlife permeability.

AGFD Flagstaff Region personnel played a crucial role in supporting the project,

including Ron Sieg, Tom McCall, Carl Lutch, and David Rigo. The outstanding capture

support provided by Larry Phoenix was vital to the success of pronghorn capture and the

project. The research team also is indebted to the capable pilots of Papillion Helicopters.

Kari Ogren, Rob Nelson, and Chad Loberger of the AGFD Research Branch provided

invaluable field support, data collection, and analysis.

Highway patrol officers with the Arizona Department of Public Safety (DPS) Flagstaff

District made an outstanding effort to record all wildlife-vehicle collisions along State

Route (SR) 64. They collected and recorded information that was important to the

research project beyond what was required on accident reports. The research team is

particularly grateful to Matt Bratz for his coordination of accurate accident data

collection by DPS and his valuable input into the 2006 Wildlife Accident Reduction

Study.

The Kaibab National Forest (NF) provided invaluable logistical support during the

project. In particular, Jeffrey Waters provided project guidance and coordination, as well

as assistance with pronghorn capture.

The Arizona Antelope Foundation, the Rocky Mountain Elk Foundation, the Arizona Elk

Society, the Arizona Deer Association, and the Mule Deer Foundation were crucial in

helping to meet matching funding requirements for the project. Their interest and

commitment to efforts to promote wildlife permeability are sincerely appreciated.

The Technical Advisory Committee (TAC) provided many suggestions toward improving

the project’s effectiveness and applicability. Its tremendous support, oversight, and

commitment throughout the project are appreciated.

EXECUTIVE SUMMARY

The ADOT Research Center funded this study through a funding allocation from the

Federal Highway Administration (FHWA) State Planning and Research Program (SPR).

The Arizona Game and Fish Department was assigned the lead role in the execution of

the study and making recommendations based on the results. This partnership was made

possible with a joint project agreement (JPA) between the two state departments. The

study would concentrate on a 57 mile stretch of SR 64 beginning at the southern end,

which is the junction with Interstate 40. The focus would be a thorough evaluation of the

movement of elk, mule deer, and pronghorn in relation to highway and habitat

characteristics, traffic volumes, wildlife related accidents, and existing highway assets

like bridges.

The incidence of wildlife vehicle collisions along State Route 64 (SR 64) in Arizona has

been on the rise and thus a growing safety issue. Data collected over a ten year period

ending in 2008 showed 42 percent of single vehicle accidents in the study area involved

wildlife. The national average for wildlife related accidents is only five percent. In

addition, on a five mile stretch of highway at the north end of the study area wildlife

related accidents accounted for 75 percent of all single vehicle accidents.

Apart from the safety issue, good wildlife management means that we need to pay

attention to whether highway infrastructure may be creating a barrier to essential wildlife

movement within its habitat. In the long term, for wildlife to flourish, it is important that

man made barriers do not create scattered ‘islands’ of smaller and smaller animal

populations. Such an unintended segregation of wildlife populations has the potential to

result in diminished genetic strength and other weaknesses related to small numbers that

ultimately leads to a slow death for the affected species. It is therefore important that

efforts to address one issue (like wildlife vehicle collisions) are not done in a manner that

worsens the situation with respect to another important consideration. Solutions need to

be developed that strike a good balance between these needs.

What the Data Shows

For purposed of data collection and analysis, the designated length of highway for the

study was divided into small sections one tenth of a mile long. This would enable

researchers to clearly identify and chart out where within the proximity of the highway

animals were located or seen to make successful crossings. Monitoring of animal

movements was made possible by the use of Global Positioning System (GPS) collars on

animals. Successful crossings by an animal were identified when two consecutive

location coordinates for a collared animal matched locations on opposite sides of the

highway.

In addition to the use of GPS collars to monitor animal movement and highway

crossings, wildlife-vehicle collision data and traffic volume data were collected during

the study as well as from other sources. Relevant evaluations by other researchers in

prior years were also reviewed to see whether they were in agreement with or

contradicted any of our results. The evaluation attempted to establish relationships

between wildlife crossing levels and corresponding wildlife vehicle accidents and traffic

levels for different highway segments.

Analysis of the data established a positive relationship between wildlife crossing figures

and vehicle collisions for both elk and mule deer. No pronghorn crossings or

accidents/collisions were documented. It is thought that the highway may constitute

enough of a barrier that pronghorn will not venture to approach it. In comparison to

highway approach and crossing data seen for elk and deer in other locations like State

Route 260, the approach and crossing levels documented for SR 64 are considerably

lower. This is thought to be explained in part by the absence of attractive wet

meadow/riparian foraging habitat areas. Overall, high traffic volumes were associated

with lower wildlife (in this case elk and mule deer) approaches to and crossings of

highways. Where these high traffic volumes lasted only short durations, and thus could

be considered temporary, animals could be expected to return to habitat close to the

highway when the period of high traffic volume ended.

Summary of Conclusions and Recommendations

 For some wildlife (like elk and mule deer), wildlife vehicle accident data can be

used reliably in the identification of locations where wildlife crossing assets can

make a big impact. When possible, GPS tracking is useful for supplemental data.

 For animals like pronghorn which have a strong tendency to keep their distance

from busy highways, GPS tracking studies are crucial for collecting the data

necessary to identify potential solutions.

 Measures to reduce noise and negative visual impacts near wildlife crossing

infrastructure have potential to enhance the effectiveness of these assets.

 Passage structure designs should consider some important characteristics, some

specific to the main animal species of concern, noted in the report to maximize the

use of these assets.

 Based on the full set of data collected and analyzed as part of this study, the

research team identified a total of eleven potential wildlife passage locations. As

opportunities arise, consideration should be made towards the implementation of

some underpasses/overpasses or the retrofitting of bridges as the case may be. The

specific details are provided for each of the potential locations.

 For existing or future wildlife passage structures, having a length of appropriate

fencing to ‘channel’ animals to the point of crossing is an important part of

achieving maximum benefits from what is typically a sizeable infrastructure

investment.

1.0 INTRODUCTION

The research team assessed wildlife-highway relationships from 2007 through 2009 along

a 57 mile stretch of State Route (SR) 64, the highway linking Interstate 40 (I-40) and

Grand Canyon National Park (GCNP) in north-central Arizona. The incidence of

wildlife-vehicle collisions (WVCs) involving elk and mule deer along this stretch of

highway is a significant and growing concern, as is the ability of wildlife to cross the

highway corridor, or permeability. This predominately two-lane highway will be

reconstructed in the future to a four-lane divided highway to address growing traffic

volume and the incidence of WVCs. The average annual traffic volume on SR 64 was

4275 vehicles per day during the study period, but traffic levels at night were low,

averaging less than 10 vehicles per hour for a 4 hour period.

In a Final Wildlife Accident Reduction Study (1991–2003), the Arizona Department of

Transportation (ADOT) commissioned the development of a proactive assessment of

WVCs and potential mitigation measures to reduce the incidence of WVCs along

approximately 50 miles of SR 64 (185.5–235.4). This assessment (ADOT 2006)

recommended that nine passage structures be integrated into future highway

reconstruction of SR 64. It also recognized the need to conduct further field evaluation

and monitoring to determine the best locations for wildlife passage structures and the

extent of fencing needed to funnel animals to the structures.

The study called for assessing wildlife use of Cataract Canyon Bridge to determine

whether its design is conducive to wildlife passage. The assessment addressed the

potential barrier effect on pronghorn and recommended that this issue also be addressed

with monitoring. As a result of these recommendations, this research project was initiated

in 2007, with the following objectives:

 Assess elk (June 2007 through October 2009), mule deer (April 2008 through

October 2009), and pronghorn (January 2008 through January 2009) movements,

highway crossing patterns, and distribution relative to SR 64 and determine

permeability across the highway corridor.

 Investigate the relationships of elk, mule deer, and pronghorn highway crossing and

distribution patterns to SR 64 vehicular traffic volume (2007 through 2009).

 Investigate WVC patterns and relationships to elk, mule deer, and pronghorn

movement and highway crossing patterns in relation to SR 64 (2007 through 2009).

 Assess the degree to which Cataract Canyon Bridge is used by wildlife for below-

grade passage (July 2008 through December 2009).

 Develop recommendations to enhance elk, mule deer, and pronghorn highway

permeability along SR 64 through the application of wildlife passage structures and

ungulate-proof fencing.

MOVEMENTS AND PERMEABILITY

The research team determined highway crossings and calculated the crossing and passage

rates for elk, mule deer, and pronghorn using Global Positioning System (GPS)

telemetry. Passage rates served as the team’s relative measure of highway permeability,

calculated as the number of times animals crossed SR 64 in proportion to the number of

times animals approached to within 0.15 mi. The research team tracked 23 elk fitted with

GPS collars and accrued 107,055GPS relocations. Elk crossed the highway 843 times, an

average of 0.12 times per day, with the highest proportion of crossings (60 percent)

occurring during the driest season (April–July).

Travel to limited water sources likely influenced movement and crossing patterns. The

elk passage rate averaged 0.44 crossings per approach, 52 percent lower than the rate

found during previous research on SR 260 sections with similar highway standards (Dodd

et al. “Evaluation of Measures,” 2007). The elk crossing distribution was not random and

exhibited several peak crossing zones, especially at the north end of the study area.

The research team tracked 11 mule deer fitted with collars and accrued 29,944GPS fixes.

Deer crossed SR 64 550 times, an average of 0.26 times per day—twice as frequently as

elk. Seasonal deer crossings were more consistent than seasonal elk crossings, though

46 percent of crossings occurred during late summer and fall (August–November). The

average deer passage rate was 0.54 crossings per approach, which was higher than the

rate for elk. The mule deer crossing distribution did not occur in a random fashion. It

exhibited two peak crossing zones at the north end of the study area, with 92 percent of

the crossings occurring along a 3.2 mile stretch between Grand Canyon Airport and the

GCNP, in the vicinity of Tusayan.

The research team tracked 15 pronghorn with GPS collars that amassed 56,433 GPS

fixes. Only a single GPS-collared pronghorn crossed SR 64 (three times), for a crossing

rate average of 0.001 crossings per day. The mean pronghorn passage rate was a

negligible 0.004 crossings per approach, indicating that SR 64 is a near total barrier to

pronghorn passage. Pronghorn approached the highway 4269 times, and the distribution

was not random. The approach distribution exhibited three peaks along SR 64, with the

largest peak near the south boundary of the Kaibab National Forest, north of Valle.

TRAFFIC RELATIONSHIPS

In cooperation with ADOT, the research team measured traffic volume using a permanent

automatic traffic recorder. The pattern of elk and mule deer distribution with fluctuating

traffic was consistent with published models that indicated reduced “habitat

effectiveness” near the highway.

The use of habitat within 990 ft of the highway, as measured by probability of presence

of all three species, was clearly reduced at higher traffic volumes. The mean proportion

for the three species occurring within 990 ft of SR 64 dropped nearly in half, from 0.34 at

less than 100 vehicles per hr to 0.19 at 200 to 300 vehicles per hour. However, elk and

deer returned to areas within 330 ft of the highway in proportions greater than 0.12 when

traffic volumes were low. The impact to habitat effectiveness for these two species thus

was temporary.

The highest levels of permeability for elk and deer (passage rates greater than

0.70 crossings per approach) occurred at night when traffic was lowest. Pronghorn, on the

other hand, are diurnal and are active when traffic is heaviest. Along SR 64, pronghorn

uniformly avoided habitats adjacent to the highway (within 330 ft), thus reflecting a

permanent loss in habitat effectiveness.

Peak daytime traffic volumes along SR 64 approach 10,000 vehicles per day, a volume

at which highways become strong barriers to wildlife passage. Pronghorn appeared more

sensitive to traffic volume impact than elk and deer, and their avoidance of the area

adjacent to the highway is problematic in terms of implementing effective passage

structures to promote permeability.

WILDLIFE-VEHICLE COLLISION RELATIONSHIPS

The incidence of WVCs along SR 64 is a growing highway safety issue, with an increase

in collisions from that documented in the 2006 Final Wildlife Accident Reduction Study

report (36.7 per year) to 52.0 per year during this study (ADOT 2006). The research team

recorded 167 WVCs, with elk accounting for 59 percent of the accidents and mule deer

accounting for 35 percent. SR 64 sections on Kaibab National Forest lands at the north

and south ends of the study area had the highest incidence of elk and deer collisions,

though the collision rate on the north end was more than twice the rate on the south end

near I-40. No WVCs involving pronghorn were recorded during the study.

The spatial association between WVCs and GPS-determined crossings at the 1.0 mi scale

was significant for elk and mule deer. From 1998 through 2008, 42 percent of all single-

vehicle accidents in the study area involved wildlife, compared with the national average

of just 5 percent. On the five miles at the north end of the study area, wildlife-related

accidents accounted for more than 75 percent of all single-vehicle accidents.

The observed frequency of elk-vehicle collisions by time of day was different from our

expectations, with the highest proportion of elk collisions (50 percent) recorded during

evening hours. There was a negative association between elk-vehicle collisions and

traffic volume by hour. Deer-vehicle collisions also varied by time of day, with 49

percent of accidents recorded during the evening. Accidents during the morning and

midday, when traffic volume was highest, accounted for 43 percent of deer-vehicle

collisions.

There was a significant difference in the observed versus expected frequency of elk-

vehicle collisions by season. The driest season of the year, early spring–summer (April–

July), accounted for 43 percent of all elk-vehicle collisions; late summer–fall accounted

for another 38 percent. The association between elk collisions and mean monthly traffic

volume was significant, which was not the case for deer. For mule deer, the incidence of

collisions was relatively constant through much of the year, except for the late summer–

fall season (August–November), when nearly half of all collisions occurred. The

association between highway crossings and collisions by month was significant for elk

and mule deer.

Using nationally accepted cost estimates associated with elk and mule deer collisions,

and based on 2007–2009 WVCs, the annual cost associated with SR 64 vehicle collisions

is estimated to be $612,513 for elk and $162,168 for deer, or a total of $774,681 per year;

over 20 years, the total cost from WVCs would exceed $15.5 million (Huijser et al.

2007).

CATARACT CANYON BRIDGE WILDLIFE USE

To quantify wildlife use of Cataract Canyon Bridge, the research team employed single-

frame cameras in each of the four box-culvert cells; these self-triggering cameras

provided infrared nighttime illumination to record animals crossing through the bridge at

night. In total, 126 wildlife images were recorded by cameras, including 13 elk and 37

mule deer. In addition to wildlife, substantial human presence was documented at the

bridge, with a total of 191 humans and 29 all-terrain vehicles passing under the structure.

Of the limited number of elk and mule deer that approached the bridge, 92 percent and

89 percent of these species, respectively, crossed through the bridge cells. The majority

(89 percent) of deer use occurred from August through October. Elk use occurred only in

October and December, with no approaches the rest of the year. Of all deer and elk bridge

crossings, 64 percent occurred in the 4 hr period between 11:00 p.m. and 3:00 a.m..

Though the documented wildlife use of the bridge was nominal, the research team’s

expectation for significant use was also low because wildlife fencing to limit at-grade

crossings and funnel animals to the bridge could not be accomplished as hoped. Despite

the limited wildlife use recorded on the cameras, the research team nonetheless believes

that the bridge has the potential to be a highly effective retrofitted wildlife passage

structure due to the comparatively high rates of mule deer and elk that crossed through

with minimal behavioral resistance.

The bridge exceeds all recommended structural and placement guidelines for effective

elk and mule deer passage structures. The high level of human use should not

significantly limit effective wildlife use of the structure because wildlife use occurs in the

evening and nighttime hours; human use occurs during daylight hours.

IDENTIFICATION OF PASSAGE STRUCTURE SITES

The research team used elk and mule deer highway crossings, WVCs, pronghorn

approaches, and the proportions of animals crossing or approaching within each segment,

among other criteria, to rate 95 0.6 mile segments for suitability as potential passage

structure locations, this 57 mile area extends into GCNP and included all areas where elk,

mule deer and pronghorn approached the highway and ranged outside of the area defined

by ADOT (2006). Additional criteria included land ownership and topography that

would support passage structure construction. The ratings ranged from 1 to 33 points on a

40-point scale and averaged 10.0 points per segment. The research team’s ratings

identified 11 priority wildlife passage structure locations; the 0.6 mile segments with

these structures averaged a 20.7 rating.

Six sites were conducive to underpasses, and five were at sites where the terrain was

conducive to overpasses and would promote pronghorn permeability. Of the nine wildlife

underpass locations identified in the 2006 Final Wildlife Accident Reduction Study report,

the research team’s rating of potential passage structure sites corroborated that eight were

warranted.

In addition to the passage structure sites recommended in the 2006 Final Wildlife

Accident Reduction Study report, which were based largely on WVC records and sites

where the topography could support a structure, the team identified three additional

passage structures. One of these was an underpass at the Kaibab National Forest–GCNP

boundary, and the other two structures are overpasses recommended for pronghorn

passage in an area where no WVC was recorded during this study or documented in the

2006 Final Wildlife Accident Reduction Study report.

A variety of passage structure types can be considered for use along SR 64, including the

single-span bridges used effectively along SR 260, cost-effective multi-plate arch

underpasses, and pre-cast concrete arches. The 11 structures recommended by the team

are spaced 1.5 to 2.3 mi apart, with this spacing generally consistent with guidelines for

elk and pronghorn.

The situation for pronghorn is very different from that for elk and deer. For pronghorn,

fencing in association with passage structures is not needed to preclude at-grade

pronghorn crossings, but it is important in providing a visual cue as to a path across the

highway barrier, provided no fencing is used at the mouth of the passages. For

pronghorn, minimizing the impact of high daytime traffic may be more critical than

fencing, especially given pronghorn avoidance of the habitats adjacent to SR 64. A

comprehensive set of measures to reduce traffic-associated impact could create “quiet

zones” along the highway corresponding to passage structures and could facilitate

pronghorn permeability.

Wildlife fencing plays an integral role with passage structures in achieving objectives for

reducing WVCs, promoting highway safety, and improving wildlife permeability,

especially for elk and deer. Failure to erect adequate fencing in association with passage

structures, even when spaced adequately, has been found to substantially reduce their

effectiveness. The research team identified a 14.2 mile section of the highway where

fencing would be needed to meet WVC reduction and permeability objectives.

2.0 LITERATURE REVIEW

2.1 BACKGROUND

Direct and indirect highway impacts have been characterized as some of the most

prevalent and widespread forces altering ecosystems in the United States (Noss and

Cooperrider 1994, Trombulak and Frissell 2000, Farrell et al. 2002). Forman and

Alexander (1998) estimated that highways have affected more than 20 percent of the

nation’s land area through habitat loss and degradation.

It is estimated that as many as 1.5 million collisions involving deer occur annually in the

United States (Conover 1997). Wildlife-vehicle collisions (WVCs) cause human injuries,

deaths, and tremendous property loss (Reed et al. 1982, Schwabe and Schuhmann 2002).

More than 38,000 human deaths attributable to WVCs occurred in the United States from

2001 through 2005, and the economic impact exceeds $8 billion a year (Huijser et al.

2007). The most pervasive impacts of highways on wildlife, however, are the barrier and

fragmentation effects resulting in diminished habitat connectivity (Noss and Cooperrider

1994, Forman and Alexander 1998, Forman 2000).

Highways block animal movements between seasonal ranges or other vital habitats.

This barrier effect fragments habitats and populations, reduces genetic interchange

(Gerlach and Musolf 2000, Epps et al. 2005, Riley et al. 2006), and limits dispersal of

young (Beier 1995), all disrupting viable wildlife population processes. Long-term

fragmentation and isolation renders populations more vulnerable to the influences of

catastrophic events and may lead to extinctions (Hanski and Gilpin 1997). Fencing that

blocks wildlife and livestock access across highways without provisions for adequate

passage may exacerbate barrier effects.

Though numerous studies have alluded to highway barrier effects on wildlife

(e.g., Forman et al. 2003), relatively few have provided quantitative data relative to

animal passage rates. Most studies have focused on the efficacy of passage structures in

maintaining wildlife permeability, the ability of animals to pass across highways

(Clevenger and Waltho 2003, Ng et al. 2004). Assessments of highway fragmentation

effects on relatively small, less mobile mammals (Swihart and Slade 1984, Conrey and

Mills 2001, McGregor et al. 2003) have proved easier to accomplish than assessments for

larger, more mobile species that are limited by cost-effective techniques to measure

permeability.

Paquet and Callaghan (1996) used winter track counts adjacent to highways and other

barriers to determine passage rates by wolves. Very high frequency (VHF) radio

telemetry has also been used to assess wildlife movements and responses to highways,

often pointing to avoidance of highways and roads (Brody and Pelton 1989, Rowland

et al. 2000).

Only a limited number of studies have addressed permeability in an experimental

(e.g., before and after construction) context with research controls (Hardy et al. 2003;

Roedenbeck et al. 2007; Olsson 2007; Dodd et al., “Evaluation of Measures,” 2007).

Olsson (2007) documented an 89 percent decrease in the mean moose-crossing rate

between before- and after-reconstruction levels along a highway in Sweden. Dyer et al.

(2002) compared actual road to simulated road network crossing rates, where caribou

crossed actual roads less than 20 percent as frequently as simulated networks.

Dodd et al. (“Assessment of elk,” 2007) stressed the value of a quantifiable and

comparable metric of permeability. They calculated elk highway passage rates from

Global Positioning System (GPS) telemetry to conduct before-after-control

reconstruction comparisons along SR 260. Dodd et al. (“Effectiveness of Wildlife,” in

review) reported that overall elk (n = 100) passage rates averaged 0.50 crossings per

approach. Among reconstruction classes, the mean elk passage rate for the before-

reconstruction control class (0.67) was 39 percent higher than the mean after-

reconstruction passage rate (0.41). They also calculated white-tailed deer passage rates

along SR 260; the rates averaged only 0.03 crossings per approach on control sections.

On reconstructed sections with passage structures, the passage rate was significantly

higher (0.16 crossings per approach).

Along United States Route 89 (US 89), Dodd et al. (“Effectiveness of Wildlife,” in

review) used the same consistent methodology and found the mean pronghorn (n = 31)

passage rate to be negligible—0.006 crossings per approach. US 89 constitutes a near-

total barrier to pronghorn passage.

In addition to the permeability insights gained from the previously discussed GPS

telemetry studies, the SR 260 and US 89 studies furthered the understanding that traffic

volume plays in the highway barrier effect. Theoretical models (Mueller and Berthoud

1997) suggest that highways averaging 4000 to 10,000 vehicles per day present strong

barriers to wildlife and would repel animals from the highway. Gagnon et al. (“Traffic

volume alters,” 2007) found that increasing vehicular traffic volume decreased the

probability of at-grade crossings by elk, which shifted their distribution away from the

highway with increasing traffic volume, consistent with Mueller and Berthoud (1997) and

Jaeger et al. (2005).

For white-tailed deer, Dodd and Gagnon (2011) found that at-grade SR 260 passage rates

were consistently low (fewer than 0.1 crossing per approach) across all traffic volumes.

Pronghorn also were consistently negatively impacted by traffic volume, even at low

levels, and distribution remained constant among all distances from US 89 and across all

traffic volumes up to 500 vehicles per hr (Dodd et al. “Effectiveness of Wildlife,” in

review). Whereas elk and deer highway crossings occur at night when traffic volume is

lowest, pronghorn are diurnal and active when traffic volumes are typically at their

highest (Gagnon et al. “Traffic volume alters,”2007) contributing to their low

permeability.

Collectively, these Arizona studies using consistent, comparable methodologies and

metrics have added substantially to the understanding of highway impact to wildlife

permeability and traffic volume relationships for multiple species and highways

exhibiting different traffic patterns. This understanding will further benefit from

continued studies that assess permeability for additional species and on highways that

expand the range of experimental conditions under which permeability is assessed

(Jaeger et al. 2005).

Numerous assessments of WVC patterns have been conducted, most focusing on deer

(Reed and Woodward 1981, Bashore et al. 1985, Romin and Bissonette 1996, Hubbard et

al. 2000). Only recently have WVC assessments specifically addressed elk-vehicle

collision patterns (Gunson and Clevenger 2003, Biggs et al. 2004, Dodd et al.

“Effectiveness of Wildlife,” in review, Gagnon et al. 2010). Insights gained from such

assessments have been instrumental in developing strategies to reduce WVCs

(Romin and Bissonette 1996, Farrell et al. 2002), including planning passage structures

to reduce at-grade crossings and to maintain permeability (Clevenger et al. 2002).

Consistent tracking of WVCs constitutes a valuable tool to assess the impact of highway

construction (Romin and Bissonette 1996) and the efficacy of passage structures and

other measures (e.g., fencing) in reducing WVCs (Reed and Woodard 1981, Ward 1982,

Clevenger et al. 2001, Dodd et al., “Evaluation of Measures,” 2007).

Increasingly, structures designed to promote wildlife passage across highways are being

implemented throughout North America, especially large bridges (e.g., underpasses or

overpasses) designed specifically for large animal passage (Clevenger and Waltho 2000,

Bissonette and Cramer 2008). Whereas early passage structures were typically

approached as single-species mitigation measures to address WVCs (Reed et al. 1975),

the focus today is more on preserving ecosystem integrity and landscape connectivity

benefiting multiple species (Clevenger and Waltho 2000).

Transportation agencies are increasingly receptive to integrating passage structures into

highways to address safety and ecological needs (Farrell et al. 2002). At the same time,

there is increasing expectation that such structures will benefit multiple species and

enhance connectivity (Clevenger and Waltho 2000), and that scientifically sound

monitoring of wildlife response to these measures will occur to improve effectiveness

(Clevenger and Waltho 2003, Hardy et al. 2003). Corlatti et al. (2009) argued for long-

term monitoring of wildlife passages to evaluate their effectiveness in maintaining

connectivity and promoting population and genetic viability, thus justifying their

relatively high cost.

Wildlife passage structures have indeed shown benefit in promoting wildlife passage for

a variety of wildlife species (Farrell et al. 2002; Clevenger and Waltho 2003; Dodd et al.,

“Assessment of elk,” 2007; Gagnon et al. 2011). Dodd et al. (“Evaluation of Measures,”

2007) found that elk passage rates along one section of SR 260 increased 52 percent to

0.81 crossings per approach once reconstruction was completed and ungulate-proof

fencing linking passage structures was erected. This pointed to the efficacy of passage

structures and fencing in promoting permeability, as well as achieving an 85 percent

reduction in elk-vehicle collisions (Dodd et al., “Evaluation of Measures,” 2007).

Gagnon et al. (“Effects of traffic,” 2007) found that traffic levels did not influence elk

passage rates during below-grade underpass crossings. This finding shows the benefit

of underpasses and fencing in promoting permeability by funneling elk to underpasses

where traffic has minimal effect compared with crossing at-grade during high traffic

volumes (Gagnon et al. “Traffic volume alters,”2007). The fivefold higher white-tailed

deer permeability along SR 260 after reconstruction with passage structures compared

with controls suggests the efficacy of passage structures; like elk, deer passage rates were

minimally affected by traffic on sections where passage structures facilitated below-grade

passage (Dodd and Gagnon 2011).

Structural characteristics and placement of wildlife crossing structures are important to

maximizing wildlife use (Reed et al. 1975; Foster and Humphrey 1995; Clevenger and

Waltho 2000, 2003; Dodd et al., “Evaluation of Measures,” 2007; Gagnon et al. 2011).

Prior studies modeled structural factors accounting for differences in wildlife use

(Clevenger and Waltho 2000, 2005; Ng et al. 2004). Gagnon et al. (2011) assessed five

factors, of which structural design and placement characteristics had the greatest

influence on elk use of SR 260 underpasses. However, given sufficient time for

habituation, most structures became equally effective for elk, even in spite of structural

or placement limitations.

The spacing between passage structures is also an important consideration (Bissonette

and Adair 2008). Dodd et al. (“Effectiveness of Wildlife,” in review) and Gagnon et al.

(2010) found considerable variation in mean elk passage rates (ranging from 0.09 to

0.81 crossings per approach) on three reconstructed SR 260 sections, likely reflecting the

corresponding variation in passage structure spacing ranging from 1.5 to 0.6 miles, with a

strong negative association with increased distance between structures (r = -0.847;

Dodd et al. “Effectiveness of Wildlife,” in review). Bissonette and Adair (2008) assessed

recommended passage structure spacing for several species tied to isometric scaling of

home ranges to define appropriate passage structure spacing distance. They

hypothesized that when used with other criteria this approach will help maintain

landscape permeability for a range of species.

Most assessments of wildlife passage structure use have been for newly constructed

structures implemented as part of major highway reconstruction projects (Clevenger and

Waltho 2000, 2005; Gagnon et al. 2011). However, some assessments have been of

primarily drainage structures retrofitted to serve as wildlife passage structures with the

erection of fencing to limit at-grade crossings and funnel animals to structures

(Gordon and Anderson 2003, Ng et al. 2004).

In Arizona, such retrofitting has considerable promise as a cost-effective approach to

minimizing WVCs and promoting permeability (Gagnon et al. 2010), particularly

compared with costly highway reconstruction that may not occur on some highways for

decades. As such, there is a need to better understand the potential effectiveness of

existing structures for retrofitting applications, including structural design characteristics

that may limit effectiveness.

2.2 RESEARCH JUSTIFICATION

The incidence of WVCs along SR 64 between I-40 and GCNP is a significant and

growing concern. In the future, this predominantly two-lane highway will be

reconstructed to a four-lane divided highway to address growing traffic volume and the

incidence of WVCs.

To help address the WVC issue, ADOT commissioned the development of a proactive

assessment of WVCs and potential mitigation measures to reduce their incidence along

SR 64. In ADOT (2006) it was reported that 48 percent of 475 accidents recorded along

SR 64 in the five-year period from October 1998 through September 2003 involved

collisions with wildlife, primarily elk and mule deer (Table 1). This study developed and

evaluated alternatives and associated mitigation measures for consideration in the

planned feasibility study for the eventual reconstruction of SR 64.

Table 1. Vehicle Accidents Involving Collisions with Elk and Mule Deer

along SR 64 from 1991 through 2003, Including the Mean Number

of Collisions (per Year and per Mile).

SR 64

section

Elk-vehicle accidents

Mule deer-vehicle

accidents

Mileposts Total

Mean

(per

year)

Mean

(per

mile)

Total

Mean

(per

year)

Mean

(per

mile)

185.5–204.7

(19.2 mi)

58 4.5 3.0 79 6.1 4.1

204.7–212.5

(7.8 mi)

2 0.2 0.3 2 0.2 0.3

212.5–214.3

(1.8 mi)

0 0.0 0.0 1 0.1 0.6

214.3–223.4

(9.1 mi)

6 0.5 0.7 3 0.2 0.3

223.4–235.4

(12.0 mi)

97 7.5 8.1 238 18.3 19.8

All

185.5–235.4

(49.9 mi)

163 12.5 3.3 315 24.2 6.3

Source: Final Wildlife Accident Reduction Study (ADOT 2006)

The earlier study report (ADOT 2006) delineated five SR 64 sections (A–E) based on

land ownership and habitat (Figure 1). This study developed two mitigation alternatives

for three of the sections (Table 2) to address the past incidences of WVCs, including the

construction of as many as seven wildlife underpasses and three overpasses, depending

on the selected alternatives (Table 2; Figure 1). None of the passage structures were

recommended along highway sections where American pronghorn were a focus (Sections

B and D), partly because no WVC involving this species was recorded from 1991

through 2003. However, SR 64 likely constitutes a significant barrier to pronghorn

passage similar to US 89 to the east, where no pronghorn-vehicle collisions were

recorded either (Dodd et al. 2011).

Table 2. SR 64 Sections and Mileposts with Proposed Wildlife Mitigation Measures for

Focal Wildlife Species Identified in the 2006 Final Wildlife Accident Reduction Study.

SR 64

section

Mileposts

Mitigation

alternative

Proposed wildlife

mitigation measures

Focal

wildlife

species

Underpass Overpass Fencing

185.5–204.7

(19.2 mi)

A(W)-1

A(W)-2

Yes

Elk, mule

deer

204.7–212.5

(7.8 mi)

None 0 0 No Pronghorn

212.5–214.3

(1.8 mi)

None 0 0 No

None;

human

develop-

ment

214.3–223.4

(9.1 mi)

D(W)-1

D(W)-2

Yes

Elk,

mule deer,

pronghorn

223.4–235.4

(12.0 mi)

E(W)-1

E(W)-2

Yes

Elk,

mule deer

Includes Cataract Canyon Bridge, which will be used as a passage structure

The same study (ADOT 2006) identified the need to conduct further field evaluation and

monitoring to determine the best locations for wildlife passage structures and the extent

of ungulate-proof fencing needed to funnel animals to passage structures. The report

indicated that the focus of such monitoring should be from mile post (MP) 222.0 to MP

235.4, where the highest incidence of WVCs involving elk and mule deer has occurred in

the past.

The report called for the monitoring of current and potential (e.g., with added funnel

fencing) wildlife use of Cataract Canyon Bridge at MP 187.3; Section A to determine

whether this multiple box culvert design is conducive to wildlife passage.

The report also addressed the potential barrier effect to pronghorn (especially along

Section B) and recommended that this issue also be further evaluated with monitoring. In

that report it was also recommended that a cooperative research project between ADOT

and the AGFD be initiated in advance of the feasibility study and final design for

highway reconstruction such that refined, site-specific information can be incorporated

into the final reconstruction plans.

Figure 1. Landownership, Mileposts, 0.1 mi Segments, Highway Sections A–E,

and Preliminary Wildlife Passage Structures in the SR 64 Study Area Identified by the

Final Wildlife Accident Reduction Study (ADOT 2006).

In 2007, an interagency agreement between ADOT and the AGFD was executed for the

SR 64 research project (Project JPA07-026T), with funding provided by the ADOT

Research Center. This research project is significant from several perspectives.

The cited study (ADOT 2006) conducted for SR 64 represents the first assessment of its

type in Arizona, forming the proactive basis from which to develop strategies to mitigate

WVCs and obtain refined information with further monitoring and research.

The project also reflects the incremental process in addressing wildlife connectivity and

permeability needs embodied in Arizona’s Wildlife Linkages Assessment (Arizona

Wildlife Linkages Workgroup 2006). General connectivity needs identified in the

assessment (e.g., Linkage No. 12; Coconino Plateau–Kaibab National Forest) were also

addressed in the 2006 Final Wildlife Accident Reduction Study, which called for further

monitoring to assess site-specific needs and refined strategies for promoting permeability.

2.3 RESEARCH OBJECTIVES

Pursued largely as a result of the 2006 Final Wildlife Accident Reduction Study, this

research project will add considerably to the understanding of wildlife movements in

relation to highways and provide information to support data-driven design planning for

the planned reconstruction of SR 64. Focusing on elk, mule deer, and pronghorn, this

research project complements previous research on wildlife-highway permeability, traffic

volume, and WVC relationships (Dodd et al. “Effectiveness of Wildlife,” in review,

2011; Gagnon et al. “Traffic volume alters,” 2007). The specific research objectives of

this research project were to:

 Assess elk (June 2007 through October 2009), mule deer (April 2008 through

October 2009), and pronghorn (January 2008 through January 2009) movements,

highway crossing patterns, and distribution relative to SR 64 and determine

permeability across the highway corridor.

 Investigate the relationships of elk, mule deer, and pronghorn highway crossing and

distribution patterns to SR 64 vehicular traffic volume (2007 through 2009).

 Investigate WVC patterns and relationships to elk, mule deer, and pronghorn

movement and highway crossing patterns in relation to SR 64 (2007 through 2009).

 Assess the degree to which Cataract Canyon Bridge is used by wildlife for below-

grade passage (July 2008 through December 2009).

 Develop recommendations to enhance elk, mule deer, and pronghorn highway

permeability along SR 64 through the application of wildlife passage structures and

ungulate-proof fencing.

3.0 STUDY AREA

SR 64 is the highway connecting I-40 to Grand Canyon National Park (GCNP). It is

classified as a rural principal arterial highway. The focus of this research project was a

57 mile stretch of highway starting at I-40, approximately 2 miles east of Williams (MP

185.5), and ending at the GCNP boundary (MP 237.0) just north of the community of

Tusayan, Coconino County, Arizona (latitude 35º25'–35º99'N, longitude 112º12'–

112º15'W; Figure 1). SR 64 runs north–south and intersects US 180 at Valle (MP 213.5);

US 180 links SR 64 to Flagstaff, 40 miles to the southeast. The majority of SR 64 now is

a two-lane highway, with occasional passing lanes.

3.1 NATURAL SETTING

The study area is at the southwest extent of the Colorado Plateau physiographic province.

The south half of the study corridor lies within the San Francisco Peaks Volcanic Field

(Hansen et al. 2004). The study corridor adjacent to SR 64 varies in elevation from

6000 ft between Red Lake and Valle to 6930 ft at the south end of the study area near

Kaibab Lake, and 6600 ft elevation at Grand Canyon Airport in Tusayan. The topography

is a mix of mesas, cinder cones, and broken terrain with rolling hills, ridges, and valleys

interspersed with large, relatively flat grassland areas (Figures 1 and 2).

Land ownership adjacent to the highway includes U.S. Forest Service (Kaibab NF) lands

(35 percent of the corridor), including 5 miles at the south end and 13 miles at the north

end of the study area (Figure 1). In between, land ownership is a mix of interspersed

Arizona State Trust (25 percent) and private lands (40 percent), with much of the private

land subdivided for development (Figure 1). Existing development is concentrated near

the communities of Red Lake, Valle, and Tusayan (Figure 1).

3.1.1 Climate

Generally, the climate is characterized as semiarid, dominated by hot summers and cool

winters. At the south and north ends of the study area, near Williams and the Grand

Canyon, respectively, the average maximum temperature is 64 °F, with July being the

warmest month (mean = 84

°F); highs can approach 95

°F. Winter daily low temperatures

average 35

°F at Williams and 32

°F at the Grand Canyon, with January being the coolest

month (mean = 19

°F); winter lows at the Grand Canyon often dip below 0

°F.

Precipitation varies considerably along the length of the study area, with Williams

averaging 21.6 inches annually, including an average snowfall accumulation of

69.3 inches. Precipitation drops off to the north along SR 64 at Valle, where it averages

only 9.4 inches annually, with 4.8 inches of annual snow accumulation. Precipitation at

Tusayan (Grand Canyon Airport) is greater than at Valle but is still less than the south

portion of SR 64; annual precipitation averages 13.7 inches, with annual snowfall of

44.3 inches. Precipitation occurs primarily during intense and localized thunderstorms

associated with the summer monsoon and more widespread frontal storms that pass

through the state in the winter.

Figure 2. Great Basin Conifer Woodland Adjacent to SR 64 (Top) with Open to Dense

Stands of Pinyon and Juniper and Cliffrose, Apache Plume, and Other Shrubs, and Plains

and Great Basin Grasslands (Bottom) Dominated by Blue and Black Grama, Galleta, and

Needle-and-Thread Grasses.

3.1.2 Vegetation

Vegetation is diverse and exhibits characteristics of the Petran Montane Conifer Forest,

the Great Basin Conifer Woodland, and the Plains and Great Basin Grassland biotic

communities (Brown 1994). Ponderosa pine dominates montane coniferous forests at the

southernmost and northernmost portions of the study areas. Gambel oak occurs in the

overstory. Big sagebrush, rabbitbrush, and cliffrose dominate the understory. Sagebrush

is particularly prevalent at the drier north extent of the study area.

The ponderosa pine–dominated forest adjacent to SR 64 is interspersed with small

openings in draws and flats vegetated by sagebrush, blue grama, and other grasses.

Though these sites are dry, they nonetheless may correspond to WVC incidence hotspots

as observed for wet meadow habitats adjacent to SR 260 by Dodd et al. (“Evaluation of

Measures,” 2007) and Manzo (2006). Sparse to dense pinyon and one-seed juniper

dominate the overstory of extensive Great Basin conifer woodlands, with sagebrush,

cliffrose, Apache plume, and other shrubs in the understory, along with blue grama and

other grasses in openings (Figure 2). Conifer woodlands transition to Plains and Great

Basin grasslands dominated by blue and black grama, galleta, and needle-and-thread

grasses, with winterfat and sage interspersed with sparse junipers (Figure 2).

3.1.3 Wildlife Species

The focal species of this study were elk, mule deer, and pronghorn. SR 64 separates

Game Management Units (GMUs) 7 and 10 south of Valle and bisects GMU 9 between

Valle and Tusayan. Elk are found in moderate densities at the south end of the study area

and at high densities at the north end of the study area, with low densities in between

(Figure 3). During the project (2007 through 2009), the AGFD surveyed an average of

424 elk in GMU 9, with a robust ratio of bulls in relation to cows and calves (39 bulls :

100 cows : 32 calves). In GMU 7, an average of 355 elk were surveyed annually

(22 bulls: 100 cows : 39 calves).

At the north extent of the study area, mule deer are commonly seen along the highway

corridor and occur in high densities; they occur in moderate densities at the south extent

and in low densities in between (Figure 3). During the study, an average of 303 mule deer

were surveyed by the AGFD each year (17 bucks : 100 does : 42 fawns). In GMU 7,

extending far east of SR 64, 151 deer were classified in surveys each year (25 bucks : 100

does : 37 fawns).

The pronghorn population levels in grassland and open woodland areas of GMUs 9 and 7

are considered average relative to other northern Arizona populations. However, pockets

of habitat east of SR 64 in GMUs 7 and 9 hold high densities of pronghorn (Figure 3),

and the population on the east side is larger than the population on the west. In GMU 9,

an average of 119 animals were surveyed each year during this project (47 bucks : 100

does : 26 fawns). For GMU 7, an average of 247 pronghorn were surveyed each year

(31 bucks : 100 does : 25 fawns).

Figure 3. Density Distributions for the Three Target Species of Research along SR 64:

Elk (Left), Mule Deer (Center), and Pronghorn (Right).

3.1.4 Cataract Canyon Bridge

Reflective of the generally rolling terrain along the highway corridor, Cataract Canyon

Bridge at MP 187.3 near Kaibab Lake is one of the most substantial bridge structures

along the 57 mi study area (Figure 4). This 44 ft wide reinforced concrete box-culvert

bridge was constructed in 2001, with four 26 ft spans, for a total length of 104 ft. The

bridge cells have a 16 ft vertical clearance. Due to the moderate to high elk numbers and

moderate mule deer density in the vicinity of this bridge, there was an opportunity to

evaluate existing wildlife use to document and better understand the efficacy of this

structure type to serve as an effective wildlife crossing structures without fencing.

Dodd et al. (“Evaluation of Measures,” 2007) stressed the need for ungulate-proof funnel

fencing to guide animals toward passage structures to achieve desired wildlife use.

Without fencing, animals continue to cross the highway at grade. Though ADOT was

amenable to such fencing near Cataract Canyon Bridge and conducted a formal analysis,

there were too many concerns to move forward with fencing to coincide with this

research project. One of the foremost concerns related to addressing potential “end run”

or forcing of elk to another location along SR 64 at the termini of the fencing, including

immediately adjacent to I-40. As such, no fencing was erected in association with this

bridge during the project.

Figure 4. Cataract Canyon Bridge on SR 64.

3.2 TRAFFIC VOLUME

Average annual daily traffic (AADT) volume on SR 64 was measured at 4343 vehicles

per day in 2008 and 4208 vehicles per day in 2009, or an average of 4275 vehicles per

day. Since late 2007, traffic volume has been continuously measured by a permanent

automatic traffic recorder (ATR) installed near Valle. Traffic volumes were highest

during daytime hours (Figure 5).

Compared with other study areas in central and northern Arizona, including SR 260

(Dodd et al. “Effectiveness of Wildlife,” in review), US 89 (Dodd et al. 2011), and

Interstate 17 (Gagnon et al. “Elk Movements Associated,” in review), SR 64 is unique in

that traffic is virtually nonexistent during the late nighttime hours, averaging less than

10 vehicles per hr for a 4 hr period (Figure 5). This reflects the predominant tourist

destination nature of motorists traveling SR 64 to and from GCNP and not using this

route for regional or interstate travel beyond GCNP at night. This is also reflected in the

relatively small proportion of commercial vehicles traveling SR 64—only 0.07 percent

during the day and 0.05 percent at night—compared with SR 260, where commercial

traffic exceeded 40 percent at night (Dodd et al. “Effectiveness of Wildlife,” in review),

and US 89, where commercial vehicles made up a third of the nighttime traffic (Dodd et

al. 2011).

Peak annual traffic volume along SR 64 between 10:00 and 17:00 averaged 355 vehicles

per hr, equivalent to an AADT volume of 8800 vehicles per day, though in summer it was

considerably higher. Mean monthly traffic volume was highest during summer (June–

August; 5710 AADT), when it was three times higher than volume during the lowest

traffic months of December–February (1775 AADT). Traffic volume was highest on

Saturdays—nearly 20 percent higher than during the rest of the week.

Figure 5. Hourly Traffic Volume (Vehicles per Hour) along SR 64, Arizona, from 2007

through 2009. Note the Low Volume of Traffic during Nighttime Hours (00:00–04:00).

4.0 METHODS

4.1 WILDLIFE CAPTURE, GPS TELEMETRY, AND DATA ANALYSIS

4.1.1 Elk Capture

The research team captured elk at 14 sites adjacent to SR 64, along Highway Sections A

and E, at the north and south extremes of the study area; 11 sites were concentrated

adjacent to Section E. Elk were trapped primarily in net-covered Clover traps (Clover

1954) baited with salt and alfalfa hay; all traps were within 0.5 mi of the highway

corridor and near permanent water sources (Figure 6).

Elk were also captured by nighttime darting from a vehicle along the highway aided with

a spotlight. The low volume of traffic late at night made such capture possible. Trapped

animals were physically restrained, and all animals were blindfolded, ear-tagged, and

fitted with global positioning system (GPS) receiver collars (Figure 6). Darted elk were

administered a reversal drug when handling was complete. Elk were instrumented with

Telonics, Inc., Model TGW-3600 store-on-board GPS collars programmed to receive a

GPS relocation fix every two hours. All collars had very high frequency (VHF) beacons,

mortality sensors, and programmed release mechanisms to allow recovery. Battery life

for the GPS units was approximately two years.

4.1.2 Mule Deer Capture

The research team attempted to trap mule deer in small Clover traps baited with sweet

feed and salt at five sites adjacent to Section E but had no success. Therefore, the team

shifted to nighttime darting from a vehicle along or immediately adjacent to SR 64, aided

by a spotlight. Again, the low volume of traffic late at night made such capture feasible.

Deer were blindfolded, ear-tagged, and fitted with GPS receiver collars (Figure 6), and

then administered a reversal drug. Most deer were fitted with Telonics, Inc. Model TGW-

3500 GPS receiver collars (Figure 6) programmed to receive a fix every two hours, with a

battery life of 11 months. Four deer were fitted with Telonics Generation IV GPS

receiver collars that had a one-year battery life. All collars had VHF mortality sensors

and programmed release mechanisms for recovery.

4.1.3 Pronghorn Capture

The research team captured pronghorn using a net gun fired from a helicopter (Firchow et

al. 1986, Ockenfels et al. 1994, Dodd et al. 2011; Figure 6). A fixed-wing aircraft and

numerous ground spotters using optics equipment were employed to search for pronghorn

during capture to minimize helicopter searching. Pronghorn were captured during winter

to minimize heat-related stress on animals as well as deleterious effects on females that

could occur if captured later in their pregnancies.

Figure 6. Photographs of Capture Techniques Used for Elk, Mule Deer and Pronghorn

along SR 64 and GPS-Collared Animals: Elk Captured with Net-Covered Clover Trap

(Top), Darting to Immobilize Mule Deer (Middle), and Net-Gunning of Pronghorn from a

Helicopter (Bottom).

The team’s capture objectives were to:

 Instrument as nearly an equal number of pronghorn on each side of SR 64 as possible

because the research team suspected that SR 64 would be a barrier similar to US 89

(Dodd et al. 2011).

 Spread the collars among as many different herds as possible along the length of the

study area.

 Capture animals within 2 mi of SR 64.

Upon capture, pronghorn were immediately blindfolded and untangled from the capture

net. Animals were fitted with a GPS collar and marked with a numbered, colored ear tag

(Figure 6). Tissue samples were taken from animals’ ears with a paper punch and

preserved for future genetic analysis. The research team instrumented pronghorn with

Telonics, Inc. Model TGW-3500 store-on-board GPS receiver collars programmed to

receive 12 GPS fixes per day, with one fix every 90 minutes between 04:00 and 22:00.

GPS units had a battery life of 11 months. All collars had VHF beacons, mortality

sensors, and programmed release mechanisms to allow recovery.

4.1.4 GPS Analysis of Animal Movements

Once GPS collars were recovered and data downloaded, the research team employed

ArcGIS

Version 8.3 Geographic Information System software (ESRI

, Redlands,

California) to analyze GPS data similar to analyses done for elk by Dodd et al.

(“Assessment of elk,” 2007), white-tailed deer (Dodd and Gagnon 2011), and pronghorn

(Dodd et al. 2011). The team calculated individual minimum convex polygon (MCP;

connecting the outermost fixes) home ranges composed of all GPS fixes (White and

Garrott 1990). Differences in means were assessed by analysis of variance, and means

were reported with ±1 standard error (SE).

Crossings were compared among the following seasons:

 Late spring–summer (April–July).

 Late summer–fall (August–November).

 Winter–early spring (December–March).

4.1.5 Calculation of Crossing and Passage Rates

The team divided the entire length of SR 64 from I-40 to the Grand Canyon village into

570 sequentially numbered 0.1 mi segments corresponding to the units used by ADOT

for tracking WVCs and highway maintenance, and identical to the approach used by

Dodd et al. (“Evaluation of Measures,” 2007, Figure 1). The number and proportion of

GPS fixes within 0.15, 0.30, and 0.60 mi of SR 64 were calculated for each animal.

To determine highway crossings, the team drew lines connecting all consecutive GPS

fixes. Highway crossings were inferred where lines between fixes crossed the highway

through a given segment (Dodd et al. “Evaluation of Measures,” 2007, Figure 7). Animal

Movement ArcView Extension Version 1.1 software (Hooge and Eichenlaub 1997) was

used to assist in animal crossing determination. The research team compiled crossings by

individual animal by highway segment, date and time, and calculated crossing rates for

individual elk, mule deer, and pronghorn by dividing the number of crossings by the days

a collar was worn.

The research team calculated passage rates for collared animals, which served as its

relative measure of highway permeability (Dodd et al., “Evaluation of Measures,” 2007).

An approach was considered to have occurred when an animal traveled from a point

outside the 0.15 mi buffer zone to a point within 0.15 mi of SR 64, determined by

successive GPS fixes (Figure 7). The approach zone corresponded to the road-effect zone

associated with traffic-related disturbance (Rost and Bailey 1979, Forman et al. 2003)

previously used for elk and white-tailed deer by Dodd et al. (“Evaluation of Measures,”

2007, Dodd and Gagnon 2011). Animals that directly crossed SR 64 from a point beyond

0.15 mi were counted as an approach and a crossing.

The research team calculated passage rates as the ratio of recorded highway crossings to

approaches. The research team tested the hypothesis that the observed spatial crossing

distribution among 0.10 mi segments did not differ from a discrete randomly generated

distribution using a Kolmogorov-Smirnov test (Clevenger et al. 2001; Dodd et al.,

“Assessment of elk,” 2007), a test that is sensitive to the difference in ranks and shape of

the distributions. The team used linear regression to assess the strength of associations

between passage rates and traffic volume, as well as between the frequency of highway

crossings and WVCs at the MP (1.0 mi) scale using WVC records from 1991 through

2009 on those highway stretches with elk or deer crossings.

Figure 7. GPS Locations and Lines between Successive Fixes to Determine Highway

Approaches and Crossings in 0.10 mi Segments. The Expanded Section Shows GPS

Locations and Lines between Successive Fixes to Determine Approaches to the Highway

(Shaded Band) and Crossings. Example A Denotes an Approach and Crossing; Example

B Denotes an Approach without a Crossing.

4.1.6 Calculation of Pronghorn Approaches

Based on previous northern Arizona pronghorn highway telemetry research (Ockenfels et

al. 1997, van Riper and Ockenfels 1998, Bright and van Riper 2000, Dodd et al. 2011),

the research team anticipated few pronghorn crossings or approaches to within 0.15 mi

associated with crossings. As such, the team used the number of approaches by

pronghorn to within 0.30 mi to determine the distribution of animals adjacent to SR 64

for the purposes of assessing the need for, and potential location(s) of, passage structures,

as was done for the US 89 study (Dodd et al. 2011). Use of this greater approach distance

also was deemed appropriate given the relatively open nature of pronghorn habitat,

pronghorn reliance on visual stimuli in risk avoidance (Gavin and Komers 2006), and

pronghorn mobility over long distances (Yoakum and O’Gara 2000) compared with other

ungulates.

Pronghorn highway approaches were determined for animals approaching from each side

of SR 64 and both sides combined. The research team tested the hypothesis that the

observed spatial approach distribution among 0.10 mi segments did not differ from a

discrete randomly generated approach distribution using a Kolmogorov-Smirnov test

(Dodd et al. 2011).

4.1.7 Calculation of Weighted Crossings and Approaches

To account for the number of individual elk, deer, and pronghorn that crossed (and

approached, in the case of pronghorn) each highway segment adjacent to SR 64, as well

as evenness in crossing frequency among animals, the research team calculated Shannon

diversity indexes (SDIs; Shannon and Weaver 1949) for each segment using this formula:

Thus, to calculate SDI (or H ) for each highway segment, the researchers calculated and

summed all the -(p

ln p

) for each animal that had approaches in the segment, where each

is defined as the number of individual collared elk and deer crossings and approaches

for pronghorn within each segment divided by the total number of respective crossings or

approaches in the segment. SDI were used to calculate weighted crossing or approach

frequency estimates for each segment, multiplying uncorrected crossings or approach

frequency by SDI. Weighted highway crossings and approaches better reflected the

number of crossing and approaching animals and the equity in distribution among elk,

deer, and approaching pronghorn (Dodd et al., “Evaluation of Measures,” 2007).

4.2 TRAFFIC VOLUME AND ANIMAL DISTRIBUTION RELATIONSHIPS

The research team measured traffic volume using a permanent automatic traffic recorder

(ATR) programmed to record hourly traffic volumes. ADOT’s Data Team provided data

collected from the ATR immediately north of Valle.

The research team examined how the proportion of elk, deer, and pronghorn GPS

relocations at different distances from the highway varied with traffic volume by

calculating the proportion of fixes in each 330 ft distance band, out to a maximum of

3300 ft. As done for elk (Gagnon et al. “Effects of traffic,” 2007), white-tailed deer

(Dodd and Gagnon 2011), and pronghorn (Dodd et al. 2011), the research team combined

traffic and GPS data by assigning traffic volumes for the previous hour to each GPS

location using ArcGIS



Version 9.1 and Microsoft

Excel

software.

This allowed the

team to correlate the traffic volume each animal experienced in the hour prior to

movement to a particular point, regardless of distance traveled.

To avoid bias due to differences in the number of fixes for individual animals, the

proportion of fixes occurring in each distance band for each animal was used as the

sample unit, rather than total fixes. The research team calculated a mean proportion of

animals and fixes for all animals within each 330 ft distance band at varying traffic

Microsoft and Excel are either registered trademarks or trademarks of Microsoft Corporation in the

United States and/or other countries.

volumes: less than 100, 101–200, 201–300, 301–400, 401–500, and 501–600 vehicles per

hr (Gagnon et al. “Effects of traffic,” 2007).

The team compared elk, mule deer, and pronghorn distribution and highway impact along

SR 64, and compared the species-specific distributions to those for elk (Gagnon et al.

“Effects of traffic,” 2007) and white-tailed deer on SR 260 (Dodd and Gagnon 2011) and

pronghorn on US 89 (Dodd et al. 2011). The team also assessed and compared species-

specific highway passage rates by time of day and associated traffic volume and used

linear regression to assess the strength of associations between WVCs and traffic volume.

4.3 WILDLIFE-VEHICLE COLLISION RELATIONSHIPS

The research team documented the incidence of WVCs along all SR 64 sections using

two methods. First, the research team relied on the submission of forms by agency

personnel, primarily DPS highway patrol officers, to determine the incidence of WVCs

during the study. DPS patrol officers made a concerted effort to record the species and

sex of animals involved in WVCs where such could be determined. These records were

augmented by regular searches of the highway corridor for evidence of WVCs by

research personnel.

The database compiled from the consolidated (non-duplicate) records included the date,

time, and location (to the nearest 0.1 mi) of the WVCs, the species involved, and the

reporting agency. WVC records were compiled and summarized by highway section by

year. Where duplicate reports of WVCs were made by DPS and research team searches,

the locations were compared to determine their accuracy (Barnum 2003, Gunson and

Clevenger 2003).

The research team used a database compiled by the ADOT Traffic Records Section

which includes DPS accident reports to determine the proportion of single-motor vehicle

accidents that involved wildlife along the respective highway sections. Huijser et al.

(2007) reported that nearly all WVCs are single-vehicle crashes. The research team

compared WVC incidence by season (using the same seasons for highway crossings),

month, day, and time (2 hr intervals), and used chi-square tests to compare observed

versus expected WVC frequencies.

4.4 WILDLIFE USE OF CATARACT CANYON BRIDGE

To quantify wildlife use of Cataract Canyon Bridge, the research team employed

Reconyx

™

professional model single-frame cameras installed within each of the four

box-culvert cells (Figure 8). The encased cameras were mounted on wood strips attached

to the culvert walls with glue, thus avoiding the need to make modifications to the walls

that might impact their integrity. These self-triggering cameras provided infrared

illumination to record animals crossing through the bridge at night. The cameras were

programmed to record up to five frames per second, providing near-video-like tracking of

animals as they approached and crossed through the bridge cells. Images were date, time,

and temperature stamped (Figure 9), digitally stored, and periodically downloaded for

analysis.

Figure 8. Reconyx

™

Camera Mounted on a Wood Strip Glued to the

Concrete Surface of Each SR 64 Cataract Canyon Bridge Culvert Cell to Monitor

Wildlife Use.

Figure 9. Images of a Mule Deer Doe (Left) and Spike Bull Elk (Right)

Recorded by Reconyx

™

Cameras Mounted

in the SR 64 Cataract Canyon Bridge Culvert Cells.

The research team analyzed camera data to determine the frequency of occurrence of

animals by species (and people) that entered and then passed through the bridge cells.

Though not able to determine underpass passage rates as done by Dodd et al. (“Video

surveillance to assess,” 2007) using video camera systems, the proportion of animals that

entered the culvert cells and ultimately passed through provided some indication of the

relative acceptance by animals to cross SR 64 below grade via Cataract Canyon Bridge.

4.5 IDENTIFICATION OF PASSAGE STRUCTURE SITES

Sawyer and Rudd (2005) identified several important considerations for locating the most

suitable sites in which to place passage structures, primarily for pronghorn, though these

criteria are applicable for other species. In this assessment of potential passage structure

sites and to validate the preliminary findings in ADOT (2006), the research team

considered each of the criteria identified by Sawyer and Rudd (2005) but recognized that

the 0.1 mile segment length used was too small and cumbersome to discern and analyze

differences among segments.

Dodd et al. (“Evaluation of Measures,” 2007) reported that the optimum scale to address

management recommendations for accommodating wildlife passage needs using GPS

telemetry or WVC data was at the 0.6 mi scale. Making recommendations at this scale

allows ADOT engineers latitude to determine the best technical location for passage

structures along the segment. Thus, the team aggregated the 570 0.1 mi segments from

MP 185.5 to MP 235.4 into 95 0.6 mi segments for analysis. The research team addressed

passage structure needs for the entire highway study area as well as each individual

highway section.

Sawyer and Rudd (2005) identified animal abundance as a primary criterion for the

consideration of passage structure sites. The research team focused this criterion on the

larger population levels adjacent to the entire study area versus by segment. For

pronghorn, Sawyer and Rudd (2005) stressed that passage structures were more

appropriate in linking populations with “abundant numbers (hundreds)” than small

isolated populations that may not benefit to the same degree and exhibit a high likelihood

of encountering passage structures. The pronghorn, elk, and mule deer populations

adjacent to SR 64 indeed number well into the hundreds, with the herds for all three on

both sides of the highway still viable and reproducing.

The team used the other segment-specific criteria identified by Sawyer and Rudd (2005)

with some modifications to rate each of the 95 0.6 mi segments, considering GPS

telemetry findings with other pertinent factors, as done for US 89 by Dodd et al. (2011).

Because passage structures that have the potential to benefit permeability for multiple

species are preferred (Clevenger and Waltho 2000), some ratings for elk, deer, and

pronghorn were additive, thus weighting those sites that may yield benefit to multiple

species. However, because pronghorn range largely did not overlap the higher-density

portions of elk and mule deer ranges (Figure 3) and because few, if any, WVCs involving

pronghorn were anticipated, the team made separate passage structure recommendations

for this species.

The team’s rating criteria/categories were as follows:

Elk highway crossings – Due to the anticipated availability of highway crossing

data for elk, this rating was based on the proportion of SR 64 crossings made by

GPS-collared elk within each aggregated 0.6 mi segment across the entire study

area. The ratings for elk crossings were additive to mule deer crossings and

pronghorn approaches. Categories used include:

0 No crossings

1 1–2% of total elk crossings

2 3–4% of total elk crossings

3 5–6 % of total elk crossings

4 7–8% total elk crossings

5 >8% of total elk crossings

Mule deer highway crossings – This rating was also based on the proportion of

SR 64 crossings made by GPS-collared mule deer within each aggregated 0.6 mi

segment. However, because deer were only captured adjacent to SR 64 Section E,

rather than adjacent to a greater length of SR 64, the ratings reflect higher

proportions of crossings. The ratings for mule deer crossings were additive to

those for elk crossings and pronghorn approaches. Categories used include:

0 No crossings

1 1–2% of total crossings for the species

2 3–4% of crossings for the species

3 5–6 % of crossings for the species

4 7–8% of crossings for the species

5 >8% of total crossings for the species

Pronghorn approaches – This criterion was considered indicative of where

animals potentially would approach and cross SR 64 via a passage structure and

was based on the proportion of approaches to within 0.3 mi on both sides of the

highway for aggregated 0.6 mi segments. This rating was additive with elk and

mule deer crossing ratings where GPS-collared animals overlapped. Categories

used include:

0 No approaches

1 1–3% of total pronghorn approaches

2 3–5% of total pronghorn approaches

3 5–15% of total pronghorn approaches

4 15–25% of total pronghorn approaches

5 >25% of total pronghorn approaches

Elk, mule deer, and pronghorn distribution – This rating was based on the number

of different GPS-collared animals that crossed SR 64 for elk and deer and were

relocated within the 0.3 mi approach zone for pronghorn. This rating was additive

for each of the three species where data overlapped. Categories used include:

0 No animals crossing or approaching

1 1–2% of all animals crossing or approaching

2 2–4% of all animals crossing or approaching

3 4–6% of all animals crossing or approaching

4 6–8% of all animals crossing or approaching

5 >8% of all animals crossing or approaching

Wildlife-vehicle collisions – The number of non-duplicate WVCs recorded by

0.6 mi segment during the project (2007–2009) for elk, mule deer, pronghorn, and

other large mammals such as mountain lion, black bear, badger, etc. categories

used include:

0 No WVC

1 1–2 total WVCs

2 3–4 total WVCs

3 5–6 total WVCs

4 7–8 total WVCs

5 >8 total WVCs

Land status – This criterion reflected the ability to conduct construction activities

on and outside the ADOT right-of-way (ROW), such as creating approaches with

fill material for overpasses. Categories used include:

0 Private

1 State Trust

3 National Park Service – GCNP (preservation and natural process

focus)

5 Federal – U.S. Forest Service (multiple-use focus)

Human activity – Ideally, no human activity should occur within the vicinity of a

passage structure; however, road access, businesses, visitor pullouts, and other

activities occur adjacent to US 89. Categories used include:

0 Significant human activity (business, housing, etc.)

1 Moderate human activity (access road, visitor pullout)

3 Limited human activity

5 No human activity

Topography – The ability to situate overpasses oriented along existing ridgelines

that pronghorn, elk, or deer can traverse, or locate underpasses in association with

wide gentle drainages is desirable. Categories used include:

0 Terrain not suited for a passage structure (steep, broken)

1 Topography marginal for a passage structure (flat)

3 Topography could accommodate a passage structure (small

drainage)

5 Topography ideally suited for passage structure (large drainage for

underpass or ridgeline for overpass)

In addition to the above criteria, the research team also considered other factors in its

identification of potential passage structure sites. These factors included whether the

0.6 mile segments coincided with the preliminary sites recommended in ADOT (2006), if

the types of structures were suited for the site, and how the priority segments from this

study relate to the minimum recommended passage structure spacing determined by

Bissonette and Adair (2008).

5.0 RESULTS

5.1 WILDLIFE CAPTURE, GPS TELEMETRY, AND DATA ANALYSIS

5.1.1 Elk Capture, Movements, and Highway Permeability

From June 2007 through October 2009, the research team tracked and recovered data

from 23 elk (13 females, 10 males) instrumented with GPS receiver collars; 17 elk were

trapped in Clover traps and six were captured by darting with immobilizing drugs. Only

three elk were captured at the far south end of the study area adjacent to Section A, while

the remainder were captured at the north end adjacent to Section E (Figure 1).

GPS collars were affixed to elk for an average of 302.9 days (33.4 SE), during which

time the collars accrued 107,055

GPS fixes for a mean of 4654.6 fixes per elk (322.6).

Of the GPS fixes, 12,483 (11.6 percent) were recorded within 0.6 mi of SR 64, and 3796

(3.5 percent) of the fixes were made within 0.15 mi; the proportion of fixes near SR 64

were considerably lower than those for SR 260, where 48.5 percent occurred within

0.6 mi of the highway (Dodd et al. “Effectiveness of Wildlife,” in review). Elk traveled

an average of 1082.1 ft (105.3) between GPS fixes. Males traveled slightly farther

between crossings (1107.1 ft 57.5) than females (1051.2 ft 142.1). Elk minimum

convex polygon (MCP) home ranges averaged 284.8 mi

(56.3), and male home ranges

(479.5 mi

165.5) which were significantly larger (t

= 2.45, P < 0.001) than female

home ranges (199.6 mi

17.8).

GPS-collared elk crossed SR 64 843 times, for a mean of 40.1 crossings per elk (11.2).

On average, elk crossed SR 64 0.12 time per day (0.03), and ranged from 1 to

200 crossings per elk, though four elk never crossed SR 64. The highest proportion of

crossings occurred in late spring–summer (April–July; 60 percent), followed by late

summer–fall (August–November; 27 percent), and only 13 percent during winter–early

spring (December–March). The overall elk passage rate averaged 0.44 crossing per

approach (0.07; Table 3). There was no significant statistical difference between mean

passage rates for female and males elk (0.46 0.08 vs. 0.43 0.12 respectively).

The crossing distribution by elk among SR 64 0.1 mi segments was not random and

exhibited several peak crossing zones (Figure 10), especially at the north end of the study

area. The observed crossing distribution differed significantly from a random distribution

(Kolmogorov-Smirnov d = 0.309, P < 0.001). The limited number of crossings at the

southern area (Section A) reflects that only three elk were captured in this area and that

these elk crossed SR 64 only an average of three times versus the study area average of

40.1 crossings per elk.

A total of 48 crossings occurred along Section D, with one apparent peak crossing zone,

and 786 occurred along Section E, where several crossing peaks were registered by elk

(Figure 10). The highest concentration of different crossing elk occurred between

Segments 470 and 500, with seven animals (30.4 percent of all elk) and a mean of 3.2

different animals per 0.1 mi segment.

Weighted highway crossings reflect the frequency of elk crossings, the number of

individual elk that crossed at each segment, and the evenness in crossing frequency

among all collared animals. The weighted elk crossing pattern (Figure 11) was noticeably

different than the uncorrected crossing distribution. Crossing peaks on Sections A and D

were absent in the weighted crossing distribution due to the relatively small number of

elk that crossed at these peak zones (Figure 10).

Table 3. Comparative Mean Values for GPS-Collared Animals by Species Determined

from GPS Telemetry along SR 64.

Parameter

Mean value per GPS-collared animal by species (±SE)

Elk (n = 23) Mule deer (n = 11) Pronghorn (n = 15)

No. of highway crossings

40.1

(11.2)

55.0

(16.4)

0.2

(0.2)

Highway crossings per day

0.12

(0.03)

0.26

(0.06)

0.001

(0.001)

GPS fixes ≤0.6 mi of

highway

per year

632.2

(164.4)

1921.4

(637.7)

791.0

(184.4)

GPS fixes ≤0.15 mi of

highway per year

346.6

(97.7)

1054.9

(354.2)

370.1

(84.1)

GPS fixes ≤0.06 mi of

highway per year

198.8

(63.2)

522.4

(167.9)

116.7

(21.4)

Highway approaches per day

0.21

(0.04)

0.58

(0.12)

0.27

(0.16)

Passage rate

(crossings per approach)

0.44

(0.07)

0.54

(0.08)

0.004

(0.002)

MCP home ranges (mi

)

284.8

(56.3)

141.1

(48.3)

85.8

(21.1)

Distance traveled between

GPS fixes (ft)

1107.1

(57.5)

942.2

(232.9)

845.2

(46.6)

Figure 10. SR 64 Crossings by GPS-Collared Elk along the Entire Study Area (Top)

and Sections A through E of the 2006 Final Wildlife Accident Reduction Study and

Enlarged to Show Crossings along Sections D and E (Bottom).

Sections: A B C D E

Section D

Section E

Figure 11. SR 64 Weighted Crossings by GPS-Collared Elk along the Entire Study

Area (Top) and Sections A through E of the 2006 Final Wildlife Accident Reduction

Study and Enlarged to Show Crossings along Sections D and E (Bottom).

Sections: A B C D E

Section D Section E

5.1.2 Mule Deer Capture, Movements, and Highway Permeability

From April 2008 through October 2009, the research team tracked and recovered data

from 11 mule deer (8 females, 3 males) instrumented with GPS receiver collars. Deer

were captured adjacent only to Section E at the far north extent of the study area.

GPS collars were affixed to deer for an average of 207.9 days (38.7), during which time

they accrued 29,944GPS fixes, for a mean of 5988.5 fixes per deer (128.0). Deer were

relocated near SR 64 more than elk, with 12,047 (57.2 percent) fixes recorded within

0.6 mi of SR 64, and 3796 (15.6 percent) of the fixes made within 0.15 mi of the

highway. Mule deer traveled an average of 942.2 ft (232.9) between GPS fixes. Males

traveled slightly farther between crossings (1003.7 ft 323.5) than females (820.9 ft

155.2). Home ranges averaged 141.1 mi

(48.3); male home ranges (189.8 mi

184.9)

were not significantly different (P = 0.343) from female ranges (132.3 mi

51.4).

Five mule deer (two males, three females) captured along SR 64 exhibited extreme long-

distance (more than 100 mi) movements away from SR 64 to the south, most

independently of each other. All five followed the same travel corridor to an area

northwest of Flagstaff and west of the San Francisco Peaks (Figure 12). The mean home

range of these five deer (342.1 mi

41.9) was 22 times greater than those that did not

make such movements (15.5 mi

3.6; t

= 10.0, P < 0.001). The factors contributing to

such movement patterns is being addressed in another study by the research team, but

such movements point to the need to maintain landscape connectivity for far-ranging

species.

Collared deer crossed SR 64 550 times, for a mean of 55.0 crossings per deer (16.4).

On average, deer crossed SR 64 twice as frequently as elk, or 0.26 times per day (0.06).

Deer crossings ranged from 2 to 147 crossings per deer, and one deer never crossed SR

64. Seasonal deer crossings were more consistent than elk, though 46 percent occurred

during late summer–fall (August–November), followed by 28 percent in late spring–

summer (April–July), and 26 percent in winter–early spring (December–March). The

overall deer passage rate was higher than that for elk and averaged 0.54 crossing per

approach (0.08; Table 3). The mean passage rate for female deer (0.59 crossing per

approach; 0.08) was higher than that for male deer (0.34 0.34).

The deer crossing distribution by 0.1 mi segments did not occur randomly and exhibited

two peak crossing zones; all crossings occurred along Section E beyond Segment 420

(Figure 13). The observed mule deer crossing distribution differed significantly from a

random distribution (Kolmogorov-Smirnov d = 0.281, P < 0.001). The two crossing

peaks occurred along a 3.2 mi stretch (Segments 480–512) between the entrances to

Grand Canyon Airport and GCNP (Figure 13); 505 crossings (92 percent of total)

occurred along this stretch of highway, though deer were captured along the length of

Section E.

Figure 12. Mule Deer GPS Fixes along the SR 64 Study Area, as well as Fixes for Two

Deer Captured North of Flagstaff (Numbers 43 and 172).

When considering the number of crossing mule deer in calculating weighted crossings,

the same two peaks in the crossing distribution were even more apparent. Deer crossings

between Segments 450 and 470 largely disappeared and were restricted to two segments

when weighted crossings were calculated (Figure 13).

5.1.3 Pronghorn Capture, Movements, and Highway Approaches

The research team instrumented and tracked 15 pronghorn (10 females, 5 males) with

GPS receiver collars from January 2008 through January 2009. Due to disparity in the

distribution of pronghorn herds (Figure 3) adjacent to SR 64, coupled with the prevalence

of closed private lands across much of the pronghorn range, the team was not able to

achieve its objective of collaring an equal number of animals on each side of the

highway; 10 were captured on the east side and five on the west.

GPS collars were affixed to pronghorn an average of 298.1 days (29.6), during which

time the collars accrued 56,433 GPS fixes for a mean of 3762.2 fixes per pronghorn

(339.0).

Figure 13. SR 64 Highway Crossings (Top) and Weighted Crossings (Bottom) by GPS-

Collared Mule Deer along Highway Section E by 0.1 mi Segment.

Of the GPS fixes accrued for pronghorn, 1426 (3 percent) occurred within 0.15 mi of SR

64, or an average of 95.1 (26.1) fixes per animal; all but one pronghorn approached the

highway to within 0.15 mi. All 15 pronghorn approached to within 0.60 mi of the

highway, accruing 9729 GPS fixes (17 percent of all fixes), with a mean of 648.6

(151.2) fixes per animal. During the duration of GPS tracking, pronghorn traveled an

average of 845.2 ft (46.6) between fixes (1.5 hr). Females traveled farther between fixes

(905.5 ft 44.6) than males (724.1 ft 72.2). Pronghorn home ranges averaged 85.8 mi

(21.1), and there was no difference between male (88.6 mi

29.6) and female (84.4 mi

34.5) home ranges (P = 0.469).

Only a single GPS-collared pronghorn crossed SR 64 during tracking—a female that

crossed three times; none of the other 14 collared pronghorn crossed the highway. The

pronghorn crossing rate averaged 0.001 crossings per day. The mean pronghorn passage

rate was a negligible 0.004 crossings per approach (Table 3).

The frequency of approaches by pronghorn to within 0.30 miles of SR 64 yielded

considerably more information than crossings for the determination of potential passage

structure locations. Pronghorn approached the highway to within 0.30 miles 4269 times

(Figure 14), for a mean of 284.6 (±69.0) approaches per animal and a range of 2 to 907

approaches.

The observed approach distribution did not occur in a random distribution (Kolmogorov-

Smirnov d = 0.883, P < 0.001). Partly owing to the disparity in the number of collared

animals on the east and west sides of SR 64, it is not unexpected that 3623 approaches

were from the east and only 465 from the west. However, the approaches per animal were

also dramatically different; 362.2 approaches per animal (±74.4) on the east side versus

91.2 approaches per animal (±121.3) from the west. All but two approaches to SR 64

between Segments 1 and 220 were made by pronghorn approaching from the east, though

a limited number of animals were captured on the west side along this stretch.

Shannon diversity index (SDI)-weighted pronghorn approaches totaled 2756.7, and the

distribution pattern changed considerably from the uncorrected approach distribution.

The peak in crossings at the south end of the study area disappeared, owing to there being

only a single male that approached here. The weighted distribution of approaches also

showed an increased peak in approaches at the north extent of pronghorn range, between

Segments 310 and 390. Between Segments 340 and 370, 11 different collared animals

(73.3 percent of total) approached SR 64, with a mean of 6.9 different animals per 0.1

mile segment. The peak in approaches between Segments 180 and 220 at the center of the

study area remained prevalent even after SDI-weighted approaches were calculated

(Figure 14), though approaches here were attributable to just two pronghorn.

Figure 14. Highway Approaches (Top) and Weighted Approaches (Bottom) Made to

within 0.3 mi of SR 64 by GPS-Collared Pronghorn and Sections A through E of the

2006 Final Wildlife Accident Reduction Study.

Sections: A B C D E

5.2 TRAFFIC RELATIONSHIPS

5.2.1 Elk-Traffic Relationships

The research team’s elk distribution analysis was based on 12,483 GPS fixes recorded

within 3300 ft of the highway. Frequency distributions of mean probabilities showed a

shift in distribution away from SR 64 with increasing traffic volume (Table 4; Figure 15).

The shift away from the highway occurred even at relatively low traffic volume

(Figure 15), with a 64 percent decrease in probability of elk occurring within 660 ft from

traffic volume less than 100 vehicles per hr (0.28 probability) to 200 to 300 vehicles per

hr (0.10). The mean probability of elk occurring within 660 ft of SR 64 remained

constant at 0.08 from 200 to 600 vehicles per hr. The mean probability of elk occurring

farther away from the highway (1650 and 1980 ft distance bands) increased 65 percent

from traffic less than 100 vehicles per hr (0.17) to 500 to 600 vehicles per hr (0.28).

Elk passage rates by 2 hr time blocks ranged from 0.01 (18:00–20:00) to 0.72 crossings

per approach (04:00–06:00), with the passage rate between midnight and 04:00 when

traffic was nearly absent along the highway (Figure 5) averaging 0.63 crossings per

approach (0.05; n = 45). This nighttime rate was more than three times higher than the

mean passage rate during the rest of the day, averaging 0.19 crossings per approach

(0.04; n = 72; Figure 16). There was a significant negative association between the elk

passage rate by 2 hour blocks and increasing traffic volume (r = -0.660, P = 0.022). The

passage rate by day averaged 0.48 crossings per approach (±0.16) and was relatively

constant for most days (0.42–0.49) except for Tuesday (0.64).

5.2.2. Mule Deer–Traffic Relationships

The research team’s mule deer distribution analysis was based on 12,047 GPS fixes

recorded within 3300 ft of the highway. Mule deer frequency distributions of combined

mean probabilities showed shifts in distribution away from the highway with increasing

traffic volume, though not as dramatic as for elk (Table 4; Figure 17). At low traffic

volumes less than 200 vehicles per hr, probabilities for deer occurring within 660 ft of the

highway averaged 0.21 but dropped when traffic was more than 200 vehicles per hr and

remained static out to 1980 ft, averaging 0.11.

Mean deer probabilities of occurring within the 1650–1980 ft distance band largely

remained unchanged across traffic volume classes (mean = 0.21) up to 500 vehicles per

hr but dropped to a mean probability of 0.14 at more than 500 vehicles per hr. The most

dramatic shift in deer distribution occurred in the intermediate distance bands, 990–1320

ft from SR 64, with the probability of deer occurring here doubling from 0.12 at less than

100 vehicles per hr to 0.24 at just 100 to 200 vehicles per hr; the probability of deer

occurring at this distance remained static up to 500 vehicles per hr and averaged 0.21.

Deer passage rates by 2 hour time blocks ranged from 0.03 (19:00–21:00) to 0.78

crossings per approach (03:00–05:00 a.m.), with the passage rate between midnight and

04:00 a.m. when traffic was absent, averaging 0.58 crossings per approach (0.03). This

nighttime rate was more than two times the mean passage rate during the rest of the day,

averaging 0.28 crossings per approach (0.02; Figure 16). Unlike elk, the deer passage

rate remained relatively high (0.61 crossings per approach [0.02]) well into the morning

hours up until the 09:00–11:00 time block (Figure 16).

Due to the passage rates remaining high into the morning hours, the negative association

between the deer passage rate by 2 hr blocks and increasing traffic volume was not

significant (r = -0.07, P = 0.831). The passage rate by day of the week averaged 0.47

crossings per approach (±0.10), which was relatively constant for most days, and ranged

from 0.42 on weekend days to 0.50 the remainder of the week.

Table 4. Mean Probabilities that GPS-Collared Elk, Mule Deer,

and Pronghorn Occurred within Distance Bands from SR 64 at Varying Traffic Volumes.

Documented from 2007 through 2009.

Distance

from

highway (ft)

by species

Probability of occurring in distance band by traffic volume

(vehicles per hour)

<100 100–200 200–300 300–400 400–500 500–600

0–990

Elk

Mule deer

Pronghorn

All

0.36

0.38

0.23

0.34

0.21

0.32

0.18

0.22

0.18

0.23

0.16

0.19

0.18

0.17

0.15

0.17

0.15

0.18

0.12

0.14

0.15

0.19

0.11

0.14

990–1980

Elk

Mule deer

Pronghorn

All

0.26

0.27

0.30

0.28

0.26

0.34

0.33

0.30

0.28

0.33

0.32

0.30

0.31

0.34

0.32

0.31

0.40

0.21

0.35

0.33

1980–2970

Elk

Mule deer

Pronghorn

All

0.28

0.29

0.37

0.30

0.38

0.26

0.38

0.36

0.27

0.34

0.43

0.40

0.38

0.39

0.38

0.43

0.37

0.47

0.43

0.33

0.43

0.45

0.40

Figure 15. Mean Probability That GPS-Collared Elk Occurred within 330 ft Distance

Bands along SR 64 at Varying Traffic Volumes.

0.1

0.2

330 660 990 132016501980

Mean probability

Distance from highway (ft)

500-600 vehicles/hr

Figure 16. Mean SR 64 Passage Rates by Two-Hour Time Blocks (Reflected by the

Midpoint of the Blocks) and Corresponding Mean Traffic Volumes during Each Time

Block for Elk (Bottom) and Mule Deer (Top).

Figure 17. Mean Probability That GPS-Collared Mule Deer Occurred within 330 ft

Distance Bands along SR 64 at Varying Traffic Volumes.

5.2.3. Pronghorn-Traffic Relationships

The team’s distribution analysis was based on 9729 pronghorn GPS fixes recorded within

3300 ft of SR 64. Regardless of traffic volume, even at the lowest levels, pronghorn

distribution within 660 ft of the highway was low, with all combined probabilities less

than 0.07 (Figure 18). In the 990–1320 ft distance bands, the mean combined probability

of occurrence dropped from 0.28 at less than 100 vehicles per hr but stayed relatively

constant thereafter (0.20–0.21) up to 400 vehicles per hr, with a slight drop to 0.13 at

400–500 vehicles per hr. In the 1980 ft distance band, the mean probability of pronghorn

occurring here increased from 0.07 at less than 100 vehicles per hr to 0.13 at just 100–

200 vehicles per hr, and up to 0.16 at 500–600 vehicles per hr (Figure 18).

The proportion of pronghorn GPS approaches made to within 0.15 mi of SR 64 (n = 951)

varied throughout the day. Nearly half the approaches (48 percent) occurred between

16:30 and 19:00, when traffic was at its highest level during the day. During the morning

hours (05:30–10:00 hr), 18 percent of the approaches occurred, as did an equal proportion

of approaches made during midday hours (10:00–16:30).

5.3. WILDLIFE-VEHICLE COLLISION RELATIONSHIPS

From 2007 through 2009, DPS highway patrol officers and research team members

recorded 157 WVCs involving elk and mule deer along the SR 64 study area (Table 5).

Elk accounted for 63 percent (n = 99) of these WVCs, followed by mule deer, which

accounted for 35 percent (n = 58). In addition, three coyotes, three rabbits and one

mountain lion, black bear, and badger each were involved in WVCs during the study

period. No collisions involving pronghorn were recorded. In total, 77 WVCs were

recorded in 2007 (46 elk, 28 deer, 4 other), 51 in 2008 (33 elk, 16 deer, 2 other), and

40 in 2009 (20 elk, 14 deer, 3 other). DPS highway patrol accident reports indicated that

27 human injuries occurred in WVCs during the study.

Table 5. WVCs Involving Elk and Mule Deer on SR 64 Sections from 2007 through

2009, including the Total Number and Mean Collisions (per Mile).

SR 64

section

Elk collisions

Deer collisions All collisions

Total

Mean

(per mile)

Total

Mean

(per mile)

Total

Mean

(per mile)

A 26 1.3 30 1.6 56 2.9

B 4 0.5 0 – 4 0.5

C 0 – 0 – 0 –

D 13 1.4 3 0.3 16 1.8

E 56 4.7 25 2.1 81 6.8

All 99 1.9 58 1.2 157 3.1

Figure 18. Mean Probability That GPS-Collared Pronghorn Occurred within 330 ft

Distance Bands along SR 64 at Varying Traffic Volumes.

Section E (MP 223.4 to MP 235.4) had the highest incidence of elk and deer collisions, as

well as collisions per mile and more than twice the collisions per mile than Section A

(MP 185.5 to MP 204.7; Figure 19); these sections account for the highest density elk and

deer range along SR 64 (Figure 3). The spatial association between elk-vehicle collisions

and crossings at the 1.0 mi scale was significant (r = 0.811, n = 27, P < 0.001), as was the

association for deer (r = 0.705, n = 10, P = 0.022).

Figure 19. Frequency of Elk and Mule Deer Collisions with Vehicles by SR 64

Milepost from 2007 through 2009.

From 1998 through 2008, 41.7 percent of all single-vehicle accidents recorded along

SR 64 involved wildlife, compared with the national average of just 4.6 percent (Huijser

et al. 2007; Figure 20). The proportion of accidents involving wildlife (recorded by MP)

was as high as 87 percent (MP 233), with wildlife-related accidents accounting for more

than 75 percent of all single-vehicle accidents along five mileposts (all from MP 229 to

MP 234; Figure 20).

For accidents where time was reported by DPS, the incidence of elk and deer collisions

varied considerably among time periods (Table 6; Figure 21). The highest proportion of

elk collisions (50 percent) occurred from 5:00 p.m. to 10:00 p.m., followed by 39 percent

from 11:00 p.m. to 04:00 a.m. Only 11 percent of elk accidents occurred from 05:00 to

10:00 a.m., and none were recorded from 11:00 a.m. to 4:00 p.m. The observed

frequency of elk-vehicle collisions by time period differed from expected values (

62.5, df = 3, P < 0.001).

Figure 20. Proportion of SR 64 Single-Vehicle Accidents by Milepost from 1998

through 2008 that Involved Wildlife.

The negative association between the occurrence of elk-vehicle collisions and traffic

volume by hour was significant (r = -0.723, P = 0.001) in spite of the disproportionately

low incidence of collisions that occurred in the morning when traffic volume was low

(Figure 21).

The timing of deer-vehicle collisions was more variable than those for elk (Table 6;

Figure 21), though the observed frequency differed significantly from the expected by

time period (

= 26.8, df = 3, P < 0.001). Though 49 percent of accidents involving deer

occurred during the evening, only 8 percent occurred at night when traffic volume was

lowest. Conversely, during the times of the day when traffic volume was at its highest,

late morning and midday, a combined 43 percent of deer-vehicle collisions occurred

(Figure 20), partly accounting for the poor association between deer collisions and traffic

volume (r = 0.016, P = 0.941).

The incidence of elk collisions by day of the week did not vary significantly (P = 0.800),

though there were fewer collisions on Thursday than other days (Figure 22). For deer,

however, the incidence of collisions on Monday was more than double that of the other

six days, and the observed frequency of collisions by day was marginally different from

what was expected (

= 11.5, df = 6, P = 0.075). Neither association between both elk

and deer collisions versus mean daily traffic volume was significant (P = 0.879 and P =

0.562, respectively).

Figure 21. SR 64 Elk and Mule Deer Collisions with Vehicles by Time of Day and

Associated Traffic Volume.

Figure 22. SR 64 Elk and Mule Deer Collisions with Vehicles by Day

and Associated Traffic Volume.

There was a significant difference in the observed versus expected frequency of elk-

vehicle collisions by season (Table 7; 

= 17.4, df = 2, P < 0.001). The driest season,

early spring–summer (April–July), accounted for 43 percent of all elk-vehicle collisions

along SR 64, while late summer–fall (August–November) accounted for another 38

percent (Table 7; Figure 23).

The association between elk collisions and mean monthly traffic volume was significant

(r = 0.789, P = 0.002). For mule deer, the incidence of collisions was relatively constant

through much of the year, except the late summer–fall season when nearly half of all

collisions occurred (Table 7; Figure 23). The association between deer-vehicle collisions

and traffic volume was not significant (P = 0.210). The association between elk crossings

and collisions by month was significant (r = 0.583, P = 0.047), as was the association for

deer (r = 0.686, P = 0.014).

Table 6. Frequency of Elk and Deer Collisions with Vehicles

along SR 64 by Time Period.

Time period Hours

Frequency of WVCs (%)

Elk Mule deer

Evening 17:00–22:00

(50.0%)

(49.2%)

Nighttime 23:00–04:00

(39.4%)

(7.7%)

Morning 05:00–10:00

(10.6)

(29.2%)

Midday 10:00–16:00

(–)

(13.8%)

Table 7. Frequency of Elk and Deer Collisions with Vehicles

along SR 64 by Season.

Season Months

Frequency of wildlife vehicle collisions (%)

Elk Mule deer

Winter–early spring Dec–Mar

(18.2%)

(20.3%)

Late spring–summer Apr–Jul

(43.4%)

(30.5%)

Late summer

–fall Aug–Nov

(38.4%)

(49.1%)

Figure 23. SR 64 Elk and Mule Deer Collisions with Vehicles by Month

and the Mean Traffic Volume.

5.4 WILDLIFE USE OF CATARACT CANYON BRIDGE

Camera monitoring of Cataract Canyon Bridge was conducted from July 2008 through

December 2009. A total of 126 wildlife images, including 13 elk and 37 mule deer, were

recorded by the four cameras in the bridge cells (Table 8).

Of the limited number of elk that approached the bridge during the study, 92 percent

successfully crossed through the bridge cells, while the remaining 8 percent turned back.

For mule deer, for which a greater number of successful crossings were recorded (n = 37)

than elk, 89 percent of crossings were successful (Table 8). For smaller mammal species,

including gray fox, raccoon, skunk, and various squirrel species, only 6 percent of these

animals went all the way through the structure, while 94 percent turned back.

The relatively low mobility of some of these species (e.g., squirrels) may have limited

their potential for crossing through the bridge. The vast majority of deer underpass use

occurred from August through October, with 89 percent of the entries into the bridge in

these three months. Elk use occurred only in October and December, with no entries the

rest of the year. Of all deer and elk bridge crossings, 64 percent occurred in the 3 hr

period from 10:00 p.m. to 01:00 a.m.

Table 8. Number of Animals by Species that Entered and Successfully Crossed through

Cataract Canyon Bridge on SR 64, and Success Rates.

Species

Animals entering bridge Animals crossing through bridge

Success

rate

No. Proportion No. Proportion

Elk 13 0.10 12

0.19 0.92

Mule deer

37 0.29 33

0.53 0.89

Gray fox

5 0.04 2

0.48 0.40

Raccoon

10 0.08 2

0.03 0.20

Skunk

7 0.06 1

0.02 0.14

Squirrel

54 0.43 0

– –

In addition to wildlife use of Cataract Canyon Bridge, substantial presence by people was

documented. In total, 191 humans were recorded, averaging 15.9 people per month;

29 all-terrain vehicles were recorded at the bridge. Human use of the bridge was largely

restricted to daytime hours from 10:00 a.m. to 5:00 p.m., when 75 percent of the use

occurred.

5.5 IDENTIFICATION OF PASSAGE STRUCTURE SITES

The research team used elk and mule deer highway crossings, WVCs, pronghorn

approaches, and the proportions of animals crossing or approaching within each segment,

among other criteria, to rate 95 0.6 mi segments for suitability as potential passage

structure locations. Additional criteria included land ownership and topography that

would support passage structure construction. Ratings of the 94 0.6 mi segments from

MP 185.5 to MP 235.4 for their suitability for potential passage structures ranged from

1 to 33 points (mean = 10 points) of a possible 40 points (Figure 24). The highest-rated

(33 points) 0.6 mi segment (Segment 82, MP 234.5 to MP 235.0) was on the Kaibab NF

just south of the south entrance to Grand Canyon Airport, which corresponded to the

stretch of highway with the highest proportion of elk crossings (14.3 percent;

121 crossings) and mule deer crossings (42.3 percent; 227 crossings), as well the

highest incidence of WVCs (n = 10).

The land ownership and terrain at this segment further make this site suited for a passage

structure. ADOT (2006) identified this site as warranting an overpass, though it was not

included in the preferred alternative. The next two highest-rated 0.6 mi segments scored

24 points each; one (Segment 81) was just south of the highest-rated segment, further

pointing to the importance of this area. The other was just south of the Kaibab NF–GCNP

boundary (Segment 85, Figure 24); the rating for this segment was largely tied to high elk

and deer crossings, though no WVCs occurred here, reflecting the 35 miles per hour

(mph) posted speed.

Figure 24. Ratings for 95 SR 64 0.6 mi Segments Using Wildlife Movement, WVC Data, and Other

Criteria to Determine the Location of Potential Wildlife Passage Structures. Red Bars Denote

Segments Where Underpasses Were Recommended in the 2006 Final Wildlife Accident Reduction Study

and Orange Where Overpasses Were Recommended (Table 9). The Green Bars Represent Segments

Where Additional Structures Are Recommended as a Result of This Study.

Section A

Section B

Section D

Section E

Of the 0.6 mi segments corresponding to the stretch of highway where weighted

pronghorn approach peaks occurred (Figure 14), the highest-rated segment (MP 221.9 to

MP 222.3) scored 20 points and corresponded to the location where ADOT (2006)

recommended either an underpass at MP 222.2 or an overpass at MP 222.0. Because only

one structure is needed here, the research team recommends an overpass to accommodate

pronghorn, elk, and deer passage.

The research team’s rating with the eight criteria was used to identify 11 priority wildlife

passage structure locations (Table 9; Figure 24). Of these, six were at sites conducive to

underpasses and five were at sites where the terrain was conducive to overpasses or the

target species was pronghorn, or both (Table 9). Of the nine wildlife crossing structure

locations identified by ADOT (2006) with underpass at MP 222.2 and overpass at 222.3

combined, including retrofitting of Cataract Canyon Bridge, the research team’s rating of

potential passage structure sites corroborated that passage structures were warranted at

eight of those locations (Table 9; Figure 24).

In addition to the passage structure sites recommended in all alternatives in that study

(ADOT 2006), which were based largely on WVC records and sites where the

topography could support a structure, the team identified an additional three potential

passage structure locations, including an underpass at the Kaibab National Forest, at the

Grand Canyon National Park boundary. The other two structures are overpasses

recommended for pronghorn passage in an area where the team recorded no WVC and

none was reported in ADOT (2006).

For the rated 94 0.6 mi segments, the average rating was 10.0 points, while the average

rating of the 11 segments where passage structures are recommended was 20.7 points

(Figure 24). By highway section, the average rating for Section A segments was 4.0. The

average rating for segments recommended for passage structures was 16.0. Structures

were recommended at half of the highest-rated segments, with the other half the highest-

rated segments being adjacent to the structures (Figure 24).

On Section B, the mean rating was 5.0 points, and an overpass was recommended at the

highest-rated segment, with 11 points (Figure 24). Section D segments averaged

10.0 points and the rating for the two segments with recommended structures was nearly

twice the mean, or 19.5 points (Figure 24). Section E segments rated the highest,

averaging 17.0 points; the six segments with recommended passage structures here

averaged 24.3 points (Figure 24). No structures have been recommended for Section C

due to the prevalence of human development.

Table 9. Wildlife Passage Structure Locations along SR 64 by Milepost and Highway Section and Types Recommended in the

Various 2006 Final Wildlife Accident Reduction Study Alternatives and Those Recommended as a Result of the Current Wildlife

Movements Study.

Passage

structure

SR 64

section

Wildlife Accident Reduction

Study recommendation

Recommendation of

current study

Passage structure recommendation

justification and focus

Underpass Overpass Underpass Overpass

187.3 A Alt. A(W)-1 Yes

Existing Cataract Canyon Bridge; elk and deer

WVCs and connectivity

188.0 A Alt. A(W)-2 No

Need better spacing of passage structures

189.2 A Alt. A(W)-1 Yes

Elk and deer WVCs and pronghorn connectivity

205.0–205.5 B Yes

Pronghorn connectivity

220.0–220.5 D Yes

Pronghorn connectivity

222.2 D Alt. D(W)-1 No

Construct overpass nearby

222.3 D Alt. D(W)-2 Yes

Pronghorn connectivity

226.6 E Alt. E(W)-1 Yes