text:
Arizona Route 260
Final Report 540
Prepared by:
Norris L. Dodd, Jeffrey W. Gagnon, Susan Boe,
Amanda Manzo, and Raymond E. Schweinsburg
Arizona Game and Fish Department
Research Branch
2221 West Greenway Road
Phoenix, AZ 85023
August 2007
Prepared for:
Arizona Department of Transportation
206 South 17th Avenue
Phoenix, Arizona 85007
in cooperation with
U. S. Department of Transportation
Federal Highway Administration
The contents of this report reflect the views of the authors who are responsible for the facts and
accuracy of the data presented herein. The contents do not necessarily reflect official views or
policies of the Arizona Department of Transportation or the Federal Highway Administration. The
report does not constitute a standard, specification, or regulation. Trade or manufacturers'
names that 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
Technical Report Documentation Page
1. Report No.
2. Government Accession No.
3. Recipient's Catalog No.
5. Report Date
4. Title and Subtitle
PERMEABILITY ACROSS HIGHWAYS: Arizona Route 260
6. Performing Organization Code
7. Au thors
Norris L. Dodd, Jeffrey W. Gagnon, Susan Boe, Amanda Manzo,
and Raymond E. Schweinsburg
8. Performing Organization Report No.
10. Work Unit No.
9. Performing Organization Name and Address
Arizona Game and Fish Department
Research Branch
2221 West Greenway Road
Phoenix, Arizona 85023
11. Contract or Grant No.
ECS File No. JPA 01- 152
13. Type of Report & Period Covered
January ' 02 – December ' 06
12. Sponsoring Agency Name and Address
Arizona Department of Transportation
206 S. 17th Avenue
Phoenix, AZ 85007
ATRC Project Manager: Estomih Kombe
14. Sponsoring Agency Code
15. Supplementary Notes
Prepared in cooperation with the U. S. Department of Transportation, Federal Highway Administration
16. Abstract
We conducted wildlife- highway relationships research from 2002– 2006 along a 17- mile stretch of State
Route 260 in Arizona which is being reconstructed in five phases with 11 wildlife underpasses and 6
bridges. Reconstruction phasing allowed us to use a before- after- control experimental approach in our
research. The objectives of our research were;
1) Assess and compare wildlife use of underpasses. 2) Evaluate highway permeability and wildlife
movements among reconstruction classes. 3) Characterize wildlife- vehicle collision patterns and changes
with reconstruction. 4) Assess relationships among highway traffic volume and wildlife vehicle collisions,
elk crossing patterns, and wildlife use of underpasses. 5) Assess the role that ungulate- proof fencing plays in
wildlife vehicle collisions, wildlife use of underpasses, and wildlife permeability. 6) Provide ongoing
highway reconstruction implementation guidance.
We used video surveillance to assess and compare wildlife use of five underpasses at which we recorded
8,455 animals and 11 different species; 5,560 of these animals ( 65.8%) crossed through the underpass. We
employed Global Positioning System telemetry to assess highway permeability across SR 260, with 65 elk
fitted with receiver collars. Elk crossed State Route 260 5,749 times. Elk permeability on reconstructed
highway ( 0.43 crossings/ approach) was half that of control sections. Permeability increased 60% after
ungulate- proof fencing was erected on a reconstructed section. Effective monitoring and adaptive
management yielded benefits to highway safety and wildlife permeability alike.
17. Key Words
Cervus elaphus, deer, elk, GPS telemetry, fencing,
highway crossings, highway impact, Odocoileus spp.,
permeability, traffic volume, video surveillance, wildlife
underpasses, wildlife- vehicle collisions.
18. Distribution Statement
No restriction. Document is
available to the U. S. public through
the National Technical Information
Service, Springfield, Virginia 22161
19. Security Classification
20. Security Classification
21. No. of Pages
22. Price
Symbol When You
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in inches 25.4 millimeters mm mm millimeters 0.039 inches in
ft feet 0.305 meters m m meters 3.28 feet ft
yd yards 0.914 meters m m meters 1.09 yards yd
mi miles 1.61 kilometers km km kilometers 0.621 miles mi
in2 Square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 square inches in2
ft2 square feet 0.093 square meters m2 m2 square meters 10.764 square feet ft2
yd2 square yards 0.836 square meters m2 m2 square meters 1.195 square yards yd2
ac acres 0.405 hectares ha ha hectares 2.47 acres ac
mi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 square miles mi2
fl oz fluid ounces 29.57 milliliters mL mL milliliters 0.034 fluid ounces fl oz
gal gallons 3.785 liters L L liters 0.264 gallons gal
ft3 Cubic feet 0.028 cubic meters m3 m3 cubic meters 35.315 cubic feet ft3
yd3 Cubic yards 0.765 cubic meters m3 m3 cubic meters 1.308 cubic yards yd3
NOTE: Volumes greater than 1000L shall be shown in m3.
oz ounces 28.35 grams g g grams 0.035 ounces oz
lb pounds 0.454 kilograms kg kg kilograms 2.205 pounds lb
T short tons
( 2000lb) 0.907 megagrams
( or " metric ton")
( or " t")
( or " t")
( or " metric ton") 1.102 short tons
( 2000lb) T
TEMPERATURE ( exact)
TEMPERATURE ( exact)
º F Fahrenheit
or ( F- 32)/ 1.8 Celsius temperature º C º C Celsius temperature 1.8C + 32 Fahrenheit
º F
fc foot- candles 10.76 lux lx lx lux 0.0929 foot- candles fc
fl foot- Lamberts 3.426 candela/ m2 cd/ m2 cd/ m2 candela/ m2 0.2919 foot- Lamberts fl
lbf poundforce 4.45 Newtons N N Newtons 0.225 poundforce lbf
lbf/ in2 poundforce per
square inch 6.89 kilopascals kPa kPa kilopascals 0.145 poundforce per
square inch lbf/ in2
SI is the symbol for the International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380
1.1 Video surveillance to assess highway underpass use by elk.............................. 2
1.2 Effects of traffic volume on elk use of wildlife highway underpasses .............. 2
1.3 Assessment of elk highway permeability using Global Positioning System
telemetry ............................................................................................................ 3
1.4 Role of fencing in promoting wildlife underpass use and highway
permeability ....................................................................................................... 4
1.5 Influence of fluctuating traffic volume on elk distribution and highway
crossings............................................................................................................. 4
1.6 Characteristics of elk- vehicle collisions and comparison to highway crossing
patterns............................................................................................................... 5
1.7 Influence of environmental factors on elk highway crossings........................... 6
1.8 Preliminary assessment of factors influencing wildlife use of highway
underpasses ........................................................................................................ 6
1.9 Overall conclusions and recommendations ....................................................... 7
2.2.1 Phased Construction and Adaptive Management ................................ 13
2.2.2 Experimental Approach ....................................................................... 13
4.3.1 Video Surveillance System Components and Layout.......................... 28
4.3.2 Video Data Analysis ............................................................................ 28
4.3.3 Time- Lapse Validation ........................................................................ 30
4.3.4 Statistical Analysis............................................................................... 30
6.2.1 Elk Capture and GPS Collars............................................................... 52
6.2.2 GPS Accuracy Validation .................................................................... 52
6.2.3 GPS Data Analysis of Elk Movements................................................ 53
6.2.4 Statistical Analysis............................................................................... 55
6.3.1 GPS Fix Accuracy................................................................................ 57
6.3.2 Elk Highway Movements .................................................................... 57
6.3.3 Habitat and Elk Movements................................................................. 60
7.3.1 Comparison of elk crossing patterns and permeability........................ 71
7.3.2 Comparison of elk- vehicle collision patterns ...................................... 72
7.3.3 Wildlife Underpass Use Comparison................................................... 73
7.4.1 Comparison of elk crossing patterns and permeability........................ 74
7.4.2 Comparison of elk- vehicle collision patterns ...................................... 76
7.4.3 Wildlife Underpass Use Comparison................................................... 79
8.4.1 Traffic volume and elk distribution ..................................................... 88
8.4.2 Traffic volume, probability of highway crossing and highway
permeability ..................................................................................................... 90
8.4.3 Elk distribution and collisions ............................................................. 90
9.2.1 Wildlife- vehicle collision tracking ...................................................... 94
9.2.2 Elk- vehicle collision relationships to AADT and elk population
estimates........................................................................................................... 95
9.2.3 Comparison of elk- vehicle collision by highway section and
construction classes.......................................................................................... 96
9.2.4 GPS telemetry assessment of elk highway crossings .......................... 96
9.2.5 Comparison of elk- vehicle collision and elk highway crossing
patterns............................................................................................................. 96
9.3.1 Elk- vehicle collision relationships to AADT and elk population
estimates........................................................................................................... 98
9.3.2 Comparison of elk- vehicle collisions by highway section and
construction class........................................................................................... 101
9.3.3 Cost- benefit of measures to reduce elk- vehicle collisions................. 103
9.3.4 Comparison of elk- vehicle collision and elk highway crossing
patterns........................................................................................................... 104
9.3.5 Spatial relationships between elk- vehicle collision and crossing
patterns........................................................................................................... 110
9.3.6 Temporal relationships between elk- vehicle collision and crossing
patterns........................................................................................................... 110
9.4.1 Elk- vehicle collision relationships to AADT and elk population
estimates......................................................................................................... 115
9.4.2 Comparison of elk- vehicle collisions by highway section and
construction classes........................................................................................ 117
9.4.3 Cost- benefit of measures to reduce elk- vehicle collisions................. 118
9.4.4 Comparison of elk- vehicle collisions and elk highway crossings ..... 118
10.2.1 Determination of elk highway crossing patterns ............................... 125
10.2.2 Habitat assessment ............................................................................. 125
10.2.3 Statistical analysis.............................................................................. 126
11.3.1 Video surveillance systems................................................................ 135
11.3.2 Assessment of wildlife use of underpasses........................................ 135
11.3.3 Modeling factors influencing wildlife underpass use ........................ 136
11.4.1 Wildlife underpass use and passage rates .......................................... 137
11.4.2 Modeling of factors influencing wildlife underpass use.................... 138
11.4.3 Influence of underpass structure and placement................................ 144
11.4.4 Influence of video surveillance monitoring length ............................ 145
11.4.5 Influence of season ............................................................................ 146
11.4.6 Influence of time of day..................................................................... 147
12.1 Highway planning and monitoring ................................................................ 153
12.2 Role of riparian and meadow habitats ........................................................... 153
12.3 Wildlife underpasses...................................................................................... 154
12.4 Influence of traffic volume on wildlife.......................................................... 154
12.5 Wildlife permeability relationships................................................................ 155
12.6 Highway safety/ wildlife- vehicle collisions.................................................... 155
12.7 Role of ungulate- proof fencing...................................................................... 155
12.8 Future State Route 260 reconstruction sections............................................. 156
Figure 3.1. Location of our study area and the five highway sections where phased highway
reconstruction has been ongoing since 2000, and the location of wildlife underpasses and
bridges........................................................................................................................ ................... 20
Figure 3.2. The existing narrow two- lane roadway ( left; Doubtful Canyon section) is being
reconstructed to a four- lane divided highway ( right; Preacher Canyon section), State Route 260,
Arizona........................................................................................................................ .................. 21
Figure 3.3. State Route 260 study area ( at the pedestrian/ wildlife underpass on the Christopher
Creek Section), Arizona................................................................................................................. 22
Figure 3.4. Aerial view of Little Green Valley riparian- meadow complex adjacent to the
Preacher Canyon section................................................................................................................ 22
Figure 3.5. Average annual daily traffic for State Route 260, Arizona. ( ADOT Control Road
monitoring station) for the period 1990– 2005. .............................................................................. 23
Figure 4.1. Little Green Valley riparian- meadow complex ( center photo) adjacent to State
Route 260 in Arizona, into which the west ( top photo) and east ( bottom photo) wildlife
underpasses open. .......................................................................................................................... 27
Figure 4.2. Layout ( top) of video surveillance system components at the west and east Little
Green Valley wildlife underpasses and the location of elk- proof and highway right- of- way
fencing, State Route 260, Arizona. ................................................................................................ 29
Figure 4.3. Frequency of elk crossing through both wildlife underpasses by time, crossing
toward and returning from the Little Green Valley meadow– riparian complex, State Route 260,
Arizona, determined from video surveillance from September 2002– September 2005................ 35
Figure 4.4. Frequency of elk observed crossing at the two Little Green Valley wildlife
underpasses by day determined by video surveillance from September 2002– September 2005,
and average daily traffic volume determined from a traffic counter along State Route 260,
Arizona, for the period December 2003- December 2004.............................................................. 36
Figure 4.5. Combined mean passage rate ( number of crossing elk/ number of approaching elk)
by month at the two Little Green Valley wildlife underpasses, State Route 260, Arizona,
determined by video surveillance conducted January 2003- September 2005. ............................ 37
Figure 4.6. Indian Gardens wildlife underpass on the Kohl's Ranch section of State Route 260,
Arizona, completed in March 2006. .............................................................................................. 38
Figure 5.1. Proportion (± SE) of individual elk exhibiting: a) alert- hesitation, b) flight- retreat,
and c) feeding behavior when observed approaching five underpasses along State Route 260,
Arizona, at varying traffic levels. .................................................................................................. 45
Figure 5.2. Mean passage rates for elk during winter and summer ( solid line) and fall and
spring migration period ( dotted line) through five wildlife underpasses at varying traffic levels
along State Route 260, Arizona, 2003- 2005.................................................................................. 46
Figure 6.1. Cow elk caught in a Clover trap ( left) and being fitted with a GPS receiver collar,
along State Route 260, Arizona. .................................................................................................... 52
Figure 6.2. Highway segments ( 0.10 mi) delineated along State Route 260, Arizona, used to
compile highway crossings by elk, and the 0.15- mi distance buffer in which approaches to the
highway were determined. ............................................................................................................. 54
Figure 6.3. Minimum convex polygon ( MCP) home range ( White and Garrott 1990) adjacent
to the study area for bull elk no. 5 in which we generated the same number of random points
( dots) as successful Global Positioning System ( GPS) fixes ( n = 3,815) to compare distribution. 56
Figure 6.4. Mean frequency of observed Global Positioning System ( GPS) fixes and random
points generated for 33 elk fitted with GPS receiver collars that occurred in buffer zones within
0.6 mi of State Route 260, Arizona................................................................................................ 60
Figure 6.5. Frequency distribution of elk highway crossings by 0.1- mi segment, highway
section, and reconstruction class along State Route 260, Arizona, determined from 33 elk fitted
with Global Positioning System ( GPS) receiver collars. ............................................................... 61
Figure 6.6. Frequency distribution of elk highway crossings by 0.1- mi segment for Christopher
Creek section, State Route 260, Arizona, and projected proportions of total crossings
intercepted with planned and proposed elk- proof fence. ............................................................... 66
Figure 7.1. Location of wildlife underpasses and bridges along the Christopher Creek section
of State Route 260, Arizona, and delineation of different treatments to deter wildlife passage
onto the highway and funnel animals toward passage structures. ................................................. 70
Figure 7.2. Alternatives to fencing to deter at- grade wildlife crossings along the Christopher
Creek section of State Route 260, Arizona.................................................................................... 71
Figure 7.3. Number of elk- vehicle collisions recorded along the Christopher Creek section of
State Route 260, Arizona, in 2004 ( top; total 51 collisions) before ungulate- proof fencing was
erected and 2005 ( bottom; 12 collisions) after fencing was erected.............................................. 77
Figure 7.4. At- grade and below- grade ( through 6 wildlife underpasses) elk passage rates at
varying traffic volume levels along State Route 260, Arizona, ( figure from Gagnon et al.
2007c). ............................................................................................................................... ........... 80
Figure 8.1. Permanent traffic counting station installed along the Little Green Valley section of
State Route 260, Arizona, in December 2003................................................................................ 85
Figure 8.2. Mean probability that Global Positioning System ( GPS)- collared elk ( n = 44)
occurred within each 330- ft distance band from State Route ( SR) 260, Arizona, at varying
traffic volumes: a) < 100, b) 100- 200, c) 200- 300, d) 300- 400, e) 400- 500, f) > 600 vehicles/ hr. .87
Figure 8.3. Probabilities of elk crossing State Route ( SR) 260, Arizona, at varying traffic
volumes under different scenarios, derived from the best possible model selected by Akaike's
Information Criterion. .................................................................................................................... 89
Figure 9.1. Bull elk killed in elk- vehicle collision along the Christopher Creek section of State
Route 260, Arizona, ( left) and a vehicle that struck an elk and sustained substantial property
damage and injured the driver........................................................................................................ 93
Figure 9.2. Number of elk- vehicle collisions ( 1994– 2006) and weighted elk highway crossings
for 33 elk fitted with Global Positioning System collars 2002– 2004 by 0.1- mi segments and
sections along State Route 260, Arizona. .................................................................................... 105
Figure 9.3. Number of elk- vehicle collisions recorded 2001– 2006 on the Preacher Canyon
( bottom), Christopher Creek ( middle), and control sections ( Little Green Valley and Doubtful
Canyon) of State Route 260, Arizona. ......................................................................................... 106
Figure 9.4. Actual elk- vehicle collisions recorded along State Route 260, Arizona, from 2001–
2006, compared to levels predicted from our multiple regression equation using average annual
daily traffic volume and elk population data. .............................................................................. 108
Figure 9.5. Number of elk- vehicle collisions along State Route 260, Arizona, by AADT levels
with and without measures to reduce collision incidence of including underpasses and fencing
assuming a 60% reduction in elk- vehicle collisions with measures, and the economic benefit
associated with the difference in the number of elk- vehicle collisions at varying AADT .......... 109
Figure 9.6. Coefficients of determination ( r2) for linear regression comparisons of elk- vehicle
collisions to elk crossings and weighted elk crossings conducted at various scales along State
Route 260............................................................................................................................ ........ 112
Figure 9.7. Frequency of elk- vehicle collisions and weighted elk crossings determined from 33
elk fitted with GPS collars 2002– 2004, by 0.6- mi sections along State Route 260. ................... 112
Figure 9.8. Proportions of elk- vehicle collisions ( solid line) and elk highway crossings ( dashed
line) by month along State Route 260, Arizona........................................................................... 114
Figure 9.9. Proportions of elk- vehicle collisions ( solid line) and elk highway crossings ( dashed
line) for bull elk by month along State Route 260, Arizona........................................................ 114
Figure 9.10. Elk- vehicle collision frequency by day and as corrected with daily AADT factors
accounting for differential daily traffic volume........................................................................... 115
Figure 9.11. Proportions of elk- vehicle collisions ( bars) and elk highway crossings ( dashed
line) by 2- hour time interval along State Route 260, Arizona..................................................... 116
Figure 9.12. Absolute departure ( by 0.5 hour increments) from sunrise or sunset for elk- vehicle
collisions along State Route 260.................................................................................................. 116
Figure 10.1. Schematic representation of the sampling block used to assess slope, aspect, and
canopy cover at 0.2- mi high and low frequency elk highway crossing sites along State Route
260, Arizona........................................................................................................................ ........ 127
Figure 10.2. Mean distance to permanent water sources and meadow habitats ( and SE bars)
from the center fn 0.2- mi elk highway crossing segments, comparing high and low frequency
crossing segments along State Route 260, Arizona. .................................................................... 128
Figure 10.3. Relationships between the mean frequency of weighted elk crossings at 0.2- mi
crossing segments along State Route 260, Arizona, and the distance to the nearest permanent
water sources ( dashed line) and meadow habitat ( solid line) ...................................................... 129
Figure 10.4. Classification and Regression Tree ( CART) modeling decision tree for variables
that described high and low frequency elk highway crossing sites, including proximity to
nearest permanent water, proximity to meadow habitat less than or greater than 680 ft, and the
proximity to meadow habitat less than or greater than 3,100 ft from 0.2- mi crossing segments
along State Route 260, Arizona ................................................................................................... 132
Figure 11.1. Aerial ( left) and entry ( right) views of the West Little Green Valley underpass
looking north.......................................................................................................................... ..... 139
Figure 11.2. Aerial ( left) and entry ( right) views of the East Little Green Valley underpass
looking north.......................................................................................................................... ..... 140
Figure 11.3. Aerial ( left) and entry ( right) views of the Pedestrian- Wildlife underpass looking
north. ............................................................................................................................... ............ 141
Figure 11.4. Aerial ( left) and entry ( right) views of the Wildlife 2 underpass looking north. ... 142
Figure 11.5. Aerial ( left) and entry ( right) views of the Wildlife 3 underpass looking north. ... 143
Figure 11.6. Number of elk groups that approached the five wildlife underpasses ( left) and
mean elk passage rate for the underpasses ( right) over three years of video surveillance
monitoring, State Route 260, Arizona. ........................................................................................ 146
Figure 11.7. Mean monthly elk passage rates for five underpasses at which video surveillance
monitoring has been ongoing for four years ( 2002– 2006) along State Route 260, Arizona. ...... 147
Figure 11.8. Number of elk underpass crossings ( left) and mean passage rate ( right) at five
underpasses along State Route 260, Arizona, determined from video surveillance 2002– 2006.148
Figure 11.9. Number of elk crossings ( left) and passage rate ( right) by time of day for five
underpasses along State Route 260, Arizona, determined from video surveillance 2002– 2006. 1148
Figure 11.10. The Indian Gardens wildlife underpass on the Kohl's Ranch section ( looking
north from the east- bound lanes bridge), State Route 260, Arizona, where nearly all originally-planned
concrete walls were removed upon construction, widening the underpass floor for
wildlife passage, retaining native vegetation, and improving openness...................................... 151
Table 2.1. Dates that highway reconstruction was initiated and completed for the five
reconstruction sections on State Route 260, Arizona, and years of research accomplished
under various construction classes as part of research conducted 2002– 2006. ....................... 14
Table 3.1. State Route 260 reconstruction sections, reconstruction status, mileposts and
length, and the number of wildlife passage structures planned or built as part of the
reconstruction................................................................................................................. ......... 19
Table 4.1. Physical characteristics associated with the two wildlife underpasses ( UP) at which
we conducted video monitoring focusing on elk from September 2002– September 2005, State
Route 260, Arizona. ................................................................................................................. 26
Table 4.2. Results obtained from the Bradley- Terry logit model for paired preferences to
assess elk preference in selection of two wildlife underpasses ( UP) on State Route 260,
Arizona, for approaching and crossing. ................................................................................... 33
Table 4.3. Probabilities ( pe) of use of two wildlife underpasses ( UP) by elk groups and 95%
confidence intervals ( CI) obtained from multiple logistic regression given the combined
effects of underpass ( east versus west) and season ( summer [ April– September] versus
winter [ October– March]), and elk passage rates and two- proportion 95% CI for the same
underpass use categories. ......................................................................................................... 34
Table 4.4. Proportion of individual elk that displayed various behaviors near the two
wildlife underpasses ( UP) while approaching and crossing, and the associated 95% CI for
differences in the proportions ( Agresti 1996).......................................................................... 34
Table 5.1. Number of successful and unsuccessful elk crossings and passage rates ( no.
crossing/ no. approaching) by elk groups observed by video surveillance at five wildlife
underpasses along State Route 360, Arizona, at varying traffic levels.................................... 44
Table 5.2. Number of individual elk exhibiting flight responses while passenger vehicles
and semis passed overhead at low and high traffic levels during attempted crossings at five
wildlife underpasses along State Route 260 in central Arizona, 2003 – 2005. ....................... 45
Table 6.1. Highway crossings by section along State Route 260, Arizona, of 33 elk fitted
with Global Positioning System ( GPS) telemetry collars, including the length and
construction status of each section, number of elk that crossed the highway within each
section, and mean passage rate. ............................................................................................... 58
Table 6.2. Mean values we calculated by elk class for highway crossing, approach, and
passage rate parameters, and minimum convex polygon ( MCP) home ranges determined
from Global Positioning System ( GPS) telemetry along State Route 260, Arizona; and
results of ANOVA ( all df = 1, 31) tests of differences in means between cows and bulls. .. 59
Table 6.3. Mean crossings/ day and passage rates ( crossings/ approach) calculated by
highway reconstruction class along State Route 260, Arizona, from 33 elk fitted with Global
Positioning System ( GPS) telemetry collars............................................................................ 61
Table 6.4. Mean proportions of vegetation types comprising minimum convex polygon
( MCP home ranges, proportion of vegetation types within 0.6 mi of State Route 260,
Arizona, mean proportion of elk Global Positioning System ( GPS) fixes by vegetation type,
and chi- squared ( χ2) comparison of observed versus expected proportions of each
vegetation type used by 33 elk fitted with GPS collars. .......................................................... 63
Table 7.1. Physical characteristics associated with wildlife underpasses ( UP) and bridges
on the Christopher Creek section of State Route 260, Arizona, and whether video
surveillance of wildlife use was conducted at the passage structure. ...................................... 69
Table 7.2. Mean elk crossings/ day and passage rate ( crossings/ approach) for elk fitted with
Global Positioning System ( GPS) telemetry collars by highway reconstruction treatment,
Christopher Creek section, State Route 260, Arizona. ............................................................ 76
Table 7.3. Mean proportion of elk highway crossings along the Christopher Creek section,
State Route 260, before and after ungulate- proof fencing was erected and the mean
proportion of change ( Δ) with fencing..................................................................................... 78
Table 7.4. Mean collisions/ season ( 2002– 2006) by season and highway reconstruction class
along the Christopher Creek section, State Route 260, Arizona.............................................. 81
Table 8.1. Parameters for the only supported model ( best model) of 22 possible for the
probability of 40 elk crossing State Route ( SR) 260, Arizona, using Akaike's Information
Criterion ( AIC), and its comparison to individual factors and the null model, - 2 log-likelihoods,
number of parameters ( k), AIC adjusted for small sample sizes ( AICc), AICc
difference ( ΔAICc), and Akaike weights ( wi).......................................................................... 88
Table 9.1. Frequency of wildlife- vehicle collisions by species and average annual daily
traffic ( AADT) volume for State Route 260, Arizona, and elk population estimates for
management units adjacent to SR 260, for the period 1994– 2006. ......................................... 99
Table 9.2. Number of animals killed in wildlife- vehicle collisions along State Route 260,
Arizona, by species documented by DPS and AGFD between 2001– 2006, with age and sex
of classified animals and proportion of classified animals. ................................................... 100
Table 9.3. Frequency of elk- vehicle collisions by State Route 260 highway section,
Arizona, recorded for the period 2001– 2006 by DPS and AFGD, and a comparison of the
total elk- vehicle collisions to the total in the ADOT database ( see Table 9.1) for the same
period ............................................................................................................................... ..... 101
Table 9.4. Proportion of single- vehicle accidents involving wildlife by State Route 260
highway section, Arizona, 1994– 2005................................................................................... 102
Table 9.5. Number of elk fitted with Global Positioning System ( GPS) collars versus those
that were killed in elk- vehicle collisions along State Route 260, Arizona, by highway
crossing frequency class. ....................................................................................................... 104
Table 9.6. Number of elk- vehicle collisions by State Route 260 highway section, Arizona,
1994– 2006, and mean collisions/ mi/ year (± SE) for each section.......................................... 107
Table 9.7. Dates of construction initiation and completion for SR 260 highway sections,
Arizona, and mean number of elk- vehicle collisions ( EVC) from 1994– 2006 (± SE) by
highway construction classes ( before, during, and after reconstruction)............................... 108
Table 9.8. Summary of elk crossings, Shannon Diversity Index ( SDI), and weighted
crossings by highway section along State Route 260, Arizona, determined from 33 elk fitted
with GPS telemetry collars, May 2002– April 2004............................................................... 109
Table 9.9. Elk- vehicle collision ( EVC) relationships between highway crossings and
weighted crossings by 33 Global Positioning System- collared elk at various scales along
State Route 260, including correlation coefficients ( r) and coefficients of determination ( r2). 111
Table 10.1. G2- statistics associated with five variables used in the Classification and
Regression Tree modeling of high and low frequency elk crossing segments along State
Route 260, Arizona. ............................................................................................................... 131
Table 11.1. Structural characteristics associated with wildlife underpasses on State Route
260, Arizona, at which video camera surveillance was conducted from 2004– 2006 to assess
wildlife use, and the year in which underpass construction was completed and monitoring
initiated. ............................................................................................................................... . 135
Table 11.2. Number of animals by species recorded by video cameras at the West Little
Green Valley underpass, number crossing through the underpass, and the passage rate ( no.
crossing/ no. approaching). ..................................................................................................... 139
Table 11.3. Number of animals by species recorded with video surveillance at the East
Little Green Valley underpass, number crossing through the underpass, and the passage rate
( no. crossing/ no. approaching)............................................................................................... 140
Table 11.4. Number of animals by species recorded with video surveillance at the
Pedestrian- Wildlife underpass, number crossing through the underpass, and the passage rate
( no. crossing/ no. approaching)............................................................................................... 141
Table 11.5. Number of animals by species recorded with video surveillance at the Wildlife
2 underpass, number crossing through the underpass, and the passage rate ( no. crossing/ no.
approaching). ......................................................................................................................... 142
Table 11.6. Number of animals by species recorded with video surveillance at the Wildlife
3 underpass, number crossing through the underpass, and the passage rate ( no. crossing/ no.
approaching). ......................................................................................................................... 143
Table 11.7. Likelihood ratio test results for five factors modeled by multiple logistic
regression analysis for successful elk crossing at five underpasses along State Route 260,
Arizona, assessed by video camera surveillance conducted 2004- 2005, including logistic
regression chi- square ( χ 2) statistic, probability, and degrees of freedom ( DF)..................... 144
Table 11.8. Probability of a successful elk crossing at five wildlife underpasses along State
Route 260, Arizona, determined by logistic regression......................................................... 145
Table 11.9. Comparison of odds of a successful crossing at 5 wildlife underpasses along SR
260 in central Arizona. The number on the left side of each ratio is associated with the
structures listed in column one .............................................................................................. 145
Table 11.10. Probability of a successful elk crossing by year at five wildlife underpasses
along State Route 260, Arizona, determined by logistic regression ...................................... 146
Table 11.11. Probability of a successful elk crossing by season at five wildlife underpasses
along State Route 260, Arizona, USA, determined by logistic regression ............................ 147
Table 11.12. Probability of a successful elk crossing by time class at five wildlife
underpasses along State Route 260, Arizona, determined by logistic regression.................. 148
Black bear Ursus americanus
Caribou Rangifer tarandus
Elk Cervus elaphus
Grizzly bear Ursus arctos
Javelina Tayassu tajacu
Moose Alces alces
Mountain goats Oreamnos americanus
Mountain lion Puma concolor
Mule deer Odocoileus hemionus
Pronghorn Antilocapra americana
Rocky Mountain elk Cervus elaphus nelsoni
White- tailed deer Odocoileus virginianus cousei
Wolf Canis lupus
Douglas fir Pseudotsuga menzeisii
Gambel oak Quercus gambelii
Juniper Juniperus spp.
Manzanita Arctostaphalos pungens
Pinyon Pinus edulis
Ponderosa pine Pinus ponderosa
White fir Abies concolor
This study was funded by the Arizona Department of Transportation's ( ADOT) Arizona
Transportation Research Center ( ATRC), and the Federal Aid Wildlife in Restoration Act,
Project W- 78- R supporting Arizona Game and Fish Department ( AGFD) research. The
Tonto National Forest ( TNF) and Federal Highway Administration ( FHWA) provided
additional funding to the project that made our application of Global Positioning System
telemetry possible. We thank Terry Brennan, Robert Ingram, and Duke Klein of the TNF,
and Paul Garrett and Steve Thomas of FHWA for their commitment to making this project
Many individuals at ADOT provided endless support and guidance in this project and were
instrumental to its success, especially Estomih Kombe, Bruce Eilerts, Siobhan Nordhaugen,
and Doug Brown ( now at the Arizona Department of Administration). Mark Catchpole,
Doug Eberline, Jami Rae Garrison, and Dale Buskirk of the Transportation Planning
Division provided invaluable traffic data and support. We sincerely thank Tom Foster,
Myron Robison, David Gerlach, William Pearson, James Laird, Tom Goodman, Jack
Tagler, and Dallas Hammit of the Prescott District for their commitment to adaptive
management and willingness to respond to our data and recommendations. Though often at
a cost of valuable time and limited funds, their commitment maximized the effectiveness of
wildlife structures and highway safety.
The cooperation of John Anderson, Walt Cline, Bob Ochoa ( Boy Scouts of America),
Mikey Marazza, and Tom Dunney ( Arizona State University), allowing us to trap elk on
private lands, contributed greatly the success of the Global Positioning System telemetry
portion of our study.
The AGFD's Mesa Region played a crucial role in the project, especially key personnel:
Tim Holt, Henry Apfel, John Dickson, Craig McMullen, and Jon Hanna. Research Branch
personnel Kari Ogren, Fenner Yarborough, Scott Sprague, and Tim Rogers assisted with the
laborious task of viewing and analyzing videotape and keeping our video camera
surveillance systems " up and running." The logistical support provided by Tonto Creek
Fish Hatchery personnel was invaluable to our project, particularly the hospitality and
assistance provided by Larry Peterson to whom we dedicate this report, John Diehl, Larry
Duhamell, Mike Weisser, and Trevor Nelson.
We offer a special thanks to the Arizona Department of Public Safety Highway Patrolmen
in the Payson District whose efforts to document wildlife- vehicle collisions were
instrumental not only to the success of our project, but invaluable in helping resolve
wildlife- vehicle conflicts across Arizona, making its highways safer.
The ATRC's Technical Advisory Committee for this project provided many suggestions
toward improving its effectiveness and applicability. Their tremendous support, oversight,
and commitment throughout the duration of the project were greatly appreciated.
We studied wildlife- highway relationships from 2002– 2006 along a 17- mi stretch of
State Route ( SR) 260, in central Arizona, USA. This stretch is being reconstructed from
a two- lane to a four- lane divided highway in five phases incorporating 11 wildlife
underpasses and six bridges. Phased reconstruction allowed us to use a before- after-control
experimental approach in our research. The objectives of our research were to:
• Assess and compare wildlife use of underpasses.
• Evaluate highway permeability and wildlife movements among reconstruction
• Characterize wildlife- vehicle collision patterns and changes with reconstruction.
• Assess relationships among highway traffic volume and wildlife- vehicle
collisions, elk crossing patterns, and wildlife use of underpasses.
• Assess the role that ungulate- proof fencing plays in wildlife- vehicle collisions,
wildlife use of underpasses, and highway permeability to wildlife.
• Provide ongoing highway reconstruction implementation guidance.
We used video camera surveillance to assess and compare wildlife use of five underpasses.
We recorded 8,455 animals and 11 different species; 5,560 of these animals ( 65.8%)
crossed through the underpasses. Our underpass passage rates ranged from 0.10– 0.68
crossing/ approach, and underpass structure and placement was the most important factor of
five modeled influencing the probability of successful underpass crossing by elk ( Cervus
elaphus nelsoni). We used Global Positioning System ( GPS) telemetry data collected from
65 elk fitted with GPS receiver collars to assess movement patterns and highway
permeability. Our elk crossed SR 260 5,749 times. Elk permeability on reconstructed
highway ( 0.43 crossings/ approach) was half that of the control sections. Permeability
increased 60% after ungulate- proof fencing was erected on a reconstructed section, linking
underpasses. Fencing also resulted in > 80% reduction in elk- vehicle collisions and
improved underpass effectiveness as elk and deer ( Odocoileus spp.) underpass passage rates
increased from 0.12 to 0.56 crossings/ approach after fencing. While we found that traffic
volume did affect elk highway crossing and distribution patterns at highway grade, it had
little or no impact on elk crossing below grade through underpasses. We assessed spatial
and temporal patterns of elk- vehicle collisions ( n = 571). Annual elk- vehicle collisions
were related to traffic volume and elk population levels. Mean elk- vehicle collisions during
reconstruction ( 11.6/ year) was higher than before ( 4.4/ year) and after reconstruction
( 6.5/ year) for each section. The benefit of reduced elk- vehicle collisions from underpasses
and fencing was projected to approach $ 1 million/ year. We compared elk- vehicle collisions
and elk GPS crossings at five scales and found that the strength of the relationship and
management utility were optimized at the 0.6- mi scale. The proximity of riparian- meadow
habitats and permanent water along SR 260 influenced the pattern of elk GPS crossings and
elk- vehicle collisions. Together, effective monitoring and adaptive management improved
both highway safety and highway permeability to wildlife.
We report our research findings in eight separate chapters or volumes addressing various
aspects of our research. A summary for each volume follows below, as well as an overall
summary of project conclusions and recommendations.
We used integrated video systems to compare wildlife use of two bridged wildlife
underpasses on the Preacher Canyon section from September 2002– September 2005.
Both underpasses opened into the same riparian- meadow complex, were situated < 850 ft
apart, and had different below- span characteristics and dimensions. Our objectives were
to compare elk response to the underpasses and test hypotheses that passage rate,
probability of use, and behavioral response at the two underpasses did not differ. We
related differences in elk use and response to underpass design characteristics. Elk
accounted for > 90% of the animals we recorded on videotape, with 3,708 elk in 1,266
groups recorded at the two underpasses. We used multiple logistic regression to predict
the probability of underpass use by elk incorporating the combined effects of underpass,
season, and year. Season had the greatest effect on underpass use, with the probability of
underpass use in summer ( 0.81) higher than winter ( 0.58) when migratory elk less
habituated to the underpasses were present. A pattern of high summer (> 0.80) and low
winter passage rates (< 0.40), regardless of underpass, existed in all three years of video
surveillance. Underpass design characteristics also had an effect on the probability of elk
crossing the underpass; the probability of use of the underpass with two times the
openness ratio, half the length for elk to traverse, and sloped earthen sides ( 0.75) was
higher than the neighboring underpass with concrete walls ( 0.66). Proportions of elk
displaying behaviors indicative of resistance to crossing were dependent on underpass
and were higher at the underpass with concrete walls. In all cases, elk preferred the more
open underpass with natural earthen sides. We believe that differences in underpass
length and the concrete walls contributed to differences in elk use and behavioral
response. Continued video surveillance of these and other underpasses will allow us to
evaluate their efficacy in promoting wildlife permeability.
Structures that allow wildlife to cross the highway corridor are increasingly used to
mitigate potential negative impacts of roadways, but little is known about how varying
traffic levels may limit their effectiveness, either by reducing wildlife passage rates or by
causing animals to cross highways at other locations where they could potentially cause
collisions. We monitored five wildlife crossings SR 260 using video surveillance to
determine if traffic levels or traffic types ( semi- trailer truck versus automobile) affected
elk use of wildlife underpasses. We examined elk crossing behavior at wildlife
underpasses at two critical points during the crossing period: 1) when elk initially
approached the underpass, and 2) after elk entered the underpass. Passage rates at low,
intermittent traffic volume ( 0- 2 vehicles/ min = 0.59, 2- 4 vehicles/ min = 0.75) and at
higher traffic levels ( 4- 6 vehicles/ min = 0.73, > 6 vehicles/ min = 0.71) were not markedly
reduced compared to passage rates when no vehicles were present ( 0.65). Passage rates
varied seasonally, likely due to the presence of migratory elk unused to underpasses
during part of the year, but even during migratory periods traffic level had minimal effect
on the passage rate. Thus, increasing traffic did not substantially reduce the effectiveness
of wildlife underpasses as viable means of mitigating wildlife population fragmentation,
at least at the traffic levels we studied. Semis were five times more likely than were
passenger vehicles to cause a flight behavior when traffic levels were intermittent versus
when traffic was continuous, possibly due to the sudden increase in sound and vibration.
If flight away from underpasses causes animals to cross the highway at other points and
thereby increase the potential for elk- vehicle collisions, measures that reduce traffic noise
and visual stimuli caused by passing vehicles at underpasses may be warranted.
Highways have significant direct and indirect impact to natural ecosystems, including
wildlife barrier and fragmentation effects, resulting in diminished habitat connectivity
and highway permeability. We used GPS telemetry to assess elk movement patterns and
permeability across SR 260. The highway was reconstructed in phases, allowing for
comparison of highway crossing and passage rates during various stages of
reconstruction. We instrumented 33 elk ( 25 female, 8 male) with GPS receiver collars
May 2002- April 2004. Our collars accrued 101,506 GPS fixes with 45% occurring
within 0.6 mi of the highway. Nearly two times the proportion of locations occurred
within 0.6 mi of the highway compared to randomly generated locations. We believe elk
were attracted to the highway corridor by riparian- meadow foraging habitats that were
seven times more concentrated within the 0.6- mi zone around the highway compared to
the mean proportion within elk use areas encompassing all GPS fixes. Elk crossed the
highway 3,057 times; crossing frequency and distribution along the highway was
aggregated compared to random. Crossing frequency within 0.1- mi highway segments
was negatively associated with the distance to riparian- meadow habitats. Mean observed
crossing frequency ( 92.6 crossings/ elk) was lower than random ( 149.6 crossings/ elk).
Cows crossed 4.5× as frequently as bulls. Highway permeability among reconstruction
classes was assessed using passage rates ( ratio of highway crossings to approaches); our
overall mean passage rate was 0.67 crossings/ approach. The mean passage rate for elk
crossing the highway section where reconstruction was completed ( 0.43
crossings/ approach) was half that of sections under reconstruction and control sections
combined ( 0.86 crossings/ approach). Permeability was jointly influenced by the size of
the widened highway and associated vehicular traffic on all lanes. Crossing frequency
was used to delineate where ungulate- proof fencing yielded maximum benefit in
intercepting and funneling crossing elk toward underpasses and reducing elk- vehicle
collisions. Use of passage rates provides a quantitative measure to assess highway
permeability, conduct future before and after construction comparisons, and develop
mitigation strategies to minimize the impact of highways on wildlife.
Ungulate- proof fencing has been used successfully to mitigate the incidence of wildlife-vehicle
collisions on highways throughout North America. While fencing is often
regarded as an integral component of effective wildlife passage structures, limited
information or guidelines exist for the application of fencing in conjunction with wildlife
passages. Fencing itself may limit wildlife permeability across highways and exacerbate
the barrier effect of highways on wildlife populations. The five- mile reconstructed
Christopher Creek section was opened to traffic six months before ungulate- proof fencing
was erected, linking four wildlife underpasses and three bridges. To assess the role of
strategically placed fencing along 49% of the section, we compared before and after
fencing elk- vehicle collision incidence, wildlife use of underpasses, and elk highway
permeability. From 2002– 2006, we documented 110 elk- vehicle collisions. The
incidence of collisions increased over three fold after highway reconstruction was
completed but before fencing was erected. After fencing, the incidence of elk collisions
declined 87%. We employed video camera surveillance systems at two underpasses to
compare wildlife use for nine months before and 11 months after fencing was erected.
Before fencing, we recorded 500 elk and deer at the underpasses, of which only 12%
successfully passed through the underpasses; 81% of animals continued to cross the
highway at grade. After fencing, of 595 elk and deer recorded, 56% crossed successfully
and no animals crossed the highway at grade. The probability of an approaching animal
crossing through an underpass increased from 0.09 to 0.56 with fencing, and the
combined odds of a crossing through the underpass after fencing was 13.6: 1 compared to
before fencing. We used GPS telemetry to assess highway permeability and crossing
patterns. We instrumented 22 elk ( 16 female, 6 male) with GPS receiver collars April
2004– October 2005, during which time our collars accrued 87,745 GPS fixes. The elk
highway passage rate after the highway was opened to traffic but before fencing was
erected ( 0.54 crossings/ approach) was 32% lower than the level determined from a
previous study for the section during reconstruction ( 0.79 crossings/ approach). Once
fencing was erected, the passage rate increased 52% to 0.82 crossings/ approach. The
proportion of elk crossings that occurred along fenced highway stretches declined 50%
while the proportion of crossings along unfenced highway increased 40%. Fencing plays
an important role in reducing the incidence of wildlife- vehicle collisions and increasing
the effectiveness of wildlife passage structures. Furthermore, fencing in combination
with a relatively high density of passages ( one structure/ 0.7 mi) promoted elk highway
permeability by funneling animals toward the underpass where resistance to crossing was
lower than that associated with crossings at grade.
We linked 38,709 GPS locations collected from December 2003– June 2006 from 44 elk
fitted with GPS collars and hourly SR 260 traffic data, totaling more than 6,470,000
vehicles to determine how elk distribution varied with traffic volume, and how traffic
volume related to highway crossings. The probability of elk occurring near the highway
decreased with increasing traffic volume, indicating that habitat near the highway was
used by elk, but primarily when traffic volumes were relatively low (< 100 vehicles/ hr).
We used multiple logistic regression combined with Akaike's Information Criteria to
identify factors potentially important in influencing probability of elk crossing the
highway. We found that increasing traffic volume reduced the overall probability of
highway crossing, but this effect depended on both season and the proximity of riparian-meadow
habitat, with elk crossing highways at higher traffic volumes during spring and
fall migratory periods and when accessing these riparian- meadow foraging areas.
Overall, our results indicate that: 1) fluctuations in traffic volume should be considered in
models of habitat effectiveness for elk, 2) the effect of traffic volume on probability of
highway crossing, and therefore highway permeability, will depend on how a highway
affects access to daily and seasonally important resources, and 3) increased traffic
volume alone will not prevent elk from crossing the roadway and therefore development
of effective wildlife passages or motorist warning signs could reduce the probability of
elk- vehicle collisions, especially during migratory periods if placed near riparian-meadow
habitat or other areas with preferred resources.
We assessed spatial and temporal patterns of elk- vehicle collisions from 1994- 2006 ( n =
571) along SR 260. We used GPS telemetry to assess spatial and temporal patterns of elk
highway crossings and compare to elk- vehicle collisions patterns. Annual elk- vehicle
collisions were related to traffic volume and elk population levels. elk- vehicle collisions
occurred in a non- random pattern. With three of the sections completed, mean elk-vehicle
collisions while they were under reconstruction ( up until ungulate- proof fencing
was erected; 11.6/ year) was higher than mean before- construction elk- vehicle collisions
( 4.4/ year) and after reconstruction ( 6.5/ year) elk- vehicle collisions for each section. On
the first section completed in 2001 with limited fencing ( 13%), elk- vehicle collisions did
not differ among before, during, and after construction classes, even though mean traffic
volume increased 67% from before- to after- construction levels, pointing to the benefit of
three passage structures and fencing. On another section completed in 2004, elk- vehicle
collisions increased > 2.5× when opened to traffic but before fencing was erected; elk-vehicle
collisions dropped > 70% once fencing was installed. The benefit associated with
reduced elk- vehicle collisions from underpass and fencing was projected to approach $ 1
million/ year. We accrued 101,506 fixes from 33 elk ( 25 females, 8 males) fitted with
GPS collars 2002- 2004. Elk crossed the highway 3,057 times in a non- random pattern.
We compared elk- vehicle collisions and crossings at five scales; the strongest
relationship was at the highway section scale. Strength of the relationship and
management utility were optimized at the 0.6- mi scale. Elk- vehicle collision frequency
was associated with proximity to riparian- meadow habitats adjacent to the highway at the
section and 0.6- mi scales. Though both fall elk- vehicle collisions and crossings exceeded
expected levels, the proportion of elk- vehicle collisions in September- November ( 49%)
exceeded the proportion of crossings and coincided with the breeding season, migration
of elk from summer, and high use of riparian- meadow habitats adjacent to the highway.
There was no difference in the proportion of elk- vehicle collisions and crossings by day,
as both reflected avoidance of crossing the highway during periods of highest traffic
volume. Though traffic volume was highest from Thursday- Saturday, the proportion of
elk- vehicle collisions was lower than expected. A higher proportion of elk- vehicle
collisions ( 59%) occurred relative to crossings ( 33%) in the evening ( 1700- 2300 hr); 34%
of elk- vehicle collisions occurred within a one- hour departure of sunset, and 55.5%
within a two- hour departure. Elk- vehicle collision data are valuable in developing
strategies to maintain permeability and increase highway safety including selecting
locations of passage structures.
Vehicle collisions with ungulates are recognized as a serious problem because they
threaten human safety and cause tremendous property damage, as well as impact wildlife
populations. Such risks to humans and wildlife make it important to understand how
environmental factors influence ungulates highway crossing patterns. Several studies
have described site- specific variables at ungulate- vehicle accident sites, but none have
used highway crossing data. Also, previous studies lack information on how riparian-meadow
habitats influence road crossings. We used GPS data from 33 elk collared from
2002– 2004 to determine where they crossed SR 260. Our GPS collars yielded more than
101,000 GPS fixes from which we determined 3,057 crossings of the highway. We
delineated 90 0.2- mi segments along a 17- mi stretch of the highway and calculated
weighted elk crossings associated with each segment. We selected the 20 0.2- mi
segments that exhibited the highest and the 20 with the lowest weighted elk crossing
frequencies, and measured various habitat factors associated with these segments. To
assess the influence of habitat factors on elk highway crossing patterns, we conducted
field validations and Geographic Information System analysis to model five
environmental parameters: 1) proximity to nearest meadow, 2) proximity to nearest
permanent water source, 3) forest canopy, 4) slope, and 5) aspect. We employed a
Classification and Regression Tree ( CART) approach to determine the hierarchical order
of importance for each variable tested for both our high and low frequency elk highway
crossing segments. Proximity to water and meadow were tied for most influential factors
associated with high frequency weighted crossing sites. The CART- derived
classification tree indicated that water occurred less than 2,500 ft from all high crossing
segments, and meadow occurred less than 3,100 ft from 85% of the high crossing sites.
The results from this study provide important information that can be used to mitigate the
incidence of wildlife- vehicle collisions and design safe and ecologically sensitive
We assessed and compared wildlife use of five underpasses on the Preacher Canyon and
Christopher Creek sections of SR 260, using data from video camera surveillance
conducted 2002– 2006. Our video surveillance systems were designed to capture animals
approaching and crossing though the underpasses, allowing us to measure passage rates
( crossings/ approach). We recorded 8,455 animals and 11 different species on 1,100
hours of videotape. Overall, 5,560 of these animals, or 65.8%, crossed through the
underpasses. Elk accounted for the majority of the animals documented at the
underpasses ( 73.8%), while white- tailed deer and mule deer accounted for 10.9% and
7.4% of the total, respectively. Our mean elk underpass passage rates ranged from 0.10–
0.68 crossing/ approach for the five monitored underpasses. We used multiple logistic
regression to select factors important in predicting probability of a successful crossing
through the underpasses by elk; we modeled the influence of underpass ( structure and
placement), season, length of monitoring, time of day, and day of the week. We used a
general linear model with a logit link to determine probabilities of a successful crossing
for each of the factors selected. We found that four factors were important in predicting
the probability of a successful crossing once elk approached the underpass. Our
underpass factor was the most important one, suggesting that underpass structure and
placement was of primary importance in predicting the probability of successful elk
passage at the underpass, with probabilities of successful elk crossing ranging from 0.09–
0.77. The length of time an underpass was monitored was the second most important
factor selected in our logistic regression modeling ( with the probability of successful
crossing increasing from 0.52 in the first year of monitoring to 0.69– 0.71 in the
subsequent four years), followed closely by season and time of day. The probability of
successful elk crossing during fall, when migratory elk not habituated to our underpasses
were present along SR 260 was 0.59 compared to 0.71 during summer. Day of the week,
our surrogate factor for traffic volume did not have a significant influence on crossing
probabilities at our below grade passage structures.
Our research underscored the ability to integrate transportation and ecological objectives
into highway construction activities, yielding tangible benefits to both highway safety
and wildlife permeability. The combination of phased construction, adaptive
management, and effective monitoring of measures to reduce wildlife- vehicle collisions
and promote permeability were instrumental to achieving transportation and ecological
objectives. We recommend that such an approach to highway construction and
monitoring be pursued elsewhere in the state whenever possible. In the instance of SR
260, ADOT prioritized the reconstruction of the five sections on the historic incidence of
wildlife- vehicle collisions. Our research validated this prioritization; the strong
association between elk- vehicle collisions and highway crossings underscored the utility
and value of wildlife- vehicle collision data in planning wildlife mitigation measures
ranging from passage structures to ungulate- proof fencing. We strongly recommend that
all work units within ADOT and other agencies make a concerted effort to collect and
archive wildlife- vehicle collision data throughout Arizona, utilizing the standardized
interagency collision report form.
We found that the presence of riparian and wet meadow habitats constituted the " engine"
that drove conflicts between the highway and wildlife along SR 260. Elk- vehicle
collision and elk highway crossing patterns were closely associated with proximity to
riparian- meadow habitats. Future highway construction activities should avoid such
limited, valuable habitats where possible. Wildlife underpasses located adjacent to
riparian- meadow habitats received high levels of use by wildlife due to their movement
toward these preferred foraging areas, as well as animal propensity to travel along
drainages. Where highway alignments near riparian- meadow habitat are unavoidable,
such sites are excellent locations to consider wildlife passage structures.
Wildlife underpasses were highly effective in promoting below- grade wildlife crossings,
with two- thirds of over 8,500 animals recorded during video surveillance having crossed
through an underpass. These underpasses were instrumental to improving highway safety
through reduction of wildlife- vehicle collisions and promoting wildlife permeability.
Structural design characteristics and placement of underpasses are important
considerations to maximizing their efficacy in promoting wildlife passage, and structural
characteristics were the most important factor in determining the probability of successful
crossings by wildlife. Underpass openness is crucial to achieving high probability of
successful underpass use. The distance that animals must travel through an underpass is
an especially important factor in maximizing efficacy, and should be minimized in
underpass design. Elk avoided an underpass where concrete mechanically stabilized
earth walls were erected for soil stabilization, compared to a neighboring underpass with
more natural 2: 1 sloped earthen sides. We recommend that the application of concrete
walls be avoided in wildlife underpasses. Visibility through underpasses should be
maximized during design and implementation. Where underpasses occur on divided
highways we recommend that the bridges be placed in line where possible to maximize
visibility by animals through the structures. Wildlife underpass placement should avoid
areas of high human activity or congregation that occur outside daytime hours.
We documented a recurring seasonal pattern where elk underpass passage rates dropped
from summer levels > 0.90 crossings/ approach to below 0.40 during the fall when
migratory elk moved through the SR 260 corridor. Migratory elk do not appear to exhibit
the same propensity for habituation to underpasses as resident elk. Additional ungulate-proof
fencing may be needed to address this seasonal drop in underpass passage rates.
Long term monitoring will provide valuable insights on changes in wildlife use patterns.
Ungulate- proof fencing in conjunction with underpasses will expedite the wildlife
learning process.
Traffic levels fluctuated greatly on an hourly, daily and seasonal basis through our study
area, averaging between 7,000– 8,500 average annual daily traffic ( AADT). We found
that traffic volume influenced elk crossing patterns and distribution at highway grade.
With increasing traffic levels, we found reduced probability of successful elk highway
crossings at grade, crossings occurred later in the evening when volume levels abated,
and elk moved away from the highway as volumes increased. Unsuccessful attempts to
cross SR 260, or " repels" typically coincided with high traffic volume. Conversely, at
our monitored wildlife underpasses, traffic volume on SR 260 overhead did not have an
effect on elk approaching and successfully crossing through the underpasses below grade.
This finding was of paramount importance to understanding the efficacy of underpasses
in promoting wildlife permeability.
GPS telemetry afforded us an unprecedented opportunity to assess and compare wildlife
permeability among highway reconstruction classes, as well as assess permeability before
and after the erection of ungulate- proof fencing. Reconstruction from a two- lane to four-lane
divided highway reduced wildlife permeability by half compared to that of our
control sections. On one section, the during reconstruction passage rate dropped 34%
after reconstruction but before fencing was erected. Yet the elk passage rate increased
54% after half of the section was strategically fenced with ungulate- proof fencing. Thus,
fencing in conjunction with underpasses promoted wildlife permeability as animals were
funneled toward underpasses and bridges where they crossed below grade with minimal
impact from traffic passing above. In addition to playing an instrumental role in
promoting permeability, ungulate- proof fencing was crucial to achieving effective use of
underpasses, especially those not located in proximity to meadow habitats. Without
fencing, elk and deer continued to cross SR 260 at grade immediately adjacent to
underpasses. The 50% of the section that was fenced was projected to intercept 89% of
elk crossings determined from GPS telemetry, and yielded an 83% reduction in elk-vehicle
collisions in the year after fencing. Fencing is an integral component of wildlife
mitigation measures in reducing elk- vehicle collisions and promoting wildlife
With two of the five SR 260 sections reconstructed to date integrating underpass and
ungulate- proof fencing, 2006 was the first year that the incidence of actual elk- vehicle
collisions dropped below the level predicted from modeling based on average annual
daily traffic ( AADT) volume and elk population levels. Our model predicted even
greater benefit as AADT increases. Thus, the complement of measures implemented to
date has achieved its objective in mitigating the impact of highway reconstruction and
increasing traffic volume, and the benefit is expected to grow now that the third section is
complete ( Kohl's Ranch) and the entire Preacher Canyon section is being fenced under
an enhancement grant project. With only a modest increase in AADT, we estimated an
annual benefit from reduced elk- vehicle collisions of nearly $ 1 million/ year.
Compared to the first three reconstructed sections, the remaining two exhibited relatively
few wildlife- vehicle collisions or collared elk crossings. The exception is the limited
areas where riparian- meadow habitat is located in close proximity to the highway.
Highways directly and indirectly create some of the most prevalent and widespread
changes to the ecosystem in the United States ( Noss and Cooperrider 1994, Trombulak
and Frissell 2000, Farrell et al. 2002). The estimated 500,000 to 700,000 deer killed each
year in collisions on U. S. highways directly affect the ecosystem ( Romin and Bissonette
1996a, Schwabe and Schuhmann 2002). Collisions also cause human injuries, deaths,
and tremendous property loss ( Reed et al. 1982, Schwabe and Schuhmann 2002), and
disproportionately affect threatened or endangered species ( Foster and Humphrey 1996).
Highways indirectly impact ecosystems by causing habitat loss and blocking animal
movements. Forman and Alexander ( 1998) estimated that highways have caused habitat
loss and degradation in more than 20% of the U. S. Blocking of animal movements
between seasonal ranges or other vital habitats is perhaps highways' most pervasive
environmental impact. ( Noss and Cooperrider 1994, Forman and Alexander 1998,
Forman 2000). Their fragmentation of habitats and populations reduces genetic
interchange ( Gerlach and Musolf 2002) and limits dispersal of young ( Beier 1995); all
serving to disrupt viable wildlife population processes. Long- term fragmentation and
isolation renders populations more vulnerable to catastrophic events and may lead to
extinctions ( Hanski and Gilpin 1997). Fencing to prevent wildlife and livestock access to
highways may exacerbate the barrier effect unless provision is made for passage.
Though numerous studies have alluded to highways' barrier effects on wildlife ( e. g., see
Forman et al. 2003), few have yielded quantitative data on animal passage rates,
particularly in an experimental context ( e. g., pre- and post- highway construction). Many
studies have focused on the efficacy of passage structures at allowing wildlife to avoid at-grade
crossings ( Clevenger and Waltho 2003, Ng et al. 2004) or have relied on modeling
to assess highways' passability, or permeability to wildlife ( Singleton et al. 2002).
Assessments of the habitat fragmentation highways cause for relatively low- mobility
small mammals have yielded quantifiable results from mark- recapture trapping, but
assessments for larger, far- ranging species have been limited by cost ( Swihart and Slade
1984, Conrey and Mills 2001, McGregor et al. 2003). Paquet and Callaghan ( 1996) used
winter track counts adjacent to highways and other barriers to determine passage rates by
wolves ( Canis lupus), something few other studies have reported. 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), but seldom directly addressing permeability as Gibeau et al. ( 2001) did for grizzly
bears ( Ursus arctos).
Numerous studies have been conducted on the spatial and temporal patterns of wildlife-vehicle
collisions, most focusing on deer ( Reed and Woodard 1981, Bashore et al. 1985,
Romin and Bissonette 1996b, Hubbard 2000). Only recently have researchers
specifically addressed patterns of collisions with elk ( Cervus elaphus) ( Gunson and
Clevenger 2003, Biggs et al. 2004). Insights gained from such studies have been
instrumental in developing strategies to reduce collisions with wildlife ( Romin and
Bissonette 1996a, Farrell et al. 2002), including planning passage structures to reduce at-grade
crossings and maintain passage ( Clevenger et al. 2002).
Consistent tracking of wildlife- vehicle collisions is a valuable tool to assess the impact of
highway construction on wildlife ( Romin and Bissonette 1996b) and the efficacy of
passage structures and other measures ( e. g., fencing) in reducing wildlife- vehicle
collisions ( Reed and Woodard 1981, Ward 1982, Clevenger et al. 2001a). Though data on
wildlife- vehicle collisions is valuable, no study has investigated or validated the
relationships between these collisions and the spatial and temporal crossing patterns of the
wildlife involved. In fact, Barnum ( 2003) reported that these data were not useful in
identifying crossing zones, largely due to inaccurate reporting.
Underpasses, overpasses and other structures designed to promote safe passage of large
animals across highways are being built more frequently throughout North America
( Clevenger and Waltho 2000). Whereas early passage structures were typically designed
to mitigate the impact on a single- species ( Reed et al. 1975), the focus today is more on
preserving ecosystem integrity and landscape continuity to benefit multiple species
( Clevenger and Waltho 2000). Transportation agencies increasingly are receptive to
integrating passage structures into highways to address both safety and ecological needs
( Farrell et al. 2002). However, they increasingly expect that such structures will benefit
multiple species and enhance access to habitat ( Clevenger and Waltho 2000); and that
scientifically sound monitoring and evaluation of wildlife response will be done to
improve future effectiveness ( Clevenger and Waltho 2003, Hardy et al. 2003).
Just as varied approaches have been used to assess wildlife passage, a multitude of
methods measures have been used to measure wildlife use of passage structures. Most
studies' data have come from underpass track counts ( Clevenger and Waltho 2000,
Gloyne and Clevenger 2001), event recorders ( Foster and Humphrey 1995), or single-frame
camera images ( Ng et al. 2004). Using frequency- of- use data to compare passage
structure use has a potential bias due to heterogeneous animal distribution or the
differential funneling caused by varying amounts of wildlife- proof fencing; and fails to
account for animals not using passage structures or that are resistant to crossing. To
address such biases, Clevenger et al. ( 2001b) estimated expected passage frequencies
derived from track assessments of relative abundance, and Clevenger and Waltho ( 2003)
calculated species performance ratios from radio telemetry, pellet transects, and habitat
suitability indices. Reed et al. ( 1975) compared animal evidences at the entrance and
exits of an underpass to calculate activity indices, while Gordon and Anderson ( 2003)
used behavioral quantification as a measure of wildlife response.
The reconstruction of State Route ( SR) 260 is one of the most comprehensive projects of
its type in North America: eleven large wildlife underpasses1 and six bridges ( 1 passage
1 Each underpass but one actually consists of two structures with an atrium between them. They will be
referred to as single structures herein.
structure/ mi) are being built to allow wildlife passage and improve highway safety, This
project rivals the landmark efforts to improve wildlife passage and reduce losses from
collisions with vehicles in Banff National Park, Alberta, Canada, which has 24 passage
structures in 28 mi ( 0.86 structures/ mi; Clevenger and Waltho 2003), as well as those
planned for the U. S. Highway 93 reconstruction in Montana, which has 42 passage
structures in 56 mi ( 0.75 structures/ mi; Western Transportation Institute 2005).
2.2.1 Phased Construction and Adaptive Management
In addition to its scope in addressing conflicts with wildlife, the SR 260 upgrade is
noteworthy for two other reasons: it is being done in phases and information gained in
early phases is being used to improve work in later ones. The section of SR 260 to be
upgraded was divided into five parts; each part is being reconstructed according to a
priority set by ADOT. These parts are identified by a settlement or prominent feature:
Preacher Canyon, Christopher Creek, Kohl's Ranch, Little Green Valley, and Doubtful
Canyon. The phasing of reconstruction has facilitated effective construction oversight by
ADOT and allowed the sections with higher priority to be done first under limited
funding. The incidence of wildlife- vehicle collisions was a key factor used in setting
priority for upgrade ( Route 260- Payson to Heber EIS, ADOT Environmental Planning
Section, Phoenix, AZ).
Doing the reconstruction in phases has also facilitated sharing of our preliminary findings
with ADOT project managers for their use in addressing wildlife- related issues.
Preliminary insights from studies done in the early phases have been used to improve
wildlife passage structures, identify appropriate stretches for ungulate- proof fencing to
maximize underpass effectiveness and minimize wildlife- vehicle collisions, and select
appropriate sites for other measures ( e. g., wildlife escape jumps and gates) in sections
whose work is still underway or is yet in the planning stage Though such an adaptive
management approach can yield continuous improvement to the quality of highway
construction, especially relating to highway safety, it does come at a potential cost when
construction delays and increased project budget expenditures occur.
2.2.2 Experimental Approach
The reconstruction of SR 260 in phases afforded us the opportunity to assess the impact
on wildlife of highway reconstruction at various stages. Hardy et al. ( 2003) and
Roedenbeck et al. ( 2007) stressed the value of conducting " before- after, control- impact"
( BACI; Underwood 1994) assessments to determine the effects on wildlife of highway
construction and the efficacy of measures to reduce wildlife- vehicle collisions and
promote passage. The phased reconstruction of SR 260 and the presence of experimental
controls gave us the opportunity to conduct such an assessment. During our research, we
assessed wildlife relationships and response to one section that was reconstructed prior to
the initiation of research, two where construction was initiated during our project,
yielding before-, during- and after- reconstruction data, and two sections that served as
research controls ( Table 2.1).
Our research focused on evaluating the effectiveness of measures designed to minimize
wildlife- vehicle collisions, especially those involving elk, and to maintain wildlife
passage across the highway. The first phase was initiated under Joint Project Agreement
01- 152, which was executed with ADOT in January 2002. . It focused on the Preacher
Canyon section, which was the first to be reconstructed. Work on this section was
completed in 2001 ( Table 2.1). Research under this phase served as a " pilot study" for
the development and evaluation of various techniques for gathering data to use in
assessing the effectiveness of the various measures to minimize wildlife- vehicle
collisions and facilitate wildlife passage across the highway corridor. Phase II of our
project continued through July 2006 under Joint Project Agreement 04- 024T, which was
executed with ADOT in December 2003. This phase focused on the Christopher Creek
section, which in late 2004 became the second section completed ( Table 2.1). We also
continued monitoring the Preacher Canyon section in this phase. In November 2005,
Joint Project Agreement 06- 004T was finalized with ADOT, which authorized Phase III
of our research. This phase focused on the Kohl's Ranch section, which was completed in
early 2006 ( Table 2.1); research under this phase will continue through June 2008.
Table 2.1. Dates that highway reconstruction was initiated and completed for the five
reconstruction sections on State Route 260, Arizona, and years of research accomplished
under various construction classes as part of research conducted 2002– 2006.
Construction upgrade Years of study by construction class
Highway section Begun Completed Before During After
Preacher Canyon
Christopher Creek 2002 2004 1 2 2
Kohl's Ranch 2003 2006 2 2 1
Little Green Valley Control 5 0 0
Doubtful Canyon Control 5 0 0
Our research addressed six objectives:
Objective 1. Assess and compare wildlife use of wildlife underpasses constructed
along State Route 260, and evaluate the efficacy of video surveillance as a means of
assessing wildlife use of underpasses.
Researchers have used various methods to gather data to assess wildlife response to
passage structures ( Hardy et al. 2003), including track counts ( Rodríguez et al. 1996;
Clevenger et al 2001b; Clevenger and Waltho 2000, 2003), event recorders ( Reed et al.
1975, Foster and Humphrey 1995), and infrared motion or heat sensor single- frame
cameras ( Servheen et al. 2003, Brudin 2003, Ng et al. 2004). Video cameras have had
only limited use in the past for assessing passage structure use ( Reed et al. 1975, Gordon
and Anderson 2003, Plumb et al. 2003). Video surveillance has an advantage over other
techniques because animal behavior can be assessed, especially when the animal resists
or fails to cross ( Hardy et al. 2003). Video surveillance also allows identification and
classification ( e. g., sex, age) of individual animals, which track counts do not ( Hardy et
al. 2003). Although video camera surveillance has been minimally used to assess use of
passage structures, such monitoring has nonetheless provided insights that were not
obtained from other methods ( Reed et al. 1975, Gordon and Anderson 2003).
To meet this objective, we evaluated the use of video surveillance to assess and compare
wildlife response to underpasses constructed during the reconstruction of SR 260 in the
first phase of our research. Focusing on the first two completed wildlife underpasses ( in
the Preacher Canyon section), we tested the hypothesis that wildlife frequency of use,
passage rates, and behavioral response did not differ at these underpasses and we
evaluated the efficacy of using passage rate and behavioral response measures to compare
wildlife use of passage structures. We explored seasonal wildlife use and response at the
two underpasses, related differences in response to underpass characteristics where
possible, and considered relationships with highway traffic volume.
Under Phase II, we expanded monitoring and assessment to the underpasses constructed
on the Christopher Creek section with three additional video surveillance systems.
Information from these underpasses is still preliminary, especially compared to the
relatively long term monitoring conducted at the two underpasses in the Preacher Canyon
section as part of Phase I. Our sixth video camera surveillance system was installed at an
underpass on the Kohl's Ranch section as part of Phase III of our project; this data is also
Our findings for this objective are reported in Sections 4 and 11.
Objective 2. Evaluate wildlife movements across SR 260 before, during, after
reconstruction using Global Positioning System ( GPS) telemetry.
The use of GPS telemetry in wildlife movement studies has become increasingly popular,
cost- effective, and reliable ( Rodgers et al. 1996). With continuous automated tracking at
set time intervals, reduced observer bias ( compared to VHF telemetry), and potential to
collect large datasets, GPS telemetry has revolutionized wildlife movement studies. GPS
telemetry is increasingly used to address previously- difficult questions ( e. g., Anderson
and Lindzey 2003), and holds tremendous potential to facilitate highway passage
assessment and determine spatial and temporal highway crossing patterns of wildlife.
Under this objective, we used GPS telemetry to investigate elk passage across SR 260,
comparing their approach, crossing, and passage rates by sections before, after and at
intermediate stages of construction. We evaluated quantitative measures of elk highway
passage using GPS telemetry; and assessed spatial and temporal influences on elk
movements. We conducted separate GPS telemetry assessments under Phases I and II of
our research, with a third assessment ongoing in conjunction with research Phase III.
Our findings for this objective are reported in Sections 6 and 7.
Objective 3. Characterize the temporal and spatial patterns of wildlife- vehicle
collisions and changes associated with highway reconstruction ( before, during, after
reconstruction), and compare wildlife- vehicle collisions to GPS- determined crossing
Under this objective, we characterized the nature of elk- vehicle collision patterns along
SR 260, and compared collision incidence associated with the highway before, during,
and after reconstruction. We sought to validate the priority for reconstruction set for the
highway sections based on wildlife- vehicle collisions. We compared spatial and
temporal patterns of elk- vehicle collisions to elk- highway crossings determined by GPS
telemetry as a means to validate the management utility of elk- vehicle collision data in
developing strategies to reduce collisions and promote passage. Overall, this objective
focused on evaluating the ultimate effectiveness in reducing elk- vehicle collisions of the
full complement of measures ( e. g., wildlife underpasses and ungulate- proof fencing)
implemented along SR 260, as well as the benefit/ cost relationships of such measures.
We reported the results of our research for this objective in Section 9.
Objective 4. Evaluate the relationships among highway traffic volume and wildlife-vehicle
collisions, elk crossing patterns, and wildlife use of underpasses.
Although researchers disagree about whether increasing traffic volume is the primary
reason for increasing ungulate- vehicle collisions ( McCaffery 1973; Reilly and Green
1974; Allen and McCullough 1976; Case 1978; Romin and Bissonette 1996), many
recognize that traffic volume is an important factor, among others such as wildlife
population fluctuations, wildlife behavior, driver behavior, and temporal and spatial
environmental factors ( Carbaugh et al. 1975, Bashore et al. 1985, Groot Bruinderink and
Hazebroek 1996, Haikonen and Summala 2001, Seiler 2004, Gunson and Clevenger
2003, Manzo 2006).
Traffic may serve as a " moving fence" that can render highways impassable to wildlife
( Bellis and Graves 1978). One theoretical model ( Iuell et al. 2003) predicted that
highways become impassable barriers to most wildlife at 10,000 vehicles/ day, potentially
leading to fragmentation and rapid genetic isolation like that documented for bighorn
sheep ( Epps et al. 2005). Alternatively, because traffic varies seasonally, weekly and by
time of day, some animals may be able to cross highways with high traffic volume during
periods when traffic volume is relatively low.
Average annual daily traffic ( AADT) along SR 260 is high and is increasing due to the
tourist, recreational, and commercial traffic that travels this highway. SR 260 links
Phoenix to White Mountain communities ( e. g., Show Low, Pinetop- Lakeside,
Springerville- Eagar) and high- mountain recreation areas ( e. g., White Mountain Apache
Reservation, Apache- Sitgreaves National Forest), as well as Interstate 40. We used GPS
telemetry data to assess relationships of AADT to elk distribution and highway approach
and crossing patterns so as to assess the impact of traffic volume at highway grade. At
wildlife underpasses, we assessed the relationships of traffic volume on wildlife crossing
below grade.
Our findings from traffic volume- related research are reported in Sections 5 and 8.
Objective 5. Assess the role that ungulate- proof fencing plays in the incidence of
wildlife- vehicle collisions, wildlife use of underpasses, and wildlife permeability
across the highway.
Research has shown that ungulate- proof fencing effectively reduces wildlife- vehicle
collisions, especially when used in conjunction with passage structures ( Ward 1982,
Lavsund and Sandegren 1991, Romin and Bissonette 1996, Clevenger et al. 2001,
Forman et al. 2003). Though fencing is generally regarded as effective in reducing
collisions with wildlife, mixed results have been reported ( Falk et al. 1978), especially
where animals cross at the ends of fencing resulting in zones of increased collisions
( Feldhamer et al. 1986, Woods 1990, Clevenger et al. 2001). Furthermore, fencing is
costly and requires substantial maintenance ( Forman et al. 2003), potentially contributing
to transportation managers' reluctance to fence extensive stretches of highways. While
fencing is often regarded as an integral component of effective passage structures ( Romin
and Bissonette 1996, Forman et al. 2003), limited information or guidelines exist for the
use of fencing with wildlife passage structures. As fences themselves constitute effective
barriers to ungulate passage across highways ( Falk et al. 1978), fencing may exacerbate
the barrier effect associated with highways alone ( see Section 6), particularly where
effective measures to accommodate animal passage are lacking.
During the reconstruction of SR 260, ADOT's practice of integrating 8- ft ungulate- proof
fencing with underpasses and bridges has been to erect limited wing fences ( fewer than
300 ft) outward from bridge abutments to funnel animals toward the structures. As part
of our research, we addressed the efficacy of this approach to fencing and used the
adaptive management approach during reconstruction to recommend and evaluate the
strategic placement of fencing to intercept crossing wildlife, reduce wildlife- vehicle
collisions, promote effective use of underpasses by wildlife, and maintain highway
The results of our research related to the role of ungulate- proof fencing are found in
Section 7.
Objective 6. Provide ongoing, highway construction and maintenance guidance
throughout all construction phases.
As our research was part of an ongoing adaptive management approach to the highway
reconstruction project; we provided guidelines for maintaining wildlife permeability,
minimizing wildlife- vehicle collisions, improving wildlife underpass design to maximize
the likelihood of high acceptance and use by wildlife, and the strategic placement of
ungulate- proof fencing.
We report the results and applications of the adaptive management part of our project in
Sections 4, 6, 7, and 11.
This report has our findings from Phases I and II, and to a limited degree Phase III. First,
we describe the study area to set the context for our research. The research conducted to
meet our objectives is reported in the following sections. In the Conclusion and
Recommendations section, we tie together the information from the previous sections.
We conducted this study along a 17- mi stretch of SR 260, beginning 9 mi east of Payson, and
extending to the base of the Mogollon Rim in central Arizona ( lat 34° 15'– 34° 18' N, long
110° 15'– 111° 13' W; Figure 3.1). The existing two- lane highway is being upgraded to a four-lane
divided highway ( Figure 3.1). In places, the footprint of the upgraded highway exceeds
0.3 mi in width ( Figure 3.2). When completed, the highway will have 11 wildlife
underpasses specifically intended to reduce at- grade elk crossings and elk- vehicle collisions,
as well as six bridges over large canyons and streams that will accommodate wildlife use
( Figure 3.1, Table 3.2). All but one of the underpasses consists of two structures, one for
each roadway, with an atrium between them. The highway reconstruction is being done in
five phases, each phase focusing on a single section ( Figure 3.1, Table 3.2). Reconstruction
of three sections is now complete.
The Preacher Canyon section was the first completed; all lanes were opened to traffic in
November 2001. This section has two bridged underpasses and a large bridge over Preacher
Canyon ( Figure 3.1); 0.4 mi ( 13%) of the section was fenced with 8- ft ungulate- proof fencing
associated with the two underpasses near Little Green Valley. The Christopher Creek section
was completed in December 2004; it has had four wildlife underpasses and three bridges in
place since 2003. All lanes in the Christopher Creek section were opened to traffic in July
2004 before all fencing associated with the underpasses was completed. Here, fencing and
alternatives to fencing ( e. g., swaths of large rock rip- rap) were implemented along half the
section in association with passage structures. The Kohl's Ranch section, the most recently
reconstructed, was completed in March 2006; this section has one wildlife underpass and 1.5
bridges ( only one bridge span was built over Thompson Draw, with the other to be done
under the Little Green Valley section). Reconstruction of the Little Green Valley and
Doubtful Canyon sections will be in or after 2008.
Table 3.1. State Route 260 reconstruction sections, reconstruction status, mileposts and
length, and the number of wildlife passage structures planned or built as part of the
Reconstruction Highway Length Wildlife passages
Highway section status mileposts ( mi) Underpasses Bridges
Preacher Canyon Completed 2001 260.0– 263.0 3.0 2 1
Little Green Valley Control 263.1– 265.5 2.5 1 0.5
Kohl's Ranch Completed 2006 265.6– 269.5 4.0 1 1.5
Doubtful Canyon Control 269.6– 272.5 3.0 3 0
Christopher Creek Completed 2004 272.6- 277.0 4.5 4 3
All 260.0- 277.0 17.0 11 6
Figure 3.1. Location of our study area and the five highway sections where phased
highway reconstruction has been ongoing since 2000, and the location of wildlife
underpasses and bridges. The shaded areas correspond to riparian- meadow habitats
located adjacent to the highway. Topographic relief reveals the study area's proximity to
the Mogollon Rim escarpment, the dominant physiographic feature within the study area.
Figure 3.2. The existing narrow two- lane roadway ( left; Doubtful Canyon section) is
being reconstructed to a four- lane divided highway ( right; Preacher Canyon section),
State Route 260, Arizona.
Our study area lies within the ponderosa pine ( Pinus ponderosa) association of the montane
coniferous forest community ( Brown 1994a). Elevations along SR 260 range from 5,220–
6,560 feet ( ft.) The Mogollon Rim escarpment to the north is the dominant landform, rising
precipitously to 7,860 ft ( Figures 3.1 and 3.3). Vegetation adjacent to the highway grades
from mixed forest of ponderosa, pinyon ( P. edulis), juniper ( Juniperus spp.), and live oak
( Quercus spp.) on the lower elevation Preacher Canyon and Little Green Valley sections, to
forests predominated by ponderosa with interspersed Gambel oak ( Q. gambelii) at higher
elevations to the east ( Christopher Creek section). Chaparral ( e. g., manzanita;
Arctostaphalos pungens) with sparse pinyon, live oak, and ponderosa pine is prevalent on the
drier south- facing slopes. In canyons emanating from the Mogollon Rim within our study
area, mixed- conifer forest of ponderosa pine, Douglas fir ( Pseudotsuga menzeisii), white fir
( Abies concolor) and Gambel oak are found. Numerous riparian and wet meadow habitats
occur at several locations along the highway corridor ( Figure 3.1); some meadows are more
than 60 acres ( Figure 3.4). Several perennial streams flow adjacent to portions of the
highway, including Little Green Valley Creek ( Preacher Canyon, Little Green Valley
sections), Tonto Creek ( Kohl's Ranch section), Christopher Creek ( Doubtful Canyon,
Christopher Creek sections), Hunter Creek ( Christopher Creek section), and Sharp Creek
( Christopher Creek section) ( Figure 3.1).
Climatic conditions within the study area are mild, with a mean maximum monthly
temperature ( July) for Payson of 90.3 º F, and mean minimum monthly temperature ( January)
of 19.6 º F. Annual precipitation averages 20.7 inches ( in.), with a mean of 21.3 in. of
snowfall in winter; precipitation has averaged ⅔ of normal since 2002.
Average annual daily traffic ( AADT) on this portion of SR 260 ( ADOT Control Road traffic
monitoring station) doubled in 10 years from 3,100 in 1994 to nearly 6,300 in 2002, and
increased to 8,700 (+ 38%) in 2003 ( Figure 3.5; ADOT Data Management Section). Over the
same period, annual wildlife- vehicle collisions involving ungulates and large carnivores on
this stretch of SR 260 increased from 28 to 44, with a mean of 35.9 (± 2.5 SE; Dodd et al.
Figure 3.3. State Route 260 study area ( at the pedestrian/ wildlife underpass on the
Christopher Creek Section), Arizona. The Mogollon Rim escarpment rises in the
distance above ponderosa pine forest adjacent to the highway corridor. The solar panels
power our video camera surveillance system to monitor wildlife use of the underpass.
Figure 3.4. Aerial view of Little Green Valley riparian- meadow complex adjacent to the
Preacher Canyon section. Such habitats are very important to wildlife for food and water,
especially in proximity to forest cover.
Rocky Mountain elk ( Cervus elaphus nelsoni) were a focus of our research for several
reasons. First, elk accounted for more than 80% of all collisions between vehicles and
wildlife ( Dodd et al. 2006) and the vast majority of property loss and human injuries
associated with these collisions. Elk are large animals that can readily support our GPS
telemetry collars, yielding substantial long- term data on movements in relation to the
highway corridor, and were relatively easy to trap.
Our study area has both resident and migratory elk herds. Resident elk were common,
especially near meadow and riparian habitats. Migratory elk come off the Mogollon Rim
with the first snowfall of more than 12 in., typically in late October ( Brown 1990,
1994b). Brown ( 1990) reported that 85% of the elk residing within his Mogollon Rim
herd unit migrated to an area below but within six mi of the base of the Mogollon Rim,
which encompasses our study area. Elk return to summer range with forage green- up at
higher elevations ( Brown 1990). The Arizona Game and Fish Department estimated the
resident elk population in the game management units encompassing our study area at
1,500- 1,600 ( Arizona Game and Fish Department, Game Management Branch,
unpublished data), though not all elk resided in proximity to SR 260. White- tailed deer
( Odocoileus virginianus cousei) were frequently seen in our study area, while mule deer
( O. hemionus) were less common and more localized on the Christopher Creek section.
Figure 3.5. Average annual daily traffic for State Route 260, Arizona. ( ADOT Control
Road monitoring station) for the period 1990– 2005.
Average annual daily traffic volume
1990 1992 1994 1996 1998 2000 2002 2004
Recognition of highways' impact on wildlife has increased dramatically in the past decade
( Forman et al. 2003). In addition to direct habitat loss ( Forman 2000), mortality from vehicle
collisions has been recognized as a serious and growing problem for wildlife and motorists
( Reed et al. 1982, Farrell et al. 2002). Annual vehicle collisions with deer alone in the U. S.
exceed 1.5 million ( Conover 1997). Highways play a pervasive role as barriers to free
movement of wildlife, fragmenting and isolating habitats, reducing genetic interchange ( Epps
et al. 2005), and increasing population susceptibility to catastrophic events ( Forman and
Alexander 1998, Trombulak and Frissell 2003).
Structures designed to promote wildlife passage across highways are increasingly being built,
particularly large bridges designed specifically for large animal passage ( Clevenger and
Waltho 2000). Whereas in the past managers typically built early passage structures as
single- species mitigations ( Reed et al. 1975), managers today have directed their focus
toward preserving habitat continuity to benefit multiple species ( Clevenger and Waltho
2000). Transportation agencies are increasingly receptive to building passage structures to
meet safety and ecological needs ( Farrell et al. 2002), and there is increasing expectation that
they will indeed yield desired benefits ( Clevenger and Waltho 2000). Scientifically sound
monitoring of wildlife response to passage structures is crucial to improving future
effectiveness ( Clevenger and Waltho 2003, Hardy et al. 2003).
Researchers have used various techniques to measure wildlife use of passage structures
( Hardy et al. 2003), including track counts ( Rodríguez et al. 1997; Clevenger et al 2001a;
Clevenger and Waltho 2000, 2003), triggered event recorders or counters ( Reed et al. 1975,
Foster and Humphrey 1995), and infrared motion or heat sensor single- frame cameras
( Brudin 2003, Servheen et al. 2003, Ng et al. 2004). Video cameras have had only limited
use ( Reed et al. 1975, Sips et al. 2002, Gordon and Anderson 2003, Plumb et al. 2003).
Several measures have been used to describe wildlife use of passage structures. Most studies
have enumerated frequency of use ( Clevenger and Waltho 2000, Gloyne and Clevenger
2001, Sips et al. 2002, Ng et al. 2004). However, frequency of use may be a biased index of
passage structure efficacy. It is subject to differential funneling of animals by varying
amounts of fencing and heterogeneous animal distribution, and does not account for non- use
attributable to structure characteristics or alternative crossing locations, as addressed by Reed
et al. ( 1975), Clevenger et al. ( 2001a), and Clevenger and Waltho ( 2003, 2005).
Video surveillance has advantages over other techniques for assessing passage because
managers can evaluate animal behavior, especially when the animals resist - or don't
2 An early version of this chapter was published in the Journal of Wildlife Management ( see Dodd et al.
complete - crossing ( Hardy et al. 2003, Gordon and Anderson 2003). Though few studies
have used video surveillance to assess use of passage structures, such monitoring has
provided insights that could not be obtained from other methods ( Reed et al. 1975, Gordon
and Anderson 2003, Plumb et al. 2003).
Our objective was to evaluate video surveillance for assessing and comparing wildlife
response to underpasses. We examined Rocky Mountain elk use of the first two underpasses
completed as part of the SR 260 reconstruction. We tested hypotheses that elk passage rate
( crossing frequency/ approach frequency), probability of use, and behavioral response did not
differ at the two underpasses. We monitored seasonal elk use of the underpasses to test the
hypothesis that passage rates and probability of use did not differ by season. We related
differences in elk use to underpass design and provided guidelines for future design to
maximize the likelihood of use by elk and other wildlife.
We conducted our study at two bridged underpasses constructed specifically for wildlife
passage along the Preacher Canyon section of SR260 ( Figure 4.1). Both opened to the south
into Little Green Valley, a relatively lush riparian- meadow foraging area contrasted by dense
forest cover on the north side of the highway ( Figure 4.1). The two underpasses were less
than 850 ft. apart ( Figure 4.1). Though both were of similar open- span bridge construction
and length ( 135 ft), the below- span characteristics and dimensions were markedly different
( Figure 4.1, Table 4.1). The east underpass had vegetated earthen sides that made it more
open and natural compared to the west underpass, which had concrete, mechanically
stabilized earth ( MSE) walls ( Figure 4.1). ADOT installed 8 ft high ungulate- proof fencing
along 0.4 mi of the highway to funnel animals to the two underpasses ( Figure 4.2).
Table 4.1. Physical characteristics associated with the two wildlife underpasses ( UP) at
which we conducted video monitoring focusing on elk from September 2002– September
2005, State Route 260, Arizona. ( see Figure 4.1).
East UP
West UP
Construction type Open I- beam span Open I- beam span
Bridge span distance 135 ft 135 ft
Maximum height above floor ( H) 22 ft 38 ft
Atrium ( between bridges) a 36 ft 36 ft
Width at floor ( W) 32 ft 52 ft
Lengthb ( L) 175 ft 365 ft
Side construction 2: 1 sloped earth/ vegetation MSEc concrete walls to 20 ft
Openness ratiod 12.3 5.5
a - Atrium = width of opening between eastbound and westbound bridge spans at each underpass
b - Length = distance for animals to fully negotiate underpass, including fill at mouth of underpass
c - MSE = mechanically stabilized earth
d - Openness = ( W × H) / L ( Reed et al. 1979)
Figure 4.1. Little Green Valley riparian- meadow complex ( center photo) adjacent to State
Route 260 in Arizona, into which the west ( top photo) and east ( bottom photo) wildlife
underpasses open. Note their proximity and the different soil stabilization features, the west
with concrete walls, and the east with 2: 1 sloped earthen sides.
4.3.1 Video Surveillance System Components and Layout
At each underpass we installed video surveillance systems comprised of four low- lux, high-resolution
black and white ( B& W) video cameras linked to a 12- volt videocassette recorder
( VCR) with alarm input and a B& W quad- screen splitter. To illuminate the area covered by
our cameras, we installed infrared ( IR) 60 LED illuminators ( 9 at the east underpass, 7 at the
west). We used five IR photo- beam triggers at each underpass to detect approaching and
crossing animals. We operated both systems on 120- volt AC power converted to 12- volt DC
power for distribution to all equipment via buried wiring. We operated the camera systems at
the east underpass from September 2002– September 2005; the camera system at the west
underpass we operated from November 2002– September 2005.
At each we oriented our video systems to record animals approaching from the north side
only ( Figure 4.2), recording the elk as they traveled from forest cover into Little Green
Valley. We believe that elk that approached from the north had a greater degree of discretion
in use of the underpasses or alternate crossing locations compared to elk already in Little
Green Valley that had to return to cover via an underpass. Nonetheless, we recorded animals
crossing from both the north and south. We installed two cameras approximately 100– 115 ft
from each underpass ( Figure 4.2) to record animals approaching to within approximately 200
ft. of the underpass along drainages leading to it. We mounted a camera atop a 15- ft pole in
each underpass to record animals entering and crossing. We oriented a camera toward the
highway to record traffic, while other cameras simultaneously recorded approaching or
crossing wildlife ( Figure 4.2); we reported results of this monitoring in Gagnon ( 2006).
We placed IR photo- beam triggers approximately 1.5 ft above ground oriented such that
animals could not approach the underpass without tripping a trigger. To avoid recording
delays, we operated all components continuously so that VCRs immediately began recording
when triggered, with all cameras recording simultaneously. We programmed our VCR alarm
to record for two minutes each time an animal successively tripped a trigger. Twelve- volt
DC blowers and heaters ensured continuous operation during heat and cold.
4.3.2 Video Data Analysis
We extracted the following data from the video tape: date, time of day, total time animals
spent in the area, species, sex, age, number of animals, number of animals approaching and
crossing through the underpass, direction of travel, and various behaviors.
We calculated monthly elk passage rates as the proportion of elk groups that passed through
the underpass from the north relative to the frequency of groups that approached from the
same direction. We counted as an approach when animals crossed over the 3.5 ft right- of-way
( ROW) fence approximately 130– 160 ft from the mouth of the underpass ( Figure 4.2).
ADOT did not remove this fence during underpass construction due to presence of livestock;
instead, at each underpass it threaded the top two stands of wire through 20- ft lengths of PVC
pipe to create elk jumps. We counted it as a group crossing when half or more the elk in a
group passed through an underpass.
Figure 4.2. Layout ( top) of video surveillance system components at the west and east
Little Green Valley wildlife underpasses and the location of elk- proof and highway right- of-way
fencing, State Route 260, Arizona. We oriented video cameras to record wildlife
approaching each underpass from the north ( 2 cameras), animals crossing through the
underpass from both the north and south ( 1 camera), and simultaneous traffic on the
highway while animals approach and pass through the underpass ( 1 camera). The bottom
photo shows a group of elk passing through the west underpass, though the lead cow is
showing resistance. Note the illumination provided by infrared lights to observe animals at
Photo- beam triggers
Ungulate- proof fence
Right- of- way fence
We classified behavioral responses of individual elk into five approach and three crossing
categories to quantify acceptance or resistance to using the underpasses, similar to
Gordon and Anderson ( 2003). For approaching elk, we assigned frequencies to 1 or more
of these categories:
• Would not cross – elk left without crossing an underpass.
• Enter underpass and retreat – elk entered an underpass but retreated outside it.
• Alarmed flight – elk that approached or entered an underpass, but rapidly departed
in an alarmed manner.
• Feeding in area – elk that fed in the area between the ROW fence and the center
of an underpass.
• Standing or milling about – elk that stood or milled about in the area between the
ROW fence and center of an underpass.
For elk that crossed through either underpass from the north, we classified the degree to
which they exhibited hesitation or paused in an alert posture ( excluding feeding
behavior). We quantified delay by the time it took the elk to move from the mouth to
beyond the center of the underpass:
• No delay – elk crossed with less than 10 seconds combined hesitation.
• Minor delay – elk crossed with 11- 30 seconds combined hesitation.
• Obvious delay – elk crossed after a combined delay more than 30 seconds. We
classified elk that retreated or fled in alarm from the underpass before finally
crossing as exhibiting obvious delay.
4.3.3 Time- Lapse Validation
We conducted 24- hour time- lapse taping on five occasions at both underpasses to
compare the number of elk groups and individual animals recorded by VCRs in time-lapse
mode to the number recorded when the VCR alarm log reflected that animals had
activated a photo- beam trigger. We relied on our VCR internal alarm counters while
viewing the time- lapse video recordings to determine what proportion of approaching and
crossing animals our photo- beam triggers detected.
4.3.4 Statistical Analysis
Clevenger and Waltho ( 2000) stressed the benefit of multi- species assessments of
passage structure use, and Little et al. ( 2002) raised issues regarding predator- prey
interactions at passage structures. Though we pursued a multi- species assessment ( see
Gagnon et al. 2006), our observations for most species were relatively small compared to
those for elk, which accounted for more than 90% of the animals recorded on our
videotapes. This limited our ability to make statistical inferences regarding underpass
use for species other than elk.
We used elk group observations to assess underpass use to address poten
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