U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2015–5180

Proceedings of the 12th Biennial Conference of 

Research on the Colorado Plateau
COVER

View from the North Rim of the Grand Canyon. Banner showing the San Francisco Peaks near Leupp, Arizona.

Photograph by Robert J. Hart, U.S. Geological Survey.

ACKNOWLEDGMENT PAGE 

Kanab Basin looking east. Photograph by Robert J. Hart, U.S. Geological Survey.
Proceedings of the 12th Biennial 

Conference of Research on the Colorado 

River Plateau

Edited by Barbara E. Ralston

Scientific Investigations Report 2015–5180

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Ralston, B.E., ed., 2016, Proceedings of the 12th Biennial Conference of Research on the Colorado River Plateau: U.S. 

Geological Survey Scientific Investigations Report 2015–5180, 128 p., http://dx.doi.org/10.3133/sir20155180.

ISSN 2328-0328 (online)
Chapters

A. Introduction........................................................................................................................................... 1

By Barbara E. Ralston

B. A Nine-Year Record of Dust on Snow in the Colorado River Basin............................................ 3

By S. McKenzie Skiles and Thomas H. Painter

C. A Comparison of Historical and Future Rates of Climate Change in the Flagstaff 

Area of Northern Arizona .......................................................................................................... 13

By Kenneth L. Cole

D. Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National 

Forest, Northern Arizona ........................................................................................................... 25

By Jeri D. Ledbetter, Lawrence E. Stevens, Marguerite Hendrie, and Arie Leonard

E. Fungal Diversity in Biological Soil Crusts of the Colorado Plateau........................................... 41

By Blaire Steven, Cedar Hesse, La Verne Gallegos-Graves, Jayne Belnap, 

and Cheryl R. Kuske

 F. Assembling a Virtual “Weevils of North America” Checklist with Symbiota—

Preliminary Insights.................................................................................................................... 49

By Nico M. Franz, Charles W. O’Brien, Sarah D. Shirota, Michael T. Shillingburg, 

Chelsey R. Tellez, and Edward E. Gilbert

G. Influence of Habitat and Region on Spider Communities Along Two Elevation 

Gradients in the Southwestern U.S. ........................................................................................ 59

By Sandra L. Brantley, Caitlin A. Chapman, and Neil S. Cobb

H. Effects of Changing Lake Level on Lake Powell Fisheries—A Hypothesis............................. 73

By A. Wayne Gustaveson

 I. Understanding Forest Restoration Effects on Water Balance—Study Design 

for the Four Forest Restoration Initiative Paired Watershed Study................................... 79

By Frances C. O’Donnell, Sharon Masek Lopez, and Abraham E. Springer

 J. Long-Term Post-Wildfire Correlates with Avian Community Dynamics in Ponderosa 

Pine Forests.................................................................................................................................... 89

 By Jamie S. Sanderlin, William M. Block, and Brenda E. Strohmeyer

 K. Hunting Methods and Harvest Demographics for Black Bears in Arizona, 1981–2011 ...... 103

 By Brian F. Wakeling

 L. Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the 

Colorado River Corridor in Grand Canyon National Park .................................................... 107

By Valerie J. Horncastle, Brett G. Dickson, and Todd A. Chaudhry

M. Bat Surveys in Pipe Spring National Monument and Ensuing Interpretive Programs ........ 123

By Cameron Jack, John R. Taylor, Andrea Bornemeier, and Amber VanAlfen
Acknowledgments

Volume Editor

Barbara E. Ralston

U.S. Geological Survey, Flagstaff, Arizona

Managing Editor

Kate Jacques

U.S. Geological Survey, Menlo Park, California

Chapter Editors

Regan Austin

U.S. Geological Survey, Menlo Park, California

Jessica L. Dyke

U.S. Geological Survey, Menlo Park, California

James W. Hendley II

U.S. Geological Survey, Menlo Park, California

Claire M. Landowski

U.S. Geological Survey, Menlo Park, California

Production Staff

Jeanne S. DiLeo

U.S. Geological Survey, Menlo Park, California

Research Assistant

Meredith A. Hartwell

U.S. Geological Survey, Flagstaff, Arizona
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter A

Introduction 

By Barbara E. Ralston1

The Colorado Plateau is a physiographic region that 

encompasses 330,000 square kilometers in parts of four states 

in the southwestern United States (Colorado, Utah, New 

Mexico, and Arizona). Known for its high deserts, the Colorado 

Plateau also includes isolated mountains, high plateaus, and 

rugged canyons. Not only is the region topographically diverse, 

but geologically, biologically, and culturally diverse as well. 

The landscape is managed by Federal entities including the 

Bureau of Land Management, the National Park Service, and 

the U.S. Forest Service; Tribal nations including the Navajo 

Nation, Kaibab Paiute, Mountain Ute, Southern Ute, Hopi, 

Zuni, Hualapai, Havasupai, and White Mountain Apache 

Tribes; State land and wildlife management agencies; and 

privately owned holdings, creating complex interactions and 

management challenges (fig. 1). Population growth, increased 

tourism to Federal and State lands, and energy development 

have increased water demands and altered land-use patterns, 

and these changes have emerged as management challenges 

facing the people working and living in the region. Climate 

change, particularly the ongoing drought, has exacerbated 

the effects of population growth, land-use change, and 

other stressors such as invasive species. As managers seek 

solutions to the challenges facing the region’s natural and 

cultural resources, the Biennial Conference of Science and 

Management of the Colorado Plateau has become an important 

venue for exchanging information about emerging management 

concerns and recent scientific research. Each biennial 

conference has sought to promote discussion, information 

sharing, and productive communication among the managers, 

scientists, students, administrators, tribal representatives, and 

others who attend the conference with the goal of enhancing 

the use of the best available science to manage the region’s 

incomparable natural and cultural resources. 

The publication and dissemination of a conference 

proceedings series expands the reach of the conference beyond 

those people in attendance and creates a record on the research 

presented. The idea of producing a conference proceedings, 

and its subsequent publication, first occurred in 1993 following 

the first biennial conference in 1991. A published volume of 

contributed papers has followed each subsequent biennial 

conference, including this volume. The venue for publishing 

proceedings has changed over the years and has included the 

National Park Service, the Government Printing Office, the 

U.S. Geological Survey, and University of Arizona Press. 

Recently, van Riper and others (2015) published a compilation 

of the abstracts from the 11 previous conference proceedings. 

Collectively, the proceedings highlight approximately 25 

years of natural- and cultural-resources research, promoting 

the integration of research with resource management across 

the Colorado Plateau. This volume is freely downloadable 

by the public, thereby further expanding the influence of this 

conference beyond the Colorado Plateau.

The 12th Biennial Conference held in Flagstaff, Arizona, 

from September 16 to 19, 2013, covered a range of topics in the 

physical, biological, and socio-cultural sciences. The conference 

was organized and hosted by Northern Arizona University’s 

(NAU) Merriam-Powell Center for Environmental Research, 

the Colorado Plateau Cooperative Ecosystem Studies Unit, 

and the U.S. Geological Survey (USGS) Southwest Biological 

Science Center. Financial and in-kind support was provided 

by a wide range of organizations including the U.S. Forest 

Service, National Park Service, Bureau of Land Management, 

Grand Canyon Trust, Colorado Plateau Research Station, and 

various NAU entities. NAU sponsors include the Landscape 

Conservation Initiative, School of Forestry, School of Earth 

Science and Environmental Sustainability, Office of the Provost, 

and Office of the Vice President of Research. Contributors 

to these proceedings include researchers and managers 

from Federal, State, and Tribal governments, universities, 

private entities, and non-profit organizations. In this regard, 

this conference has wide-ranging support and participation 

among private and public entities involved in the science and 

management of natural resources on the Colorado Plateau. 

Topics at the conference included broad subject sessions in 

animal, plant, and fire ecology, and special topical sessions that 

included piñon-juniper woodland health, arthropod biodiversity, 

biological soil crusts, and climate change mitigation and 

adaptation. The 12 contributed papers appearing in this volume 

represent a subset of topics presented at the conference. The 

order of articles follows a progression from the physical 

sciences to biology, and ends with the outcomes of research 

used in an interpretive setting. Each paper in this biennial 

conference proceedings represents original research that is 

previously unpublished and has been peer reviewed by scientists 

familiar with the subject matter.

Numerous people contributed to the success of both the 

conference and this proceedings volume. In particular, Lara 

Schmit, the conference coordinator, and Ron Hiebert, my 

conference co-chair, helped identify special session organizers, 1

U.S. Geological Survey
2 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

developed the conference agenda, and worked with NAU’s 

conference staff to find a location that accommodated the 

conference and all of the presentations. Volunteers from both 

NAU and the USGS moderated sessions. Peer reviewers 

provided insightful and critical comments that improved 

all of the submitted manuscripts. Additionally, the copy 

editors with the USGS Science Publishing Network provided 

outstanding editorial oversight. As with previous publications 

of the conference proceedings, the USGS provided financial 

support. In this case, David Lytle, the Director of the Southwest 

Biological Science Center, provided on-going support for the 

completion of this volume.

Reference

van Riper, C., III, Drost, C.A., and Selleck, S.S., compilers, 

2015, A quarter century of research on the Colorado 

Plateau—A compilation of the Colorado Plateau Biennial 

Conference proceedings for 1993–2015: U.S. Geological 

Survey Open-File Report 2015‒1115, 186 p., http://dx.doi.

org/10.3133/ofr20151115. 

Green 

River

River

Lake

Powell

Lake

Mead

Virgin

40°

115° 110°

35° Flagstaff

St. George

Moab

Albuquerque

Durango

Salt Lake City NEVADA UTAH

ARIZONA

COLORADO

NEW MEXICO

San Juan River

Colorado Rive r

Little

Colorad o River

Tribal Land (BIA)

National Historic Park (NPS)

National Monument (BLM or NPS)

National Park (NPS)

National Recreation Area (NPS)

National Forest (USFS)

EXPLANATION

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Figure 1. Map identifying the extent of the Colorado Plateau that covers parts of Arizona, Colorado, New 

Mexico and Utah with National Parks, Monuments, Historic Sites, Recreation Areas, and Forests, as well as 

Tribal land boundaries identified. (NF, National Forest; NHS, National Historic Site; NM, National Monument; 

NP, National Park; NRA, National Recreation Area) 
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter B

A Nine-Year Record of Dust on Snow in the Colorado River 

Basin 

By S. McKenzie Skiles1 and Thomas H. Painter1

Abstract

Mountain snow cover of the Colorado River Basin 

(CRB) has had a fivefold increase in dust loading since Anglo 

settlement of the western United States in the mid-19th century. 

This dust deposition accelerates snowmelt by reducing albedo 

through surface darkening and enhanced snow grain growth. 

Here we present a nine-year (water years [WY] 2005–2013) 

record of dust loading and advanced snowmelt at Senator Beck 

Basin Study Area, San Juan Mountains, Colorado (SBBSA). 

Since observations of dust events began in WY 2003, a single 

dust-free season has not occurred. The majority of these dust 

events occur in the spring, which enhances the efficacy of 

dust-advanced snowmelt, as it coincides with increasing solar 

irradiance. The fewest springtime events occurred in WY 

2003 (three events) and then increased through WY 2008 

(nine events) before stabilizing with an average of 9±1 spring 

events between 2008 and 2013. The amount of dust entrained 

and deposited in each event is variable; therefore, the number 

of dust events is not a good indicator for dust loading, which 

exhibits greater interannual variability. End of snow season 

total dust concentrations range from 0.2 to 4.8 milligrams of 

dust per gram of snow melt water (mg/g), resulting in earlier 

snowmelt by 3 to 7 weeks. Results from snowmelt modeling 

show that the degree of advanced melt is linearly related to the 

amount of dust deposited each season. 

Introduction

The Colorado Plateau region of the western United States 

is one of the main dust producers in North America (Neff and 

others, 2008). Southwesterly winds entrain sediment from this 

region and deposit it downwind in the snow-covered Colorado 

Rocky Mountains. Remote sensing imagery, back-trajectory 

analysis, and chemical analysis of dust in snow from the 

San Juan Mountains in southwestern Colorado indicate that 

the dust source is the Colorado Plateau, namely the lowland 

arid landscapes of the Four Corners Region (Lawrence and 

others, 2010; Neff and others, 2008; Painter and others, 2007). 

Understanding the impact of dust deposition onto snow cover 

in this region is of interest because snowmelt is a critical 

resource that contributes to more than 70 percent of the flow in 

the Colorado River, an overallocated river that provides water 

to seven states in the western United States and to Mexico. 

Additionally, the current level of dust loading is a relatively new 

phenomenon in this region. Sediment cores from high elevation 

lakes in the San Juan Mountains show that dust deposition rates 

have increased fivefold over background since Anglo settlement 

and grazing disturbance of the western United States in the mid19th century (Neff and others, 2008).

Dust radiative forcing is defined as the instantaneous 

enhanced surface absorption of solar radiation by dust deposited 

in snow (Painter and others, 2007). This impact is direct, 

enhancing absorption of solar radiation by darkening the snow 

surface, and indirect, enhancing absorption by accelerating grain 

growth (Painter and others, 2007). As snow absorption increases, 

snow albedo is reduced further, and a feedback process is 

initiated. The additional energy supplied by dust radiative forcing 

has been shown to advance melt by 3 to 7 weeks, shift timing 

and intensity of peak runoff, and reduce total water yield (Painter 

and others, 2010; Skiles and others, 2012). Ongoing research 

indicates that not accounting for dust radiative forcing may be a 

factor contributing to operational river runoff forecast errors in 

the Colorado River Basin (CRB) (Bryant and others, 2013). 

Previous Work

In 2005, two snow energy balance instrumentation array 

towers were installed in Senator Beck Basin Study Area 

(SBBSA), San Juan Mountains, Colorado. These instrumentation arrays, along with another tower on Grand Mesa in 

west-central Colorado installed in 2009, are the only full snow 

energy balance towers in the Colorado River Basin that allow 

for energy balance based snowmelt modeling. This record is 

highly valuable in a region that depends so heavily on snow for 

water resources. Painter and others (2007) utilized this data to 

isolate the effects of dust from other controls and show that the 

acceleration of melt by the shortwave radiative forcing of dust 

results in a shortening of snow cover duration in this region by 

about a month. 1

Jet Propulsion Laboratory, California Institute of Technology
4 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Skiles and others (2012) expanded this analysis to 

assess the interannual variability in dust loading, radiative 

forcing, and snowmelt rates over a 6-year record (2005–

2010); mean springtime dust radiative forcing across the 

period ranged from 31 to 75 watts per square meter (W/m2

), 

shortening snow cover duration by 21 to 51 days. The dustadvanced loss of snow cover was found to be linearly related 

to total dust concentration at the end of snow cover, despite 

temporal variability in dust exposure and solar irradiance. 

The advanced melt owing to dust resulted in faster and 

earlier peak snowmelt outflow, with daily mean snowpack 

outflow doubling under the heaviest dust conditions. Skiles 

and others (2012) also compared the relative capacity of 

dust and warmer temperatures to advance melt and found 

dust efficacy to be greater: increases of 2–4°C advanced 

melt by 5–18 days in the absence of dust and 0–6 days in the 

presence of dust. 

Current Work

The current work is designed to be an update to, and 

supplement, the dust-on-snow data record presented in Painter 

and others (2007) and Skiles and others (2012), and the reader 

is referred to these papers for additional discussion and detail 

on methods. Here, we expand on these studies to encompass 

the full observation period at SBBSA study sites, from 2005 

through 2013. Interannual variability in dust deposition and 

loading is assessed from the snow observations and sampling 

record. Interannual variability in dust radiative forcing and 

advanced snowmelt is assessed from the radiative forcing and 

snowmelt reconstruction modeling.

Methods

Study Area

Senator Beck Basin Study Area is in the Ouray Ranger 

District of the Uncompahgre National Forest in the western 

San Juan Mountains of southwestern Colorado (location 

within the Colorado River Basin is shown in fig. 1). The 

study area is managed by the Center for Snow and Avalanche 

Studies (CSAS). Observations of dust events began during 

WY 2003 (October 2002 through September 2003), and two 

meteorological and energy-flux instrumentation towers were 

installed in early WY 2005. 

The instrumentation towers are located within dedicated 

study plots, where regular snow sampling also takes place. 

Senator Beck Study Plot (SBSP, fig. 1) is located in the alpine 

tundra (3,719 meters [m]) above tree line at a level site near 

the center of SBBSA. Swamp Angel Study Plot (SASP, fig. 1) 

is located in a clearing below tree line in a subalpine forest at 

3,368 m. Hereafter, SBSP is referred to as the “alpine site” and 

SASP as the “subalpine site.”

Measurements from the instrumentation towers are 

used to determined dust radiative forcing and reconstruct 

snowmelt, as described below. Measured parameters 

include wind speed and direction, air temperature and 

relative humidity, snowpack depth, incoming and outgoing 

broadband (BB) solar radiation, incoming and outgoing near 

infrared/shortwave infrared (NIR/SWIR) solar radiation, and 

incoming longwave radiation. Incoming and outgoing visible 

(VIS) radiation is the difference between the BB and NIR/

SWIR. Snow albedo is the ratio of incoming to outgoing 

solar radiation. Outgoing longwave radiation is inferred 

from measurement of snow surface temperature from an 

infrared sensor. Precipitation is measured at the subalpine 

site by a standalone precipitation gauge but is not measured 

at the alpine site owing to the high winds. A more detailed 

description of these sites and data can be found in Painter and 

others (2012) and Landry and others (2014). Data from these 

sites are publically available by the CSAS at http://www.

snowstudies.org/.

Observations and Snow Sampling

The presence of airborne dust over the study area and 

subsequent deposition in the snow, referred to as dust events, 

are visually identified and recorded by CSAS. The evolution 

of dust layers, and snow properties, are monitored through 

snowpit measurements that take place within study plot 

boundaries. The measurements are collected once a month 

during the winter, when few dust events occur and snow is 

accumulating, and then increases to weekly in the spring, 

when the majority of dust deposition occurs and snowmelt 

begins. Snow sampling is more frequent at the subalpine 

site, which is easier to access. Snowpits are excavated 

to the ground, and measurements include the following: 

snow depth, 10-centimeter (cm) temperature profile, visual 

notation of snow and dust stratigraphy, liquid water content, 

and snow density. Depth and density measurements are used 

to calculate snow water equivalent (SWE), the hydrologically 

relevant variable that describes how much water is contained 

within the snowpack. 

In addition to snow observations, snow samples are 

collected in snowpits when dust is at or near the surface to 

quantify dust concentrations that may be impacting albedo and 

radiative forcing. Samples are collected in the top 30 cm of 

the snow column, across a horizontal area of 500 cm2

, at 3-cm 

intervals. Because of the attenuation of incident solar radiation 

in snow surface layers, 30 cm is the estimated maximum depth 

to which dust influences snow albedo (Warren and others, 

1982). The 3-cm sampling interval is useful to capture dust 

layers, which tend to be thin. Consistent sampling volumes, 

maintained using a gravimetrics board, allow for a consistent 

comparison between collections/years. Dust is typically not 

entrained in melt, and end-of-year (EOY) dust concentrations 

are reported from samples collected just prior to snow 

depletion, when the majority of dust has combined at the 

surface. Additional dust was not deposited in the time between 
A Nine-Year Record of Dust on Snow in the Colorado River Basin 5

the last sample collection and snow all gone (SAG) in any 

year of the study.

Snow samples (3 cm × 500 cm2) are melted, stored 

individually in Nalgene bottles, and sent to the Snow 

Optics Laboratory at National Aeronautics and Space 

Administration’s (NASA’s) Jet Propulsion Laboratory, where 

each sample is filtered through preweighed 0.495-micrometers 

(μm)-diameter Nuclepore filters. After filters have dried, they 

are reweighed to find dust mass. Dust concentrations are 

reported in milligram of dust per gram of snow sample (mg/g), 

which is equivalent to parts per thousand by weight (pptw). 

Dust Radiative Forcing Modeling 

Solar radiation measurements at the instrumentation array 

towers allow us to determine the range of potential radiative 

forcing owing to dust, using the treatment described in Painter 

and others (2007). Briefly, to bracket the potential dust impact, 

radiative forcing (in W/m2) is calculated by way of two 

scenarios. The minimum impact scenario addresses the direct 

effect of absorption by dust in snow:

 Fdmin = Evis Δvis (1)

UTAH COLORADO

ARIZONA NEW MEXICO

San Juan Mountains

39°

38°

37°

110° 109° 108° 107° 106°

Senator Beck

Basin

A

B Senator Beck study plot alpine site C Swamp Angel study plot subalpine site

Base from NASA/MODIS 

Colorado River

Basin

STUDY

AREA

0

0

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50 100

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Figure 1. Senator Beck Basin Study 

area is located in the upper Colorado 

River Basin in southwestern Colorado 

(A). The alpine Senator Beck Study Plot 

(B) and subalpine Swamp Angel study 

plot (C) are pictured.
6 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

where Evis is visible irradiance (W/m2

) and Δvis = 0.92−αvis, 

where αvis is measured visible albedo and 0.92 is the mean 

visible albedo for relatively dust-free snow at SBBSA (Painter 

and others, 2007). 

The maximum impact scenario addresses both the 

direct effect and the indirect effects of dust on snow by also 

accounting for changes in grain size, using an empirical 

relationship developed at SBBSA that gives the proportion of 

the change in NIR albedo owing to the presence of dust versus 

grain growth in the absence of dust:

 Fdmax = 0.5(Evis Δvis+Enir αnir ((1/ξ)−1)) (2)

where

 if Δvis ≤ 0.17, then ξ = 1−1.689 Δvis ;

 if Δvis ≥ 0.17, then ξ = 0.67; 

 Enir is the NIR/SWIR net shortwave flux; and

 αnir is the NIR/SWIR albedo (Painter and 

others, 2007).

Snow Energy Balance and Melt Modeling

Dust radiative forcing (RF) can change timing and 

intensity of snowmelt runoff and reduce total water yield. 

To estimate the influence of dust on snowmelt, the depletion 

of SWE is modeled in the presence and absence of dust 

using the snow energy balance snowmelt model, SNOBAL 

(Marks and others, 1998). The presence of dust represents 

observed conditions and is modeled from energy balance 

measurements directly. The absence of dust represents 

a clean snowpack, which is modeled by removing the 

minimum and maximum RF owing to dust from net solar 

radiation, then averaging the daily values of these two 

scenarios to represent a conservatively clean snowpack. 

Snowmelt is modeled from April 15, the average date of peak 

SWE at SBBSA, to capture snowmelt evolution and SWE 

depletion over the ablation season (the time period between 

peak SWE and SAG).

The SNOBAL model, which reconstructs snow cover 

evolution from snow energy balance measurements, was 

first described conceptually by Marks and Dozier (1992) and 

then described in detail by Marks and others (1998). Both 

the model and modeling process are described in greater 

detail in Skiles and others (2012), along with a site-specific 

sensitivity analysis. Briefly, changes in snow depth and density 

(SWE) and point runoff are predicted by SNOBAL using 

snow properties (temperature, density, and water content), 

energy exchanges from the instrumentation towers (net solar 

radiation, incoming longwave radiation, air temperature, 

wind speed, and vapor pressure), precipitation, and tower 

measurement heights. Dust and dust-free (clean case) model 

runs are initiated with measured snow properties from the 

snowpit, which is excavated closest in time to April 15 and run 

at an hourly time step until complete snowmelt is achieved. 

The only variation between scenarios is net solar 

radiation, which is altered in the clean case to remove dust 

radiative forcing. Thus, the difference between when the dust 

scenario and clean scenario time series reach SAG indicates 

the number of days that dust radiative forcing advances 

complete melt under observed meteorological conditions. 

Snowpack evolution is reconstructed well by SNOBAL over 

the ablation season; on average, modeled dust scenario SAG 

occurs within a day of observed SAG across all years.

Results and Discussion

Dust Deposition and Loading

Since 2003, a total of 87 dust events have been recorded 

during the presence of snow cover in SBBSA. An average of 

eight events per water year occur when snow cover is present, 

although events are not evenly distributed (fig. 2). The fewest 

events occurred in 2003 and 2004, with three events each 

season. The number of events exhibited an increasing trend 

through 2009, when the number of events peaked at 12. From 

2009–2013, the number varied between 9 and 12 events. 

Although there is a clear increase in the number of events 

per season, it is possible that this is an artifact of improving 

recognitions of dust events. It is not possible to draw any 

conclusions about long-term trends because the data record is 

still relatively short.

Dust events are not distributed evenly across the snow 

cover season; 80 percent of them occur in March, April, and 

May (fig. 2). This is not unexpected. The drying of semiarid 

landscapes and increased wind speeds (owing to atmospheric 

momentum exchange) coincide in the spring and thereby 

increase dust emission and dust event frequency at that time. 

The number of springtime events follows a similar pattern 

to total number of events per season throughout the study 

period, increasing from 2003–2008 and then becoming less 

variable over the last 5 years with an average of 9±1 events 

each spring.

The timing of dust deposition is important for the 

radiative impact. Dust deposited in the spring has the 

greatest impact because solar irradiance is increasing and the 

internal energy in the snow pack is great enough to initiate 

snowmelt. Additionally, dust remains in the layer in which it 

was deposited and is not entrained in melt water, a process 

which results in newly deposited spring dust layers persisting 

at the surface and previously buried layers resurfacing and 

converging as snow cover is depleted, darkening the surface 

and compounding albedo decay. The stationary behavior of 

dust layers as snow melts has been consistently observed 

at SBBSA and has also been reported for light-absorbing 

impurities in other regions (Conway and others, 1996; Doherty 

and others, 2013).
A Nine-Year Record of Dust on Snow in the Colorado River Basin 7

End-of-year dust concentrations exhibit greater 

interannual variability than number of seasonal dust events 

because the amount of dust entrained in each event is variable; 

the number of events each season is therefore not a predictor 

of total dust loading (fig. 3). Although still a relatively short 

data record, it appears that seasonal loading is roughly 

1.0 mg/g or less in most years (2005–2008, 2011–2012). In 

these years, seasonal dust loading ranges from 0.2 to 1.3 mg/g

and averages 0.8 mg/g at the subalpine site, and at the alpine 

site, it ranges from 0.3 to 1.1 mg/g and averages 0.7 mg/g. In 

the other years, dust loading is more extreme (2009, 2010, and 

2013), ranging from 4.3 to 4.8 mg/g and averaging 4.6 mg/g, 

which is five times the average dust loading in the lower dust 

years. Concentrations of dust at the windy alpine site tend 

to be slightly lower than the subalpine site, with an average 

concentration of 3.6 mg/g in the higher dust years.

The highest end-of-year dust concentration was sampled 

in 2013, with 4.8 mg/g at the subalpine site; the next highest Number of dust events

14

12

10

8

6

4

2

0

Season total

Spring total

EXPLANATION

35

30

25

20

15

10

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0 Oct Nov Dec Jan Feb Mar Apr May JunAvg basin SAG data

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

A

B

Figure 2. Bar graphs showing observed dust events in 

Senator Beck Basin Study Area in the San Juan Mountains of 

southwestern Colorado, from 2003 to 2013. A, The number of 

observed dust events (2003–2013). B, Monthly distribution of these 

events over the full record. The months of August and September 

are not shown in the distribution plot because dust events are 

not recorded when snow cover is absent. The basinwide average 

date of snow all gone (SAG) is shown by the dotted line.

dust concentration year, 2009, had an end-of-year dust 

concentration of 4.6 mg/g at the subalpine site. Because of 

high avalanche danger, the alpine site was inaccessible for 

an end-of-year sample in 2013. The variation in dust loading 

between alpine and subalpine is greatest in heavy dust loading 

years, with an average difference of −1.2 mg/g for 2009 and 

2010, whereas if you average across all 9 years, the difference 

minimizes to −0.4 mg/g. Therefore, the end-of-year dust 

concentration at the alpine site in 2013 is estimated to be 

between 3.6 and 4.4 mg/g. 

It is outside the scope of this paper to discuss in detail 

the potential source for this high interannual variability in 

dust loading, which could relate to surface dynamics in the 

source region and synoptic meteorology. A recent study by 

Li and others (2013) found a relation between the amounts 

of bare ground, as identified from remote sensing imagery, 

and dust loading at SBBSA. Likely, heavy dust years cannot 

be attributed to a single factor, and additional study is 

needed better understand the relation between source region, 

atmospheric transport, and deposition processes.

Dust Radiative Forcing 

Dust radiative forcing typically initiates in March with 

the deposition of spring dust events and increases through the 

ablation season with additional dust deposition, increasing 

irradiance, and snow albedo feedbacks. To capture the rise 

2005 2006 2007 2008 2009 2010

0

1.0

2.0

3.0

4.0

5.0

2011 2012

Alpine

Subalpine

2013

Dust concentration, 

in parts per thousand by weight 

EXPLANATION

Year

Figure 3. Bar graph showing sampled end-of-year dust 

concentrations in parts per thousand by weight (pptw, mg/g

equivalent) at Senator Beck Basin Study Area in the San Juan 

Mountains of southwestern Colorado. The alpine site is located in 

the upper basin in alpine tundra above tree line, and the subalpine 

site is located in the lower basin below tree line.
8 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

in spring radiative forcing, it is calculated each year from 

March 15 to the date of modeled clean SAG. The average

of the two RF scenarios (max/min) is plotted for 2011–2013 

at the subalpine site as daily means along with broadband 

albedo, dust events, and modeled/measured snow water 

equivalent evolution in figure 4. Similar plots for 2005–2010 

can be found in Skiles and others (2012).

Snow albedo typically decreases, and radiative forcing 

increases, with or shortly after a dust event, although not all 

dust deposition events increase RF immediately. Dry events, 

those not associated with precipitation, result in the quickest 

reduction in albedo because they can interact with solar 

irradiance directly upon deposition. The impact of events 

that occur just ahead of or during precipitation events are 

dampened by new snow, and radiative forcing increases as 

the new snow increases in grain size (no longer dominating 

attenuation) or as it melts (bringing the dust layer closer to 

the surface). On a seasonal scale, RF increases steadily in the 

spring as previously buried dust layers converge at the snow 

surface, and the largest RF values occur at the end of the 

season just prior to SAG. 

Average radiative forcing between April 15 and 

observed SAG gives an indication of how much additional 

energy dust is contributing toward melts over the ablation 

season (table 1). 

The highest daily mean RFs over this time period occur 

in the extreme dust years, ranging from 58 to 75 W/m2

 at the 

subalpine site. Although 2013 had a higher end-of-year dust 

concentration, 2009 had a higher ablation season RF because 

the lack of new snow and absence of cloud cover (both 

factors that influence radiative forcing magnitude) reduced 

snow albedo to below 0.35, unprecedented values, for the 

final 11 days of snow cover. This resulted in an average RF 

of 125 W/m2 during the last 7 days of snow cover, whereas 

RF was 108 W/m2 over this same time period in 2013 (when 

snow albedo fell below 0.35 for the final 5 days). The lower 

dust concentration years (2005–2008, 2011–2012) have a daily 

mean RF of 50 W/m2, ranging from 45 to 56 W/m2. Radiative 

forcings at the alpine site tend to be lower because of lower 

dust concentrations and higher albedos, and corresponding 

daily mean RFs over the ablation season are 52 W/m2 and 

36 W/m2 for extreme and lower dust years, respectively.

120 140 160 180 200

0

50

100

150

200

0

0.2

0.4

0.6

0.8

1.0

Broadband snow albedo

Radiative forcing, in watts per square meter

120 140 160 180 200 120 140 160 180 200

200

400

600

800

1,000

1,200

Snow water equivaent,

 in millimeters

0

Dust

Clean

Snow all gone

Sampled SWE

EOY dust: 

1.3 pptw 

49 days

EOY dust: 

0.8 pptw 

EOY dust: 

4.8 pptw 

32 days 24 days

Albedo

Dust RF

Dust event

Day of year, 2011 Day of year, 2012 Day of year, 2013

EXPLANATION

EXPLANATION

Figure 4. Graphs showing the evolution in snow albedo and dust radiative forcing (RF), from April 15th (day of year 105), along with 

dust event timing and end-of-year (EOY) dust concentration in parts per thousand by weight (pptw) for 2011–2013 (top panel), and the 

depletion of snow water equivalent (SWE) for the dust and clean scenarios (bottom panel). Measured SWE and observed snow all gone 

(SAG, dotted line) are plotted to show that the model is doing a good job of reconstructing observed conditions.
A Nine-Year Record of Dust on Snow in the Colorado River Basin 9

Table 1. End-of-year dust concentrations (mg/g, milligrams per 

gram), ablation season daily mean radiative forcing (W/m2, watts 

per meter squared), and ∆SAG at the subalpine/alpine study plots 

in Senator Beck Basin Study Area in the San Juan Mountains of 

southwestern Colorado (the number of days difference between 

snow all gone dates for dust and clean scenarios).

Water 

year

EOY dust 

concentration 

(mg/g)

Daily mean dust 

radiative forcing 

(W/m)

DSAG

(days)

2005 0.23 / 0.29 45 / 27 28 / 23

2006 0.98 / 0.89 56 / 45 31 / 21

2007 0.85 / 0.87 45 / 38 35 / 27

2008 0.86 / 0.53 54 / 38 27 / 26

2009 4.58 / 3.86 75 / 50 51 / 44

2010 4.34 / 2.67 58 / 50 48 / 37

2011 1.30 / 1.08 50 / 32 32 / 28

2012 0.82 / 0.54 53 / 34 24 / 27

2013 4.76 / not collected 64 / 57 49 / 38

The time period between dust SAG and clean SAG is 

the number of days when there would still be snow cover in 

the absence of radiative forcing by dust (fig. 4). These values 

provide a measure of how much additional solar radiation 

the ground is absorbing owing to earlier reduction of snowcovered area by dust radiative forcing. The RF varies over 

this period from 173 W/m2 (2013) to 127 W/m2 (2009), 

with an average of 151 W/m2 at the subalpine site, and from 

166 W/m2 (2013) to 115 W/m2 (2009) with an average of 

148 W/m2 at the alpine site. The lowest RF values occur in 

2009, despite being highest over the ablation season, because 

frequent cloud cover lowered total irradiance and therefore 

radiative forcing.

Mean daily radiative forcing from April 15 to clean 

scenario SAG provides an encompassing measure of dustforced snow-albedo feedbacks, including reduced albedo, 

enhanced grain growth, and earlier removal of snow cover. 

The daily mean RF over this period is again highest in 

extreme dust years with 113 W/m2 and 91 W/m2 at the 

subalpine and alpine site, respectively. In a lower dust year, 

mean daily RF was 86 W/m2 and 71 W/m2 at the subalpine 

and alpine site, respectively. In all years of the record, there 

is an additional 30–60 W/m2 of radiative forcing for the 

period of April 15 to clean scenario SAG relative to dust 

scenario SAG; this contribution comes from earlier reduction 

in snow-covered area owing to dust radiative forcing.

Advanced Snow Melt

The additional energy from dust radiative forcing results 

in earlier snowmelt. Without direct observation of zero-dust 

conditions, the modeled clean snow scenario represents the 

best understanding of the evolution of the snowpack in the 

absence of dust. As discussed in the Methods section, the clean 

snowpack is modeled by removing the minimum and maximum 

RF attributed to dust, then averaging the daily values of these 

two scenarios to represent a conservatively clean snowpack. 

Because there is dust deposition in every year, the dust scenario 

always melts out earlier than the clean scenario. The difference, 

in numbers of days, between when the dust and clean scenarios 

reach SAG is referred to as ∆SAG.

As might be expected, the greatest degree of dustadvanced melt takes place in the extreme dust years with 

∆SAG values of 51 (2009), 48 (2010), and 49 (2013) days at 

the subalpine site, respectively. The ∆SAG at the alpine site is 

on average 6 days less, owing to the lower dust concentrations 

and RF values (44, 37, and 38 days for 2009, 2010, and 2013, 

respectively). The average of both sites gives an idea of the 

basinwide ∆SAG, which would be 44 days for extreme dust 

years. The lower dust years, 2005–2008 and 2011–2012, 

exhibit advanced melt on the scale of about a month: 24–34 

days at the subalpine site and 23–28 days at the alpine site, 

with a basin wide ∆SAG of 27 days (table 1).
10 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Radiative forcing by dust varies on multiple temporal 

scales, and the degree to which dust RF advances snowmelt

can be influenced by factors such as the amount of SWE at the

beginning of the ablation season (Painter and others, 2007) and 

the amount of new snowfall in the spring (Skiles and others, 

2012). These factors do not influence the relationship between 

∆SAG and end-of-year dust concentrations exhibited at each 

site over the 9-year record, which is linear (fig. 5; R2 value 0.93, 

both sites). It should be noted that the relationship between 

reductions in snow albedo and increases in dust concentration 

is log-linear (Warren and Wiscombe, 1980), and given this, a 

nonlinear relationship between ∆SAG and dust concentration 

would be expected as well. A longer data record from the 

SBBSA will allow us to better examine this relationship with 

additional data points to more fully populate the plot.

0 1.0 2.0 3.0 4.0 5.0

Dust concentration, in parts per thousand by weight

20

30

40

50

Change in snow all gone, in days

Subalpine

Alpine

EXPLANATION

Figure 5. Graph showing the relationship between end-of-year 

dust concentration in parts per thousand by weight (equivalent 

to mg/g) and change in snow all gone date, or ∆SAG (the number 

of days that dust advances melt) from 2005–2013 at Senator Beck 

Basin in the San Juan Mountains of southwestern Colorado. 

Acknowledgments

We acknowledge Chris Landry and Kim Buck of the 

Center for Snow and Avalanche Studies for their efforts 

in assisting with data retrieval and availability. This work 

was funded by the National Science Foundation grants 

ATM04323237 and ATM0431955 and NASA project 

NNX10AO97G. Part of this work was performed at the Jet 

Propulsion Laboratory, California Institute of Technology, 

under a contract with NASA. 

References Cited

Bryant, A.C., Painter, T.H., Deems, J.S., and Bender, S.M., 

2013, Impact of dust radiative forcing in snow on accuracy 

of operational runoff prediction in the Upper Colorado 

River Basin: Geophysical Research Letters, v. 40, no. 15, 

p. 3945–3949. [Also available at http://dx.doi.org/10.1002/

grl.50773.]

Conway, H., Gades, A., and Raymond, C.F., 1996, Albedo 

of dirty snow during conditions of melt: Water Resources 

Research, v. 32, no. 6, p. 1713–1718. [Also available at 

http://dx.doi.org/10.1029/96WR00712.]

Doherty, S.J., Grenfell, T.C., Forsström, S., Hegg, D.L., 

Brandt, R.E., and Warren, S.G., 2013, Observed vertical 

redistribution of black carbon and other insoluble 

light-absorbing particles in melting snow: Journal of 

Geophysical Research, v. 118, no. 11, p. 5553–5569. 

[Also available at http://dx.doi.org/10.1002/jgrd.50235.]

Landry, C.C., Buck, K.A., Raleigh, M.S., and Clark, M.P., 

2014, Mountain system monitoring at Senator Beck 

Basin, San Juan Mountains, Colorado—A new integrative 

data source to develop and evaluate models of snow and 

hydrologic processes: Water Resources Research, v. 50, 

no. 2, p. 1773–1788. [Also available at http://dx.doi.

org/10.1002/2013WR013711.]

Lawrence, C.R., Painter, T.H., Landry, C.C., and Neff, J.C., 

2010, Contemporary geochemical composition and flux 

of aeolian dust to the San Juan Mountains, Colorado, 

United States: Journal of Geophysical Research, v. 

115, no. G03007. [Also available at http://dx.doi.

org/10.1029/2009JG001077.]

Li, J., Okin, G.S., Skiles, S.M., and Painter, T.H., 2013, 

Relating variation of dust on snow to bare soil dynamics 

in the western United States: Environmental Research 

Letters, v. 8, no. 4, p. 1–8. [Also available at http://

dx.doi.org/10.1088/1748-9326/8/4/044054.]

Marks, D., and Dozier, J., 1992, Climate and energy 

exchange at the snow surface in the alpine region of the 

Sierra Nevada—2, Snow cover energy balance: Water 

Resources Research, v. 28, no. 11, p. 3043–3054. [Also 

available at http://dx.doi.org/10.1029/92WR01483.]

Marks, D., Kimball, J., Tingey, D., and Link, T., 1998, 

The sensitivity of snowmelt processes to climate 

conditions and forest cover during rain-on-snow—a 

case study of the 1996 Pacific Northwest flood: 

Hydrological Processes, v. 12, no. 10–11, p. 1569–

1587. [Also available at http://dx.doi.org/10.1002/

(SICI)1099-1085(199808/09)12:10/11<1569::AIDHYP682>3.0.CO;2-L.]
A Nine-Year Record of Dust on Snow in the Colorado River Basin 11

Neff, J.C., Ballantyne, A.P., Farmer, G.L., Mahowald, N.M., 

Conroy, J.L., Landry, C.C., Overpeck, J.T., Painter, T.H., 

Lawrence, C.R., and Reynolds, R.L., 2008, Increasing 

eolian dust deposition in the western United States linked 

to human activity: Nature Geoscience, v. 1, p. 189–195. 

[Also available at http://dx.doi.org/10.1038/ngeo133.]

Painter, T.H., Barrett, A.P., Landry, C.C., Neff, J.C., 

Cassidy, M.P., Lawrence, C.R., McBride, K., and Farmer, 

G.L., 2007, Impact of disturbed desert soils on duration 

of mountain snow cover: Geophysical Research Letters, 

v. 34, no. 12, 6 p. [Also available at http://dx.doi.

org/10.1029/2007GL030284.]

Painter, T.H., Deems, J.S., Belnap, J., Hamlet, A.F., Landry, 

C.C., and Udall, B., 2010, Response of Colorado River 

runoff to dust radiative forcing in snow: Proceedings 

of the National Academy of Sciences, v. 107, no. 40, p. 

17125–17130. [Also available at http://www.pnas.org/

content/107/40/17125.]

Painter, T.H., Skiles, S.M., Deems, J.S., Bryant, A.C., and 

Landry, C.C., 2012, Dust radiative forcing in snow of 

the Upper Colorado River Basin—1, A 6 year record of 

energy balance, radiation, and dust concentrations: Water 

Resources Research, v. 48, no. W07521, 14 p. [Also 

available at http://dx.doi.org/10.1029/2012WR011985.]

Skiles, S.M., Painter, T.H., Deems, J.S., Bryant, A.C., and 

Landry, C.C., 2012, Dust radiative forcing in snow of the 

Upper Colorado River Basin--2. Interannual variability 

in radiative forcing and snowmelt rates: Water Resources 

Research, v. 48, no. W07522, 11 p. [Also available at http://

dx.doi.org/10.1029/2012WR011986.]

Warren, P.L., Reichhardt, K.L., Mouat, D.A., Brown, B.T., and 

Johnson, R.R., 1982, Vegetation of Grand Canyon National 

Park—technical report no. 9: Tucson, Ariz., Cooperative 

National Park Resources Studies Unit, University of 

Arizona and National Park Service, 140 p. [Also available 

at http://sdrsnet.srnr.arizona.edu/data/techreports/

TECHRPT09.pdf.]

Warren, S.G., and Wiscombe, W.J., 1980, A model for the 

spectral albedo of snow—II, Snow containing atmospheric 

aerosols: Journal of the Atmospheric Sciences, v. 37, 

no. 12, p. 2734–2745. [Also available at http://dx.doi.

org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0

.CO;2.]
Looking west towards the San Francisco Peaks from near Leupp, Arizona. Photograph by Robert J. Hart, U.S. Geological 

Survey.
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter C

A Comparison of Historical and Future Rates of Climate 

Change in the Flagstaff Area of Northern Arizona

By Kenneth L. Cole1

Abstract

Climate observations from 

the Flagstaff region document 

a warming trend over the last 

40 years of 0.041 °F per year. 

A compilation of multiple 

General Circulation Models 

(GCMs) of the globe that 

were downscaled to focus 

on the southern Colorado 

Plateau projected a similar 

warming trend of 0.059 °F 

per year to occur during this 

same period. If this warming 

were to continue linearly 

at its recent observed rate, 

Flagstaff’s climate will be 

4.9 °F warmer by the 2090s. 

The GCMs applied here 

projected an accelerating rate 

that would result in a climate 

that is 7.2 °F warmer by the 

2090s. Annual precipitation in the Flagstaff area has declined 

about 11 percent over the last 20 years from the prior 

70-year period. This decline occurred during the cool-season 

period while the summer monsoon precipitation increased. 

These shifts in both total and seasonal precipitation mirror 

the results from suites of multiple GCMs applied to the 

same period. Future comparisons between observed trends 

and older GCM downscaled projections should become 

more valuable through time because they can illustrate the 

strengths and weaknesses of GCM modeling in projecting 

future climate trends. The strong agreement between 

observed climate trends and GCM projections of rising 

temperatures and decreasing precipitation for the Flagstaff 

area is a cause for concern about future ecological effects 

and water availability, although some ecological impacts 

could be mitigated by an increase in summer monsoon 

precipitation projected by a few GCMs.

Figure 1. Map showing the locations of climate stations near Flagstaff, Arizona, relative to 

major topographic features.

0

0

10

10 20

MILES 

KILOMETERS 

Hwy 89

I-40

I-17

I-40

6,000 feet

6,000 feet

8,000 feet

4,000 feet

Mogollon Rim

Arizona Divide

Arizona Divide

Fort Valley

Williams

Flagstaff Airport

N

Climate of the Flagstaff Area

The climate of northern Arizona is distinct from that 

of the more populated desert areas surrounding Phoenix 

and Tucson. Unlike the hot desert areas of Arizona, the 

high-elevation plateaus north of the Mogollon Rim (fig.1) 

support winter-cold forests that are mildly pleasant in summer 

(Staudenmaier and others, 2007). These high plateaus 

typically receive winter snowfall of 100 inches or more, and 

temperatures are rarely above 85 °F in the summer.

Analyzing Arizona’s climate using the traditional seasons 

found in Europe or eastern North America—winter, spring, 

summer, and fall—can be misleading. Northern Arizona has 

three discrete rainfall seasons and one extreme dry season, 

but they conform to a distinctive monthly sequence (Sellers 

and Hill, 1974; Sheppard and others, 2002; Steenburgh and 

others, 2013). Between late November and early April, Pacific 

cyclonic storm systems bring gentle rain across the area 

(fig. 2). This important period produces about two-thirds of 1

Northern Arizona University and 12 Pinyons Research Station
14 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

the annual precipitation. These cool-season rains are crucial 

for groundwater recharge because they add high-elevation 

snowpack and snowfall while evaporation rates are low. 

However, the earliest of the cool-season precipitation (between 

November and December) falls while upper elevation plants 

are dormant, and it is not important for most middle to upper 

elevation plant species (Cole and others, 2008) or the animals 

dependent upon those plant species (van Riper and others, 

2014). If these early season rains are not supplemented by 

later rains, the surface soils become desiccated by May when 

the upper elevation growing season finally begins.

These cyclonic storm events become less frequent by 

late March, and rarely occur after mid-April, leading to the 

second distinct season; the late-spring/early-summer dry 

months of May and June. This stressful drought period can 

have critical effects on plant and animal life (Breshears and 

others, 2005) because it occurs just when evapotranspiration 

is increasing owing to the increasing temperatures. By about 

mid-July, summer monsoon convective storms begin in 

the Flagstaff area (Liebmann and others, 2008) and moist, 

southerly air masses from the Gulf of California, the Gulf of 

Mexico, and sometimes the Pacific Ocean offshore of Baja 

California can lead to extreme monsoon high-precipitation 

events. This summer monsoon period extends from July 

through September, and by mid-September additional 

precipitation may occasionally be generated by tropical 

cyclonic storms (hurricanes, locally known as chubascos) off 

the west coast of Baja California. The moisture from these 

cyclonic storms can occasionally lead to high-precipitation 

events in this fourth season, typically between midSeptember and early November.

65

60

55

50

45

40

35

30

25

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0 Jan Feb Mar Apr May June

Month

Mean monthly temperature,

in degrees Fahrenheit

Mean monthly precipitation, in inches

July Aug Sept Oct Nov Dec

Precipitation

 1981–2010

 1961–1990 

Monthly temperature

 1981–2010

 1961–1990

EXPLANATION

Figure 2. Climograms showing the monthly climates of Flagstaff 

measured at Flagstaff Pulliam Airport during two 30-year periods. 

Mean monthly precipitation is shown with wider light blue bars 

(1981–2010) and narrower dark blue bars (1961–1990). Mean 

monthly temperatures are shown with a black line (1981–2010) and 

a dashed gray line (1961–1990).

Projections of Climate Change

The scientific basis for climate changes caused by 

the increase of atmospheric carbon dioxide and other 

greenhouse gases has been understood for well over 

a century, whereas the confirmation of these ongoing 

changes in atmospheric composition have been accurately 

measured for over 60 years. The effects of these changes 

on the Earth’s climate have been continually refined using 

computer-based General Circulation Models (GCMs) 

for about the past 40 years. But it was only within the 

last decade or two that the projections of these global 

models became detailed enough that their effects could be 

reasonably projected to specific regions.2

As regional GCMs climate projections increase in 

precision, they can be compared to station-based climate 

observations within individual regions. And, with an 

increasing number of years of recorded climate records 

since the onset of likely greenhouse-caused climate shifts 

(International Panel for Climate Change [IPCC], 2013), 

these comparisons increase every year in their statistical 

power. However, climate change is highly dynamic, and 

adjoining regions can be affected in opposite ways. So, 

changes in any one region, or even a subcontinent, will 

not prove or disprove that climate change is occurring, 

yet analyses of regional trends may be reaching the point 

of yielding predictive value for that region. This paper 

compares regional-scale GCM projections and ground-based 

measurements within the same region. This comparison 

is preliminary in nature, as GCMs are continually being 

improved, and observational records increase in detail 

every year. Yet this comparison for the Flagstaff area of 

northern Arizona shows surprising agreement for trends of 

temperature change, and it raises important concerns for 

trends in precipitation resources, which are crucial in this 

arid region.

A series of future climate projections completed prior 

to 2004 were compiled and published by Garfin and others 

(2010) using the Fourth Assessment Report (AR4) GCM 

data compilation with the A1B CO2

 scenario (IPCC, 2007). 

These projections had been downscaled from the extremely 

large spatial grids (~200 miles by ~200 miles) produced 

by GCMs, to landscape grids of about 1.2 by 1.2 miles 

using a procedure known as statistical downscaling.3 This 

2

The first IPCC climate assessment report detailed GCM results based upon 

grid sizes of more than 300 miles (~500 km, T21) (IPCC, 1990). By the fourth 

assessment, some models were included using grid sizes of only 68 miles 

(~110 km, T106) (IPCC, 2007).

3

The following is a brief synopsis of downscaling procedures, as fully 

described by Garfin and others (2010). The GCMs were aligned to a common 

grid—the 4-km grid used by PRISM (Daly and others, 1994)—using inversedistance weighting (Eischeid and others, 2000). Next, they were statistically 

downscaled (Salathé, 2005) using the 1950 to 1999 PRISM estimates from 

each grid cell to impose spatial structure to the GCM-simulated monthly precipitation and temperature while preserving the atmospheric processes driving 

the simulations. Bias between the large-scale simulated climate parameter and 
A Comparison of Historical and Future Rates of Climate Change in the Flagstaff Area of Northern Arizona 15

downscaling procedure projects the changes from the large 

GCM grid to specific regional landscapes. It can be thought 

of as an elevation lapse-rate correction at each local grid cell 

relative to the larger GCM grid (Salathé, 2005). Although the 

method does not incorporate a few of the effects of the local 

topography on the region’s climate such as rain shadows, 

the high correlation of temperature with elevation results in 

statistical downscaling that is very reliable for temperature 

variations in arid regions of high topographic diversity. In 

Arizona, temperatures are highly correlated with elevation 

(Smith, 1956) owing to the uniform physical properties of 

gases under varying pressures. Precipitation is also well 

correlated with elevation, but it can be a much more episodic 

and localized variable, and thus more difficult to model.

The data from Garfin and others (2010) can serve as a 

baseline prediction for comparison with subsequently monitored 

climates. An optimal test of scientific prediction versus 

subsequent results would require even earlier projections resulting 

in a longer subsequent test period. Unfortunately, these GCM 

results were the first such results with sufficient spatial detail to 

portray the southern Colorado Plateau as a specific region.

The GCM data applied in this paper from Garfin and 

others (2010) were a product of GCMs run between about 

2001 and 2003 that were available from the 2005 AR4 

compilation from the IPCC (2007). More recent and refined 

GCM data are now available through the Fifth Assessment 

Report (AR5) (IPCC, 2013), although the differences within 

the region are not great (Seager and others, 2012). The older 

data used in this report have the advantage of having been 

produced over a decade ago allowing them to be treated as 

forming testable hypotheses of future climate change. Also, 

there remains skepticism that they may have been adjusted 

to produce a reasonable output,4

 although actual 20th or 

21st century climate changes have not been entered into the 

models (IPCC, 2007, 2013). But, older regional GCM output 

could serve as a forceful scientific tool for testing their 

reliability since they could not possibly have been adjusted 

after their results were published. 

the observed climate parameter at each grid cell was corrected by month. As 

a result, at each grid cell the average magnitude of the GCM historic period 

simulations conformed to the average 1950–1999 observation from that gridcell, as extrapolated from the PRISM dataset.

Spatial biases and magnitudes were corrected independently at each grid 

point for each model by multiplying the simulated parameters by a monthly 

bias factor (for precipitation) or by calculating the difference between the 

simulation and the bias factor (for temperature), as described with equations 

adapted from Salathé (2005). This additive method may be thought of as a 

lapse-rate correction owing to the elevation difference of the local gridpoint 

relative to the GCM grid (Salathé, 2005). No allowance is made for possible 

changes in the lapse rate as a consequence of climate change.

4

The retired physicist, Freeman Dyson, is widely quoted as having stated 

in an interview “They [the global climate models] are full of fudge factors 

that are fitted to the existing climate, so the models more or less agree with 

the observed data” (Solomon, 2007). However, there are detailed published 

analyses that demonstrate their reliability including Risbey and others (2014) 

and Raisanen (2007).

These types of comparisons will become even more 

statistically reliable in the future as the subsequently 

measured climates increase in duration. In this way, a past 

synthesis of GCM data could become scientifically as 

valuable as newer refined GCM projections. They represent 

the most extreme test of science, prediction of future 

experimental results. Unfortunately this experiment is being 

run on our only planet.

The GCMs used here projected a warming trend on 

the southern Colorado Plateau starting about 1975 and 

extending into the future. The vast majority of both the 

models used in this paper (Seager and others, 2007) and 

the next generation of models (Seager and others, 2012) 

project a trend of decreasing annual precipitation in the arid 

southwest throughout the 21st century, although individual 

models differ greatly in their projections of the monsoon 

precipitation (see Comparing Precipitation Trends section). 

These combined trends of higher temperatures, coupled with 

decreasing rainfall, can be highly detrimental to the flora and 

fauna (Allen and others, 2010).

This paper is focused on the following questions: 

(1) Is this warming trend projected from the GCMs evident 

in the recent recordings from regional climate stations? and 

(2) If so, how do the recent rates of change compare to those 

projected into the future? Most analyses of this type apply 

large complex datasets from many climate stations across 

multi-state regions. However, in this study three long-term 

stations in the homogeneous climate area close to Flagstaff 

are analyzed, minimizing the irregularities that often can be 

overlooked when compiling large datasets.5

Historical Climate Records from the 

Greater Flagstaff Area 

Historical records contain the most accurate climate 

data in both the precision of measurements and the size of 

their represented area. Here, a review of the recent trends 

in northern Arizona’s local climate data is provided prior 

to a comparison with climate model projections, which are 

inherently more general in scope. 

I averaged the records from three stations in a local 

area because of the variability inherent even in adjacent 

climate stations for monsoon precipitation (table 2). Three 

5

Large data compilations require uniform data across hundreds of individual 

climate stations even though close examination might reveal instances where 

some individual station data are inappropriate. For example, Garfin and others 

(2013), in order to apply uniformity to a vastly larger data set encompassing 

six States, examined rates of climate warming between the years 1901 and 

2000. However, prior to 1909 the most reliable station in the Flagstaff area, 

Williams (used by Garfin and others to represent the area), contained more 

months without data than recorded months. Additionally, until 1940 Williams 

recorded anomalously low temperatures compared to the other two stations 

analyzed here (fig. 2). This paper takes an intermediate approach, closely 

examining each individual station record, yet averaging the data from three 

closely spaced stations to reduce variability.
16 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

long-term climate stations in the Flagstaff area—Williams, 

Flagstaff Pulliam Airport, and the Fort Valley Experimental 

Station6

—are all located within 30 miles (50 km) of each 

other, and at relatively similar elevations when compared to 

other stations in the region. These three stations have longer 

records than many others in the arid southwest and are not 

adjacent to intense urban development that could potentially 

generate urban heat. The three stations differ somewhat 

in climate primarily because of their minor differences in 

elevation, ranging from 6,750 feet (2,057 m) at Williams, 

to 7,010 feet (2,137 m) at Flagstaff, and to 7,360 feet 

(2,243 m) at Fort Valley.

Although some measurements at these stations began as 

early as 1894 (Flagstaff), 1897 (Williams), and 1909 (Fort 

Valley), these early instruments were moved frequently 

and had many missing months until about 1924, when all 

three records became fairly continuous.6,7 Several other 

climate stations in northern Arizona were not used in this 

analysis owing to their shorter records and the effects of 

complex topography on precipitation. For example, climate 

stations at Sunset Crater (recorded since 1969 at 6,980 feet 

elevation) and Wupatki National Monument (recorded since 

1940 at 4,900 feet elevation) detail significant records. But 

these are farther from the other stations, have much shorter 

records, and are located northeast of the central Arizona 

Divide and San Francisco Peaks. These stations, and other 

stations farther to the north (for example, Cameron and Lees 

Ferry), record much lower cool-season precipitation values 

than stations to their west likely owing to their position in 

the rain shadow of the higher elevations of north-central 

Arizona.

The data analyzed here were obtained from the Western 

Regional Climate Center (2013), and the National Oceanic 

and Atmospheric Administration (2014). Summaries of 

climate records for the Flagstaff station can be found in 

Staudenmaier and others (2007). 

6

Station metadata

Station Coop 

number

Elevation, 

(feet)

Years with at least 10 months 

temperature data

Williams 029359-2 6,750 1987, 1904, 1905, 1912, 1916–

1946, 1948–1957, 1959–2014

Flagstaff 023010-2 7,010 1894–1895, 1897, 1924–1940, 

1950–2014

Fort Valley 023160-2 7,360 1909–1993, 1994–2000, 2004–

2013, 2014 (temperature only)

7

Some missing monthly temperature values from one of the three stations 

still had to be estimated using Period of Record correlations from the other 

two stations for 119 months (out of 3,240 possible) since 1924, including 6 

missing years from Flagstaff data during the 1940s. All monthly index values 

contained observed data from at least two of the three stations.

Temperature Trends 

Climate changes can be studied statistically in different 

ways, usually as continuous trends through time, or as step 

changes between different periods of multiple years. With 

either approach, there can be disagreements over the selection 

of the specific beginning and end dates for each period. For 

example, because 1998 was the warmest year ever recorded 

up to that point globally, selecting a period starting in 1998 

generated a trend that showed little subsequent warming 

(Silver, 2013). This began a period that has been referred 

to as the climate warming “hiatus” (Kaufmann and others, 

2011; Karl and others, 2015). However, three ensuing years 

(2005, 2010, and 2014) have subsequently been recorded as 

even warmer than 1998 (National Oceanic and Atmospheric 

Administration, 2015). Statistically, the year-to-year variability 

in measurements is far too great to infer significance from a 

single year or even a few years. For this reason, climatologists 

typically represent climates as averages of 30 years, as shown 

in figure 1. But if climate has been more rapidly changing 

over just the last 10 or 20 years, as inferred by GCMs and the 

atmospheric concentrations of carbon dioxide, then this 30-year 

period would not fairly represent trends that might be apparent 

on a shorter time period. As a result, this study illustrates 

trends in 10-year (decadal) averages, but will only statistically 

compare different periods of 20 years or more.8

The average temperatures recorded each year from three 

stations in the local Flagstaff area from 1924 through 2013 are 

shown on figure 2. The considerable differences between the 

stations are due to their different elevations and other landscape 

factors. Over the entire period, temperatures in Williams 

averaged 3.25 °F warmer than Flagstaff, and temperatures at 

Fort Valley averaged 2.97 °F cooler. Despite these differences, 

all three stations show similar decadal trends to the regional 

decadal trend throughout the period.9 The trends are especially 

similar since 1950 as the current station locations were fixed 

and the readings became more consistent and reliable. The 

Flagstaff station shows values very similar to the regional trend 

as it is located at mid-elevation between the other two stations.

Determining rates of any temperature changes 

through time requires critical decisions on the length of 

the time period studied. Because of the high year-to-year 

variability (black points on figure 3), accuracy is lost when 

analyzing any period of less than 20 years. But, applying 

too long a time period, such as the last century, does not 

8

The high year-to-year variability of climate (figs. 2, 3) causes parametric 

statistical tests comparing groups of values fewer than 20 to be far less effective. Thus, for annual values, 20 years is a practical compromise between the 

30 years typically used by climatologists and shorter periods with less statistical power. Moving averages of 10 years are shown on figures 3, 4, and 5 for 

illustrative purposes but are not analyzed statistically.

9

This illustrates the general feature that regional temperatures are a much 

more robust variable when compared to sporadic local rainfall records. A cold 

day in Flagstaff is often a cold day in Phoenix. But rainfall records are far less 

predictable. Rainfall in Flagstaff may or may not indicate rainfall in Phoenix, 

or even as nearby as Williams.
A Comparison of Historical and Future Rates of Climate Change in the Flagstaff Area of Northern Arizona 17

account for the escalation in atmospheric CO2 starting around 

the years 1960 to 1970 (fig. 5; IPCC, 2007). Atmospheric 

CO2 only increased from about 297 parts per million (ppm) 

to 326 ppm in the 70 years between 1900 and 1970 (about 

0.1percent per year). In contrast, it increased nearly five 

times as fast from 326 ppm to 398 ppm between 1970 and the 

end of 2013 (about 0.5 percent per year), making this time 

period the most important for study of any relation between 

atmospheric CO2 levels and climate. Examining periods 

of several different lengths is preferable to selecting any 

particular period during this interval.

Table 1 shows the mean annual temperatures for the 

first 70 years of records (1924–1993) and the most recent 

20 years (1995–2014). Although the difference between the 

periods is only +1.5 °F, it is a statistically significant shift. 

The rate of this temperature increase can be extrapolated by 

fitting linear regressions through the last 20, 30, or 40 years 

producing warming rates of 0.021 °F/year (r2=0.01, ρ=0.79), 

0.044 °F/year (r2=0.13, ρ=0.48), and 0.041 °F/ year (r2=0.18, 

ρ=0.01). The similarity of these longer rates suggests that this 

increase has been a fairly steady rate of increase, rather than 

a step-change within any part of the period. The lower rate 

calculated over the last 20 years could suggest a reduction 

in the rate of warming (Risbey and others, 2014; Raisanen, 

2007), although 20 years does not appear to be a sufficient 

statistical sample for a measure of such high variability as 

shown on figure 3. Note that the warmest 2 years for this 

index for the Flagstaff area were 2003 and 2014. Analysis 

of this average temperature-warming trend suggests that it is 

primarily driven by higher nighttime temperatures, especially 

from October through May (Hereford, 2007).

52

50

48

46

44

42

1930 1940 1950 1960 1970 1980

Year

Mean annual temperature,

in degrees Fahrenheit

1990 2000 2010

 Area decadal trend

 Area average by year

Decadal trend

 Williams 

 Flagstaff 

 Fort Valley

EXPLANATION

Figure 3. Plot showing the decadal 

moving averages for mean annual 

temperatures from 1924 to 2014. Black 

line is regional temperature index 

(an average of all three stations) and 

average yearly values are shown 

as black dots. Decadal trends from 

individual stations are Williams (red), 

Flagstaff (green), and Fort Valley (blue).

Table 1. Mean annual temperatures measured during selected 

periods.

[ft, feet; °F, degrees Fahrenheit]

Time

period

Climate station mean annual 

temperature (°F) Regional index1

mean annual 

temperature (°F) Williams 

(6,750 ft)

Flagstaff 

(7,360 ft)

Fort Valley 

(2,137 ft)

1924–1993 49.2±1.6 45.9±1.3 42.9±1.7 2

46.0±1.0

1995–2014 50.8±1.0 46.7±1.1 44.8±2.0 2

47.5±1.1

1

Average of all three stations.

2

Student’s t-test shows the 1924–1993 and 1995–2014 averages are statistically different (P=<0.001).

Precipitation Trends 

The average precipitation amounts recorded for the 

Flagstaff region from 1924 through 2014 are shown in 

figure 4 and table 2. Although all three stations together 

averaged 20.8 inches for the period, averages for individual 

years were as high as 37.9 inches (1965) and as low as 

11.7 inches (1956), demonstrating the high variability in 

annual rainfall even when averaged over the entire year 

and multiple stations. Precipitation was even more variable 

among stations, especially for late summer (July through 

September) monsoon precipitation. Monsoon precipitation 

has been particularly high in recent years, including 2014 

(third highest) and 2013 (seventh highest). Average monsoon 

precipitation at all three stations did not show a significant 

difference between the first 70 years (1924–1993) and the 

most recent 20 years (1995–2014), but the variability was 

too high to reject the conclusion that a change has occurred 

(fig. 4; table 2). Individual stations record the high spatial 

variability of this rainfall, even when averaged across 

3-month periods. For example, in the 2009 monsoon period, 

Williams received over twice the total precipitation of 

Flagstaff, whereas in 2013, Flagstaff received over twice the 

amount measured at Fort Valley (table 2).

The annual precipitation averaged at all three stations 

was less for the most recent 20 years (1995–2014), than for 

the first 70 years (1924–1993) (table 2; P=0.059; significant 

to the 94.1 percent level versus the 95.0 percent level that 

is usually considered to be statistically significant). This 

step-like change in annual amount of 2.4 inches (11 percent), 

as shown by the decadal moving average (fig. 4), seemed 

to start in the mid-1990s. Most of this change appears to 

have been caused by a decrease in the cool-season cyclonic 

storms that characterize much of the rainfall between late 

November and April. At least half of this decrease is evident 

in the statistically significant decrease in March through June 

precipitation (fig. 4; table 2). March through June is shown 

here rather than just the most extreme late-spring/earlysummer drought months of May and June because these two 

months have always had negligible precipitation. A further 

reduction in rainfall from something that was very little 
18 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

is probably not as relevant as the fact that this dry season 

period now seems to be lengthening by starting earlier in 

March and April. For ecosystems dependent on stored soil 

moisture, abundant precipitation in March and April can 

offset a dry May and June, or even a dry November through 

February. Available soil moisture at the start of the growing 

season is of crucial importance (Breshears and others, 

2005); the specific month supplying that moisture is of less 

importance.

A warm and dry 4-month period can increase plant 

mortality as well as fire severity (McDowell and others, 

2009; Westerling and Swetnam, 2003). The combination 

of decreasing March through June precipitation and higher 

temperatures is most crucial for sustaining ecosystems 

(Breshears and others, 2005). These higher temperatures 

increase soil evaporation and plant evapotranspiration further 

compounding the decreasing moisture and increasing the soil 

moisture deficit.

35

30

25

20

15

10

5

0 1930 1940 1950 1960 1970 1980

Year

Mean annual precipitation, in inches

1990 2000 2010

Annual precipitation

Decadal trend of annual total

Monsoon trend (July–Sept)

Late spring trend (Mar–June)

EXPLANATION

Figure 4. Plot showing the decadal moving mean of annual 

precipitation for 1925–2014. Black line is regional precipitation 

index (an average of Williams, Flagstaff, and Fort Valley stations). 

Average yearly values are black dots. Most annual precipitation 

(58 percent) falls from October through April. But important 

seasonal trends are the summer monsoon (July through 

September, green line), and the late-spring/early-summer (March 

through June, red line).

Table 2. Annual and seasonal precipitation measured during 

selected periods.

[ft, feet]

Time period

Climate station precipitation (inches) Regional 

index1

precipitation

(inches)

Williams 

(6,750 ft)

Flagstaff 

(7,360 ft)

Fort Valley 

(2,137 ft)

Mean annual total

1924–1993 20.9±5.9 20.9±5.9 22.4±5.0 2

21.4±5.0

1995–2014 19.7±5.4 18.6±4.9 19.0±4.4 2

19.1±4.1

Mean March through June total

1924–1993 4.4±2.2 4.6±2.1 5.1±2.1 3

4.7±2.0

1995–2014 3.2±2.1 3.0±1.8 3.1±1.9 3

3.1±1.7

Mean monsoon total (July through September)

1924–1993 7.6±2.9 7.2±2.7 8.2±2.7 7.7±2.3

1995–2014 8.4±3.0 8.0±3.2 7.8±3.3 4

8.2±2.6

Monsoon total (July through September)

2009 5.4 2.5 3.9 3.9

2013 11.3 15.7 7.1 11.4

1

Average of all three stations.

2

Student’s t-test shows these totals are very close to statistically different 

(P=0.059).

3

Mann-Whitney rank-sum test shows these totals are statistically different 

(P=0.005), but the data are too variable to apply the t-test.

4

Student’s t-test shows these totals are not statistically different (P=0.63), 

but low power indicates it is less likely to detect a difference when one exists.

Climate Model Projections for 1924 to 

2099

The GCM data applied in this paper are just a small 

part of the results described in Garfin and others (2010). 

They projected monthly temperatures and precipitation for 

the continental United States west of Kansas (west of long 

110° W.) to every 1.2 by 1.2 mile grid point (about 800,000 

grid squares) for every year between 1900 and 2099 (2,400 

sequential monthly values). Furthermore, they compared 

results from 5 individual simulations, each produced by 

different GCMs, and 1 data compilation (or ensemble) that 

averaged 48 simulations from 22 different GCMs.

All five of the GCMs compared in the Garfin and others 

(2010) study produced similar future projections of mean 

annual temperature, although each model varied around a 

central trend similar to annual variations found in an actual 

climate record. Earth’s climate varies from year to year 

because of random factors and complex influences from 

prior years. The ensemble data, averaging the results from 

48 simulations from 22 different GCMs, is more useful for 
A Comparison of Historical and Future Rates of Climate Change in the Flagstaff Area of Northern Arizona 19

comparison with observed temperature trends 

because individual GCM year-to-year variability 

has been removed by combining multiple runs 

of multiple models. Because every year in this 

ensemble dataset is essentially an average of 48 

different modeled years, it doesn’t show the yearto-year variation that characterizes either the results 

of years portrayed by a single model, or years of 

real climate. These annual averages were further 

averaged into a weighted average of multiple years 

to emphasize the underlying trends. As a result, the 

past and future projected temperature trends shown 

in figure 5 are generalized trends, not unlike the 

Earth’s climate would produce if somehow decades 

of history could be re-run 48 times.

Although these GCM averages are the product 

of non-linear models producing a different value 

every year, linear regressions can still be fit to their 

yearly projections. This results in rates of increase of 

0.059 °F/year for the most recent 40 years, 0.070 °F/

year through 2050, and 0.070 °F/year through 2099.

Comparing Warming Rates

Over the last 20 years (1995 through 2014), 

temperatures in the Flagstaff area averaged 1.7 °F 

higher than the 1924–1973 50-year baseline 

(table 3). The GCMs modeled the same amount of 

increase during this period. 

Future temperatures in table 3 are estimated 

using a linear continuation of recent trends as well 

as using GCM projections. The linear extrapolation 

does not incorporate the highly complex nonlinear 

atmospheric and oceanic processes or environmental 

feedbacks that are incorporated into the GCM 

700

650

600

550

500

450

400

350

300

53

52

51

50

49

48

47

46

45

1940 1960 1980 2000 2020 2100

Year

Atmospheric CO2, in parts per million

Mean annual temperature, in degrees Fahrenheit

2040 2060 2080

Recorded Flagstaff temperature

GCM temperature projection

A1b CO2 scenario

Recorded atmospheric CO2

EXPLANATION

Figure 5. Plot of measured and modeled mean annual temperature 

and CO2. The regional index of historical measured temperatures (black 

line) is contrasted with the GCM model projections (brown dashed 

line) for the southern Colorado Plateau for the time period 1924–2099 

(modified from Garfin, 2007). Both trends are 10 year running averages. 

Measured atmospheric CO2

 in parts per million (red dotted line) from 

the Scripps CO2

 Program (2013) is contrasted with the A1B CO2 scenario 

(black dots and cyan dashed line) (IPCC, 2007).

Table 3. Historical mean annual temperatures of the Flagstaff area and estimated future temperatures, comparing GCM projections 

and linear continuation of recent trends.

[°F, degrees Fahrenheit]

Technique

Mean annual temperature (°F) Estimated mean annual 

temperature (°F)

1924–1973 1924–1993 1995–2014 40 years in 

future (2050s)

80 years in 

future (2090s)

Average projection from 22 GCMs 45.8 1

Set at 46.0 47.5 51.1 53.1

Warming above 1924–1973 baseline 0 0.5 1.7 5.1 7.3

Historical values and linear continuation of recent trends 45.8±1.0 46.0±1.0 47.5±1.1 49.1 50.8

Warming above 1924–1973 baseline 0 0.2 1.7 3.3 5.0

1

The southern Colorado Plateau GCM trend-line is quantified as a relative change across the southern Colorado Plateau. This modeled trend can be extrapolated to any specific location as a change relative to the 1924–1993 mean measured at that location, which was 46.0 °F in the Flagstaff Area.
20 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

estimates. The simplistic nature of the linear extrapolation 

likely underestimates warming beyond the next 30 years. 

Instead, the linear trend data can be considered as a 

simplistic, best case scenario for the time periods within a 

shorter planning horizon. It does not account for the effects of 

unknown future political or technological changes, or model 

poorly understood, highly complex atmospheric and oceanic 

processes or environmental feedbacks. The higher GCM 

results also shown in table 3 are more likely to occur since 

they take into account the ever-increasing concentrations of 

greenhouse gasses in the atmosphere and those feedbacks that 

can be well quantified. But the complexity of the models can 

increase skepticism by nonscientists suspicious of climate 

change science (Lahsen, 2013). In fact, GCMs have proven to 

be excellent predictors of the temperatures yet to occur when 

the models were produced (Raisanen, 2007). 

Extrapolating the 0.041 °F per year rate of temperature 

increase measured over the past 40 years to the next 40 

and 80 years projects temperatures much higher than 

the most recent two decades, the warmest in Flagstaff’s 

recorded history. Applying this low, best case scenario rate 

of temperature increase projects increases of 3.3 °F by the 

2050s and 5.0 °F by the 2090s. These temperature increases 

are well above the historic average (table 3, fig. 3) of what 

had been the more stable climate prior to 1974.10 Because 

the GCM average projects an accelerating warming trend 

through the next 40 years, its estimated temperatures for 

later in this century are even higher, producing temperatures 

5.1 °F warmer by the 2050s and 7.3 °F warmer by the 2090s 

(table 3).

There are probably not sufficient data points yet in this 

observed warming trend to permit the addition of meaningful 

parametric confidence limits to the projections. But the 

downscaled climate projections made possible through this 

type of modeling could become a subject for the future 

application of Bayesian inference (Min and others, 2007). 

Because we now have predictions of climate changes that 

would occur in specific regions, Bayesian probabilities 

could be updated as additional climate observations become 

available (Silver, 2013).

The measured temperature warming trends of the past 

40 years are very similar to those projected for this period 

by the GCMs (fig. 5), although the measured temperatures 

display greater year-to-year variability than the GCM 

ensemble that averages 48 simulations. The predictions of 

the A1B CO2

 scenario used in these downscaled GCMs has 

corresponded very well to ongoing measurements (fig. 5). 

The atmospheric CO2 concentration for the end of 2013, 

using the A1B CO2 scenario of 403 ppm, was very close to 

the observed 398 ppm (Scripps CO2 Program, 2015).

10Between 1924 and 1973 the standard deviation of mean annual temperatures was 0.99 °F within a total range of 4.4 °F, while between 1974 and 

2014 the standard deviation had increased to 1.11 °F within a range of 5.2 °F.

Comparing Precipitation Trends 

The measured annual precipitation during the most recent 

20 years (1995–2014) was 11 percent less (2.3 inches) than the 

1924—1993 baseline (table 2). This decrease in precipitation 

occurred over all seasons except the summer monsoon season. 

The monsoon season precipitation has been increasing, 

although it has been highly variable from year to year. The 48 

runs of 22 GCMs also suggested a roughly 12 percent decline 

in mean annual precipitation from the 20th century average 

by the year 2013 (not shown). Although this multimodel mean 

correlates well with the actual recorded trends, the individual 

GCM models were extremely variable, especially for the 

seasonal amounts of precipitation.

An analysis of each GCM’s hindcast of 20th century 

seasonal precipitation suggested that many models lacked 

sufficient spatial resolution to account for the southwestern 

monsoon process. Two models, the UKMO-HADGEM1 and 

the MPI-ECHAM5 GCMs, performed the best (of 22 GCMs) 

on portraying 20th century seasonal relative precipitation 

percentages, accurately reproducing the seasonal precipitation 

swings that typify the southwestern states (Garfin and others, 

2010). Yet these two models depicted opposite futures for 

monsoon precipitation totals. The UKMO-HADGEM1 

suggested a future strengthening of the monsoon especially 

toward the northwest, into Nevada. In contrast, the MPIECHAM5 suggested a weakening of the monsoon and its 

withdrawal to the southeast. The most recent 2 years (2013 

and 2014) were excellent examples of the type of monsoon 

strengthening that was projected by the UKMO-HADGEM1 

model. This model’s projections are an outlier, given that the 

majority of models did not project any monsoon increase. But 

the fact that it outscored the other 21 models in hindcasting 20th 

century seasonal precipitation percentages (Garfin and others, 

2010) should invite closer examination.

Because of the arid environment of the southwest, the 

ongoing and predicted future decline in precipitation depicted 

by the GCMs is the subject of intense concern and multiple 

studies. The results, thus far, from both the AR4 (IPCC, 

2007) and AR5 (IPCC, 2013) GCM compilations suggest that 

the ongoing decrease in annual precipitation may continue 

to worsen (Seager and others, 2007; Seager and others, 

2012). Drought conditions will be greatly exacerbated by 

higher temperatures during dry periods, and the cool-season 

precipitation could be lowered further by the disappearance 

of arctic ice cover (Sewall and Sloan, 2004). Although the 

total amount of precipitation from the summer monsoon is not 

expected to decline, the arid late-spring drought will continue 

to worsen through a combination of less precipitation and more 

severe evapotranspiration (Cook and Seager, 2013).

Many forest species, such as ponderosa pine (Pinus 

ponderosa) and piñon pine (Pinus edulis), are stressed by higher 

temperatures during dry periods such as the arid late spring 

(Allen and others, 2010) and are highly dependent upon the 

relief from the subsequent summer monsoon. Yet, because the 

majority of the GCMs incorporated into the AR5 model set do 
A Comparison of Historical and Future Rates of Climate Change in the Flagstaff Area of Northern Arizona 21

not project any future increase in summer monsoon (except 

for the UKMO-HADGEM1 model), these plant species are 

modeled to disappear from the Colorado Plateau within this 

century (McDowell, 2014). However, if the future climate 

projections from the UKMO-HADGEM1 model are applied 

instead of the averages from the AR5 dataset, these pine species 

suffer no mortality (K. Ironside and K.L. Cole, Northern 

Arizona University, unpub. data, 2011). The agreement between 

the GCMs and measured recent trends in both decreasing latespring precipitation and increasing late-spring temperatures 

suggest that the persistence of the forests of the Flagstaff area 

may be dependent on a future increase in precipitation from 

summer monsoons. Accordingly, results showing strengthening 

monsoons such as those from the UKMO-HADGEM1 model 

are encouraging for the Flagstaff region. A strengthening 

monsoon may be the most crucial aspect of our changing 

climate that will be revealed in the coming years.

Summary 

The similarity between temperature and precipitation 

trends measured during the past 40 years in the Flagstaff 

area and trends projected by multiple GCMs may be a cause 

for concern with regard to the region’s natural resources, 

water supply, and some economic resources such as winter 

industries. Measured ongoing trends of climate warming thus 

far are nearly identical to those from GCMs and the trends 

of precipitation are very similar, although changes in the 

monsoon period remain uncertain. The ensuing years will 

likely reveal even more about the magnitude of these trends in 

warming and changes in precipitation.

Acknowledgments

The development of the downscaled GCMs applied in this 

paper was supported by funding from the U.S. Department of 

Energy National Institute for Climatic Change Research project 

MPC35TV-02 and the U.S. Geological Survey National Climate 

Change and Wildlife Science Center. Helpful comments were 

received from two anonymous reviewers and the editor.

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Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter D

Ecological Inventory and Assessment of Springs 

Ecosystems in Kaibab National Forest, Northern Arizona

By Jeri D. Ledbetter,1 Lawrence E. Stevens,1 Marguerite Hendrie,1 and Arie Leonard2

Abstract 

Springs are ecosystems in which groundwater emerges at, 

and generally flows from the Earth’s surface. Although poorly 

mapped, studied, and managed, springs function as hotspots 

of biodiversity that support a wide array of rare and endemic 

taxa, and commonly serve as keystone ecosystems. To assist 

Kaibab National Forest with improved stewardship and greater 

understanding of springs ecosystem integrity, we conducted 

inventories on the ecohydrology of forest springs from 2010 

to 2012. We compiled data for 205 springs from several public 

sources and data contributed by collaborating researchers 

and conservation organizations. We conducted surveys of 

52 springs and measured site characteristics, flow, water 

quality, biota, habitat, and human influences on a total area of 

6.01hectares (ha) of springs-influenced habitat. 

The Kaibab National Forest region is estimated to support 

1,325 plant taxa across 647,500 ha (0.0034 species per hectare). 

We detected 441 plant species on our inventories, for a density of 

72.5 plant species per hectare at springs. Although species-area 

constraints influenced these results, we detected one-third of the 

regional flora in <0.001 percent of the landscape, representing 

a remarkable concentration of plant species richness at springs. 

Here, we present the results of a 2-year ecological inventory and 

characterization of the springs ecosystems in Kaibab National 

Forest that will provide a basis for prioritizing springs protection 

and restoration and for detecting ecologic changes over time.

Introduction

Springs are ecosystems in which groundwater emerges 

at, and generally flows from the Earth’s surface (Springer and 

Stevens, 2008; Stevens and Meretsky, 2008). Although springs 

have generally been poorly mapped, studied, and managed, 

they are far more abundant than is usually recognized. Springs 

are hotspots of biodiversity, supporting a wide array of rare and 

endemic taxa, and commonly function as keystone ecosystems—

small habitats that play disproportionally large roles in the 

1

Springs Stewardship Institute, Museum of Northern Arizona

2

Kaibab National Forest

ecology of the surrounding landscape (Perla and Stevens, 2008). 

Springs are widely regarded by indigenous cultures as important 

sites for religious, educational, and resource uses. Springs also 

are commonly used and managed for domestic and livestock 

water supplies. Despite their biological, cultural, and economic 

importance, springs are commonly degraded by human activities.

Knowledge of the distribution, ecosystem ecology, and 

ecological integrity of the springs in North America (Ceroici 

and Prasad, 1977; Williams and Danks 1991; Stevens and 

Meretsky, 2008; Hallam, 2010) is increasing, and the springs 

in northern Arizona and southern Utah are beginning to receive 

scientific attention (Grand Canyon Wildlands Council, 2002; 

Flora, 2004). The Springs Stewardship Institute (SSI)’s mapping 

of Arizona springs provides one of the few comprehensive statelevel mapping efforts in the Western United States (Ledbetter 

and others, 2012; see also Howard and Merrifield, 2010). 

Although the flow and quality of water have been monitored 

at several individual springs in northern Arizona, most springs 

in the region have not been studied, and many have not been 

mapped. Although scientific studies have begun to contribute to 

a wider understanding of the distribution and ecologic integrity 

of northern Arizona springs, we still have much to learn. 

Such efforts are timely, given society’s increasing demand for 

water and other springs-related resources, and the declines in 

some springs-specialist taxa, such as the northern leopard frog 

(Ranidae: Lithobates pipiens).

Methods

Study Area

Here, we present the results of a 2-year ecohydrologic 

inventory and characterization of the springs in Kaibab National 

Forest, Ariz. (fig. 1). The forest, which occupies an area of 

6,475 square kilometers(km2) in central northern Arizona, is 

topographically diverse, with elevations ranging from 900 meters 

(m) in Kanab Canyon to 2,800 m near the North Rim of the 

Grand Canyon. The forest comprises three ranger districts: the 

North Kaibab Ranger District, north of the Grand Canyon (area, 

2,652 km2

); the Tusayan Ranger District, south of the Grand 

Canyon (area, 1,341 km2); and the Williams Ranger District, 
26 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

west of Flagstaff, Ariz. (area, 2,482 km2

). The North Kaibab 

Ranger District lies across the East Kaibab monocline, a northtrending dome that rises 1,000 m above the Coconino platform, 

bounded by Marble Canyon to the east and the Kanab Creek 

drainage to the west. The Tusayan Ranger District occupies 

the tablelands of the Coconino platform immediately south of 

the Grand Canyon, and the Williams Ranger District includes 

the volcanic hills and mountains near the southern edge of the 

Colorado Plateau in the vicinity of Williams, Ariz. (fig. 1). 

The climate of Kaibab National Forest is arid and 

continental, with cold high-elevation winters, hot lowelevation desert summers, and a short growing season (Sellers 

and others, 1985). Precipitation averages ~550millimeters 

per year (mm/yr) and is distributed bimodally, with summer 

monsoonal rains and winter snowfall and rain, which is the 

primary contributor to groundwater recharge. The forest 

includes floristic elements of the Cordilleran, Intermountain, 

Mohavean/Sonoran, and Madrean biomes, with a 

Springs 

Inventory status

EXPLANATION

Surveyed

Location verified

Reported: not verified

Reported; not found

Interstate highway

Major road

Forest area

0 5 10 20KILOMETERS

0 5 10 20

0 5 10 20KILOMETERS

0 5 10 20 MILES

MILES

ARIZONA

A

B

Figure 1. Map of northern 

Arizona, showing two 

study areas for springs 

inventory in North Kaibab 

(A) and Williams (B) Ranger 

Districts.
Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National Forest, Northern Arizona 27

low-elevation desert habitat occupied by Madrean, Sonoran, 

and Mohavean Desert shrub vegetation, middle-elevation 

habitats dominated by intermountain Great Basin grasslands 

and piñon-juniper woodlands, and upper elevation plateaus 

occupied by cordilleran Rocky Mountain ponderosa pine 

(Pinus ponderosa) and mixed conifer forests, with large grassor sagebrush- (Artemesia spp.) dominated meadows.

Data Compilation

Before conducting our fieldwork, we completed 

a level1 springs inventory of Kaibab National Forest, 

according to the protocols of Stevens and others (2011). Our 

inventory involved compiling data from available sources to 

locate previously reported springs. Datasets from the Arizona 

State Land Office (1993, 2008) provided the bulk of location 

information. We also visually scanned each 7.5-minute U.S. 

Geological Survey (USGS) digital-raster-graphic (DRG) 

topographic-quadrangle map to locate additional springs, and 

reviewed applicable environmental impact statements (U.S. 

Forest Service, 1986; Bureau of Land Management, 2011). 

We imported data from several previous springs inventories, 

including those by Brown and Moran (1979), the Grand 

Canyon Wildlands Council, (2002, 2005), and the Grand 

Canyon Trust (unpub. data, 2011), as well as information 

provided by the U.S. Forest Service (FS). We compiled these 

data into the SSI Springs Inventory Database (Ledbetter and 

others, 2012).

Field Methods

Our field inventory used the level 2 springs inventory 

protocols of Stevens and others (2011). These rapid survey 

and inventory methods include a comprehensive suite of 

field measurements that describe physical characteristics of 

the ecosystem. A team of three to five researchers visited 

each site for 1–3 hours, measuring and (or) assessing site 

characteristics, flow, geochemistry, geomorphology, habitat 

structure, vegetation composition, aquatic and wetland fauna, 

human influences, and resource management context. 

Upon arriving at a springs ecosystem, we noted any avian 

and wildlife presence and then prepared a field sketchmap 

of the site, identifying the location, orientation, size, slope 

angle, and soil characteristics of each springs-supported 

geomorphologically defined microhabitat, as well as the 

locations of photographs and the places where we measured 

water quality, solar radiation, and flow. 

We measured water flow downstream from the source at a 

point that was judged to represent the total discharge from the 

springs. Flow was typically measured using timed volumetric 

capture or a weir plate. We measured field water-quality 

variables as close to the source as possible, recording pH, 

water and air temperatures, specific conductance, dissolved 

oxygen concentration, and alkalinity, where possible. For 

some springs, we also collected filtered water samples for 

laboratory analyses of stable isotopes (18O and 2

H), nutrients, 

and major cations and anions. 

We visually estimated the percentage cover of surficial 

grains or soils using a modified sediment grain-size scale 

ranging from 1 (clay) to 8 (bedrock), or organic soils/peat. We 

also visually estimated the percentage cover of each vascular 

plant species growing on or hanging over each microhabitat, 

using the taxonomic nomenclature, nativity, and habitat 

affiliation of the U.S. Department of Agriculture. We evaluated 

vegetation structure within the following cover strata: aquatic, 

nonvascular, basal (large-woody-stem emergence at ground 

level), ground (graminoid or herbaceous-deciduous), shrub 

(>4-m-high woody shrub), middle canopy (4–10-m-high woody 

perennial), and tall canopy (>10-m-high woody perennial). 

We opportunistically sampled and, where sufficient 

flow permitted, conducted replicated quantitative kicknet 

sampling for springs’ benthic invertebrates by sampling an 

area-defined portion of the benthos for 1 minute. Specimens 

were returned to the laboratory for enumeration, preparation, 

and identification, using the taxonomy of Merritt and 

others (2008), with identification of species where possible. 

Specimens are curated at the Museum of Northern Arizona.

We assessed the overall quality of the springs by using 

the SSI Springs Ecosystems Assessment Protocol (SEAP) of 

Stevens and others (2011) to determine the ecologic integrity 

(condition) of, and risks to, the springs in relation to the 

adjacent landscape. In this assessment, the condition or value 

and the risk (inverse of rehabilitation potential) were scored 

on a scale from 0 (no condition or value, no risk) to 6 (high 

condition or value, overwhelming risk with no recovery 

potential) for each of 42 resource or management variables, 

among 6 categories of information: aquifer and groundwater 

quality, geomorphology, habitat quality, biota, freedom from 

human impacts, and administrative context (see http://docs.

springstewardship.org/PDF/SEAPCulturalApril2012.pdf). 

The administrative context variables were quantified through 

interviews with the springs steward(s). 

Analyses

Analysis of nutrients and major cations and anions 

was conducted at the University of Arizona’s Water Quality 

Certification (WQC) laboratory, and analysis of stable 

isotopes (18O and 2H) at Northern Arizona University’s water 

quality laboratory. We used visual graphic inspection and 

simple linear regression to evaluate the extent of linearity 

in response variables. We used compiled data to determine 

relation among springs types and associated ecosystem 

characteristics (Springer and Stevens, 2008), and evaluated 

the differences in some of those characteristics between the 

North Kaibab and Williams Ranger Districts. We calculated 

the geomorphic diversity of each springs ecosystem as the 

Shannon-Weiner diversity index (as reviewed by Spellerberg 

and Fedor, 2003), using the proportional cover of each 

geomorphic microhabitat influenced by the spring. We also 

calculated plant morphospecies richness within strata and sites 
28 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

(Shannon-Weiner plant morphospecies diversity) and visually 

estimated percentage cover within each vegetation stratum. We 

used the SEAP protocols to determine the ecologic integrity 

of each springs visited, and whether the springs’ ecological 

condition met the FS stewardship expectations.

Information Management

We entered the inventory data, along with a 

representative photograph and a sketchmap, into the SSI 

Springs Inventory Database (Ledbetter and others, 2012). 

Each study site was included in a geodatabase to relate survey 

data to Geographic Information Systems (GIS) mapping 

applications using a common site identification number. All 

data were also provided to the FS.

Results

SSI staff and volunteers visited 71 springs in the Kaibab 

National Forest in 2011–12, conducting level 2 inventories 

on 52 springs and level 1 (georeferencing) inventories 

on 8 springs, and observed 12 springs to be either dry or 

nonexistent (table 1). In addition, we compiled survey data 

from previous studies on 134 springs, including those studies 

by the Grand Canyon Wildlands Council (2002, 2005) and 

the Grand Canyon Trust (unpub. data, 2011). Including the 

pre-fieldwork literature search, we obtained at least some 

information on 205 springs, although 19 were either dry 

or unlocatable. Survey crews also found 7 springs that had 

not previously been mapped or described. Miller Spring, 

the only springs ecosystem reported in the Tusayan Ranger 

District, had previously been reported as dry and was dry 

at the time of our site visit in November 2012. Not all data 

were available for each spring; therefore sample size differs 

among variables.

Aquifers

Many of the aquifers in Kaibab National Forest are 

perched and stacked, with uppermost, younger basalt flows 

overlying deeper Paleozoic strata, particularly the Kaibab, 

Toroweap, Coconino, Supai Group, and Redwall Formations 

(Kreamer and Springer, 2008; Crossey and Karlstrom, 

2012). Most springs in the Williams Ranger District emerge 

from shallow aquifers in basalt flows, whereas those in the 

North Kaibab Ranger District primarily emerge from the 

deeper Kaibab, Toroweap, and Coconino Formations. Water 

temperature and solute concentration typically increase with 

depth through these strata, but groundwater emerging from the 

Toroweap/Coconino Formation contact in the North Kaibab 

Ranger District is more solute rich than other waters in Kaibab 

National Forest, as discussed below.

Springs Habitat Area

A sample of 81 springs for which sufficient data existed 

for detailed comparison occupied a total area of 6.01 ha of 

springs habitat, ranging widely in area from 4.5 to 15,036m2

and averaging 742 m2 (95-percent-confidence interval, 

406.3m2), with a median area of 166 m2. 

Springs Types

We detected 9 of the 12 types of terrestrial springs 

recognized by Springer and Stevens (2008), among 

90 springs for which sufficient data were available to 

determine springs types (table 2). Because much of 

Kaibab National Forest consists of rolling plateaus, the 

preponderance of low-gradient springs types (for example, 

hillslope, rheocrene, and helocrene) is not surprising. 

However, lands in the steep terrain of the Kanab Creek 

drainage in the North Kaibab Ranger District support 

hanging gardens, and additional unmapped springs remain 

to be located, georeferenced, and inventoried there.

Flow

The average flow of the 75 springs for which data 

were available varied widely (mean, 0.53 liters per 

second (L/s); 95-percent-confidence interval, ±0.365L/s). 

Springs discharge ranged from perennial, apparently 

groundwater-fed ponds and minor seeps with no detectable 

flow, to springs with a flow of >1.0L/s, including Big Spring 

(as much as 12.3L/s) in the North Kaibab Ranger District, 

Slide Spring (4.4 L/s), Lower North Canyon Spring (2.78 L/s), 

Pipe Springs on Camp Navajo (1.91L/s), and Glenn Rink 

Spring (1.9 L/s). Glenn Rink Spring was notable, owing to 

its lengthy (>600m) runout and significant contribution 

to North Canyon Creek, which supports Apache trout 

and at least five narrowly endemic invertebrate taxa. In 

spite of its having the fourth-highest flow measured in 

the North Kaibab Ranger District, Glenn Rink Spring had 

not previously been mapped or described. Few repeated 

measurements of flow have been performed on the springs 

in the Kaibab National Forest, but the Grand Canyon 

Wildlands Council (2002, 2005) reported that the flow 

from Big Springs in the North Kaibab Ranger District 

decreased from 12.3 to 0.867 L/s in the period 2000–01. 

Such widely varying flows characterize the cold-water 

springs emanating from the Redwall and Muav Formations 

in nearby Grand Canyon National Park (for example, 

Vaseys Paradise, Roaring Springs, Thunder River Spring, 

and Dutton Spring). Big Springs in the North Kaibab 

Ranger District is one of the few Coconino Aquifer 

springs known to have such a widely varying flow. Our 

surveys indicated that at least 9.3 percent of the springs in 

the Kaibab National Forest are ephemeral.
Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National Forest, Northern Arizona 29

Table 1. Data for springs at which inventories were conducted in Kaibab National Forest from 2010 to 2012. 

[Locations: NC, central North Kaibab Ranger District; NE, east side of North Kaibab Ranger District; NW, west side of North Kaibab Ranger District; RD, 

ranger district; S, Williams Ranger District. Springs types: C, cienega/helocrene/springfed fen; G, gushet; H, hillslope; HG, hanging garden; L, limnocrene; 

LGC, low-gradient cienega; R, rheocrene. Aquifers: B, neogene basalt; C, Coconino; H, Hermit Shale; K, Kaibab; Su, Supai; T, Toroweap Formation. °C, 

degrees Celsius; –, no data]

Spring Survey 

date

Elevation 

(m) Area (m2

) Discharge 

(L/s)

Locationtype-aquifer

Total 

alkalinity 

(mg/L)

pH 

(field)

Dissolved 

oxygen 

(field; 

mg/L)

Specific 

conductance 

(field; μS/cm)

Water 

temperature 

(°C)

27 Mule 10/30/09 1,379 40 – – – – – – –

Acer 5/21/11 1,535 22 0.034 NW-H-H 205.6 7.38 4.46 1,146.7 13.6

Aconitum 6/23/06 2,359 69 0.42 NC-HG-C 290 8.11 – 371.3 6

Bear Lake 7/14/05 2,775 976 – NC-L-K – 6.31 – 27 22

Bear 7/27/10 2,662 1,855 0.094 NW-R-K – 6.58 2.36 75.2 10.4

Bear 5/10/12 2,016 273 0.13 S-R-B 40 7.77 – 160 14.3

Bee 6/30/00 2,388 5 0 NC-H-K – 7.6 – 492 8.5

Big (North Kaibab RD) 7/2/02 2,150 2,445 12.303 NW-HG-C – 7.7 – 354 8.8

Big (Williams RD) 5/12/12 2,072 359 0.62 S-H-B – 7.01 – 108 10.1

Bitter 5/19/09 1,212 10 – NW-R-Su – 7.82 2.76 1,835 15.7

Boulder 5/17/11 1,390 48 1 NW-R-H 210.8 7.84 11.07 730 11.8

Buck 5/12/12 2,078 708 0 S-M-B 0 6.71 – 69 10.1

Burro 6/28/11 1,887 209 – NE-L-T 142.5 8.11 8.06 641.5 11.6

Castle 6/14/12 2,195 2,320 0.15 NC-HG-C 330 7.44 12.77 469 15.4

Coconino Fracture 6/17/12 2,499 147 0.073 NC-G-T 330 8.12 364.8 8.2

Cottonwood Cienega 5/20/09 1,346 – 0.092 NW-M-Su – 7.3 5.31 1,582.7 20.7

Cottonwood Pourover 5/20/09 1,300 – 0.443 NW-R-Su – 7.3 2.88 1,675.3 13.7

Cottonwood 5/19/09 1,290 – 0.133 NW-R-Su 240 7.42 3.37 1,803 14.5

Crane Lake 6/30/02 2,605 1,035 – NC-L-K – 6.9 – 63 21

Crystal 7/18/05 2,682 182 0.236 NC-H-K – 7.5 – 231 5.4

Dead Fawn 12/21/11 2,374 50 0.052 NC-R-K 230.6 7.62 7.82 263.7 7

Dog Lake 6/28/00 2,682 736 – NC-L-K – 9.2 – 88 14.3

Dry Cottonwood 5/20/09 1,511 – 0 – – – – – –

Dry Park Lake 6/28/11 2,553 1,140 – NC-L-K 30 6.27 – 45 20.5

Elsie, Camp Navajo 8/30/10 2,210 1,256 – – – – – – –

Franks Lake 8/12/10 2,641 5,000 – – – – – – –

Glenn Rink 12/20/11 2,560 142 1.9 NC-H-T 260 8.17 – 299.3 5.2

Hat Tank Lower 5/8/12 2,036 674 0.21 S-R-B – 7.38 – 95 10.8

Hat Tank Upper 5/8/12 2,065 178 0.11 S-R-K 30 6.97 – 108.5 10.1

Isham 8/29/11 2,072 – 0 – – – – – –

Jumpup 5/21/11 1,548 529 0.077 NW-HG-C 195 7.93 9.61 1,118.7 13.7

Kwagunt Columbine 5/21/09 1,118 – 0.021 NW-M-Su – 8.14 8.25 1,473 18.5

Kwagunt Hollow 5/22/09 1,340 – 0.068 NW-R-Su – 7.67 6.2 1,376 18.1

Kwagunt Hollow Zen 5/21/09 1,128 – 0.063 NW-HG-Su – 8.03 6.07 1,750 19.9

Little 5/17/11 1,455 344 0.75 NW-R-T 209.2 7.89 10.71 753.3 14.5

Little Willow 5/17/11 1,415 112 NW-R-H 175.3 8.01 7.51 980 14.2

Lockett 5/7/12 2,159 15,036 0.19 S-LGC-K 32 7 – 65 7.3

Locust 7/30/10 2,452 203 0.086 NW-M-K – 7.16 – 599.3 6.7

Lookout Lakes 6/25/11 2,668 2,388 – NC-L-K 30 7.3 6.74 41 22.7

Lower Cottonwood 5/20/09 1,403 – 0 – – – – – –

Lower Jumpup 7/10/09 1,212 – 3.6 NW-R-Su – 7.23 – 1,946.7 20.2

Lower McDermit 8/30/11 2,165 1,670 0.001 S-H-B – 7.86 – 236 15

Lower Sowats 5/22/09 1,084 – 0.004 NW-HG-Su – 7.94 – 1,465.7 16.8

Lower Two 1 7/28/10 2,316 74 0.421 NW-G-K – 7.67 – 402 6.5

Mangum 1 6/27/11 2,225 27 0.085 NW-HG-C 224.1 7.97 – 455 8.8

Mangum 2 6/27/11 2,167 – – – – – – – –

Mangum 6 6/27/11 2,166 20 – – – – – – –
30 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 1.—Continued

Spring Survey 

date

Elevation 

(m) Area (m2

) Discharge 

(L/s)

Locationtype-aquifer

Total 

alkalinity 

(mg/L)

pH 

(field)

Dissolved 

oxygen 

(field; 

mg/L)

Specific 

conductance 

(field; μS/cm)

Water 

temperature 

(°C)

Mangum 7 6/27/11 2,174 55 0.029 NW-HG-C 320 7.73 7.27 325.7 8.8

Mangum 6/27/11 2,163 100 0.02 NW-HG-C 280.6 8.05 7.9 322.6 9.4

McDermit 8/30/11 2,200 400 0.022 S-M-K – 7 – 206 21.1

Mountain Sheep 8/17/09 1,130 467 1.7 NW-H-S – 7.29 – 1,726.7 18.4

Mud 5/10/12 2,119 640 0 S-M-B 40 6.81 – 123 11.3

North Canyon Lower 6/28/00 2,485 250 2.776 NC-H-T – 8.4 – 323 5.8

North Canyon Middle 6/25/01 2,534 16 0.419 NC-H-T – 8.7 – 322 6.5

North Canyon Upper 6/28/00 2,513 5 0.059 NC-H-T – 9 – 313 5.7

Oak 6/16/12 2,054 94 0.011 NW-H-T 310 7.88 – 354.2 12.1

Oquer 6/28/11 2,550 24 0.018 NW-H-K 148.8 7.72 6.9 189.6 8.8

Parissawampitts 7/15/05 2,364 132 0.122 NW-M-K – 7.57 – 464.7 6.9

Pasque 5/20/11 1,765 745 – NW-HG-C 385 8.28 10.55 650 13.1

Pasture 7/14/05 2,412 39 0.055 NW-H-K – 7.75 – 522 9.2

Phragmites 5/17/11 1,396 342 0 – – – – – –

Pigeon 5/18/11 1,506 125 0.001 NW-HG-T 243.3 7.2 4.46 1,960 12.1

Pipe, Camp Navajo 9/24/10 2,167 1,298 1.91 S-LGC-K – 6.82 – 203 12.6

Quaking Aspen 7/13/05 2,398 114 0.153 NE-C-K – 7.5 – 444 9.6

Riggs 6/29/11 2,264 – 1.322 NE-C-K 205.4 7.84 – 390 9.5

Rock Lower 11/2/09 1,328 1,323 1 NW-HG-M – 8.1 – 2,600 13.9

Rock Upper Bowl 11/2/09 1,345 166 0.1 – – – – – –

Rocky Tule 9/27/10 2,012 – – – – – – – –

Rosilda 5/12/12 2,046 591 0.29 S-R-K 0 6.68 77 10.1

Slide 7/8/10 1,490 125 4.3 NW-R-T 240 7.46 9.6 1,216.7 15.5

Solidago 5/20/11 1,765 165 0.023 NW-HG-C 199.2 8.46 10.82 540 12.1

South Canyon 6/29/11 2,577 38 0.017 NC-R-C 250 8.01 6.04 283.1 10.4

Sowats Middle 6/14/12 1,850 278 – – – – – – –

Sowats 6/23/06 1,835 131 0.239 NW-H-T 270 7.85 – 696 11.5

Sowats Upper 6/23/06 1,828 36 0.014 NW-HG-T 280 8.2 – 840 13.3

Sowats Veronica 6/23/06 1,840 136 0.1 NW-H-T 270 8.2 – 768 12.3

Squaw 7/30/10 2,443 312 0.389 NW-H-K – 7.36 – 533.7 8.1

Stonefly 6/26/11 2,399 72 0.1 NC-R-K 269.4 8.16 8.4 315.7 7.5

Summit 5/10/12 2,212 39 0 – – – – – –

Table Rock 5/16/11 1,613 450 0.004 NW-HG-T 148.3 8.28 11.26 1,496.7 13.5

Tappan, Camp Navajo 9/21/10 2,233 3,050 0.017 – – – – – –

Tater Canyon 7/15/05 2,260 113 – NE-HG-C – – – – 11.5

Tater Canyon Upper 7/15/05 2,345 42 0.257 – – – – –

Tilton 6/27/11 2,099 60 0.003 NW-H-H 264.4 8.11 – – 12.1

Timp 8/3/05 2,416 1,550 0.186 NC-H-K – 7.46 – 478 7.3

Trailview 6/17/12 2,538 120 0 – – – – – –

Typha 5/19/11 1,408 43 0.015 NW-H-H 210 7.95 9.43 1,256.7 14.2

Unnamed 8 5/19/09 1,410 – – NW-HG-H – 7.56 – 1,269 17.4

Upper Sowats 5/22/09 1,107 – – NW-HG-T – 7.96 – 1,579.7 18.9

Upper Two 8/7/00 2,377 100 – NW-R-K – 7.6 – 517 21.5

VT Lake 6/16/12 2,689 3,666 – NC-L-K 50 9.33 – 41.9 14.5

Warm 12/30/05 2,109 37 0.36 NW-Ca-C 165.6 7.8 – 610 14.5

Watts 7/14/05 2,442 151 0.076 NW-H-K – 7.5 – 497 8

White 5/19/09 1,390 – – NW-H-H – 7.66 – 1,322.7 18.7

Wild Horse 5/11/12 2,041 77 0.034 S-R-B 40 6.62 – 75 14.6

Wild Horse Upper 5/11/12 2,050 167 0.004 S-R-B 80 8.58 – 94 20.9

Wildband 5/18/11 1,545 1,198 0.008 NW-H-T 160 8.44 10.08 2,000 13.2

Willow 8/29/11 1,980 720 – – – – – – –
Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National Forest, Northern Arizona 31

Table 2. Abundance of springs types reported in Kaibab National Forest.

[n=90; from Springer and Stevens (2008)]

Springs type Description Frequency Percent 

occurrence

Hillslope Emerges from a hillslope (typically >15°), often indistinct or multiple sources 27 30

Rheocrene Lotic channel floor; spring emerges directly into one or more stream channels 20 22

Hanging 

garden

Dripping flow emerges usually horizontally along a geologic contact along a cliff wall of a perched, 

unconfined aquifer

20 22

Helocrene Low-gradient cienega, springfed-fen, or wet meadow: Marsh or wet meadow emerges on shallow 

gradient; flow is often diffuse and indistinct, with multiple sources

10 11

Limnocrene Emergence of confined or unconfined aquifers in lentic pool(s) 6 7

Gushet Discrete source flow gushes from a cliff wall of a perched, unconfined aquifer 3 3

Hypocrene Buried spring where flow does not reach surface, typically owing to very low discharge and high 

evaporation or transpiration

2 2

Cave Emergence in a cave in mature to extreme karst with sufficiently large conduits 1 1

Exposure Cave, rock shelter fractures, or sinkholes where unconfined aquifer is exposed near the land surface 1 1

Water Quality

We measured field water-quality variables and compiled 

water-quality data for 83 springs (table 1). Temperature, pH, 

and specific conductance were recorded for 52 springs. In 

addition to these variables, dissolved oxygen concentrations 

were measured at 29 sites, and field alkalinity was measured at 

43 springs (table 1). 

Field water quality varied by elevation, aquifer parent 

rock, and across the Kaibab monocline. In the North Kaibab 

Ranger District, the central Kaibab Plateau had higher field pH 

values, lower specific conductances, and intermediate water 

temperatures relative to springs on the east and west sides of 

the plateau (table 1). Springs on the west side of the monocline 

commonly discharge from or near the bottom of the Toroweap 

Formation and drain into Kanab Creek. These springs typically 

had higher specific conductances and water temperatures and 

lower pH values, particularly those at the lowest elevations (for 

example, the Rock Springs complex). In contrast, many of the

springs on the east side of the monocline emerge from the base 

of the Coconino Formation. Springs in the Williams Ranger 

District, which primarily emerge from Neogene basalt flows, 

had the lowest average pH values and specific conductances, 

and the highest average water temperatures (table 1).

Stable isotope ratios for oxygen generally indicated 

relatively low levels of 18O enrichment, consistent with the 

dominant, shallow, high-elevation aquifers of the Colorado 

Plateau. This relation was particularly true in perennial ponds, 

which have significant direct input from precipitation and, 

therefore, a higher concentration of lighter oxygen isotopes. 

Specific conductance was negatively related to varying 

degrees among strata and hydrologic subbasins, and therefore 

by elevation (fig. 2). This relation may also be due to 

3,000

2,500

2,000

1,500

1,000

500

0

0 500

SCWWKM = (–1.0615* elev) + 2,879.3

R2 = 0.693

SCWEKM = (–0.685* elev) + 1,973.9

R2 = 0.661

West Kaibab monocline

Williams District

East Kaibab monocline

EXPLANATION

1,000 1,500

Elevation, in meters

Specific conductance,

in microsiemens per centimeter

2,000 2,500 3,000

Figure 2. Plot of specific conductance verses elevation at 

springs on the west and east sides of the Kaibab monocline 

in the North Kaibab and Williams Ranger Districts in Kaibab 

National Forest, northern Arizona. See figure 1 for locations.
32 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

variation in the levels of evaporative concentration of water 

pre-recharge and (or) post-discharge. Although specific 

conductance did not vary much across the limited elevation 

gradient in the Williams Ranger District, it varied in a strongly 

inverse fashion across elevation on both the East and West 

Kaibab monoclines (R2

, 0.661 and 0.693, respectively). 

Springs on the west side of the Kaibab upwarp occur across 

a wider elevation range than elsewhere in Kaibab National 

Forest, and the lowest elevation springs (that is, those in the 

Kanab Creek drainage) commonly had fivefold greater specific 

conductance relative to springs at higher elevations. 

Geomorphic Diversity

Geomorphic diversity (Shannon-Weiner proportional 

area of different microhabitats in relation to the overall springs 

ecosystem) has been positively correlated with plant species 

richness at springs (Hallam, 2010; Springer and others, 2014). 

Geomorphic diversity varied among springs types, with an 

overall average of 0.32. Gushets and hanging gardens had 

the highest overall geomorphic diversity, averaging 0.35. 

Limnocrene and hillslope springs had somewhat lower 

geomorphic diversities, followed by helocrene, rheocrene, and 

hypocrene springs, which respectively had progressively lower 

geomorphic diversities.

Solar Energy Budget and Rare Plants

Aspect strongly influences springs vegetation composition, 

particularly at higher elevations (Stevens, 2012). Analyses of 

available solar radiation at the study sites indicated that hanging 

gardens, hillslope springs, gushets, and some rheocrene springs 

sustained substantially reduced solar radiation budgets owing 

to shading by adjacent cliffs. The first three of these springs 

types are known to support unusual, rare plant species, such as 

the populations of Primula specuicola we observed in Pasque 

and Solidago Hanging Gardens in the Sowats Creek drainage. 

Rheocrene springs are less likely to support rare plant species, 

owing to the frequency of flooding in those surface-flowdominated habitats. Although orchids may grow at helocrene 

and limnocrene springs, those springs types often are sunny 

and frequently disturbed by grazing; therefore they may be less 

likely to support shade-tolerant plant species. 

Vegetation

We detected 441 plant species in the springs inventory 

during our 2010–2012 surveys and in data compiled from other 

sources. Plant species richness varied considerably among 

springs types (fig. 3). Exposure springs (which were rare and 

strongly influenced by human activities) had the lowest species 

richness, with only seven plant species per site, whereas marshforming helocrene springs had the highest species richness, 

with an average of 40 plant species per site. Rheocrene and 

hillslope springs and the sole hypocrene springs averaged 

a somewhat higher plant species richness than did gushet, 

limnocrene, hanging garden, or cave springs types.

Plant cover similarly varied considerably among springs 

types (fig. 4). Not surprisingly, the greatest percentage of 

aquatic vegetation cover occurred in pool-forming limnocrene 

springs, with only modest aquatic cover in other springs 

60

50

40

30

20

10

0

n=1

n=1

n=2 n=15

n=9

n=25 n=2

n=6

n=15 n=76

Cave

Exposure

Gushet

Helocrene

Hillslope

Hypocrene

Limnocrene

Rheocrene

All springs

Mean number of plant species

Springs type

Hanging garden

Tall canopy

Middle canopy

Shrub cover

Wetland ground cover

Nonwetland ground cover

Nonvascular

Aquatic

EXPLANATION

Figure 3. Chart showing native and nonnative plant species 

richness among nine springs types in Kaibab National Forest, 

northern Arizona (fig. 1).

80

70

60

50

40

30

20

10

0

n=1

n=2

n=15

n=9

n=25 n=2

n=6

n=15 n=75

Cave

Gushet

Helocrene

Hillslope

Hypocrene

Limnocrene

Rheocrene

All springs

Mean visually estimated percentge cover

Springs type

Hanging garden

Tall canopy

Middle canopy

Shrub cover

Wetland ground cover

Nonwetland ground cover

Nonvascular

Aquatic

EXPLANATION

Figure 4. Chart showing mean visually estimated percentage 

cover of native and nonnative vegetation among eight springs 

types in Kaibab National Forest, northern Arizona (fig. 1).
Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National Forest, Northern Arizona 33

types. Mosses and other nonvascular taxa reached maximal 

cover at gushet and hillslope springs, somewhat less cover 

at helocrene springs, and little cover at other springs types. 

Native ground cover exceeded 30 percent at limnocrene, 

helocrene, and hypocrene springs and reached nearly that 

extent at hillslope and hanging garden springs. Scour and 

steep terrain limited native ground cover to slightly more than 

20 percent at rheocrene and gushet springs. Nonnative ground 

cover exceeded 20 percent at hillslope and helocrene springs, 

averaging 9.5 percent across all springs types. Shrub cover 

vastly exceeded all other cover types at gushet springs, was 

>25 percent at hillslope, hypocrene, and rheocrene springs, 

and averaged 20 percent across all springs types. Middle and 

tall canopy cover was generally 10–20 percent at rheocrene, 

hypocrene, hillslope, and hanging garden springs, <10 percent 

at gushet springs, and very low at helocrene springs.

Kaibab National Forest is estimated to support 

~1,325plant taxa, for an overall average density of 0.002 

plant species per hectare. Our surveys encompassed a total 

area of 6.01 ha of springs habitat, indicating that the plant 

species density at springs was 72.5 species per hectare. 

Although species-area constraints are responsible for much 

of this difference, we detected one-third of the entire flora in 

<0.001percent of the forest landscape. These results attest 

to the remarkable concentration of plant species richness at 

springs in Kaibab National Forest.

Invertebrates

The richness and abundance of aquatic invertebrate 

species varied considerably among the array of springs in 

Kaibab National Forest. A total of 58 springs had sufficient 

flow to allow us to sample benthic invertebrates. We observed 

at least 410 aquatic, wetland, and riparian invertebrate 

morphospecies among 2,530 individual specimens collected. 

The North Canyon drainage is well known for supporting 

elevated numbers of unique or rare aquatic and riparian 

invertebrates, including at least five narrowly endemic taxa, 

as well as at least one undescribed stonefly (Plecoptera). 

However, other springs in the forest also support elevated 

densities of aquatic taxa, particularly Big Springs and those 

in the Sowats Creek drainage, all in the North Kaibab Ranger 

District. Few of the springs in the Williams Ranger District 

could be conclusively confirmed as perennial, a condition that 

limits the presence of many aquatic invertebrates. However, 

Big Springs in the Williams Ranger District may warrant more 

detailed entomological study because it supports at least two 

unusual aquatic beetle taxa, including Optioservus divergens

(Elmidae) and an as-yet-unidentified scirtid marsh beetle.

The sum of Ephemeroptera, Plecoptera, and Trichoptera 

(the EPT score) in a sample is a commonly used water 

quality metric (table 3; Lenat, 1988). EPT scores at springs 

in Kaibab National Forest were weakly positively related 

to perennial spring elevation, discharge, and pH, and were 

weakly negatively related to specific conductance and water 

temperature, with R2

 values ranging from 0.013 to 0.36. 

EPT species vary widely in their tolerance of water quality. 

Nonetheless, the slightly negative relationship between 

specific conductance and water temperature may partly 

explain the relatively high concentrations of flatworms, 

stoneflies, and other aquatic invertebrate species in upper 

elevation, cool, low-solute springs, such as those in the North 

Canyon drainage, and the limited presence of those taxa at 

middle- and low-elevation, warmer springs with elevated 

solute concentrations.

Vertebrates

We detected a wide array of vertebrates during our 

springs inventory of Kaibab National Forest; however, the 

list of vertebrates is incomplete for any individual springs 

ecosystem. Such a list would require a focused study of 

vertebrate habitat use, which was beyond the scope of the 

present effort. Surveyors observed only a few herpetofaunae, 

including tiger salamander (Ambystoma tigrinum) and 

wandering garter snake (Thamnophis elegans). They reported 

a wide array of woodpecker, raptor, and songbird species; 

and mammals, ranging from pocket gophers (Thomomys) to 

ungulates and predators. Among the mammals, four introduced 

taxa directly impact springs. In addition to cattle and sheep, 

nonnative Rocky Mountain elk (Cervus elaphus) are abundant 

in the Williams Ranger District, and evidence of elk browsing 

and trampling is ubiquitous in springs wetland habitats. Small 

numbers of elk also have reportedly colonized the North 

Kaibab Ranger District, but we did not detect elk impacts on 

springs there. We observed signs of introduced bison at several 

sites in the North Kaibab Ranger District, particularly around 

natural ponds. The bison population is reportedly expanding, 

and their wallowing, trampling, and grazing may adversely 

affect wetlands in the North Kaibab Ranger District.

Assessment of Springs Ecosystem Integrity

We used the SSI SEAP to evaluate the ecologic integrity 

of and risks to natural resources, human influences and risks, 

and the administrative context of 86 of the springs inventoried 

in Kaibab National Forest (table 4). We first examined the 

array of human impacts on springs between the two larger 

districts. This analysis demonstrated that adjacent forest 

conditions, flow regulation, and herbivores affected springs 

the most, with differences in the form and extent of impact 

between the North Kaibab and Williams Ranger Districts. 

Springs in the North Kaibab Ranger District have sustained 

less intensive human use and typically are in slightly better 

ecologic condition than those in the Williams Ranger District. 

Many springs in the North Kaibab Ranger District lie within 

wilderness areas and (or) are in areas that are difficult to 

access, and so they are buffered from some direct human 

uses. Increased impacts from flow regulation, herbivory, and 

adjacent land uses are more likely to affect surface water 
34 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 3. Selected springs in Kaibab National Forest that support elevated key aquatic indicator taxon species richness. 

[Ranger district: N, North Kaibab; W, Williams. Asterisk (*), springs that support both Plecoptera and Turbellaria]

Spring Ranger 

district

Taxa E/P/T 

taxon 

sum

Minimum 

total number 

of taxa

Number of 

individuals 

specimens

EphemeropteraPlecopteraTrichoptera Coleoptera Diptera Odonata Turbellaria

Aconitum* N 1 1 2 2 2 0 1 4 14 33

Bear N 1 0 1 2 2 2 0 2 18 27

Big (North Kaibab RD)* N 3 4 11 17 7 8 1 18 128 279

Big (Williams RD) W 0 0 1 5 1 3 0 1 22 27

Bitter N 1 0 0 2 1 0 0 1 3 3

Castle N 0 0 1 1 5 0 1 1 35 67

Coconino Fracture* N 0 1 1 3 1 0 1 2 10 18

Cottonwood Cienega N 1 0 0 0 0 1 0 1 2 2

Cottonwood N 1 0 0 0 1 0 0 1 2 2

Crane Lake N 0 0 1 8 11 6 0 1 50 107

Crystal N 0 0 4 3 7 1 1 4 24 101

Dead Fawn* N 0 2 1 4 4 0 1 3 20 44

Dog Lake N 0 0 1 10 7 4 1 1 40 116

Dry Park Lake N 0 0 1 0 1 3 0 1 7 26

Elsie W 1 0 0 7 2 7 0 1 29 50

Glenn Rink* N 1 1 1 6 5 0 1 3 29 76

Hat Tank Lower W 1 0 2 6 5 4 0 3 38 58

Jumpup N 1 0 0 4 4 2 0 1 13 18

Lookout Lakes N 0 0 1 6 3 5 0 1 45 70

Lower Jumpup Below N 1 0 0 10 3 2 0 1 20 27

Lower Sowats Seep N 0 0 1 1 0 0 0 1 3 3

Mangum1 N 0 1 0 1 2 0 0 1 8 8

Mangum * N 0 2 1 2 6 0 1 3 22 31

McDermit W 1 0 0 5 2 0 0 1 14 32

Mountain Sheep N 1 0 0 18 6 4 0 1 40 53

North Canyon Lower N 1 1 4 1 1 0 0 6 9 9

North Canyon Middle* N 1 2 5 4 3 0 0 8 42 79

North Canyon Upper N 0 1 3 1 3 0 0 4 27 46

Oak N 0 0 2 5 3 1 0 2 22 30

Pasture N 0 1 3 1 2 0 0 4 8 10

Pipe Camp Navajo W 1 0 1 8 4 3 0 2 33 59

Riggs Tank N 0 0 1 3 6 0 0 1 20 29

South Canyon N 0 0 2 3 1 0 0 2 8 13

Sowats N 2 0 2 3 1 1 1 4 19 33

Sowats Upper N 0 0 2 6 3 0 0 2 28 42

Sowats Veronica N 1 0 2 5 2 2 1 3 27 45

Stonefly N 0 1 1 0 1 0 0 2 4 5

Table Rock N 1 0 0 0 5 0 0 1 13 21

Tappen W 1 0 0 6 1 6 0 1 26 36

Tater Canyon Upper N 0 0 1 0 1 0 0 1 2 2

Timp N 1 0 1 0 4 0 0 2 11 36

VT Lake N 0 0 2 4 6 5 1 2 30 40

Watts N 0 0 2 2 2 0 0 2 10 14
Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National Forest, Northern Arizona 35

quality in the Williams Ranger District. Grazing is likely to 

exert stronger impacts on higher elevation sites owing to the 

shorter growing season and, therefore, reduced recovery time 

(for example, Samuelson and Rood, 2011).

We plotted the overall scores for natural resource 

conditions of each springs ecosystem for which SEAP data 

were available against the human risk category score for that 

site. Using resource condition values of 3 (moderate ecologic 

condition or value) and human risk scores of 3 (moderate 

risk but moderate rehabilitation potential) as midpoints, we 

identified springs in and near the upper right quadrant of the 

graph that have high natural resource value but are at risk 

from human impacts (fig. 5). This graphic approach indicated 

springs in the two larger ranger districts that may warrant 

further investigation and (or) management attention. Those 

springs in the Williams Ranger District that ranked highest 

in this regard include Lockett, Tappan, Pipe, and Mud, 

with Rosilda and McDermit (fig. 5A). Springs in the North 

Kaibab Ranger District that ranked highest in priority for 

management attention included Big Springs, Crystal, Pigeon, 

Table Rock, Pasture, and Castle (fig. 5B). Management 

needs range from simple construction of a steppingstone 

access trail to the source (for example, at Big Springs in 

the North Kaibab Ranger District), to potential geomorphic 

rehabilitation (for example, Mud and Rosilda Springs in 

the Williams Ranger District). Three common management 

recommendations involved (1) fencing springs to protect the 

sources from livestock impacts, while providing for springs 

flow downstream; (2) removing unnecessary springs boxes 

and piping; and (3) maintenance of existing flow-regulation 

structures and piping, where such infrastructure is needed. 

Specific management recommendations were included in the 

reports on each inventoried springs ecosystem.

Discussion and Management 

Considerations

The springs in Kaibab National Forest are abundant 

but generally small ecosystems that support a wide array of 

physical, biological, and cultural/socioeconomic resources. 

Extrapolating the average habitat area of measured springs 

to the 205 known springs in Kaibab National Forest 

suggests that the total area of springs habitat in the forest is 

approximately 15 ha. Although additional springs remain 

undocumented, particularly along the Coconino SandstoneHermit Shale contact in the headwaters of Kanab Creek, 

the overall habitat area of springs is unlikely to exceed 

20ha. Similar extrapolation of flow data from the group 

of 75 springs with adequate flow-measurement data to the 

205 total known springs, including the maximum flow from 

North Kaibab Big Springs (12.3 L/s), gives an estimated total 

springs discharge of 40 L/s.

In addition to surface flows from the North Kaibab 

Ranger District, a substantial but less well monitored flow 

(~2,500 L/s) emerges as cool- or cold-water springs from 

the Redwall/Muav Aquifer in Tapeats, Deer, Shinumo, and 

Bright Angel Creeks and from the Upper Marble Canyon 

springs complex near Vaseys Paradise on the north side of 

the Colorado River in Grand Canyon National Park. These 

aquifers underlie some or much of the North Kaibab Ranger 

District. Also, the deep-aquifer Blue Springs complex 

in the lower Little Colorado River drainage and Havasu 

Springs in Cataract/Havasu Creek on the east and south 

sides of the Grand Canyon, respectively, as well as other 

small tributaries, contribute another ~7,500 L/s (Kreamer 

and Springer, 2008). Kaibab National Forest overlies a 

substantial portion of the regional perched aquifers, which 

discharge approximately 10 cubic meters per second (m3/s) 

of springs flow into the Colorado River in the Grand Canyon. 

Analysis of pre- and post-dam flows of the Colorado River at 

Lees Ferry, immediately upstream from the Grand Canyon, 

reveal that autumn and winter average monthly minimum 

6

5

4

3

2

1

0

6

5

4

3

2

1

0

0

A

B

1 2 3 4 5 6

Summit

Lower McDermitt

Isham Rosilda

Tappan Lockett

Pipe

Mud

Bear

Wild Horse,

upper and lower

Riggs tank

Pasture

Pigeon

Crystal

Big

North and Sowats

Castle Table

Rock

Risk score from human impacts

Natural resources condition

Figure 5. Plots of stewardship risks to springs from human 

impacts in relation to overall score for natural resources condition 

in the Williams Ranger District (A) and the North Kaibab Ranger 

District (B) in Kaibab National Forest, northern Arizona (fig. 1). 

Springs in upper right quadrant may warrant management 

attention.
36 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 4. Springs ecosystem assessment category condition and risk scores, overall natural resource condition and risk score, and null scores.

[Locations: NC, central North Kaibab Ranger District; NE, east side of North Kaibab Ranger District; NW, west side of North Kaibab Ranger District; RD, ranger district; 

S, Williams Ranger District. Spring types: C, cienega/helocrene/springfed fen; G, gushet; H, hillslope; HG, hanging garden; L, limnocrene; LGC, low-gradient cienega; R, 

rheocrene. Aquifers: B, neogene basalt; C, Coconino; H, Hermit Shale; K, Kaibab; Su, Supai; T, Toroweap Formation. Estimated scores are in bold]

Spring

Locationtypeaquifer

Aquifer 

functionality 

water quality

Geomorphology Habitat Biotic

integrity

Natural 

resource

Freedom from 

human impact

Administrative context Null

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

27 Mule NW-R-H 4 1.7 4 2 3.8 2.6 4.9 2.3 4.2 2.1 4.9 1.6 2.8 2.4 0 0

Acer NW-H-H 4 2 4.2 1.2 4 1.6 4.6 1.4 4.2 1.5 5 1.3 2.7 2 1 0

Aconitum NC-HG-C 4.8 1 4.6 2 5.4 1.8 5 1.8 5 1.6 5.5 1.4 4.3 3 2 4

Bear NW-R-K 4.5 1.8 5.2 3 5.5 2.3 4.5 3 4.9 2.5 4.6 2.6 3.4 1.9 33 33

Bear S-R-B 4.2 1.2 4.2 1.2 4.2 1.6 4.3 1.9 4.2 1.5 4.2 1.5 3.9 2.4 0 1

Bear Lake NC-L-K 4.2 2 4.8 2.2 4.2 2.2 4.1 2.4 4.3 2.2 4.3 2.5 4 2.4 2 1

Bee NC-H-K 2.2 4.5 1 4.4 2 4.8 2.4 4.3 1.9 4.5 3 2.9 3.7 2.1 0 1

Big (North Kaibab RD) NW-HG-C 4.8 1.7 3.2 3.8 4 4 4.4 3.4 4.1 3.2 3.9 3.4 4.4 2.7 0 0

Big (Williams RD) S-H-B 4 3.2 4.6 2.2 4 2.8 3.3 3.1 4 2.8 3.9 2.4 4.9 2.4 1 1

Boulder NW-R-H 5.3 0.8 4.6 0.8 3.2 1.8 4 2.6 4.3 1.5 4.9 1.3 2.7 1.9 0 1

Buck S-M-B 2 4.2 2.6 3.6 3.6 3.2 2.8 4 2.7 3.7 2.8 3.8 4.4 3.1 2 1

Burro NE-L-T 5 1.4 4.6 1 4.3 1.6 4.8 2.5 4.7 1.6 4.2 2.5 3.4 2 3 1

Cane Aqueduct NE-H-C 4.5 1.8 2.8 3.6 3.4 3.2 4.3 3.1 3.8 2.9 3.7 2.8 3.6 2.8 0 1

Castle NC-HG-C 3.8 2.3 2.6 4.2 3.2 3.2 2.1 4.1 2.9 3.5 2.7 3.9 3.1 3 2 3

Coconino Fracture NC-G-T 5.2 1 5.4 1 4.4 1.6 5.5 1.3 5.1 1.2 5.2 1 3.4 2.3 9 9

Crane Lake NC-L-K 3.6 3 3.3 3.3 4 2.4 3.8 3.5 3.7 3 4.3 2.4 4.3 2.3 2 2

Crystal NC-H-K 4 1.5 3.6 3.4 3.2 3.4 4 3 3.7 2.8 3.9 3.3 3.4 2.3 9 9

Dead Fawn NC-R-K 5 1 5.2 0.8 4.2 1.4 5 1.4 4.9 1.1 5.1 1 2.6 1.9 1 2

Deer Lake NC-L-K 5.6 2 4 2.5 4.2 3.2 4.6 3.1 4.6 2.7 2.4 2.8 3.7 2.1 2 2

Dog Lake NC-L-K 4.2 2.3 1.8 5 2 4.6 3.3 4.3 2.8 4 3.1 2.9 3.9 2 1 1

Dry Park Lake NC-L-K 4.3 1.6 3 2.8 3.6 2.8 3.9 3.1 3.7 2.6 3.6 2.8 3.8 2.1 3 3

Elsie S-He-K 2.8 3.2 2.3 4 3,4 4 3.6 3.4 2.9 3.7 3.4 3.5 4.2 2.1 1 1

Franks Lake NC-L-K 5 2 5 2 6 2 3.5 2 4.9 2 5.1 1.6 – – 24 23

Glenn Rink NC-H-T 5.5 2 5.2 1.8 4.6 1.8 5.4 1.6 5.2 1.8 5.3 1.5 3.4 2.3 9 9

Hat Tank Lower S-R-B 4 2 4.2 2.6 3.8 2.6 3.9 3 4 2.6 4.6 1.6 3.7 2.5 1 1

Hat Tank Upper S-R-K 4.2 1.8 3.6 2.8 3.8 3 4.5 2.1 4 2.4 4.4 1.9 3.9 3.4 0 1

Isham S-H-Dry 0 6 1.6 4.4 2.2 4.6 3.6 4 1.9 4.8 3 2.9 3.7 2.3 0 1

Jumpup NW-HG-C 3.8 2.6 4.8 1.2 4.8 1.6 4.8 1.3 4.5 1.7 4.6 1.9 4.2 2.5 1 2

Little NW-R-T 5 0.8 4.8 1.4 4.2 1.8 4.4 2.3 4.6 1.6 4.9 1.6 3.9 2.1 0 1

Little Willow NW-R-H 4.7 0.7 5 0.4 4 1 4.6 2 4.6 1 4.9 1.3 3.3 1.6 0 1

Lockett S-LGC-K 3.3 3.6 2.8 4.8 4.8 2.7 3.9 3.3 3.7 3.6 3 4.1 4.7 2.8 0 5

Locust NW-M-K 4 1.8 2.8 5 2 5 2.5 5 2.8 4.2 3.8 3.3 3.3 2.3 7 6

Lookout Lakes NC-L-K 5.3 1.5 4 3 4.2 3 4.1 3.6 4.4 2.8 3.8 2.5 3.4 2 4 4

Lower McDermit S-H-B 1.2 5 1.4 4.6 3.2 4.2 2.9 4 2.2 4.5 2.8 3.6 3.7 2.3 0 1

Lower Sowats Seep NW-HG-Su 4.3 1.3 6 1 5.2 2 5 2.3 5.1 1.6 5.3 1.8 3.6 1.6 6 5

Mangum NW-HG-C 3.5 1.8 3.2 3 2.4 3.8 3 2.8 3 2.8 4.2 1.6 4.3 1.9 0 1

Mangum 1 NW-HG-C 4.5 1.2 3 3.8 3.2 4 3.9 2.9 3.6 3 4 1.9 4.2 1.9 0 1

Mangum 6 NW-HG-C 4 2 4 1 4.8 1.2 5 2 4.5 1.6 5.8 1 4.4 1.8 11 12

Mangum 7 NW-HG-C 4.8 1 3.6 2.2 4 2.6 4.1 2 4.1 2 4.4 1.8 4 2.3 24 25

McDermit S-M-K 3 3.3 1.4 4.6 3.8 2.6 2.6 4.1 2.7 3.7 2.8 3.9 3.7 2.3 9 9

Mountain Sheep NW-H-S 4.8 2.8 5.2 1.6 4.2 2.2 5.3 1.9 4.9 2.1 4.7 2.4 4.5 2.1 1 1
Ecological Inventory and Assessment of Springs Ecosystems in Kaibab National Forest, Northern Arizona 37

Table 4.—Continued

Spring

Locationtypeaquifer

Aquifer 

functionality 

water quality

Geomorphology Habitat Biotic

integrity

Natural 

resource

Freedom from 

human impact

Administrative context Null

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Score Risk 

score

Mud S-M-B 3.2 1.5 3 2.8 3.4 3.6 2.8 3.8 3.1 2.9 3.2 3.1 4.1 2.4 1 1

North Canyon Lower NC-H-T 5.3 0.8 5.2 1.2 5.8 1.2 5.8 1.4 5.5 1.2 5.5 1 3.2 2.3 0 1

North Canyon Middle NC-H-T 5 1 4.6 1.6 5.4 1.8 5.8 1.3 5.2 1.4 5.3 1.5 3.4 2.3 0 1

North Canyon Upper NC-H-T 5 0.8 4.8 1.2 5.8 1.2 5.5 1.4 5.3 1.2 5.5 1 3.2 2.3 0 1

Oak NW-H-T 3.8 2 1.8 4.8 3.4 3.8 3.5 3.4 3.1 3.5 4.3 2.1 3.5 2.5 8 9

Oquer NW-H-K 4.5 1.8 3 2.4 3.6 2.4 3.5 2 3.7 2.2 3.1 2.4 3.3 2.1 1 2

Parissawampitts NW-M-K 3.4 3.2 2.2 3 2.4 3.2 2.8 3 2.7 3.1 2.3 3.3 3.9 2 6 7

Pasque NW-HG-C 4.7 1 5.2 1 4.4 1.2 5.3 1 4.9 1.1 5.2 1.5 2.7 1.9 0 1

Pasture NW-H-K 3.6 3 3.4 3 4.3 3 2.8 3 3.5 3 4.4 3 – – 16 16

Phragmites NW-Hy-H 3.2 1 4.2 2.2 3.8 2 4.8 2 4 1.8 4 2 2.7 1.9 0 1

Pigeon NW-HG-T 4.5 2 3.2 3.2 3.6 3.2 3.8 3.1 3.8 2.9 3.2 3.9 3.9 2.6 0 1

Pipe Camp Navajo S-LGC-K 4 3.2 2.3 4.2 3.6 4.2 3.5 3.6 3.4 3.8 3.6 4 4.3 2.2 1 0

Quaking Aspen NE-C-K 3.8 3 3.4 3 4.4 3 3.3 3 3.7 3 3.7 2.7 3.9 2.1 6 7

Riggs Tank NE-C-K 3.2 3 0 6 0.8 5.2 0 6 1 5.1 3.5 2.5 3.8 2 11 11

Rock Lower NW-HG-M 4.5 2.7 3.4 3 3.8 2.8 4.8 2.5 4.1 2.7 3.9 2.8 4 2 1 1

Rock Upper Bowl NW-HG-M 4 2.7 4.6 2 3.4 3 4.3 3.3 4.1 2.7 4.4 2.1 2.9 2.3 0 1

Rocky Tule S-H-B 3.8 3.2 1.5 3 2.3 2.8 2.8 3 2.6 3 2 3.1 – – 35 34

Rosilda S-R-K 3.3 2.2 3 2.8 3.8 3 3.9 2.8 3.5 2.7 3.6 2.9 3.7 2.5 0 1

Slide NW-R-T 4.5 1.8 4.6 1 4 1.2 4.1 2.1 4.3 1.5 3.8 2.3 3.8 2.3 0 1

Solidago NW-HG-C 4.7 1.2 5.2 1 4.2 1.4 5.1 1.1 4.8 1.2 5.2 1.5 2.7 1.9 0 1

South Canyon NC-R-C 3.2 3.4 4 3.2 4.3 2.8 – – 3.8 3.1 3.7 2.7 3.9 2.6 11 12

Sowats NW-H-T 4.7 1 5 1.2 4 2.6 4.7 2 4.6 1.7 5.6 0.6 3.1 1.9 7 8

Sowats Middle NW-H-T 4.7 1 5 0.6 5 1.2 4.9 1.4 4.9 1 4.8 1.4 3.1 1.1 1 1

Sowats Upper NW-HG-T 4.2 1 4.8 1.8 3.8 2 4.9 2 4.4 1.7 4.9 1.4 3.4 1.6 1 3

Sowats Veronica NW-H-T 5 1 4.8 1.8 4 1.4 4.9 2 4.7 1.6 4.8 1.5 4 1.9 0 1

Stonefly NC-R-K 4.8 1 5.4 1 4 1.4 5.1 1.8 4.8 1.3 5 1.1 2.7 1.9 0 1

Summit S-H-B 1 5 2.2 4.2 2.6 3.8 2.8 3.8 2.1 4.2 2 4.5 3 4 12 8

Table Rock NW-HG-T 4.3 1.2 2.2 4.8 3.6 4.4 3.8 3.1 3.5 3.4 3.4 3.3 4 2.1 0 1

Tappan S-He-K 4.4 2.4 2 5 3.2 4.6 3 4.3 3.2 4.1 2.9 4.1 4.3 2.1 2 2

Tater Canyon NE-HG-C 4.2 2 1.4 4.6 2.2 4.4 2.6 3.6 2.6 3.7 3.9 2.6 3.3 2.4 1 0

Tater Canyon Upper NE-HG-C 4.7 1.8 2.8 4 3.6 3.8 4.8 3 4 3.2 5 2.1 4.7 2.3 0 1

Tilton NW-H-H 4.5 1 2.8 2.2 4.2 2.6 4.9 1.9 4.1 1.9 3.8 2.9 3.7 1.9 0 1

Trailview NNEF-C 3.7 1.2 4.6 0.6 4.4 1.2 5.5 1 4.5 1 4.8 0.9 2.4 0.4 0 0

Typha NW-H-H 4.4 1 5.4 1 5.2 1.2 3.8 3.5 4.7 1.7 5.6 1.3 2.7 1.9 5 6

Unnamed 8 NW-HG-H 4.3 1.3 6 1 5.2 2 5 2.3 5.1 1.6 5.4 2 3.6 1.6 6 5

Upper Sowats Seep NW-HG-T 4.2 1.7 6 1 5.2 2 6 1 5.3 1.4 5.4 2 3.6 1.6 6 5

Upper Two NW-R-K 3.6 1.8 1.8 4 2.8 3.6 3.5 2.5 2.9 3 4.4 2.3 – – 10 9

VT Lake NC-L-K 5.6 2 4 2.5 4.2 3.2 4.6 3.1 4.6 2.7 2.4 2.8 3.7 2.1 2 2

Warm NW-Ca-C 3.5 2.7 2.4 4.4 2 4.2 2.9 4.5 2.7 3.9 2.9 3.4 4.3 1.8 9 9

Watts NW-H-K 3.8 2.2 2.4 3.6 2 3.6 2.5 3.6 2.7 3.2 3.9 3.1 3.7 1.9 9 9

White NW-H-H 4.3 1.3 6 1 5.2 2 4.8 2.5 5.1 1.7 5.8 1.3 4.1 1.4 6 5

Wild Horse S-R-B 4.2 1 4.4 1.8 4 1.8 4.6 1.9 4.3 1.6 5.3 1.6 3.7 2.5 0 1

Wild Horse Upper S-R-B 4.2 1 4.4 1.8 4 2.3 4.8 2 4.3 1.8 4.7 1.9 3.7 2.5 0 3

Wildband NW-H-T 4.7 0.8 4.6 1.4 4.4 2.4 4.8 2.3 4.6 1.7 4.3 1.6 3.9 2 0 1

Willow S-R-B 4.3 3 4.8 1.4 4.2 1.6 3.4 3.8 4.2 2.4 4.6 1.6 4.1 1.6 0 1
38 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

flows varied between 50 and 100 m3

/s (U.S. Geological 

Survey, 2016). Post-dam minimum flows are presently kept 

at or above 141.6m3/s through a political agreement (U.S. 

Geological Survey, 2016). Thus regional springs contributed 

10–20percent of the pre-dam Colorado River base flow in the 

Grand Canyon, and presently provide more than 7 percent of 

the post-dam base flow. 

The springs ecosystems in Kaibab National Forest offer 

a wide array of research and stewardship opportunities. 

Further inventories of the springs will help refine our 

understanding of them and of springs-dependent species 

distribution and status, and monitoring of selected springs 

may help the FS better understand natural variability and 

aquifer responses to climate change. Further research on 

the role of physical variables affecting springs vegetation 

and invertebrate species presence also may help the FS 

better understand why and how so many rare species occur 

at springs. Springs in the southern Colorado Plateau have 

been recognized as keystone ecosystems, habitats that 

disproportionally contribute to the physical and biological 

processes of surrounding landscapes (Perla and Stevens, 

2008). Improved understanding of how and how much the 

springs in Kaibab National Forest influence adjacent lands 

may help further refine management priorities and actions.

We observed nearly one-third of the flora in Kaibab 

National Forest on only 6.1 ha of springs habitat. This 

remarkably tight packing of plant species at springs has also 

been reported at springs in the Spring Mountains of southern 

Nevada and in southern Alberta. Springs support not only 

wetland plant species but also many upland species, and 

larger springs and some particular springs types (for example, 

rheocrene, hillslope, and helocrene springs) may passively 

acquire more plant species because of the relatively strong 

species-area relation at springs, including anemochores and 

hydrochores. Greater soil moisture may locally reduce the 

intensity of wildfires around springs, and thus allow springs to 

serve as regeneration hotspots after fire or drought, at least for 

some biota.

The inventory and SEAP data presented here are a first 

step in determining stewardship options and priorities. Using 

our data and additional information, Paffett (2014) conducted 

a refined prioritization assessment of the springs in Kaibab 

and adjacent Coconino National Forests. He led a discussion 

with forest staff to rank springs stewardship needs in relation 

to issues of management urgency, efficiency, and cost. In 

concert with a revised Forest Plan that identifies springs 

stewardship and restoration as management priorities, and in 

collaboration with the Hopi and North Kaibab Paiute Tribes 

and the primary allotment holder, the FS recently initiated 

restoration actions on Big and Castle Springs in the North 

Kaibab Ranger District. Monitoring of those restoration efforts 

may help the FS better understand the costs and benefits of 

such collaborative resource management efforts (for example, 

Stacey and others, 2011). Thus, for many reasons, attention 

to and sustainable stewardship of springs ecosystems may 

contribute to FS overall management objectives. 

Acknowledgments

This research was funded by a Kaibab National Forest 

cost share agreement to the Museum of Northern Arizona 

under FS Project Agreement 10-CS-11030700-023. We thank 

the Museum of Northern Arizona for administrative and 

curatorial support in this project, particularly its president, 

Robert Breunig. We greatly appreciate the Grand Canyon 

Wildlands Council, the Grand Canyon Trust, and Stevens 

Ecological Consulting for contributing data and volunteers 

in support of our study. We thank Stephen Monroe from the 

Colorado Plateau Network of the National Park Service in 

Flagstaff, Ariz., for his contribution of springs inventory 

data. Boris Kondratieff (Colorado State University) and 

Richard Durfee assisted in the identification of Coleoptera 

and Plecoptera, and we thank Richard A. Bailowitz for his 

assistance in the identification of Odonata and Lepidoptera. 

Dustin Berger and Kit MacDonald provided insight into the 

administrative context of springs management in the North 

Kaibab and Williams Ranger Districts, respectively. We 

particularly thank Abe Springer and his students for hydrology 

assistance, and Glenn Rink for his indefatigable logistical 

assistance and botanical expertise. Additional funding from 

the Nina Mason Pulliam Charitable Trust through the Grand 

Canyon Wildlands Council provided transportation, meals, and 

participation by Beep Jenkins from the Hopi Tribe and Andrea 

Hazelton, botanist for the Navajo Nation. We warmly thank 

the many students and volunteers for their field and laboratory 

assistance, particularly Victoria Hermosillo, Kumi Isobe, Vera 

Markgraf, Frank Romaglia, Krista Sparks, Phoebe Stevens, 

and Dan Taylor. Barbara Ralston and anonymous reviewers 

provided a helpful review of the manuscript. Data from this 

project are archived at Springs Online (http://springsdata.org).

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restoring hydrology, geomorphology, and invertebrate and 

plant species composition comparable to natural springs 

with minimal anthropogenic disturbance?: Flagstaff, Ariz., 

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in biogeography: Rijecka, Croatia, InTech, p. 169–208, 

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in North America—Ecology and conservation: Tucson, 

University of Arizona Press, 406 p.
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Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter E

Fungal Diversity in Biological Soil Crusts of the Colorado 

Plateau

By Blaire Steven,1* Cedar Hesse,1 La Verne Gallegos-Graves,1 Jayne Belnap,2 and Cheryl R. Kuske1

Abstract

We report the results of a replicated fungal large-subunit 

ribosomal RNA gene sequencing survey, conducted on 

biological soil crusts (biocrusts) of the Colorado Plateau. 

Fungal communities were characterized from two sites 

approximately 15 kilometers apart; the soil at each of 

the sites was derived from sandstone and shale material, 

respectively. Fungal large-subunit ribosomal RNA gene 

sequences recovered from the biocrusts on soil derived 

from sand were consistently more diverse than those from 

shale. Sample to sample heterogeneity was high, and the 

majority of fungal sequence types were unique to a single 

sample. This suggests a high level of spatial variability in 

the biocrust fungal community. A small number of fungal 

lineages were found that were shared between the two soil 

types. The conserved fungal taxa were predominantly within 

the class Dothideomycetes, order Pleosporales or were not 

reliably classified. This replicate sequencing strategy has shed 

light on the heterogeneity of biocrust fungal communities at 

different spatial scales, and has identified conserved fungal 

lineages that may play essential roles in biocrust functioning. 

These results suggest that fungi, particularly certain lineages 

of Pleosporales, should be considered as common and 

potentially important biocrust members. 

Introduction

Biological soil crusts (hereafter referred to as biocrusts) 

are an important ecosystem component of the Colorado 

Plateau. Biocrusts are biological communities, consisting 

of bacteria, algae, archaea, fungi, lichens, and mosses that 

colonize the topsoils in drylands and are a conspicuous aspect 

of the Colorado Plateau landscape (fig. 1; Belnap, 2003; 

Pointing and Belnap, 2012). Biocrusts play many functional 

roles in the environment including soil stabilization, reduction 

1

Biological Science Division, Los Alamos National Laboratory

2

U.S. Geological Survey

*

Now at Department of Environmental Sciences, Connecticut Agricultural 

Experiment Station

of erosion, moisture retention, and carbon and nitrogen 

fixation (Belnap, 2002, 2003; Housman and others, 2006).

In this study we focus on the fungal diversity of 

biocrusts. In soil environments fungi perform multiple and 

diverse roles. They are important in the decomposition and 

recycling of carbon, in modification of the soil environment, 

as partners in mutualistic symbioses, and as potential 

pathogens of plants (Christensen, 1989). In biocrusts, fungi 

are algal symbionts in lichenized biocrusts (Bates and others, 

2010a). Nitrogen transformations, particularly the production 

of N2O, are likely driven by biocrust fungi (Crenshaw and 

others, 2008; Marusenko and others, 2013). The fungal 

populations in dryland soils have also been implicated in 

forming nutrient bridges between local plants and the biocrusts 

that may translocate carbon and nitrogen (Green and others, 

2008; Porras-Alfaro and others, 2010).

Although the bacterial community structure in the 

biocrusts of the Colorado Plateau has been described in detail 

(for example, Yeager and others, 2004; Steven and others, 

2013), few studies document the diversity and structure of the 

fungal populations. Two previous studies employing the DNA 

Figure 1. Photograph of a Colorado Plateau biocrust. Biocrusts 

appear as the dark pigmented surface layers. The dark coloration 

is due to the biological pigment, scytonemin, produced by 

cyanobacteria. 
42 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

fingerprinting technique denaturing gradient gel electrophoresis 

(DGGE) and quantitative polymerase chain reactions (qPCR) 

found that Colorado Plateau biocrust fungi were generally less 

abundant and diverse than the biocrust bacteria. The dominant 

fungal lineages recovered in these studies were within the 

phylum Ascomycota, predominantly related to fungi within 

the order Pleosporales (Bates and Garcia-Pichel, 2009; Bates 

and others, 2010b). Similarly, abundant fungal populations 

recovered in culture-based studies of New Mexico grassland 

biocrusts and Mojave Desert biocrusts were related to the 

Pleosporales (Porras-Alfaro and others, 2010; Steven and 

others, 2014). In these diverse studies the identified fungi were 

predominantly microcolonial lineages. The microcolonial fungi 

have been identified in desert soils worldwide and form darkly 

pigmented, melanized hyphae and spores that allow them to 

survive the desiccating conditions of dryland soils (Sterflinger 

and others, 2012). However, because of the limited resolution 

and replication applied in previous studies there is still a 

knowledge gap concerning the spatial patterning, patchiness, 

diversity, and composition of fungal populations in Colorado 

Plateau biocrusts.

The goal of this study was to document patterns of fungal 

taxonomy and distribution in biocrusts of the Colorado Plateau 

employing a replicated, high-resolution sequencing study. 

Fungal communities were characterized from two soil types 

using sequencing of the large subunit (LSU) ribosomal RNA 

(rRNA) gene to generate an inventory of fungal populations in 

the biocrusts, and to characterize how these populations varied 

between biocrusts collected at two sites that differed in soil 

parent material.

Site Description and Methods

Biocrust samples were collected from two distinct soil 

types in the vicinity of Moab, Utah on October 14, 2011. The 

sand soil samples (Arches Series) were collected from within 

the Island of the Sky district of Canyonlands National Park 

(38o27’27.21”N, 109o32’27.93”W, 550 meters [m] elevation). 

The soil was a sandy loam consisting of eolian and residuum 

deposits derived from the local sandstone (Navajo Sandstone). 

The shale soil (Rizzo Series, Moenkopi Formation) was located 

near Castle Valley, Utah (38o

40’29.5”N, 109o49’58.7”W, 399 m 

elevation). The soil characteristics and bacterial communities 

of these soil types have been documented previously (Steven 

and others, 2013). Three replicate surface biocrust samples 

were collected at each location. Biocrust samples were 

defined as the soil bound together by the cyanobacterial 

mats at a depth of <1 centimeter. Samples were specifically 

collected from areas not showing visible lichen patches to 

focus on cyanobacterially dominated biocrusts. Each sample, 

approximately 5 grams, was immediately placed on dry ice for 

transportation to Los Alamos National Laboratory. Samples 

were stored at –70 °C until processing. 

Total community DNA was extracted from the biocrust 

samples using the FastDNA prep kit (MP Biomedicals) using 

the included protocols. Successful DNA extractions were 

verified by gel electrophoresis and were quantified using the 

Quant-iT PicoGreen dsDNA kit (Invitrogen). Polymerase 

chain reaction amplification for 454 Titanium sequencing (Los 

Alamos National Laboratory Sequencing Center, standard 

protocols) was performed using 0.5 nanograms of each 

template DNA. Fungal LSU rRNA genes were amplified with 

the primers LR0R and LR3 using amplification protocols 

described previously (Steven and others, 2014). The recovered 

sequences were processed and analyzed in the mothur software 

package (v.1.25.1; Schloss and others, 2009). Sequences were 

quality checked and processed as described previously (Steven 

and others, 2013). Sequences were screened for nonfungal 

eukaryotes using BLAST and MEGAN5 software (Altschul 

and others, 1990; Huson and others, 2007). Fungal sequences 

were aligned using the fungal 28S rRNA alignment tool from 

the Ribosomal Database Project (RDP; Cole and others, 2014) 

and classification of sequences was performed using the naïve 

Bayesian classifier with the January 2014 version of the fungal 

LSU database, available through the RDP website (Liu and 

others, 2012; Porras-Alfaro and others, 2014). Operational 

taxonomic units (OTUs) were determined as sequences sharing 

>97 percent sequence identity in the mothur software package. 

For calculations of diversity statistics and proportion of shared 

OTUs the datasets were randomly subsampled to the size of 

the smallest dataset to maintain equal sampling effort. For 

analysis of molecular variance (AMOVA), sample similarities 

were calculated employing the Bray-Curtis similarity metric. 

AMOVA statistics, testing whether the genetic diversity of two 

samples differs significantly, were calculated in the mothur 

software package. The fungal LSU sequences are publicly 

available on the MG-RAST website (Meyer and others, 2008) 

under the ID number 4550096.3 (http://metagenomics.anl.gov).

Results

Sequencing and Diversity Statistics

A total of 7,563 high quality fungal LSU sequences with 

an average length of 275 base pairs were analyzed in this 

study. The number of sequences recovered from each sample, 

the estimated coverage, and the diversity statistics for each 

sample are shown in table 1.

The number of OTUs from sand soils was consistently 

and significantly higher than in the shale soil (paired t-test; 

p=0.02). Fungi were also more diverse (H’) in the sand 

soils than the shale soils (table 1). Across the datasets the 

estimated coverage of fungal diversity ranged from 80 to 

89 percent, suggesting that the majority of the fungal OTU 

diversity expected to occur in these samples was recovered 

with this sampling effort. Finally, the ChaoI diversity estimate, 

a nonparametric species estimator, suggests approximately 

580OTUs would be recovered from sand soils compared to 

346 from shale when completely sampled (table 1). AMOVA 
Fungal Diversity in Biological Soil Crusts of the Colorado Plateau 43

statistics were determined to test if the sand and shale soils 

harbored significantly different fungal communities. The 

difference in community structure between the two soil types 

was highly significant (p<0.001).

Taxonomic Composition of Fungal Communities

The LSU sequences were classified to the class-level to 

compare the taxonomic composition of the fungal communities 

(fig. 2). The majority of the LSU sequences belonged to two 

fungal phyla, the Ascomycota and the Chytridiomycota. 

Additionally, a small number of sequences were also identified 

within the phylum Basidiomycota, but do not appear in figure 

2 as they generally accounted for <1percent of the sequence 

Table 1. Sample size, coverage and diversity statistics for the 

large subunit (LSU) datasets.

[OTU, operational taxanomic unit, %, percent]

Sample

Number

H’2 Coverage 

(%)2 ChaoI2

Sequences OTUs2

Sand_crust_1 1,355 228 4.7 80.3 582

Sand_crust_2 1,129 240 4.8 79.7 558

Sand_crust_3 1,087 217 4.4 81.5 602

Average 1,190.3 228.3 4.6 80.5 580.7

Shale_crust_1 7681 150 4.1 88.5 351

Shale_crust_2 1,009 129 3.0 88.5 354

Shale_crust_3 2,215 148 3.9 88.4 334

Average 1,330.7 142.3 3.7 88.5 346.3

1Smallest dataset to which the other datasets were randomly subsampled.

2

Calculated with an operational taxonomic unit (OTU) definition of 97 

percent sequence identity.

datasets. The majority of sequences within the Ascomycota 

belonged to six classes that varied widely in relative abundance 

between the replicates (fig.2). Finally, a relatively large

proportion of the sequences could not be assigned to known 

fungal taxa using the naïve Bayesian classifier. Putative 

taxonomic identities based on the best BLAST match reveal 

that many unclassified Ascomycota-related OTUs likely belong 

to lichenized lineages, including Heppia (89 percent sequence 

similarity, class Lichinomycetes) and Parmeliella (90 percent 

sequence similarity; class Lecanoromycetes). It is important 

to note that these classifications are based on best BLAST 

matches and may not represent the true taxonomic affiliation 

of the recovered sequences. However, these sequences were 

>90percent similar to fungal sequences recovered from 

Mojave Desert biocrusts (Steven and others, 2014), suggesting 

these fungal lineages are consistently recovered in biocrusts of 

the Southwest U.S. These data indicate that biocrusts contain 

significant populations of previously undescribed fungal and 

lichen diversity. 

The only fungal class that was present in both soil types 

was the Dothideomycetes, which accounted for ~3 to 40percent of the sequences (fig. 2). Within this class, the order 

Pleosporales accounted for 81 to 92 percent of the sequences, 

making them the most numerically abundant order recovered 

(data not shown). Within the order Pleosporales, sequences 

could be classified into 17 distinct genera, of which only 

two, Alternaria and Preussia, were present across all of the 

samples. Many plant-associated dark-septate fungi have been 

identified within the Pleosporales from similar arid environments; therefore it is likely that the prevalence of Pleosporales in these samples reflects an abundance of dark-septate 

fungi. Taken together our compositional data show high heterogeneity among biocrust fungal communities both locally 

(between soil type replicates) and between soil types. Members of the Eurotiomycetes were encountered in two of the 

shale soil samples and one of the sand soil samples. These 

Unclassified

Sordariomycetes

Lichinomycetes

Leotiomycetes

Lecanoromycetes

Pezizomycetes

Eurotiomycetes

Dothideomycetes

100

90

80

70

60

50

40

30

20

10

0

Unclassified fungi

Chytridiomycetes

EXPLANATION

Percent of LSU sequences

Ascomycota

Sand Shale

Figure 2. Plot of class-level taxonomic 

bins in the large subunit (LSU) datasets. 

Only those classes that accounted for 

>1 percent of sequences in at least one 

dataset are displayed, so the bars do not 

sum to 100 percent.
44 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

sequences were primarily in the order Verrucariales, which 

contains lichenized fungi that likely evolved independently 

from the other two major lichenized classes (Lichinomycetes 

and Lecanoromycetes). The largest group of “rare” Ascomycota was found in a single shale soil sample and represents 

a single OTU closely related to the lichenized genus Psora

(Lecanoromycetes).

Defining a Core Biocrust Fungal Community

The datasets were investigated for shared OTUs in order 

to define a core fungal community. In each comparison, OTUs 

composed of a single sequence (singletons) were removed. 

OTUs were identified that were common between replicate 

samples for each soil type and between the two soil types 

(fig.3). Many OTUs were unique to the individual datasets, 

ranging from 36 to 54 percent of the identified nonsingleton 

OTUs (fig. 3A,B). The proportion of OTUs unique to each 

dataset was higher in the sand soil biocrusts. However, given 

the higher diversity of fungi in the sand soil biocrusts (table 1), 

this may be due to under-sampling of the sand communities 

when the datasets were normalized to the same size. A small 

number of OTUs were also common between the sand and 

shale soils (fig. 3C).

Shared OTUs (those in common between the two 

soils) were primarily in the Dothideomycetes (fig. 3D) and 

unclassified Ascomycota, which were often most closely 

related to Dothideomycete fungi (data not shown). Many 

of the conserved OTUs were unclassified fungi, suggesting

that even fungi that are common in biocrusts may be poorly 

represented in current taxonomic databases. Only five OTUs 

were conserved among all replicates of the two soil types 

(table 2). These OTUs likely represent fungi that have been 

strongly selected for their presence in the biocrusts.

With the exception of a single OTU (OTU-5) all of the 

OTUs belonged to the class Dothideomycetes. Two of the 

OTUs were further classified to the order Pleosporales and 

the final two OTUs were classified to the genera Alternaria

and Preussia within the order Pleosporales (table 2). The 

one OTU that could not be assigned to a class was only 

79 percent similar to its nearest neighbor in GenBank, 

suggesting that this is either an example of a novel 

fungal lineage in the biocrusts or potentially a nonfungal 

eukaryote. Interestingly, two of the OTUs (OTUs 1 and 

3) were most closely related to LSU sequences recovered 

in a previous study of fungal diversity in Mojave Desert 

biocrusts (Porras-Alfaro and others, 2014) and another 

(OTU-5) was related to an LSU sequence recovered from 

a sandy soil sample collected under a piñon pine in New 

Mexico (Eichorst and Kuske, 2012). In this respect, these 

OTUs may not only be conserved between biocrusts of the 

Colorado Plateau but may be distributed among drylands at 

a much larger scale.

Unclassified fungi

Chytridiomycota

Basidiomycota

Unclassified Ascomycota

Rare Ascomycota

Dothideomycetes

EXPLANATION

A Sand crusts B Shale crusts C Between sand and shale

108 121

22 12

25 18

21 24 12 8

88 79

40

(37%)

50

(41%)

46

(52%)

39

(36%)

45

(54%)

41

(52%)

210

(66%)

136

(56%)

Sand Shale

Sand

(25 OTUs)

Shale

(18 OTUs)

Sand and Shale

(107 OTUs)

109

Total

number

of OTUs 83 317

107

243

100

80

60

40

20

0

Percent of shared OTUs

D Taxonomic composition of conserved OTUs

Figure 3. Plots of shared 

operational taxonomic units (OTUs) 

between datasets. The number 

of shared OTUs was determined 

between replicate samples (A, 

B) and between the sand and 

shale soils (C). The total number 

of OTUs in each dataset (after 

removal of singletons) is indicated 

outside of the circles. OTUs shared 

between datasets are indicated 

in the overlapping portions of 

the diagrams. Inset percentages 

indicate the proportion of OTUs 

unique to each dataset. D, displays 

the taxonomic composition of the 

shared OTUs. Note that they are 

the OTUs shared between all three 

replicates or in the compiled sand 

and shale soil datasets, so each 

bar represents a different number 

of OTUs (indicated by the number 

below the bars).
Fungal Diversity in Biological Soil Crusts of the Colorado Plateau 45

Table 2. Classification of conserved operational taxonomic units (OTUs) between all sequence 

datasets.

OTU Closest BLAST match

% 

Sequence 

ID1

Isolation 

source

Accession Taxonomy2

1 Uncultured clone NTS_064A_3 97 Moab biocrust KC558465.1 Pleosporales (83)

2 Fungal sp. BG29 94 Endophyte JQ249257.1 Alternaria (85)

3 Uncultured clone NTS_054A 95 Moab biocrust KC558210.1 Pleosporales (83)

4 Fungal sp. BG80 95 Endophyte JQ249246.1 Preussia (85)

5 Uncultured clone OTU91-167 79 Piñon soil JQ310904.1 Fungi (86)

1

Percent sequence identity to nearest neighbor in GenBank.

2

Represents the finest scale taxonomy to which the representative sequence could be classified reliably (>60 

percent confidence score). The confidence score of the classification is indicated in parentheses. 

Discussion

This sequence-based study documented the composition 

and biogeography of fungal communities in biocrusts of 

the Colorado Plateau. Our results show that edaphic factors 

related to soil parent material may influence the structure 

of the fungal communities. The structure of the fungal 

communities was found to differ significantly between the 

two soils sampled in this study (AMOVA analysis, p<0.001). 

More specifically, the sand soils supported a more rich and 

diverse fungal community than the shale soils (table 1). 

Furthermore, the shale soils contained lower relative 

abundances of Dothideomycetes compared to sand biocrusts 

(fig. 2). The decrease in Dothideomycetes in the shale soil was 

accompanied by an increase in the proportion of unclassified 

Ascomycota-related fungi (fig. 2). Our preliminary 

examination of the unclassified sequences suggests a large 

part of these sequences likely represent Dothideomyceterelated fungi that are poorly represented in current databases. 

Patterns in fungal diversity related to soil type differed 

from observations of bacterial communities from the same 

biocrusts, which were generally more similar in their diversity 

and composition across large spatial scales and different soil 

types (Steven and others, 2013). In this regard, biocrust fungal 

populations appear to be affected differently than bacteria 

by local edaphic factors. We suggest defining the specific 

soil characteristics that control the composition of fungal 

communities be pursued to determine the environmental 

drivers of biocrust fungal community structure.

Variability was high among the replicate field samples 

(fig. 2). These patterns suggest that fungal community 

composition varied substantially between samples separated 

at relatively small spatial scales. For example, 36 to 41 

percent of the fungal OTUs in the sand biocrusts were unique 

to each dataset compared to 52–54 percent in the shale 

biocrust datasets (fig. 3B). Prior measures of fungal biomass, 

culturable fungal counts, DGGE fingerprints, qPCR, and 

Sanger sequencing were variable among replicate soil samples 

(Bates and Garcia-Pichel, 2009; Steven and others, 2014). 

Our replicated sequencing survey provided higher community 

sampling coverage than the previous DGGE fingerprinting 

or Sanger sequencing studies, and provide a more robust and 

quantitative measure of fungal community composition and 

the spatial heterogeneity of biocrust fungi. 

Most of the fungal OTUs that were shared between the 

two soil types were members of the class Dothideomycetes 

(fig. 3D). A recent meta-analysis of fungal diversity across 

different drylands found that the most common lineages of 

fungi encountered through both culture-dependent and cultureindependent studies of biocrusts were Dothideomycetes, 

primarily of the order Pleosporales (Bates and others, 

2010b). The only genus that was consistently found across 

the different studies was Alternaria. Similarly, our analyses 

identified conserved OTUs predominantly related to the 

Pleosporales including an OTU belonging to Alternaria

(table 2). The data presented here adds to a growing body 

of literature that suggests Pleosporales-related fungi are 

important biocrust constituents that are present in biocrusts 

from many types of drylands. This conservation of specific 

fungal lineages between biocrusts suggests these fungi are 

providing important ecosystem services to the biocrusts, which 

are yet to be determined. While dark-septate fungi within the 

Pleosporales have been hypothesized to form nutrient bridges 

between biocrusts and nearby grasses (Green and others, 

2008), strong evidence for this interaction is still needed.

Although we attempted to avoid visible lichens in our 

sampling strategy, we detected numerous lichenized fungal taxa 

in our sequence survey. The morphology and pigmentation of 

these lichens may make them difficult to discern on filamentous 

cyanobacterial biocrusts. For example, the black pigments of 

species within the Verrucariales may resemble scytonemicpigmented cyanobacterial mats. These results suggest that 

cyanobacteria-dominated biocrusts are a mixed community 

of both Cyanobacteria and lichen. Finally, a large proportion 

of sequences in our survey were not classifiable with the most 

current LSU databases. This suggests there is still insufficient 
46 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

representation of fungal taxa in current reference databases. 

However, based on the low identity of many sequences 

to named taxa in the National Center for Biotechnology 

Information nonredundant nucleotide database, it is likely that 

arid land biocrusts represent a reservoir of fungal diversity that 

has not been previously described.

Acknowledgments

This study was supported by the Laboratory Directed 

Research and Development program of the Los Alamos 

National Laboratory, and a Science Focus Area grant 

(2009LANLF260) from the U.S. Department of Energy, 

Biological and Environmental Research Division. This is 

LANL unclassified report LA-UR-14-20460. 

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Kanab Basin looking north. Photograph by Robert J. Hart, U.S. Geological Survey.
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter F

Assembling a Virtual “Weevils of North America” 

Checklist with Symbiota—Preliminary Insights

By Nico M. Franz,1 Charles W. O’Brien,1 Sarah D. Shirota,1 Michael T. Shillingburg,1 Chelsey R. Tellez,1 and 

Edward E. Gilbert1

Abstract

We report on preliminary insights gained from the 

virtual Weevils of North America (WoNA) project. WoNA 

represents a novel, collaborative solution to the challenge 

of assembling monographs for taxonomically complex 

groups such as the weevils (Coleoptera: Curculionoidea) 

by leveraging the large-scale, specimen-level digitization 

efforts of thematic collections networks. The project has 

been developed online since 2012 within the framework 

of the Southwest Collections of Arthropods Network 

(SCAN). Current WoNA holdings include voucher-based 

data on more than 350 genera and 1,100 species, as well as 

high-resolution images for more than 600 species. Efforts 

to enrich these data with additional images, taxon profile 

pages, and interactive identification keys are underway. 

SCAN and WoNA are supported by the Symbiota software 

platform, which promotes diversified forms of engagement 

with research collections, taxonomic experts, and the general 

user community. In each case, the sources and actions related 

to data holdings are properly accredited. Future Symbiota 

modules will facilitate an increasingly monograph-like 

representation and publishable output under this approach. 

Integration of Filtered Push technology allows external users 

to submit annotations amounting to an open, distributed, and 

dynamic system of peer review that can increase support and 

trust in such virtual taxonomic knowledge environments.

Introduction 

The natural history collections community is making 

a strong push to bring specimen data online at a global 

scale. Specimen-based monography is transitioning from 

traditional, one-time publication in print media to the 

continuous assembly of dynamic, virtual environments 

(Smith, 2014). Large-scale investments in biodiversity data 

mobilization, in North America and elsewhere, are shifting 

the landscape (Baker, 2011). Such game-changing initiatives 

include the National Science Foundation’s Advancing 

Digitization of Biological Collections program (National 

Science Foundation, 2011) and the iDigBio HUB, which 

jointly sustain 15 Thematic Collections Networks (TCNs) 

that have been initiated since 2011. The mobilization of 

virtual collections for research and outreach is also a central 

objective of the Network Integrated Biocollections Alliance 

and related initiatives (Wheeler and others, 2012; Hanken, 

2013; Hardisty and others, 2013). 

Digitization of specimen data, though, requires 

systematic synthesis and discovery in order to be useful 

on a broad scale. Natural history collections require 

continuous growth and curation to best serve diverse user 

communities (Drew, 2011; Johnson and others, 2011). 

Curation, in turn, requires the time of taxonomic experts 

who are distributed across many institutions and must also 

respond to pressures to produce peer-reviewed and highly 

cited research (Valdecasas, 2011). Thus digitization finds 

itself at a crossroads where taxonomic experts are presented 

with stark choices: data quantity versus quality; build-up of 

research-supporting infrastructure versus genuine research 

and innovation; publication in online databases versus 

highly ranked journals (Wägele and others, 2011; Costello 

and others, 2013). Moreover, trust in data quality is at the 

top level of the Semantic Web stack—the hierarchy of 

languages and logic operations for research queries—that 

specimen data sustain at lower levels (Berners-Lee and 

others, 2006). To earn that trust and maximize return on 

investment, we must offer experts new incentives to engage 

in the virtual curation of digitized specimen data (Vollmar 

and others, 2010).

Numerous efforts to bridge the divide between 

digitization of specimen data and accredited publication are 

underway (for example, Smith and others, 2013). Here we 

offer preliminary insights into one such effort intended to 

create a new model of collaborative virtual monography for 

a major lineage of North American insects—the weevils. 

The Weevils of North America (WoNA) project (http://

symbiota4.acis.ufl.edu/scan/portal/checklists/checklist. 1

Arizona State University
50 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

php?cl=1) is the first to build directly upon an NSFsupported TCN, that is, SCAN—the Southwest Collections 

of Arthropods Network (http://symbiota4.acis.ufl.edu/scan/

portal/). SCAN came online in mid-2012 and has since 

(as of October 2015) accumulated ~6 million specimen 

occurrence records representing 66,000 arthropod species 

(including GBIF records and moth observations which 

are also hosted in SCAN), primarily from North America. 

Of these specimen records, approximately 75 percent are 

georeferenced with latitude/longitude data; about 50 percent 

of the records are identified to the level of species. 

SCAN and WoNA are hosted by the iDigBio HUB server 

infrastructure and sustained by the innovative, open source, 

and exclusively Web-based Symbiota software platform 

(http://symbiota.org). Symbiota facilitates the bottom-up 

assembly of voucher-based biodiversity information 

communities—called portals—of which the Southwest 

Environment Information Network (SEINET; Http://

swbiodiversity.org/portal/index.php) is a primary example.

In the past 5 to 10 years, floristic treatments within 

SEINet have matured into monograph-like resources of high 

information density and accessibility (Makings, 2006; see 

also http://symbiota.org). Such treatments include abundantly 

vouchered and searchable species lists, species profile 

pages with high-resolution images and descriptive data, 

interactive occurrence maps, and dynamic keys to identify 

taxa based on checklist- or map-driven entry points. Hence, 

many components for delivering a Symbiota-based WoNA 

monograph are already in place, as are platform-inherent 

advantages related to the continuous expansion, annotation, 

and enhancement of checklists by contributors and users.

Our interim review of WoNA assembly includes special 

taxonomic challenges related to weevils, an overview of 

Symbiota’s design principles and checklist support functions, 

current data holdings, and future developments. Throughout, 

our motivation is to further promote this approach for adoption 

by other arthropod research communities.

Taxonomic Challenges and 

Opportunities 

Weevils—Coleoptera: Curculionoidea, in the sense of 

Bouchard and others (2011)—are among the most diverse, 

economically impactful, and taxonomically challenging 

lineages of arthropods globally (Oberprieler and others, 

2007) and particularly in North America (herein limited to 

Canada and the United States). Weevils are ubiquitous in 

all terrestrial habitats where they are herbivores of native 

or cultivated plants as well as premier control agents 

(O’Brien, 1995). Anderson’s (2002) synopsis and other 

curculionoid treatments in American Beetles (Arnett and 

others, 2002) account for 7 families with 317 genera and 

2,728 species in the region. The fungus-cultivating bark and 

ambroisa beetles (Harrington, 2005), which are members 

of the superfamily Curculionoidea (Kuschel, 1995), add at 

least 77 more genera and 532 more species to this diversity 

(Rabaglia, 2002). Managing such richness is challenging 

enough when taxonomic groups are well circumscribed, but 

in weevils this challenge is further amplified by an unnatural 

and impenetrable mid-level classification that limits the 

recognition of monophyletic lineages.

The taxonomic history of the weevil superfamily can be 

abridged into (1) a period of foundation laying by 19th-century 

authors such as Schoenherr (Schoenherr, 1823, 1826, 1833–

1845) and Lacordaire (Lacordaire and Chapuis, 1863, 1866), 

and (2) a prolonged period of incremental extensions of that 

basic framework throughout the 20th century. It is well known 

that Lacordaire’s definitions of weevil groups (Lacordaire and 

Chapuis, 1863, 1866) are too simplistic to yield natural taxon 

circumscriptions (Kuschel, 1995; Anderson, 2002; Oberprieler 

and others, 2007). Nevertheless, these definitions remain 

highly influential in modern classifications (Alonso-Zarazaga 

and Lyal, 1999), which recognize some 16 subfamilies, 195 

tribes, and 5,800 genera in the Curculionidae alone (again, 

excepting bark beetles). Molecular projects conducted over 

the past 15 years, (for example, Farrell, 1998; McKenna and 

others, 2009; Bocak and others, 2014) have under-sampled this 

diversity so dramatically as to yield only minimal classificatory 

realignments (Franz and Engel, 2010).

The Weevils of North America project represents a novel, 

bottom-up effort that emphasizes specimen-level data and 

phenotype-based taxonomy and thus critically complements 

parallel top-down phylogenetic efforts. Past decades have 

shown that major advances in weevil systematics exceed the 

abilities of individual researchers. No fruitful synthesis is 

possible unless both bottom-up and top-down components are 

covered and coordinated. Hence we require a better structured 

and distributed research and synthesis platform that (1) is 

up to date with developments and standards in biodiversity 

informatics and (2) can utilize these tools to scale up to the 

vast dimensions of the task.

Symbiota Design Principles 

Thematic Collections Networks Increase 

Information Quality Control

Symbiota portals provide a framework for collaboratively 

publishing biodiversity information. Regional checklists 

are built upon vouchered records with direct linkages to 

the sources that manage the data and specimens (fig. 1). 

This ensures that data updates undertaken within the source 

collections can flow back to the virtual checklists, and vice 

versa (Vollmar and others, 2010). 

Symbiota portals adhere to a particular regional and (or) 

taxonomic theme and scope. A portal’s full potential may be 

reached if it is supported on a broad scale among numerous 
Assembling a Virtual “Weevils of North America” Checklist with Symbiota – Preliminary Insights 51

Figure 1. Screenshot of Weevils of North America checklist homepage in SCAN (Southwest Collections of Arthropods 

Network), showing current occurrence record statistics (as of January 2014) and taxa with the “Taxon Authors” and 

“Notes & Vouchers” options activated (see checkmarks in the Options/Filter box). Specimens are annotated with their 

collector and source collection (displayed in square brackets). The Options/Filter box includes a search function (with 

synonymy) to further narrow down the taxon list, as well as print and spreadsheet output options (icons to the right of 

“Rebuild List”). The top right corner includes icons for checklist administration (“A”), managing linked vouchers (“V”), 

and adding species (“Spp”).

thematically interconnected data contributors. Portals promote 

a positive feedback loop that includes (1) making data public 

instantaneously, which can serve to expose errors; (2) using 

Web-based editing tools and workflows that allow such errors 

to be resolved as they are identified; (3) redirecting data 

repairs back to a source’s internal platform; and (4) rendering 

repairs permanent with the subsequent data update. Thus, 

Symbiota takes advantage of the themed data mobilization 

approach towards improving the quality of the individual 

collections’ datasets (Costello and others, 2013).

Portals Promote Diversified Forms of 

Engagement

Symbiota portals allow different forms of engagement 

for contributing collections, research teams, individual experts, 

and public groups or citizens. In particular, collections and 

researchers have the option to contribute specimen information 

through either direct “Live Data” management or a “Data 

Snapshot” harvested periodically from another database 

system. In the former case, Symbiota’s content management 

system (CMS) is used to achieve day-to-day digitization 

and data assembly tasks, whereas in the latter case the portal

merely exposes information generated in another (preferred) 

environment. Symbiota has features to allow for such flexibility, 

including manual or API-supported data uploads using Darwin 

Core-compliant formats (Wieczorek and others, 2012). In 

addition, any user can readily access a portal’s occurrence 

records by downloading auto-configured datasets in spreadsheet 

format. Users can submit comments to data owners following a 

simple login procedure. Jointly these features lower the threshold 

for engaging new member collections and taxonomic experts.

Functional Modularity, Data Integrity, and 

Customization

Symbiota allows communities to acquire distinct 

identities and functions while ensuring database integrity at 

a broad scale. The aim to integrate biodiversity information 

sets practical strictures on the degree to which configurations 
52 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

can vary. These limitations are most apparent in the format 

of the single occurrence data table (fig. 2), which is linked 

to a portal’s taxonomic thesaurus. The latter constitutes a 

continuously curated reference classification that subtends all 

taxonomically based search functions and output displays. 

Joining a Symbiota portal requires acceptance and coordination 

of these conventions (Scoble, 2004; Franz and others, 2008).

Beyond sharing data formats, Symbiota portals are 

customizable in numerous ways that suit specific community 

needs. Modularity is manifested at different levels: (1) 

application modularity—the modules for managing 

specimens, biotic inventories, identification keys, and taxon 

profile pages are designed to function independently of one 

another; (2) data modularity—collections are represented 

as independent units with their own management regimes; 

and (3) portal modularity—multiple Symbiota portals can 

maintain distinct front-end appearences while connecting to 

a single back-end database.

Modularity also means that existing Web services can 

be deployed while optimizing for specific functions. For 

instance, services related to mapping occurrence records are 

provided through Google Maps (https://maps.google.com/). 

Georeferencing tasks are supported by GEOLocate tools 

(Rios and Bart, 2010). Voucher images can be imported from 

external sites such as the Encyclopedia of Life (Wilson, 2003).

Checklist Assembly Functions—A 

Primer 

We limit our Symbiota review to the creation of “static” 

checklists, created by single or collaborating experts on top 

of baseline digitizations efforts (see also http://symbiota.

org/docs/research-species-lists/). WoNA exemplifies this 

collaborative checklist model.

Biotic Inventories

Once a portal and member collection nodes are 

established, contributing experts can create regional checklists 

by either creating a species list within the network or by 

linking a group of species lists within a Symbiota project. 

Static checklists give researchers complete and continuous 

control over species composition, taxonomic placements, 

region specific comments, and voucher assignments. 

Additional partitioning of an all-inclusive list into 

multiple smaller lists—for example, for states, counties, or 

conservation areas—is also possible.

Symbiota offers a flexible user interface for exploring 

checklists (fig. 1). Users can filter these lists by taxon and 

display results with author names, habitat information, and (or) 

voucher details. Checklists can also be displayed as a set of 

thumbnail images, thus making it easier to visualize the taxa 

represented in the region (fig. 3).

Interactive Keys

Symbiota’s interactive keys are generated directly from 

descriptive data stored in a relational DELTA (description 

language for taxonomy) format (see http://delta-intkey.

com/). Such keys have many advantages over traditional 

dichotomous keys (Hagedorn, 2007; Brach and Boufford, 

2011). Computerized identification tools have advanced to 

the point where specifying only two to five characters often 

reduces a long list of candidate taxa to a manageable size. 

Subsequent browsing of taxon images, distribution maps, and 

descriptions can further speed up the process.

The Symbiota key-editing interface is coordinated 

with the hierarchy defined within the taxonomic thesaurus, 

allowing for character inheritance and other efficient coding 

tools. If a large region or taxonomic group is treated, keys are 

usually broken down into several levels of complexity, and 

the user proceeds from one key to the next. 

Taxon Profile Pages 

Symbiota offers taxon profile pages that complement 

the aforementioned modules (fig. 4). Natural language 

descriptions as well as images may be provided for each 

taxon. An occurrence map generator pulls information from 

the collections records to produce a distribution map. The 

corresponding CMS includes separate tabs to (1) provide 

synonyms and vernacular names; (2) view all annotated 

images; (3) specify the sequence in which images appear; (4) 

add new images; and (5) add natural language description(s) 

or notes on natural history and distribution. Links to source 

repositories for primary taxonomic literature such as the 

Biodiversity Heritage Library (http://www.biodiversitylibrary.

org/) are supported.

Checklist Management 

Symbiota’s CMS for checklist management (fig. 5) allows 

lead authors to manage contributor access rights and related 

checklists, add new taxa and images, and link new vouchers 

based on dynamic searches. Tabs to resolve conflicting voucher 

identifications and output reports round out the checklist CMS.

Current WoNA Holdings

From its beginnings in 2012, WoNA has been 

exclusively Symbiota-based (fig. 1). The project has 

benefitted from donations of authoritatively identified 

specimens of the Charles W. O’Brien (CWOB) collection 

(Green Valley, Arizona; see O’Brien and Wibmer, 1982; 

fig. 2). In October 2015, searches for North American 

curculionoids in SCAN yielded >43,500 specimens and 

>1,800 species; the results showed a strong Southwest 
Assembling a Virtual “Weevils of North America” Checklist with Symbiota – Preliminary Insights 53

Figure 2. Screenshot of one record in the SCAN (Southwest Collections of Arthropods Network) occurrence data 

table (see also http://symbiota.org/docs/symbiota-occurrence-data-fields-2/). Top-level tabs include screens for 

editing the specimen’s identification (with options to submit to Filtered Push; see “Conclusions” section below), 

viewing annotations, adding images or GenBank links, and occurrence record administration. Switching from frontend (fig. 1) to back-end (fig. 2) displays of specimen-level data is instantaneous in Symbiota for users with editing 

rights.
54 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Anthonomus

alboannulatus

Anthonomus

albopilosus

Anthonomus

albus

Anthonomus

bicorostris

Anthonomus

canoides

Anthonomus

cognatus

Anthonomus

confusus

Anthonomus

corvulus

Anthonomus

decipiens

Anthonomus

disjunctus

Anthonomus

elongatus

Image not

yet available

Anthonomus

eugenii

Anthonomus

floralis

Anthonomus

fulvus

Anthonomus

grandis

Anthonomus

grandis

Figure 3. Screenshot with thumbnail view of species in the weevil genus Anthonomus Germar 

(in the sense of O’Brien and Wibmer, 1982) linked to WoNA. This view is produced by searching for 

“Anthonomus” in the checklist’s Search/Filter box (fig. 1) and then selecting “Display as Images” 

and “Rebuild List”. Clicking on each thumbnail will lead to the corresponding taxon profile page, 

where additional high-resolution images and specimen data are available.

Figure 4. Screenshot of species profile page in the Southwest Collections of Arthropods 

Network/Weevils of North America database for Acalles sablensis Blatchley, with link to 

the original description (Blatchley, 1920), available through the Biodiversity Heritage Library. 

Thumbnails of dorsal and lateral habitus photographs and an interactive map of specimen 

occurrence records are also shown on each species page. The pen icon in the top right corner 

links to the taxon profile page content management system (see fig. 5).
Assembling a Virtual “Weevils of North America” Checklist with Symbiota – Preliminary Insights 55

bias mirroring that of the source collections (that is, 

~10,000 specimens and 400 species are from Arizona alone). 

WoNA itself has 1–100 specimen occurrence records for 

>350 genera (~88 percent genus-level coverage for North 

America) and >1,100 species (~30 percent species-level 

coverage for North America). High-resolution images 

(frequently with a scale bar) are available for more than 

600species (~15–20 percent; fig. 3). Roughly 300diagnoses, 

imported from the Biodiversity Heritage Library, have 

been added to WoNA (fig. 4). The following URL for the 

cryptorhynchine genus Acalles Schoenherr (in the sense of 

O’Brien and Wibmer, 1982) illustrates how these components 

are integrated in Symbiota: http://symbiota4.acis.ufl.edu/scan/

portal/taxa/index.php?taxon=4682. 

WoNA’s interactive key module is available at http://

symbiota4.acis.ufl.edu/scan/portal/ident/key.php?cl=1. Basic 

functionality of this module is projected for late 2015 although 

authoring a powerful tool will require considerable new 

taxonomic research. The SCAN identification module will 

include a new feature that allows authors to illustrate specific 

characters by way of voucher-based, scalable vector graphics 

(SVG)-highlighted detail images (Ramírez and others, 2007; 

Yoder and others, 2010). So far, 50 multi-state characters for 

curculionoid families and subfamilies have been extracted 

from the American Beetles chapters (Anderson, 2002; Arnett 

and others, 2002) and reformulated for the WoNA key.

Conclusions—Prospects for 

Arthropod-Centric Symbiota 

Monographs

We close with discussing a broader question—what 

are the advantages and limitations of the WoNA approach 

to systematic “monography,” and under what circumstances 

should prospective authors make use of this approach?

Mayr and Ashlock (1991, p. 421) define a monograph 

as “an exhaustive treatment of a higher taxon in terms of all 

available information pertinent to taxonomic interpretation; 

[this] usually involves full systematic treatment of the 

comparative anatomy, biology, ecology, and detailed 

distributional analyses of all included taxa.” While WoNA 

Figure 5. Screenshot of 

Weevils of North America 

checklist administrator interface 

for managing linked vouchers. 

Checklist authors can create 

custom searches (“Create SQL 

Fragment”) to identify vouchers 

recently added to the Southwest 

Collections of Arthropods 

Network as potential candidates 

for inclusion in the checklist.
56 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

fits this definition in a general sense, we acknowledge that 

publishing by way of Symbiota is not identical to a one-time 

submission of a manuscript to a traditional, peer-reviewed 

journal. Academic recognition of new and significant 

systematic contributions in such a medium is arguably the 

greatest challenge.

However, the landscape of acceptable models 

for publishing taxonomic information is changing 

(International Commission on Zoological Nomenclature, 

2012; Smith and others, 2013; Smith, 2015). There are 

many advantages inherent in shifting to a dynamic, 

collaborative monography model—especially for a project 

of WoNA’s dimensions. For one, Symbiota—as a result 

of prior NSF awards and strong iDigBio/TCN community 

support (Gries and others, 2014)—is an immensely robust, 

production-level platform with some 470 collections, 

2,000 users, and 18 million specimen occurrence records 

managed in more than 20 portals including SCAN. SEINet 

alone has ~ 20,000 monthly site visits, according to 

Google Analytics. Such widely distributed data input and 

institutional/user support are valuable assets to taxonomist 

authors. Additional synergy may be obtained through 

linking WoNA with the nationwide network of Symbiota 

vascular plant portals. Connecting weevil and plant 

specimen data at such a scale opens up new possibilities 

for analyses of co-occurrences and interactions.

Symbiota’s capabilities to support monographic 

treatments can be further improved with new modules that 

allow (1) the citable exposition (author/year/reference 

URL) of Symbiota monographs within existing portals; 

(2) custom-configurable (taxon-/region-specific) static 

output functions (for example, extract the checklist as a 

PDF; show taxa, images, and key for Arizona); and (3) 

a standard-compliant reference module for taxon profile 

pages. These modules can mature as WoNA and similar 

treatments receive more authoritative input and weight. 

Integration with ZooBank (Krell and Pyle, 2010) would 

open up possibilities for publishing new nomenclatural 

actions.

Another advantage is the ability to accommodate 

different forms of institutional and author engagement 

with proper intellectual attribution. This allows WoNA to 

transparently accredit the provenance of each specimen, 

image, description, or key identification component (figs. 1 

and 4). Attribution of credit is critical for engaging additional 

collections and experts into such collaborative projects. 

Lastly, Symbiota is developing tools to promote 

peer review by way of expert annotations using the novel 

Filtered Push technology (Morris and others, 2013), see 

http://wiki.filteredpush.org/wiki/. This technology amounts 

to an open, distributed, and dynamic system of contributor 

engagement and peer review. The coming years will reveal 

to what extent these developments contribute to altering 

the cost/benefit ratio for taxonomic experts interested in 

leveraging large-scale specimen-level digitization efforts 

towards monographic projects with Symbiota.

Acknowledgments

The authors thank Neil Cobb and Paul Heinrich of 

Northern Arizona University for their support of the SCAN and 

WoNA projects. We also acknowledge contributions to WoNA’s 

data holdings by Gregory Setliff (Kutztown University), 

Martha Camacho, Dustin Daniel, Nicole Henry, Soon Flynn, 

Jennifer Guzzardi, Lisa Höfler, Andrew Jansen, Sangmi Lee, 

Joshua Persson, and Naomi Pier (all Arizona State University). 

Support for WoNA-related digitization and research activities 

by the National Science Foundation (EF-1207107) and the 

United States Department of Agriculture (Agreement 58-1275-

1-335) is kindly acknowledged. 

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Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter G

Influence of Habitat and Region on Spider Communities 

Along Two Elevation Gradients in the Southwestern U.S.

By Sandra L. Brantley,1 Caitlin A. Chapman,2 and Neil S. Cobb2

Abstract

There has been little research examining the changes 

in community composition of ground-dwelling arthropods 

along elevation gradients, and yet, understanding how 

species are distributed along elevation gradients is critical 

for understanding how such communities may respond to 

climate change. This report is one of few comparative studies 

of spider communities along elevation gradients in the 

Southwest U.S. We examined the abundance and community 

composition of ground-dwelling spiders along elevation 

gradients in northern Arizona (San Francisco Peaks) and 

New Mexico (Bandelier National Monument), respectively. 

Our questions were (1) how are spider species distributed 

across elevation gradients that span piñon-juniper, ponderosa 

pine, and mixed-conifer habitats, and are there fundamental 

changes in community composition, or degree of habitat 

specialization from low to high elevations; (2) how do these 

patterns compare between similar habitats in two regions 

of the Southwest; and (3) what is the importance of habitat 

structure versus geographic distance in terms of community 

structure and abundance. Key findings from this study were 

(1) species composition was distinct among habitats for 

both elevation gradients, despite no consistent differences in 

overall abundance and species richness; (2) species displayed 

a strong affinity for specific habitats, 59 percent of the 79 

taxa were found in only one habitat type; (3) there was also 

a strong degree of regionalization, where 77 percent of the 

79 species were only found in northern Arizona or northern 

New Mexico; and (4) habitat affinity was more important 

than geographic distance, in that spider communities were 

more similar among the same habitats in different states than 

communities in adjacent but different habitats in the same 

area.

Introduction

Many arthropod groups in the southwestern U.S., 

including spiders, are still poorly described (Parmenter and 

others, 1995). Ground-dwelling spiders are dominant predators 

in the region, typically composing 16–38 percent of grounddwelling arthropod species in some habitats (Crawford, 1989; 

Ellis and others, 2000, 2001). Although scores of species may 

occupy a single habitat, many spiders are locally rare, although 

wolf spiders (Lycosidae) and ground spiders (Gnaphosidae) 

can be abundant (Bradley, 2013). Spiders are defined as 

generalist predators (Wise, 1993), but their distributions may 

reflect more specialized preferences for climate and habitat 

structure, such as the amount of precipitation, canopy cover, 

or litter depth (Uetz, 1991). Spider community composition 

may change dramatically over meters in response to changes 

in habitat structure, suggesting that they can be extremely 

sensitive to microhabitat changes (Higgins and others, 2014).

Because the southwestern U.S. is characterized by varied 

topography and gradients (Brown, 1982), spider communities 

can vary dramatically over short distances in response to 

changing habitat types (Chatzaki and others, 2005; Bowden 

and Buddle, 2010; Higgins and others, 2014). Additionally, 

directional habitat change over elevation gradients provides a 

proxy for climate change, replacing space for time and allows 

us to better understand how climate change impacts may affect 

species and communities. Since higher elevation habitats are 

often restricted to isolated areas throughout most of the Colorado 

Plateau, they are considered “at-risk” habitats under rapid 

climate change (Rehfeldt and others, 2006, 2009). At a larger 

geographic scale, higher elevation habitats can be separated 

by more than 50 kilometers (km), creating an archipelago of 

fragmented habitats. It is unclear how consistently these changes 

are reflected between these isolated mountain habitats.

During the past 13,000 years, vegetation zones in the 

Southwest, including the Colorado Plateau, have moved up in 

elevation and migrated northward as the climate has warmed 

(Vankat, 2013). In the process, it is likely that ecosystems 

ranging from mixed conifer forests to subalpine have 

become fragmented in a sea of lower elevation ecosystems. 

Concomitantly, species that are adapted to warmer and drier 

environments, for example, piñon pine (Pinus edulis), juniper 

1

Museum of Southwestern Biology, University of New Mexico

2

Colorado Plateau Biodiversity Center and Department of Biological 

Sciences, Northern Arizona University
60 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

(Juniperus sp.), and ponderosa pine (Pinus ponderosa), 

that have come to define southwestern woodlands, have 

radiated northward. There has been considerable interest in 

documenting the biogeography of species and communities 

associated with isolated mountains in southern Arizona, 

otherwise referred to as sky islands (Masta, 2000). However, 

there has been virtually no research examining community 

patterns on elevation gradients throughout the Colorado 

Plateau, which also contains complex gradients (Vankat, 2013).

An example of recent work on gradients in the region is 

Lightfoot and others (2008), which compared orthopterans 

and spiders along a latitudinal gradient that spanned from 

the Jornada Long-Term Research Program site in southern 

New Mexico to Bandelier National Monument in northern 

New Mexico. One of the most interesting results from that 

study was that spider assemblages were more similar to 

adjacent but different vegetation types, than they were to 

spider assemblages in the same vegetation type in different 

ecoregions. However, the only tree-dominated vegetation 

type represented in more than one area was piñon-juniper 

woodland. It is unclear whether these patterns would be 

apparent in higher-elevation forests. Because higher elevation 

forests are more fragmented regionally, we expect that spider 

communities at higher elevations would show even stronger 

patterns in regional differences than was reported in Lightfoot 

and others (2008).

In this paper, we compare spider communities from two 

elevation gradients that are separated longitudinally from 

each other, one in the San Francisco Peaks (SFP) in northern 

Arizona, and the other in the Jemez Mountains (Bandelier 

National Monument [BAND]) in northern New Mexico. We 

address the following questions: (1) which species make up 

the ground-dwelling spider communities and do they show 

preferences for a given habitat type or do they occur in 

multiple habitats; (2) how do species composition and habitat 

preference compare between two widely separated locations 

with the same designated habitat types; and (3) from this 

comparison can we show the strength of habitat type versus 

ecoregion (in other words, adjacent but different vegetation 

types) in structuring these communities?

Flagstaff

San Francisco

Peaks

Bandelier

National Monument

Prescott

ARIZONA NEW MEXICO

Mesa

Phoenix

Albuquerque

Gallup

Santa Fe

Flagstaff

San Francisco

Peaks

Bandelier

National Monument

Prescott

ARIZONA NEW MEXICO

Mesa

Phoenix

Albuquerque

Gallup

Santa Fe

0

0

50 100

50 100

MILES 

KILOMETERS 

San Francisco Peaks habitats

 Piñon-juniper

 Ponderosa pine

 Mixed conifer

Bandelier National Monument

 Piñon-juniper

 Ponderosa pine

 Mixed conifer

EXPLANATION

Figure 1. Map of the southern Colorado Plateau including the San Francisco Peaks (circles) and Bandelier National 

Monument (squares) study areas. Darker green areas between the two study areas typically represent ponderosa pine 

forests and mixed conifer vegetation types, as well as higher elevation forests. Piñon-juniper woodlands are nearly 

continuous between the two areas, whereas higher elevation forests are more fragmented.
Influence of Habitat and Region on Spider Communities Along Two Elevation Gradients in the Southwestern U.S. 61

Table 1. Habitat and climate characteristics at the two study locations, San Francisco Peaks (SFP), AZ and Bandelier National 

Monument (BAND), NM. Mean annual precipitation, maximum temperature, and minimum temperature are based on modeled 800 m 

PRISM data, which are not specific enough for each line of traps.

[°C, degrees Celsius; m, meters; cm, centimeters]

Area Elevation 

(m) Latitude Longitude Habitat Dominant trees

Annual 

precipitation 

(cm)

Annual temperature, 

(°C)

Maximum Minimum

SFP 2,011 35.5143 –111.6235 Piñonjuniper

Pinus edulis, Juniperus monosperma 39.6 19.0 2.5

BAND 1,948 35.7618 –106.2654 39.6 18.1 0.9

SFP 2,285 35.3726 –111.5875 Ponderosa 

pine

Pinus ponderosa 52.7 15.5 0.6

BAND 2,454 35.8296 –106.3683 55.7 16.5 –1.0

SFP 2,633 35.3624 –111.7413 Mixedconifer

Pinus strobiformis, Pseudotsuga menziesii, Abies 

concolor, Populus tremuloides

67.7 12.4 –0.7

BAND 2,712 35.8511 –106.4102 71.5 13.6 –0.4

Sites and Methods

The San Francisco Peaks are located in Coconino County, 

Arizona; Bandelier National Monument is located in Sandoval 

County, New Mexico (fig. 1). Both areas are of volcanic origin 

with volcanic activity beginning about 6 Ma for SFP (Priest 

and others, 2001) and 18 Ma for BAND (Goff, 2009). 

In both studies we compared three vegetation types, 

piñon-juniper woodland (PJ; low elevation), ponderosa pine 

(PIPO; medium elevation), and mixed-conifer/aspen forest 

(MC; higher elevation). The areas are at a similar latitude 

and approximately 475 km apart. SFP is part of the Arizona 

mountains forests ecoregion, BAND is part of the Southern 

Rockies forests ecoregion, and both are part of the broader 

temperate coniferous forest major habitat type (Ricketts and 

others, 1999). The data used for these comparisons were 

collected in 2009–11.We restricted the analysis to the three 

vegetation types where we had comparable data from each 

of the two gradients. The BAND study has been ongoing for 

more than 20 years (1992–present), sampling continuously 

during the growing season for many ground-dwelling 

arthropod taxa from piñon-juniper woodlands through mixed 

conifer (Lightfoot and others, 2008). The SFP study was 

initiated in 2010 and focused on ants, beetles, and spiders. 

The three habitats studied at SFP and BAND are 

comparable with regard to elevation, dominant tree species, 

and climate (table 1). Temperature and precipitation data are 

based on modeled 30-year mean data using an 800-m pixel 

size (PRISM Climate Group, 2014), and are similar between 

sites. We established meteorological stations for all three 

habitats in the SFP since 2002 (Merriam-Powell Center for 

Environmental Research, 2014). The meteorological data 

do vary 2–8 centimeters (cm) in precipitation and 3–5 °C 

in temperature from PRISM data, indicating that modeled 

PRISM values do not precisely reflect climate at our scale of 

interest. We do not have comparable meteorological station 

data available for all of the BAND habitats. 

To sample ground-dwelling spiders we used pitfall 

traps, which differed in trap type and sample duration 

between BAND and SFP studies. The SFP pitfalls consisted 

of 32 × 200 millimeters (mm) lipped borosilicate test tubes 

inserted in polyvinyl chloride (PVC) sleeves with a PVC 

cover, and filled with diluted ethylene glycol (Higgins and 

others, 2014). Pitfalls were placed in 2 lines of 10 traps in 

PJ; the lines in PIPO and MC were arranged with one set in 

the open and one set in forested areas to sample these two 

very distinct microhabitats that do not occur in the more 

open woodlands. Traps within a line and between lines were 

10 meters (m) apart. We had 20 total traps per site for PJ,

and 40 traps per site for PIPO and MC. Sampling occurred 

at three sites for each of the three vegetation types. Samples 

were collected after one month (approximately late August to 

late September) in 2010 and 2011.

The pitfall traps at BAND were somewhat larger (plastic 

cups inside tin cans, 7.5 cm top diameter and 10 cm deep) and 

placed in 5 lines of 6 traps, for a total of 30 traps per site. Within 

a line, traps were 10 m apart; the lines were at least 100 m apart. 

Propylene glycol was used as the preservative. Although we did 

not directly compare trap types, the smaller pitfall traps used 

for the SFP study were probably more efficient at collecting 

smaller individuals as reflected by the relatively high number 

of immature spiders. We also suggest they were more effective 

in sampling a larger number of species, as most of the species 

found in the SFP study and not found at BAND during the same 

time period have been recorded at BAND in other years. The 

open areas and closed canopy areas of PIPO sites at SFP were 

sampled separately and then combined. At BAND, the PIPO 

site was sampled continuously through open and closed canopy 

areas without separating them. More detail about the sampling 

can be found in Lightfoot and others (2008).

Samples were collected after two months (approximately 

late July to late September) in 2010 and 2011. In summer 

2011, the Las Conchas wildfire burned most of the trap areas 

at BAND in the PIPO and MC habitats; therefore we used 
62 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

pre-fire data from the fall of 2009 and 2010 for comparison. 

In order to compare results from both studies, we 

standardized the metric for spider catches that reflected the 

number of spiders captured per 10-cm pitfall trap perimeter 

per 30 days. Specimens from both studies were collected into 

70 percent ethanol, sorted, and identified at the University of 

New Mexico (UNM) or Northern Arizona University (NAU). 

For both studies, the primary reference collection was 

deposited at UNM Museum of Southwestern Biology and a 

second reference at the NAU Colorado Plateau Museum of 

Arthropod Biodiversity.

Data Analysis

To address our first and second research questions, we 

focused on five metrics that describe the spider communities 

at both locations and in the three habitats: (1) species richness 

and total abundance, (2) species composition, (3) familylevel patterns, (4) dominant species, and (5) indicator species. 

These metrics then allowed us to address our third question 

3

2

1

0

9

6

3

0

A B

C D

PiñonjuniperPonderosa

pine

Mixed

conifer

PiñonjuniperPonderosa

pine

Mixed

conifer

Species richness Relative abundance

Bandelier

National Monument

San Francisco

Peaks

Total

Adults

Immatures

EXPLANATION

Figure 2. Graph of species richness (A, B) and relative abundance 

(C, D) in three habitat types: piñon-juniper, ponderosa pine, and 

mixed-conifer at the San Francisco Peaks (SFP) and Bandelier 

National Monument (BAND) study sites. Species richness in mean 

number of species per standardized trap/day of ground-dwelling 

spider species and relative abundance per standardized trap/day. 

Standardized trap size explained in Methods section. Significance 

values between SFP and BAND given in Results section.

concerning the strength of habitat type or ecoregion on 

structuring the communities.

We combined data from the open and closed canopy SFP 

sites in PIPO and MC to more closely match the sampling 

at BAND. We defined spider species as generalists if they 

occurred in more than one habitat type and as specialists if 

they occurred in one habitat only. Single representatives of a 

given species were removed from all analyses. We analyzed 

the data two ways: (1) with immature spiders included in 

the analysis because they make up a large part of the active 

life stages and biomass (Norris, 1999; Jimenez-Valverde and 

Lobo, 2006), and (2) with adult stages only. To minimize the 

differences in sampling technique (trap design and placement) 

between SFP and BAND, we standardized the spider 

abundance values to reflect number of individuals per 10-cm 

length of the pitfall circumference per day of sampling. 

For both locations, we compared spider species 

composition, species richness, and relative abundance in 

the habitats. To analyze differences in species richness and 

abundance between SFP and BAND, we used a two-factor 

analysis of variance (ANOVA): the factors were location 

(SFP and BAND) and habitat type (PJ, PIPO, and MC) 

nested within each location. We used species composition 

and the relative abundance of adults to analyze habitat and 

location effects (IBM Corp., 2013) for mean trap numbers, 

and Primer-6 software for multidimensional scaling (MDS) 

and an unweighted Bray-Curtis cluster analysis (Clarke and 

Gorley, 2006). Because samples were collected multiple times 

from the same traps, we used the PERMANOVA software 

(Anderson and others, 2008a) to analyze differences between 

locations and habitats. Data were square root transformed 

before analysis. Indicator species analysis was performed 

using PC-ORD software (McCune and others, 2002). The 

analysis is based on (1) the relative frequency of species and 

(2) the concentration of their abundance within groups, called 

fidelity and exclusivity, respectively, by McCune and others 

(2002). Species that are consistently found in only one habitat, 

even if they are rare, are strong indicators for that habitat. 

Results 

Species Richness and Abundance

A total of 1,741 individuals were collected in 79 taxa 

identified at the genus or species level (appendix). For all 

habitats at both SFP and BAND, Lycosidae and Gnaphosidae 

were the dominant families, as is common for grounddwelling spiders throughout North America (Bradley, 2013). 

For both SFP and BAND, total species richness decreased 

with increasing elevation between PJ (38 species) and PIPO 

(24 species) habitats but increased again in MC (30 species), 

with no mid-elevation increase in richness, as seen in some 

other groups (Rahbeck, 1995). Examining species richness on 
Influence of Habitat and Region on Spider Communities Along Two Elevation Gradients in the Southwestern U.S. 63

San Francisco Peaks 

 Piñon-juniper

 Ponderosa pine

 Mixed conifer

Bandelier National Monument

 Piñon-juniper

 Ponderosa pine

 Mixed conifer

2D Stress 0.15

A All spiders

Multidimensional scaling 1

EXPLANATION

2D Stress 0.09

C Lycosidae

Multidimensional scaling 2

2D Stress 0.04

B Gnaphosidae

Figure 3. Multidimensional scaling ordination of grounddwelling spider species (immature spider population removed). 

A, All spider taxa; B, Gnaphosid species only; C, Lycosid species 

only. Each symbol represents a line of traps. San Francisco 

Peaks site, circles; Bandelier National Monument site, squares; 

piñon-juniper habitat, green; ponderosa pine habitat, blue; mixedconifer habitat, red. 

Table 2. Number of species occurring in single and multiple 

habitats at San Francisco Peaks (SFP) and Bandelier National 

Monument (BAND) sites. Piñon-juniper habitat, PJ; ponderosa 

pine habitat, PIPO; and mixed-conifer habitat, MC.

Habitat SFP only BAND only SFP and 

BAND Total

PJ 14 6 0 20

PIPO 5 5 0 10

MC 8 7 2 17

PJ and PIPO 7 5 7 19

PIPO and MC 0 2 4 6

PJ, PIPO, and MC 1 1 5 7

Totals 35 26 18 79

a per sample basis indicated overall differences among habitats 

(df=2, F=4.789, P=0.009) with a weak trend in decreasing 

species richness with increasing elevation at SFP and no 

significant differences at BAND (fig. 2). As was the case for 

most of the analyses, taxa dominated by adults (rather than 

immature individuals) showed differences within and between 

study areas. There were no differences in abundance among 

habitats (df=2, F=0.068, P=0.934). Differences for location 

* habitat were not significant for richness (df=2, F=1.469, 

P=0.231) nor for abundance (df=2, F =1.280, P=0.279). 

Species richness was significantly greater at BAND (df=1, 

F=31.698, P<0.001), but significantly more individuals were 

collected at SFP (df=1, F=75.821, P<0.001) (fig. 2). More 

species that occurred at BAND also occurred at SFP, while 

more unique species were collected at SFP (table 2, appendix). 

Spider Communities Among Habitats and 

Locations

We found strong differences in the composition of spider 

communities among habitats and less so between locations; 

this clarified the ANOVA results, which looked only at number 

of species (richness) and abundance (table 2, appendix). In 

agreement with these values, the results of PERMANOVA 

tests showed the effect of location (SFP versus BAND) was 

not significant for all spiders combined (pseudo-F=1.9918, 

P=0.231), for gnaphosids (pseudo-F=1.4559, P=0.299), or 

for lycosids (pseudo-F=1.8578, P=0.322). However, the 

effect of habitat was significant for all spiders combined 

(pseudo-F=5.9398, P=0.001), for gnaphosids (pseudo-F=3.7594, 

P=0.001) and for lycosids (pseudo-F=10.697, P=0.001). 

We used multidimensional scaling ordination (without 

including the immature stages) to visualize differences (fig.3A–C) 

among habitats and locations. Spiders in the SFP habitats 

showed less scatter than BAND; PJ habitat was well separated 

from the other two habitats. Some of the trap lines in PIPO 

and MC sites overlapped at both locations. The Gnaphosidae 

showed less separation by habitat or location (fig. 3B) than the 
64 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Lycosidae, which were important contributors to the separation 

of habitats (fig. 3C). Analysis at the family level indicated that 

lycosids, gnaphosids, and linyphiids showed significant trends 

in abundance as elevation increased; lycosids (df=2, F=10.786, 

P<0.001) and linyphiids (df=2, F=8.305, P<0.001) increased, and 

gnaphosids (df=2, F=20.380, P<0.001) decreased.

The cluster analysis (fig. 4) provided more resolution for 

the patterns seen in the MDS (fig. 3). The main division in 

the dendrogram is between PJ sites and combined PIPO and 

MC sites, with about a 20 percent similarity. Later divisions 

separated along location and habitat, with some mixing of 

both. The similarity levels among these sites never exceeded 

70 percent, showing that there was a large amount of variation 

in both habitat and location.

The five most abundant species at each location and 

habitat type showed little overlap among locations (table 

3A–C). The top five species made up about 50 percent of the 

abundance in PJ at SFP and BAND, but for the other two 

habitats, the proportion increased, as expected with fewer 

species at higher elevations (with the exception of SFP PIPO), 

but the proportion increased more at BAND (61.6 percent at 

PIPO and 82.6 percent at MC) than at SFP (48.4 percent and 

76.6 percent at MC). The numbers of immature stages were 

high enough in most habitats to make up a large proportion of 

total spider numbers. Families contributing the most immature 

stages were Gnaphosidae and Lycosidae. 

Indicator Species Analysis

Another way of looking at habitat specificity is through 

indicator species analysis (table 4), which detects more than 

just numerically dominant species occurring in a habitat, as 

described in the Data Analysis section. For SFP, two of the 

five dominant PJ species were also indicators; at PIPO one 

of the dominant five was an indicator; and at MC two of the 

five were indicators. For BAND, one of the dominant five 

species was an indicator, and two of the five were indicators 

in PIPO and MC. Species that were not numerically abundant 

but important in distinguishing habitat types were Pardosa 

montgomeryi at SFP and Anyphaena hespar, Trochosa 

terricola, Varacosa gosiuta, and Zorocrates karli at BAND. 

Lycosids were indicators in all habitats (fig. 3C). Gnaphosids 

were indicators in PJ and MC, although present in all habitats, 

often as immature stages of Zelotes. 

In general, spider species were specialists in habitat 

preference, with 20 in PJ only, 10 in PIPO only, and 17 in MC 

only or 59 percent of the total (table 2). Nineteen occurred in 

PJ and PIPO, 6 in PIPO and MC, and 7 in all three habitats 

(including immature stages of Callilepis, which probably 

represent multiple species, and the genus Cicurina, whose 

taxonomy is unsettled at present but at our locations probably 

includes several species). Within location, the number of 

species occurring in only one habitat at SFP was 14 in PJ, 5 in 

San Francisco Peaks sites

 Piñon-juniper

 Ponderosa pine

 Mixed conifer

Bandelier National Monument sites

 Piñon-juniper

 Ponderosa pine

 Mixed conifer

EXPLANATION

Similarity Dendrogram

Similarity

BPJE

BPJD

BPJC

BPJABPJB

SFPJ2SFPJ3SFPJ1

SFPIPO1

BMCC

BMCE

BMCABMCB

SFMC1SFMC2SFMC3

BPIPOBBPIPOA

BMCD

SFPIPO2SFPIPO3

BPIPOE

BPIPOC

BPIPOD

0

20

40

60

80

100

Figure 4. Dendrogram from Bray-Curtis 

cluster analysis for the effect of habitat 

and location on spider communities.
Influence of Habitat and Region on Spider Communities Along Two Elevation Gradients in the Southwestern U.S. 65

Table 3. The top five taxa, in relative abundance, in each habitat type. A, piñon-juniper; B, ponderosa pine, 

and C, mixed-conifer. 

[imm, immature individuals]

A) Piñon-juniper

San Francisco Peaks Bandelier National Monument

Taxon

Percent 

of total 

abundance

Cumulative 

percent Taxon

Percent 

of total 

abundance

Cumulative 

percent

Zelotes anglo 19.1 19.1 Schizocosa mccooki 14.4 14.4

Drassyllus mexicanus 9.9 29.0 Drassodes gosiutus 13.3 27.7

Zelotes imm. 8.9 37.9 Zelotes imm. 9.9 37.6

Drassodes imm. 5.7 43.6 Cicurina spp. 7.7 45.3

Habronattus imm. 5.7 49.3 Alopecosa kochi 6.1 51.4

B) Ponderosa pine

San Francisco Peaks Bandelier National Monument

Taxon

Percent 

of total 

abundance

Cumulative 

percent Taxon

Percent 

of total 

abundance

Cumulative 

percent

Alopecosa kochi 16.7 16.7 Pardosa yavapa 17.1 17.1

Pardosa yavapa 9.2 25.9 Alopecosa kochi 13.7 30.8

Zelotes imm. 8.1 34.0 Varacosa gosiuta 11.8 42.6

Xysticus montanensis 7.7 41.7 Cicurina spp. 11.4 54.0

Pardosa imm. 6.7 48.4 Zelotes imm. 7.6 61.6

C) Mixed-conifer

San Francisco Peaks Bandelier National Monument

Taxon

Percent 

of total 

abundance

Cumulative 

percent Taxon

Percent 

of total 

abundance

Cumulative 

percent

Alopecosa kochi 55.6 55.6 Cicurina spp. 50.5 50.5

Pardosa imm. 7.0 62.6 Pardosa uncata 11.1 61.6

Zelotes fratris 6.2 68.8 Helophora orinoma 9.2 70.8

Zelotes imm. 4.1 72.9 Alopecosa kochi 8.6 79.4

Micaria pulicaria 3.7 76.6 Zelotes fratris 3.2 82.6

PIPO, and 8 in MC, for a total of 27 out of 35 (77 percent); at 

BAND there were 6 in PJ, 5 in PIPO, and 7 in MC, for a total 

of 18 out of 26 species (69 percent). 

Discussion

A major pattern that emerged was a significant degree of 

partitioning of communities among habitats in a predictable 

transition from piñon-juniper woodlands to ponderosa forests 

and mixed-conifer forests. We also found differences in spider 

communities among the regions, but not nearly to the degree 

as reported by Lightfoot and others (2008) who found that 

adjacent but different habitats were much more similar than the 

same habitats in two areas. MDS (fig. 3) clearly showed that 

both habitat and location influenced the spider communities; 

but cluster analysis (fig. 4) and PERMANOVA results showed 

that habitat was the stronger of the two. Our study differed in 

that both areas are located on the southern Colorado Plateau 

and there has been continuity of habitats between SFP and 

BAND in the last 20,000 years, although higher elevation 

habitats have become more fragmented in the last 10,000 years 

(Vankat, 2013). Both the SFP and BAND also share the same 

forested vegetation types, whereas the three areas studied 

in Lightfoot and others (2008) ranged along a latitudinal 

gradient from the southern Chihuahuan desert (Jornada) to the 

northernmost Chihuahuan desert (Sevilleta National Wildlife 
66 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 4. Indicator species by habitat for San Francisco Peaks 

(SFP) and Bandelier National Monument (BAND) sites. PJ, 

piñon-juniper habitat; PIPO, ponderosa pine habitat; and MC, 

mixed-conifer habitat; NC, species not collected, therefore could 

not contribute to indicator value for combined locations; NP, not 

performed since species did not occur at SFP and BAND; NS, 

species not statistically significant as an indicator; imm., immature 

individuals.

Habitat Species

P-value

SFP & BAND SFP BAND

PJ Schizocosa mccooki 0.0002 0.0342 0.0012

Habronattus imm. 0.0004 NS 0.0134

Latrodectus hesperus 0.0036 NS 0.011

Drassyllus mexicanus 0.0066 NS NS

Drassodes imm. NP 0.0342 NC

Zorocrates karli NP NC 0.011

Zelotes anglo NP 0.0342 NC

Schizocosa imm. NP NC 0.0134

Drassodes gosiutus NS NS 0.0134

PIPO Pardosa yavapa 0.0002 0.0372 0.0034

Phrurolithus camawhitae 0.0092 NS 0.023

Varacosa gosiuta NP NC 0.0106

Hololena hola 0.0352 NS 0.0346

Xysticus imm. 0.049 NS NS

Anyphaena hespar NP NC 0.0372

MC Pardosa uncata 0.0006 NS 0.0016

Cicurina spp. 0.0038 NS 0.0016

Trochosa terricola NP NC 0.0116

Zelotes fratris 0.0492 NS NS

Alopecosa kochi NS 0.0084 NS

Pardosa montgomeryi NP 0.0322 NC

Refuge) and finally to the southern Colorado Plateau (BAND). 

This suggests that there has been a much longer period of 

separation or fragmentation of habitats among the three areas.

A possible methodological reason explaining the different 

results between our study and that of Lightfoot and others 

(2008) may be the length of sampling time: two years and only 

the fall season for our study and seven years of year-round 

sampling for Lightfoot and others (2008). It is possible that 

short-term sampling collects mainly the more widespread or 

dominant species within habitats while longer-term sampling 

picks up more rare species (which might include more 

geographically restricted species), leading to a pattern of 

greater regional influence on species composition. A way to test 

this idea with the data from Lightfoot and others (2008) is to 

analyze the data for a few years only, and then add subsequent 

years to see if the pattern changes or not, and if so, determine 

how many years of sampling are needed before a change is 

detected. Even with our smaller dataset there were indications 

that location influenced spider richness and abundance, but 

probably not as strongly as habitat type (fig. 3A–C). There were 

more unique species at SFP (35) than BAND (28) (appendix), 

which was driving some of the location difference. This may 

be due to regional distribution differences of species, such as 

Calilena spp., which occur westward from Arizona, Utah, and 

Nevada, but some of the species are widespread in the western 

U.S. (A. utahana, M. pulicaria, and Z. lasalana, so at least a 

part of the difference was likely due to undersampling. The 

three species just mentioned occurred at BAND, but not during 

the years used in this study (S. Brantley, unpub. data, 2015). 

Long-term studies may help us differentiate how habitat type 

and region organize spider communities.

We found the common pattern of decreasing species 

number with increasing elevation to be more pronounced at 

SFP (33 species in PJ, 28 in PIPO, and 18 in MC) with no 

mid-elevation increase, while at BAND richness changed 

little over the 3 habitats (22 species in PJ, 24 in PIPO, and 20 

in MC), with only a slight mid-elevation increase (appendix). 

In mountain ranges in Canada, Bowden and Buddle (2010) 

did not show consistent changes in spider species numbers 

with elevation, in part because habitat structure did not 

always change (for example, forested sites occurred at 

different elevations). From this, we expected that species 

occurring in two of our habitats would be more likely to 

occur in PIPO and MC, because of similar canopy cover and 

litter. Instead, our results showed that species occurring in 

two habitats were more likely to be in PJ and PIPO at SFP, 

and at BAND were as likely to be in PJ and PIPO as PIPO 

and MC. Elevation changes cover a mixture of abiotic and 

biotic factors, which can confound explanation of species 

patterns (Körner, 2007); Bowden and Buddle’s (2010) 

study design was able to separate abiotic elevation factors 

from vegetation and other habitat factors on their species 

distribution. In our study, we may be seeing the increasing 

importance of abiotic features at high-elevation besides 

forest cover (table 1 and Methods section), such as lower 

temperatures and higher precipitation (Rahbeck, 1995). 

Gnaphosids on Crete (Chatzaki and others, 2005) included 

many species that were widely tolerant of elevation changes. 

The gnaphosids at SFP and BAND were also somewhat less 

restricted by elevation (fig. 3B), showing less distinction 

between habitats compared with the lycosids (fig. 3C).

Otto and Svensson (1982) found different patterns in spider 

occurrence on an elevation gradient in Norway. At their sites, 

spiders at the highest elevation were widely distributed and 

occurred at some lower elevations, while the lowest elevation 

species were more restricted. An explanation they proposed 

is that high-elevation species disperse widely (referred to as 

fugitive species) and therefore occupy large areas. Spider species 

found in this high-latitude location may have already overcome 

the harsh abiotic conditions (compared to the southwestern US), 

where our mixed-conifer species were restricted perhaps by 

relatively hot and dry conditions at lower elevations and by less 

area where cooler conditions occur. The Linyphiidae at our sites 

were generally restricted to mixed-conifer habitat (appendix). 
Influence of Habitat and Region on Spider Communities Along Two Elevation Gradients in the Southwestern U.S. 67

Although spiders are typically described as generalists 

in prey choice (Wise, 1993), they are often more specialized 

in their habitat preferences (Uetz, 1991). Of the 79 species 

collected over 3 habitats and 2 locations in this study, 59 

percent were found in 1 habitat only (table 2, appendix). Within 

the dominant families Lycosidae and Gnaphosidae, which 

are speciose, there were examples of both habitat specialists 

and generalists. In the Lycosidae, specialists included P. 

montgomeryi, P. uncata, and T. terricola in MC only, P. 

orophila, P. yavapa, and S. mccooki in PJ and PIPO; an 

example of a generalist is A. kochi, found in all habitats at both 

locations. In the Gnaphosidae, lower-elevation species included 

those in the genera Drassodes, Herpyllus, and Micaria, while 

MC genera included Gnaphosa and Haplodrassus. In the large 

gnaphosid genus Zelotes, Z. anglo, and Z. lasalanus occurred 

in PJ and PIPO, while Z. fratris occurred in PIPO and MC. 

Mallis and Hurd (2005), working on spider communities in 

six successional habitats in Virginia, found 50 percent were 

specialists (occurring in only one habitat) and no species 

was found in more than four habitats. Pardosa species were 

important there, and the widespread T. terricola also occurred 

in two habitats (the authors’ disturbance recovery site and old 

field), whereas in our study it occurred only in mixed-conifer. 

Chatzaki and others (2005) sampled gnaphosids in five habitats 

on elevation gradients on Crete, finding that 14 (26 percent) of 

54 species were found in only one habitat, and that the highest 

elevation sites had the fewest species and very little overlap 

with species from the other four habitats. 

All habitats were strongly dominated by 4–6 species. The 

five most abundant taxa in each habitat were often different 

between locations (table 3A–C); when the habitats shared 

species, they were in different rank order. In contrast Roughley 

and others (2006) found four out of five of the most abundant 

spider species were shared between tallgrass prairie and forest 

in an ecotone setting in Manitoba, Canada. In fact, the dominant 

spider, Pardosa moesta, occurred in both habitats. While these 

habitats appear very different to people, they may provide some 

common features, such as cover and soil moisture, which are 

likely important from the spiders’ point of view. In a Colorado 

grassland and mixed-grass/shrub landscape, Weeks and Holtzer 

(2000) found virtually no species overlap among species of 

Lycosidae or Gnaphosidae between the two habitats. Though the 

area is described broadly as shortgrass steppe, the two habitats 

were very different in the amount of ground cover (much less 

in the grass/shrub habitat), which probably influenced the 

microhabitat tolerances and mobility of the spiders. 

Examples of species that were significant indicator 

species but were not among the dominant taxa (tables 3, 4) 

include L. hesperus and Z. karli in PJ, P. camawhitae and H. 

hola in PIPO, and T. terricola in MC. Useful indicator species 

for environmental categories (such as habitat type) can be 

overlooked if only the dominant taxa are examined. We do not 

yet have a complete understanding of the factors influencing 

spider habitat occurrences. 

Consistent among habitats and locations in our study was 

the high relative abundance of immature-stage individuals, 

which generally cannot be assigned to a species, but can be 

a large part of spider biomass in a given area (Weeks and 

Holtzer, 2000), creating difficulties in assessing richness and 

turnover (Norris, 1999; Jimenez-Valverde and Lobo, 2006). 

Immature stages were included in the lists of dominant taxa 

for each habitat type (table 3A–C) to show their importance 

in relative abundance, but were excluded from the MDS to 

clarify habitat associations (an MDS including immature 

stages did not alter the overall patterns; data not shown).

An interesting result from this study was the greater 

overlap in spider species between PJ and PIPO habitats, 

rather than between PIPO and MC (appendix), which 

appeared to be more similar in vegetation structure to 

each other than to PJ. A possible explanation is the shared 

geologic and climatic history of the PJ and PIPO habitat 

types in the southwestern U.S. About 14,000 yr. before 

present (B.P.) subalpine forests were found at elevations 

below 2,300 m (Anderson and others, 2008b) in parts of 

the southwestern U.S., so that these forests covered a much 

wider area than they do today (compare with our current 

habitat types, fig. 1, table 1). By 11,500 yr B.P. the climate 

had warmed, causing a retreat of mixed-conifer plant species; 

summer monsoon rains also increased, which allowed 

ponderosa pine, oaks, and piñon pine to move into some 

of the areas vacated by mixed-conifer species (Toney and 

Anderson, 2006; Anderson 2008b). Under these conditions 

the spider species from PJ and PIPO sites perhaps arrived in 

the area at about the same time, while the MC species were 

retreating with the wetter forests.

The southern Rocky Mountains and Colorado Plateau 

form an ideal landscape in which to study the distributions 

of higher-elevation arthropod species, as forested areas that 

are more connected in the Rockies become increasingly 

fragmented on the Colorado Plateau. Species may be 

“filtered” out by fragmentation (isolation) and (or) a 

warmer, drier climate (Wiescher and others, 2012). Such 

studies will allow us to predict distribution changes due to 

global warming, which is expected to increase temperatures 

and extreme climate events in the southwestern U.S. 

(Diffenbaugh and Ashfaq, 2010). While much work remains 

in understanding regional patterns in spider communities in 

the southwestern U.S., the cumulative results of this study 

and others (Lightfoot and others, 2008; Higgins and others, 

2014) are steadily leading to a more complete picture.

Acknowledgments

We thank Craig Allen and Kay Beeley of Bandelier 

National Monument for their continued support of the long-term 

arthropod study there; Jason McVay for PRISM meteorological 

data; and Barbara Ralston, Mike Draney and David Richman 

for constructive reviews that improved the paper. Brad 

Butterfield and Carrie Cobb both provided insightful comments 

on the manuscript. We acknowledge the support of NSF grant 

EF-1207371 for the Symbiota/SCAN database. 
68 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

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Appendix
Influence of Habitat and Region on Spider Communities Along Two Elevation Gradients in the Southwestern U.S. 71

Appendix. List of taxa and total specimen numbers collected at the San Francisco Peaks (SFP) and Bandelier National Monument 

(BAND) sites by habitat. 

[PJ, piñon-juniper; PIPO, ponderosa pine; MC, mixed-conifer; imm., immature individuals]

Family Species

Habitat Type

PJ PIPO MC Total

SFP BAND SFP BAND SFP BAND SFP BAND BOTH

Agelenidae Agelenopsis utahana 0 0 0 0 1 0 1 0 1

Novalena lutzi 0 0 0 0 0 1 0 1 1

Calilena arizonica 3 0 1 0 0 0 4 0 4

Calilena restricta 0 0 0 0 1 0 1 0 1

Hololena hola 1 4 3 14 0 0 4 18 22

Anyphaenidae Anyphaena hespar 0 1 0 5 0 0 0 6 6

Clubionidae Clubiona oteroana 0 0 0 0 0 1 0 1 1

Corinnidae Castianeira luctifera 0 0 5 0 0 0 5 0 5

Castianeira occidens 1 1 13 3 0 1 14 5 19

Phrurolithus camawhitae 5 0 4 3 1 1 10 4 14

Phrurolithus schwarzi 1 0 0 0 0 0 1 0 1

Phrurotimpus certus 4 0 3 0 0 0 7 0 7

Dictynidae Cicurina spp. 0 14 19 24 6 159 25 197 222

Dictyna apacheca 0 0 2 0 0 0 2 0 2

Dictyna personata 0 1 0 0 0 0 0 1 1

Lathys delicatula 0 1 0 0 0 0 0 1 1

Euctenizidae Neoapachella rothi 0 0 0 0 3 0 3 0 3

Filistatidae Kukulcania imm. 1 0 0 0 0 0 1 0 1

Gnaphosidae Callilepis imm. 9 0 2 0 1 0 12 0 12

Drassodes gosiutus 14 24 1 0 0 0 15 24 39

Drassodes neglectus 0 0 1 0 0 0 1 0 1

Drassyllus dromeus 0 0 0 5 0 0 0 5 5

Drassyllus lepidus 3 0 0 0 0 0 3 0 3

Drassyllus mexicanus 28 6 29 0 0 0 57 6 63

Gnaphosa muscorum 0 0 0 7 0 4 0 11 11

Haplodrassus bicornis 0 0 0 2 0 0 0 2 2

Haplodrassus signifer 0 0 0 0 0 8 0 8 8

Herpyllus cockerelli 1 0 0 0 0 0 1 0 1

Herpyllus propinquus 1 0 0 0 0 0 1 0 1

Micaria longipes 3 0 0 0 0 0 3 0 3

Micaria pulicaria 0 0 0 0 9 0 9 0 9

Nodocion rufithoracicus 1 0 0 0 0 0 1 0 1

Zelotes anglo 54 0 6 0 0 0 60 0 60

Zelotes fratris 0 0 3 4 15 10 18 14 32

Zelotes lasalanus 1 0 1 0 0 0 2 0 2

Hahniidae Neoantistea gosiuta 0 0 0 4 0 0 0 4 4

Linyphiidae Agyneta imm. and females 0 0 3 0 0 0 3 0 3

Aphileta misera 0 0 0 0 4 0 4 0 4

Ceratinella brunnea 0 0 0 0 1 0 1 0 1

Grammonota gentilis 0 0 0 0 0 1 0 1 1
72 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Appendix.—Continued

Family Species

Habitat Type

PJ PIPO MC Total

SFP BAND SFP BAND SFP BAND SFP BAND BOTH

Helophora orinoma 0 0 0 0 0 29 0 29 29

Lepthyphantes turbatrix 0 0 0 0 2 0 2 0 2

Mermessus taibo 0 0 2 0 4 2 6 2 8

Pityohyphantes cristatus 0 0 0 0 1 1 1 1 2

Spirembolus pallidus 0 0 0 1 0 0 0 1 1

Wubana drassoides 0 0 0 5 0 3 0 8 8

Lycosidae Alopecosa kochi 5 11 85 29 135 27 225 67 292

Hogna sp. 3 0 0 0 0 0 3 0 3

Hogna carolinensis 0 0 2 0 0 0 2 0 2

Pardosa montgomeryi 0 0 0 0 6 0 6 0 6

Pardosa orophila 0 3 8 1 0 0 8 4 12

Pardosa uncata 0 0 0 0 1 35 1 35 36

Pardosa yavapa 0 0 47 36 0 6 47 42 89

Schizocosa mccooki 7 26 0 1 0 0 7 27 34

Schizocosa saltatrix 0 1 0 0 0 0 0 1 1

Trochosa terricola 0 0 0 0 0 8 0 8 8

Varacosa gosiuta 0 1 0 25 0 0 0 26 26

Mimetidae Mimetus hesperus 1 0 0 0 0 0 1 0 1

Philodromidae Apollophanes texanus 0 1 0 1 0 0 0 2 2

Ebo imm. 0 4 0 0 0 0 0 4 4

Thanatus altimontis 1 0 0 0 0 0 1 0 1

Thanatus coloradensis 0 4 0 1 0 0 0 5 5

Pholcidae Psilochorus imitatus 0 3 0 1 0 0 0 4 4

Psilochorus utahensis 12 0 28 0 0 0 40 0 40

Salticidae Habronattus geronimoi 2 0 0 0 0 0 2 0 2

Mexigonus arizonensis 0 0 0 1 0 0 0 1 1

Pellenes imm. 1 0 0 0 0 0 1 0 1

Phidippus imm. 1 0 0 0 0 0 1 0 1

Theraphosidae Aphonopelma imm. 1 0 0 0 0 0 1 0 1

Theridiidae Euryopis scriptipes 2 1 8 2 0 0 10 3 13

Latrodectus hesperus 4 8 16 0 0 0 20 8 28

Steatoda hespera 0 0 0 0 0 2 0 2 2

Thymoites sclerotis 2 0 1 0 0 0 3 0 3

Thomisidae Synema sp. probably

neomexicanum

3 0 1 0 0 0 4 0 4

Xysticus apachecus 1 2 13 0 6 0 20 2 22

Xysticus gulosus 1 0 0 0 0 0 1 0 1

Xysticus locuples 0 1 0 2 0 1 0 4 4

Xysticus montanensis 0 0 39 5 1 6 40 11 51

Zorocratidae Zorocrates karli 0 8 0 0 0 0 0 8 8

Totals Abundance 178 126 346 182 198 306 722 614 1,336

Totals Species 33 22 28 24 18 20 54 43 79
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter H

Effects of Changing Lake Level on Lake Powell 

Fisheries—A Hypothesis 

By A. Wayne Gustaveson1

Abstract

Lake Powell fisheries are greatly influenced by changes 

in lake level and nutrient concentrations. Various fish species 

can be stocked, but lake conditions determine which species 

thrive. Largemouth bass (Micropterus salmoides) depend 

on inundated terrestrial vegetation for spawning and nursery 

cover, and thrive or decline based on the amount of brush in 

the water. Abundant threadfin shad (Dorosoma petenense)

allow striped bass to grow and reproduce without limit. 

Predation from an overpopulation of striped bass eliminates 

pelagic shad, causing a decline in the health and reproductive 

success of striped bass. Threadfin shad are dependent on 

plankton that is abundant near inflowing tributary water. 

Sediment and nutrients transported by spring runoff settles out 

near the plunge point where the river enters the lake. Nutrients 

available for plankton production are transported throughout 

the main stem of the lake by overflowing density currents. The 

side canyon nutrient transport is impacted in the same manner 

by a flash flood event. It takes a reservoir-wide event such as a 

high flow event to impact all the side canyons simultaneously 

and increase plankton and shad production.

Introduction

Lake Powell is located on the Utah–Arizona state line 

where the Colorado River crosses the border. Lake Powell is 

the second largest reservoir by volume in the United States 

when it is at full pool. The reservoir was impounded by Glen 

Canyon Dam beginning in March 1963, and the initial filling 

was completed by June 1980. With over 3,000 kilometers (km) 

of shoreline, Lake Powell has a surface area of 65,960 hectares 

(ha). Full pool elevation is 1,128 above mean sea level with a 

maximum water depth at the dam of 170 meters (m).

The reservoir stratifies in the summer months with an 

inflow current overflowing a denser bottom layer. Stratification 

typically breaks down in November due to convective mixing 

and advective currents. Due to the great depth (170 m at 

full pool) at the lower end of the lake, the water below the

penstocks remains homogenous and separate from the water

above. During winter a cold, dense inflowing current sometimes 

moves along the bottom and over time displaces this layer up 

and through the penstocks. Average water retention time for 

the reservoir is two years, however it is only eight months for 

the upper part of the lake. This is due not only to the shallower 

water depth, which is more susceptible to convective mixing, 

but also the proximity to the Colorado River, which allows for 

more lateral circulation (Potter and Drake, 1989).

Two major droughts have affected the reservoir in the 

last 20 years. The first began in the mid-1980s and lasted until 

1992; the second began in 1999 and ended in 2005. Drought 

results are tracked by annual water level fluctuations (fig.1). 

The second drought, in particular, appears to have had a 

profound effect on the water chemistry of the lake, affecting 

productivity and the fishery (Vernieu, 2009, 2010).

Recently, water has been released from the Glen Canyon 

Dam in “high flow events” to transport sediment deposited 

near the dam downstream towards the Grand Canyon in order 

to rebuild beaches that have been eroded due to fluctuating 

flow patterns. The high flow events (HFE) occurred in 1996, 

2004, 2008, and 2013. The 1996 high flow event featured 

flows of 1.273 m3/ssustained for 7 days. The elevation of Lake 

Powell dropped 3 m during the flood event. In November 

2004 a 60-hour high flow event with an outflow of 1.16 m3/s

dropped the lake level almost 1 m. The third HFE occurred 

March 5–7, 2008, with similar flows of 1.17 m3/s and dropped 

the elevation of Lake Powell almost 1 m.

Methods

Pelagic sampling for shad has historically been conducted 

by tow survey on Lake Powell by the Utah Division of Wildlife 

Resources, Lake Powell Project crew, utilizing a 1-m-wide net 

to quantify larval shad production in the backwater at the heads 

of selected canyons, and a midwater depth trawl-net survey that 

measured the quantity of open-water shad populations.

Larval shad tow gear consisted of a 500-micron mesh net 

with a 1-m circular open end, tapering to 76.2 millimeter (mm) 1

Utah Division of Wildlife Resources
74 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

diameter at the cod-end. The overall net length was 3 m, with 

a detachable cup for fish removal on the terminal end. The net 

was towed at a speed that allowed it to suspend just below the 

surface for 2minutes. Approximately 102 m3 of water was 

sampled in each tow. Three to six tows were performed at each 

station with the goal of bringing the coefficient of variation 

of the mean catch to 0.20 or less based on recommendations 

by Boxrucker and others (1995). Down-lake tow sites were at 

Wahweap Creek, Warm Creek, and Navajo Canyon. Midlake sites were primarily at Bullfrog Creek and Halls Creek. 

Historically, Red Canyon and Ticaboo Creek were the most 

consistently sampled up-lake sites, but we have since shifted 

to sampling at Red Canyon and Trachyte Creek. These 

sites were selected based on declining water levels near the 

Colorado River inflow. The new sites consistently had inflow 

during the threadfin shad spawning season and, in most cases, 

were in close proximity to open water trawl sampling stations. 

In conjunction with the tows, surface temperature and secchi 

disk readings were obtained.

Mid-water trawl was used to sample the pelagic part of 

the threadfin shad population. The annual survey began in 

1976 with equipment based on a design described by Von 

Geldern (1972). The net had a 3.048 m2 opening, was 15.24

m 

long with 20.3 centimeter (cm) bar-mesh tapering to 3 mm 

mesh at the terminal end. Lead weighted steel depressors were 

attached to the lower corners, and aluminum hydrofoils with 

attached cork floats were fastened to the top corners to float 

and spread the net opening. Each tow consisted of spooling 

the net out for 61 m, then immediately retrieving it while 

running the boat at 1,100 rpm. This produced an oblique tow 

that sampled from the surface to a depth of 10 m. The water 

volume sampled was 8,178 square meters (m3).

Three standard trawl stations were established at 

Wahweap Bay, Bullfrog Bay, and Good Hope Bay following 

the main channel of the lake. A station on the San Juan arm of 

the lake was also sampled when lake elevations and logistics 

permitted. Sampling was done after dark, during the period 

of the new moon in July and August. At least three tows were 

conducted at each site. Results from the three sampling sites 

were averaged to provide a standard shad abundance figure for 

each year. 

The standardized November gill-netting survey has been 

conducted in its present form since 1981. From 1975 to 1981 

the same gill-net survey was conducted during March instead 

of November. The survey sites include four stations at Warm 

Creek/Wahweap Bay (down-lake), Rincon (mid-lake), Red 

Canyon at Good Hope Bay (up-lake), and the Piute Canyon/

Neskahi Wash area on the San Juan arm. Nets were deployed 

for two consecutive nights at each location. Total length 

and weight were obtained from all fish sampled. Additional 

information was obtained on certain target species including 

sex, stomach contents (percent occurrence), fat index, parasite 

index, and scale samples for aging. 

The survey utilized 10 experimental, sinking-style gill 

nets at each location. Each net consisted of four panels with 

progressively increasing bar mesh sizes of 19, 25.4, 38.1, 

and 50.8 mm, respectively. Nets were attached to the shore, 

alternating large and small mesh sizes on the inshore side. 

Nets were set in a perpendicular orientation to shore with the 

open-water end usually at a 6 to 21 m depth range.

Sampling for striped bass consists of a young-of-year 

(YOY) electrofishing survey conducted in September and the 

annual gill-netting survey in November. Relative abundance 

of YOY striped bass and length information were collected 

from the electrofishing survey. The gill-netting survey yields 

information on relative abundance, total length, weight, 

condition, fat index, diet information, and sex. Scale samples 

are used for age and growth analysis.

The condition of striped bass is based on Fulton’s 

condition factor, K, which assumes that the standard 

weight of a fish is proportional to the cube of its length. In 

sampling Lake Powell striped bass we have chosen to use 

3,725

3,700

3,675

3,650

3,625

3,600

3,575

3,550

200

160

120

80

40

0

Maximum yearly lake elevation, in feet

Number of largemouth bass collected in

annual fall gill-net survey

Year

EXPLANATION

1980 1985 1990 1995 2000

Lake elevation

Number of largemouth bass

2005 2010

Figure 1. Graph of the 

maximum yearly lake 

elevation compared to 

largemouth bass 

abundance, Lake 

Powell, Utah, 1981–2012.
Effects of Changing Lake Level on Lake Powell Fisheries—A Hypothesis 75

the fork length instead of total length, hence the term Kfl 

when discussing relative robustness of individual fish. This 

description is consistent throughout our long-term studies 

beginning in 1974. Black bass species are defined using the 

term “K” which means condition is based on total length 

instead of fork length.

Water levels at Lake Powell and all reservoirs in the 

Colorado River system are collected daily by the Bureau of 

Reclamation (BOR) and posted on their website (http://www.

usbr.gov/uc/water/rsvrs/ops/crsp_40_gc.html). Water levels 

found in this report come from BOR statistics.

Results and Discussion

Largemouth Bass

Shortly after Lake Powell was impounded in 1963 

almost one million largemouth bass (Micropterus salmoides) 

were stocked, followed by another two million largemouth 

bass fry in 1964 (Stone and others, 1965). These stockings 

proved to be adequate for largemouth bass to dominate the 

Lake Powell sport fishery for the next 20 years (Blommer 

and Gustaveson, 2014). Each spring the lake level would 

increase, covering new terrestrial vegetation, and allowing 

bass to use submerged brush as spawning and nursery cover. 

These species thrived while other stocked fish, rainbow trout 

(Oncorhynchus mykiss) and kokanee salmon (Oncorhynchus 

nerka), struggled in an inhospitable environment. Trout 

could be stocked annually to maintain a put-grow-and-take 

fishery, but successful reproduction was uncommon due 

to silty river bottoms that smothered eggs on nests built in 

inflowing streams. Kokanee salmon were stocked in 1963 and 

1964, but were never recorded as being caught by anglers or 

sampled in fish collection gear thereafter (Gloss and others, 

1971). Seven native fish species that thrived in the Colorado 

River prior to impoundment were poorly suited for life in 

the new reservoir environment (Gustaveson and Blommer, 

2013). Within five years, only a few individual native fish 

remained in the flat water of the lake, with most native fish 

disappearing. Presently, only an occasional flannelmouth 

sucker (Catostomus latipinnis), razorback sucker (Xyrauchen 

texanus), or Colorado pikeminnow (Ptychocheilus lucius) 

remain at the inflow waters where tributaries enter the lake 

(Blommer and Gustaveson, 2014).

Threadfin shad (Dorosoma petenense) were stocked 

in 1968 and 1969 to provide forage for all game fish and in 

anticipation for stocking of striped bass that occurred in 1974. 

Shad were immediately preyed on by all lake predators and 

increased the growth and condition of game fish species. Shad 

have been the primary food source of striped bass from initial 

stocking to the present day.

Lake Powell filled in 1980 and water levels began 

fluctuating thereafter. Shoreline vegetation was no longer 

covered by rising water each spring; declining water levels, 

combined with wind and wave action eliminated terrestrial 

vegetation in the reservoir fluctuation zone. Without 

vegetation to use as nursery cover, the largemouth bass 

populations also declined (Bryan and Scarnecchia, 1992).

During the 1980s, largemouth bass remained low in 

number until the drought of 1988–92 reduced the lake level 

and allowed terrestrial vegetation enough time to grow and 

develop before the lake level rebounded in 1993 (fig. 1). 

Rising water again flooded new terrestrial vegetation including 

tamarisk and native willow. Largemouth bass responded as 

the few remaining adults used the new habitat for spawning 

and nursery cover. Largemouth bass YOY numbers increased 

to higher levels and maintained that peak in abundance from 

1994 to 1996 (fig. 2).

The lake level again stabilized in 1996, but then 

fluctuated through the end of the decade. Fluctuation again 

reduced vegetation, resulting in poor survival of young 

largemouth bass. Numbers declined back to the range seen 

prior to the previous drought.

The relation of bass population to brush was again 

demonstrated during the next drought cycle. From 2000 to 

2004, largemouth bass numbers were lower than the long-term 

average; as brush was covered by rising water in spring 2005, 

0.6

0.5

0.4

0.3

0.2

0.1

0

Catch per unit effort of YOY

largemouth bass, log 10 (cpue +1)

Change in lake elevation, in feet

–40 –30 0 10 20 30 40

r = 0.57

p = 0.005

–20 –10

Figure 2. Graph of correlation between the maximum 

lake level and the catch of young-of-year largemouth 

bass in the annual electrofishing survey, 1988–2012.
76 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

largemouth bass numbers rose. As the lake level continued 

to rise each spring through 2008, bass numbers increased to 

the same levels (95 bass/gillnet in 2008 and 97 bass/gillnet 

in 1984) seen when the lake originally filled in 1980 and 

overfilled in 1984 (fig. 1). Thus, it was demonstrated once 

more that bass rely on vegetation for nursery cover. When 

submerged brush of the right variety is available, largemouth 

bass thrive; without brush in the water, bass numbers decline.

Striped Bass

Striped bass (Morone saxatilis) were stocked in 

Lake Powell beginning in 1974. Stocking was curtailed 

in 1979 after natural reproduction was discovered. Prior 

to introduction it was expected that some limited natural 

reproduction would occur, according to literature references 

at the time of stocking (Gustaveson and Blommer, 2013). The 

original expectation was that striped bass would not be able to 

produce enough young to provide a significant sport fishery, 

which would have to be maintained by annual stocking. 

The magnitude of reproduction proved to be exactly the 

opposite, with unprecedented survival rates of striped bass 

in Lake Powell. Striped bass size and numbers increased 

dramatically in the early 1980s. The lake’s top level predator 

grew quickly by feeding on the previously unexploited threadfin 

shad (Dorosoma pentenense) population. The growth in size 

and number of striped bass continued until the shad population 

was eliminated from open water and forced to hide in shallow 

thermal refuges to avoid striped bass predation. The striped bass 

limiting factor then became shad availability (fig.3). 

Shad persisted in warm thermal refuges in turbid water 

in the backs of many canyons where striped bass foraging 

success was limited. Growth of striped bass decreased during 

periods of low shad forage (Gustaveson, 1999); adult striped 

bass suffered from malnutrition (Kfl less than 1.0) when 

shad resources were low or absent in open water (fig. 4). 

2.0

1.6

1.2

0.8

0.4

0

300

240

180

120

60

0

1980 1985 1990 1995

Sample year

EXPLANATION

Pelagic shad abundance

Adult striped bass condition

2000 2005 2010

Striped bass condition factor (Kfl)

Pelagic shad abundance from midwater trawl

Figure 3. Graph of pelagic shad abundance (midwater trawl) 

compared to adult striped bass condition (Kfl), 1976–2013.

1.4

1.3

1.2

1.1

1.0

0.9

0.8

0.7

Mean Kfl of adult striped bass

Pelagic shad abundance

0 50 100 150 200 250 300 350 400

r = 0.49

p = 0.01

n = 34

Figure 4. Graph of correlation between mean condition of adult 

striped bass (Kfl) and pelagic shad abundance from trawl tows, 

1979–2012.

Juvenile striped bass were always present in large numbers 

and able to maintain healthy body condition by foraging on 

zooplankton when shad were absent. When Lake Powell shad 

abundance increased, adult striped bass growth, condition, and 

reproductive ability immediately improved.

Shad

Open water shad abundance was high during the 1970s 

preceding striped bass establishment. From 1982 to 2002, shad 

abundance peaks in open water were rare. This suggests that 

intense striped bass predation reduced shad numbers from the 

open water zones where trawl sampling was conducted. 

Modest shad abundance peaks did occur unexpectedly on 

a few occasions. During the 1980s and 1990s, open water shad 

abundance peaked at levels higher than 120 shad/midwater trawl 

haul in 1984, 1991, and 1996. Shad abundance peaks occurred 

more often from 2003 to 2010 but these peaks consisted 

mostly of open water gizzard shad (Dorosoma cepedianum) 

(unintentionally introduced in 2000) mixed with threadfin shad 

that were responsible for peaks in the previous decades.

High Flow Events (HFE)

It is interesting to note that modest shad abundance 

peaks occurred each year that a high flow event (HFE) release 

from Glen Canyon Dam occurred (fig. 5). HFE releases were 

conducted in March 1996, November 2004, March 2008, 

and November 2013. It is possible that nutrients important 

to plankton production were transported into open water by 

the quick drop of 0.75 m in lake level. It is suggested that 

shad production is greater if the HFE is conducted in the 

springtime, when shad are spawning, instead of in the fall after 

spawning is complete.

The magnitude and dynamics of these flood events are 

described in several U.S. Geological Survey reports (Vernieu, 

2009, 2010). However. the possible impact on Lake Powell 
Effects of Changing Lake Level on Lake Powell Fisheries—A Hypothesis 77

fish can be summarized by anecdotal evidence found during 

the March 2008 flood event. Nitrogen and phosphorus nutrient 

levels were sampled in Navajo Canyon before and after the 

event. Nutrient concentrations upstream, toward the tributary 

inflow of Navajo Creek, increased after the HFE (Bill Vernieu, 

Glen Canyon Monitoring and Research Center, oral commun., 

2013). Further details of profiling methodologies and sampling 

protocols are described in Vernieu (2009). Although the 

increases in nitrogen and phosphorus were not statistically 

significant, they were attributed to the resuspension of deltaic 

sediments in the upper end of the tributary. The short-term 

drawdown during the HFE may result in similar increases in 

nutrient concentrations and primary productivity. Therefore, 

it is possible that high-release operations at Glen Canyon 

Dam, which result in significant drawdown of the reservoir 

during a short period of time, may result in similar nutrient 

increases in the tributary arms of the reservoir, and may cause 

subsequent increases in productivity in the tributary arms and 

the reservoir in general. The results of the Navajo Canyon 

transect sampling, although not robust enough to show 

statistical significance, reflect anecdotal conditions before and 

after the HFE; while indicating that nutrient concentrations 

are higher near the inflows of tributaries, as is commonly 

seen at Lake Powell, they also indicate further increases in 

nutrient concentrations as a result of the short-term drawdown 

of the reservoir during the HFE (Bill Vernieu, GCMRC, oral 

commun., 2013). 

It is important to determine if inflowing currents travel 

across the surface of the reservoir, rather than plunging to 

deeper layers. After the HFE, on March 11, 2008, higher 

dissolved oxygen (DO) concentrations were observed in an 

approximately 30-m-thick layer at the bottom of the reservoir. 

This represents the winter inflow density current approaching 

350

300

250

200

150

100

50

0

Mean number of shad captured

1980 1985 1990 1995 2000 2005 2010 2015

Year

Figure 5. Annual shad abundance (midwater trawl) with high 

flow years (1996, 2004, 2008, and 2013) highlighted in black.

Glen Canyon Dam that was not present in the pre-HFE profile. 

No discernable differences were observed at the penstock 

withdrawal depth (Vernieu, 2010).

Isopleths of temperature, specific conductance, and DO, 

pre- and post-HFE, show the stratification in the reservoir 

below the mixed epilimnion, the progressive movement of the 

inflow density current through the reservoir, and the gradual 

evacuation of the previous year’s hypolimnion (Vernieu, 

2010). When conditions are right, an HFE may result in 

increased production of fish and a refreshing of the stagnant 

layers of water near the dam. 

Summary and Conclusions

Lake level fluctuations play a large role in determining 

which species of fish thrive in the lake while others barely 

survive. Lake stabilization at full pool creates a fluctuation 

zone where terrestrial vegetation is absent. Declining water 

levels expose previously flooded terrestrial vegetation to wind 

and wave action that tends to uproot plants in sandy soil. 

Refilling into the same 7-m-zone following the next spring 

flood does not allow enough time for new vegetation to grow. 

The result is a sandy beach with little or no brush to be used as 

fish habitat. 

Without submerged vegetation, brush-loving species find 

little habitat for spawning and nursery cover. Largemouth 

bass do not thrive in these conditions. Extended drought that 

causes the lake level to decline also allows more brush to grow 

along the new, lower elevation lakeshore. A resurgence of 

brush-loving fish occurs after the lake overtakes fast-growing 

terrestrial vegetation, such as tamarisk (Tamarix ramosissima), 

which is used as spawning and nursery habitat. 

Fish not dependent on submerged brush, like striped 

bass and threadfin shad, depend on other factors for optimum 

survival. Striped bass depend on open-water shad production. 

When shad numbers are high, open-water predators thrive. 

When shad numbers decline, the total number and physical 

condition of pelagic predators decline.

Shad abundance is greatly influenced by plankton 

availability. Circumstances such as resuspension of deltaic 

sediment that release nutrients previously unavailable due 

to sediment coverage can enhance a shad year class when 

resuspension occurs just prior to, or during, the shad spawning 

period. Spring runoff from the tributaries enhances lake 

productivity each year, but inflowing sediment that contains 

important nutrients often settles out of the water column 

and some of the nutrients are then covered by a sediment 

delta. Those nutrients flowing into the reservoir that remain 

in suspension are transported through the main stem of the 

reservoir by overflowing density currents. The side canyons 

are less affected by density current flow unless a perennial 

stream occurs in that canyon. 

Resuspension of deltaic sediment by flash flood events 

can increase nutrients in each canyon where flash floods 

occur. It takes a lake-wide event, such as the rapid decline 
78 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

in reservoir level that occurs during an HFE, to enhance 

nutrient levels in all 96 major side canyons simultaneously. 

An event of that magnitude can increase plankton 

production, which is then followed by an increase in pelagic 

shad. The end result is a positive impact on the Lake Powell 

food chain and total fish numbers.

References Cited

Blommer, G.L., and Gustaveson, A.W., 2014, Lake Powell 

fisheries investigations completion report—May 2010-

April 2013: Salt Lake City, Utah Department of Natural 

Resources, Division of Wildlife Resources, Publication no. 

14-0, 161 p.

Boxrucker, J., Degan, D., DeVries, D., Michaletz, P., Van 

Den Avyle, M., and Vondracek, B., 1995, Sampling shad 

in southern impoundments: U.S. Fish and Wildlife Service 

and American Fisheries Society, Special publications 

sponsored by USFWS and Reservoir Committee of the 

Southern Division, AFS, 22 p.

Bryan, M.D., and Scarnecchia, D.L., 1992, Species richness, 

composition, and abundance of fish larvae and juveniles 

inhabiting natural and developed shorelines of a glacial 

Iowa lake: Environmental Biology of Fishes, v. 35, no. 

4, p. 329–341, accessed October 2013, at http://dx.doi.

org/10.1007/BF00004984.

Gloss, S.P., Starostka, V., and Thompson, C., 1971, Glen 

Canyon Reservoir post impoundment report: Salt Lake 

City, Utah Division of Wildlife Resources, 18 p.

Gustaveson, A.W., 1999, Cyclical population dynamics of 

self-sustaining striped bass in Lake Powell, Utah-Arizona, 

1974-1998: Salt Lake City, Utah Division of Wildlife 

Resources, Sport Fish Restoration Act project F-46-R.

Gustaveson, A.W., and Blommer, G.L., 2013, History of striped 

bass management in the Colorado River, in Bulak, J.S., 

Coutant, C.C., and Rice, J.A., eds., Biology and management 

of inland striped bass and hybrid striped bass: Bethesda, Md., 

American Fisheries Society Symposium 80, p.15–23.

Potter, L.D., and Drake, C.L., 1989, Lake Powell—Virgin flow 

to dynamo: Albuquerque, University of New Mexico Press.

Stone, R., Fields, L., and Miller, K., 1965, Lake Powell post 

impoundment investigations: Utah State Department of 

Fish and Game, Glen Canyon Unit, Colorado River Storage Project, Progress report no. 2, Section 8 program, 35 p. 

[Also available at http://cwcbweblink.state.co.us/WebLink/

DocView.aspx?id=133590&page=20&dbid=0.]

Vernieu, W.S., 2009, Historical physical and chemical data for 

water in Lake Powell and from Glen Canyon Dam releases, 

Utah-Arizona, 1964-2013: U.S. Geological Survey Data 

Series 471, 23 p., accessed October 2013, at http://dx.doi.

org/10.3133/ds471.

Vernieu, W.S., 2010, Effects of the 2008 high-flow experiment 

on water quality in Lake Powell and Glen Canyon Dam 

releases, Utah-Arizona: U.S. Geological Survey Open-File 

Report 2010–1159, 25 p., accessed October 2013, at http://

pubs.usgs.gov/of/2010/1159/.

Von Geldern, C.E., Jr., 1972, A midwater trawl for threadfin shad, 

Dorosoma petenense: California Fish and Game, v. 58, no. 

4, p. 268–276. [Also available at https://archive.org/stream/

californiafishga58_4cali/californiafishga58_4cali_djvu.txt.]
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter I

Understanding Forest Restoration Effects on Water 

Balance: Study Design for the Four Forest Restoration 

Initiative Paired Watershed Study 

By Frances C. O’Donnell,1 Sharon Masek Lopez,1 and Abraham E. Springer 1

Abstract

A legacy of past forestry and grazing practices, and a 

century of fire suppression in semiarid forests have led to 

declining forest health and an increasing incidence of larger and 

more severe wildfires, making it imperative to rapidly reduce 

forest density. Changes in vegetation cover after thinning may 

affect water balance in forests in the Salt and Verde River 

watersheds of central Arizona. This planned project will analyze 

forest restoration treatment effects at the catchment scale on 

water resources from forested lands in central Arizona, as well 

as the broader ecologic responses of forest structure, fuels and 

fire behavior, and vegetation. Comprehensive water balance data 

collected from a 12-site paired watershed study will quantify 

the effects of various restoration treatments and the efficacy 

of prescribed burning for maintaining postrestoration water 

yields. Results from the paired watershed study will be used to 

develop, parametrize, and verify hydrologic models in order to 

scale up predictions to the catchment scale. Products from this 

project will assist downstream water managers in forecasting 

and planning for water availability and sedimentation, and will 

be key components in the adaptive-management process being 

implemented for the Four Forest Restoration Initiative. 

Introduction

A legacy of intensive logging and grazing practices, along 

with a century of fire suppression in the semiarid ponderosa 

pine forests of Arizona, have led to declining forest health and 

an increasing incidence of larger and more severe wildfires 

(Covington and Moore, 1994). The U.S. Forest Service is 

planning a decades-long, landscape-scale forest restoration 

project called the Four Forest Restoration Initiative (4FRI). 

Thinning to reduce tree density, along with the reintroduction 

of surface fire, is one of the most common approaches proposed 

for the restoration of ponderosa pine watersheds (Covington and 

others, 1997; Allen and others, 2002). In addition to reducing fire 

risk, a goal of 4FRI is to improve the hydrologic function and 

resilience of the forests that cover the watersheds of the Salt and 

Verde Rivers (U.S. Forest Service, 2011), an important water 

source for the Phoenix metropolitan area.

Altering vegetation cover affects the water balance mainly 

through changes in two processes: evapotranspiration (ET) 

and snow dynamics. Thinning in ponderosa pine forests results 

in an overall decrease in ET due to a reduction in overstory 

transpiration (Dore and others, 2012), although understory 

transpiration and soil evaporation may increase because of 

reduced interception and shading (Breshears and others, 1998; 

Simonin and others, 2007). Thinning typically increases snow 

accumulation by reducing canopy interception (Winstral and 

Marks, 2002) while, unlike in completely defoliated forests, 

sufficiently shading the snowpack to moderate melting rates 

(Seibert and others, 2010). Snow water input to soil moisture 

and runoff is a balance between interception and shading, and so 

it is generally highest in forests with intermediate canopy density 

(Musselman and others, 2008; Gustafson and others, 2010).

Paired watershed studies have commonly been used to 

evaluate the effects of forest management on hydrology. A paired 

watershed study utilizes two basins that are as similar as possible 

in terms of size, morphology, geology, soil type, climate, and land 

use. One watershed is treated, and the other is left as a control to 

determine watershed response to treatments (Hewlett and Pienaar, 

1973; Andréassian, 2004). The best paired watershed studies test 

hypotheses and yield information about the hydrologic cycle 

that has applications beyond the local level. To evaluate water 

balance changes due to forest restoration treatments, a paired 

watershed study is required. Plot-based and other types of studies 

may address parts of the water balance (such as ET or soil water 

storage), but only a paired watershed study can examine the 

entire water balance (Clausen and Spooner, 1993). 

Several paired watershed studies conducted in the 1950s to 

1980s have evaluated the effects of forest management on water 

yield and water quality in the semiarid forests of the Arizona 

highlands (Baker, 1999). Forest thinning treatments applied 

to mixed conifer ecosystems in two of three subbasins of the 

Workman Creek watershed were found to increase water yields 

(Rich and Gottfried, 1976), a result that was reproduced in three 1

Northern Arizona University
80 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

more pairs of mixed conifer (Thomas and Willow Creeks) and 

ponderosa pine (Castle Creek) forests on the White Mountains. 

Long-term monitoring at Castle Creek showed that the increases 

persisted for at least 21 years after thinning treatments designed 

to mimic commercial forest management (Gottfried and 

DeBano, 1990). The most extensive series of paired watershed 

experiments were conducted on 20 small catchments in the 

Beaver Creek Experimental Watershed in ponderosa pine and 

piñon-juniper ecosystems (Brown and others, 1974; Baker, 

1986). The Beaver Creek studies found that both overstory 

removal and strip-cut thinning treatments that removed at least 

30 percent of basal area resulted in significant increases in water 

yield, but, in contrast to the Castle Creek study, streamflow 

returned to pretreatment levels 4–10 years after treatment.

Past experimental watershed studies have provided a 

wealth of data on how forest management practices that were 

common at the time affected water yields. Over the past several 

decades, priorities for forest management have shifted, and 

silvicultural techniques have progressed, leading to a change in 

the forest treatment strategies that are used in the southwestern 

United States. Past forest management techniques were designed 

to maximize sustainable timber yield or to increase water yield 

(Baker, 1986; Gottfried and DeBano, 1990). Although wateryield improvement and economic gain from timber are still 

goals of forest management, they are parts of a more holistic 

view of ecosystem services that includes wildfire-risk reduction, 

restoration of presettlement conditions, reduction of soil erosion, 

improvement of wildlife habitat, and maintenance of recreational 

lands (U.S. Forest Service, 2011). This suite of priorities is better 

served by newer treatment methods (U.S. Forest Service, 2013), 

the effects of which have not yet been investigated. In light of 

conflicting results from past studies regarding the persistence of 

yield increases, further study of the recovery of the water budget 

are needed, especially ET. The authors of the two Beaver Creek 

studies cite increasing ET as the mechanistic cause of declining 

yields but have no data to support this conclusion. Furthermore, 

the efficacy of maintenance treatments, usually prescribed burns, 

for restoring water yield increases has not been evaluated. It is 

critical to quantify the effects of treatment in the early years of 

forest restoration in order to inform 4FRI adaptive-management 

decisions across the landscape. 

Our study seeks to test the following five hypotheses:

1. After restoration treatments, runoff will increase and 

then decline to pretreatment levels within 5–10 years. 

Groundwater recharge will increase proportionally to 

runoff.

2. Increases in runoff and recharge will result from 

maintenance treatments but will be lower than those 

observed after initial restoration.

3. ET will be reduced by restoration treatments but will 

recover, mainly because of increases in understory ET.

4. Restoration will increase snowpack, leading to higher 

spring soil moisture levels.

5. Restoration will not significantly affect sediment yield.

Study Area

To quantify hydrologic responses to restoration treatments 

at the watershed scale, 12 watersheds—9 treated and 3 

control—were selected within the 4FRI project area, allowing 

for 3 replicates of the treatment intensities (low, medium, and 

high) as well as the controls. The watersheds range in size 

from 153 to 555 hectares (ha) with an average of 323 ha. They 

are located within two hydrologic unit code level 5 (10 digit 

code) watersheds (Seaber and others, 1987): Sycamore Creek 

in the Verde River Basin and Walnut Creek (which includes 

the Flagstaff municipal Lake Mary watershed) in the Little 

Colorado River Basin (fig. 1, table 1). Baseflow does not occur 

in any of the watersheds, except for the Sycamore Creek control 

(MS-1), which has a spring within the watershed. A streamflow 

gauge or pressure transducer will be used to measure and 

control for flow from the spring. Watersheds have consistent 

geologic parent material and are covered by contiguous 

ponderosa pine forest. The consistency of vegetation 

characteristics among catchments will be determined when our 

initial surveys are performed. Changes in surface water and 

groundwater storage are assumed to be negligible at the annual 

scale in these small headwater catchments. 

Untreated controls would be ideal, but because of the 

U.S. Forest Service’s concern about wildfire risk, the minimal 

treatment in the initial 4FRI plan is “burn only” (<10 percent 

basal area change). These minimally treated watersheds will 

serve as proxy control basins. On the basis of previous research, 

the minimally treated watersheds would not be expected to 

significantly affect water balance because controlled burning 

causes only slight tree mortality in mature forest, and less than 

a 30-percent decrease in ponderosa pine basal area has not 

resulted in significant changes in water yield (Baker, 1986; 

Sackett and others, 1996). Along with pretreatment data from all 

basins, this plan should provide an adequate control. 

For this study, researchers will not be prescribing 

treatment types but will work within the context of the 

treatments proposed by the U.S. Forest Service for the 4FRI. 

Therefore, this study is observational rather than experimental. 

Because this study tests treatment types that will be applied 

across ~1 million hectares (ha) of forest, utilizing the alreadyselected treatment types is a realistic approach that will 

provide results to inform adaptive forest management that 

must incorporate multidisciplinary resource objectives.

Determination of Treatment Effect and Study 

Duration

In the paired watershed approach, runoff is observed during 

a pretreatment calibration period. A linear regression model is 

developed from the calibration-period data that predicts annual 

runoff in the treatment basin based on runoff in the control basin. 

After treatment, the effect of the treatment is determined as the 

difference between runoff measured in the treated basin and that 

predicted by the regression model using measurements in the 
Understanding Forest Restoration Effects on Water Balance: Study Design for the Four Forest Restoration Initiative 81

control basin. According to the algorithm of Kovner and Evans 

(1954), recommended for use in the Environmental Protection 

Agency’s guidelines for the design of paired watershed studies 

(Clausen and Spooner, 1993), the length of the measurement 

periods needed pretreatment and posttreatment to provide 

statistically significant results can be determined if an expected 

change in the mean is known. The minimum detectable 

change in the mean, d, for n1 and n2 years of pretreatment and 

posttreatment observation, respectively, is given by

d 2= sxy

2 n1+ n2

n1 n2

F (1+

F

n1+ n2− 2

)

, (1)

where s2

xy is the estimated variance of the residuals of the 

regression equation and F is the F statistic at the desired 

significance level (usually 0.95) with degrees of freedom 1 and 

n1 + n2 – 3.

Determining the value of s2

xy requires calibration-period 

data to establish a regression equation, so observation times 

cannot be determined until several years after data collection 

has begun. To estimate a timeframe for the proposed study, 

we analyzed historical runoff data from the Beaver Creek 

Experimental Watershed, which has climate and vegetation 

characteristics similar to that of the proposed study sites. A 

similar dataset is not available for the other variables of interest 

in this study. However, we expect the interannual variation, 

Table 1. Paired watershed IDs, locations, elevations, soil types, 

and proposed treatment intensities.

[Soil hydrologic groups use the USDA Natural Resource Conservation 

Service’s system based on soil water transmissivity: A (>0.76 cm/h), B 

(0.38–0.76 cm/h), C (0.13-0.38 cm/h), and D (<0.13 cm/h). ha, hectare; m, 

meters; %, percent; ft, feet]

ID Area

(ha)

Elevation

(m)

Dominant 

hydrologic

soil groups

Treatment 

intensity

Basal area

Reduction 

(%)

Final 

(ft2

/

acre)

Sycamore Creek watershed

MS-1 393 2,245 C, D Control 24 22

MS-2 488 2,245 C, D Medium 33 19

MS-3 555 2,260 C, D High 28 16

MS-4 339 2,374 C Low 27 18

LS-1 342 2,080 C, D Control 26 23

LS-2 153 1,996 C, D Medium 34 16

Lake Mary watershed

LM-1 202 2,242 B, C Control 17 11

LM-2 453 2,342 B, C Low 27 24

LM-3U 204 2,237 B, C Low 28 20

LM-3L 184 2,191 B, C High 47 12

LM-4 235 2,209 B, C High 44 16

LM-5 323 2,206 B, C Medium 49 16

Flagstaff

Walnut Creek

Oak Creek

35°20’

112°10’ 112° 111°50’ 111°40’ 111°30’

35°10’

35°

I-40 and I-17

Stream route

5th HUC watersheds

4 FRI proposed trtmt area

Treatment intensity

High

Medium 

Low

Control

Sycamore Creek

EXPLANATION

0

0

5 10 15

5 10 15

MILES 

KILOMETERS 

Figure 1. Central 

Arizona, showing 

locations of 

watersheds 

designated 

for study of 

effects of forest 

management 

(colored patches).
82 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

and thus the calibration-period length, to be similar for all 

hydrologic variables. The improvement in estimating the 

accuracy as the length of the calibration period increases 

is plotted in figure 2. On the basis of this estimate, we plan 

to calculate the necessary calibration and treatment lengths 

8years after data collection begins. We estimate that the total

length of the calibration period will be 8–10 years. The initial 

period before maintenance treatment will be 6–8 years, and the 

watersheds will be monitored for an additional 7–10 years after 

maintenance treatments, approximating the timeframe needed to 

detect a change in runoff of 2.5centimeters (cm), the minimum 

change observed in the year immediately after treatment across 

all of the Beaver Creek treatments conducted in ponderosa 

pine forests (Baker, 1986). These estimated lengths are based 

on ideal values for statistical analysis. We will also consider 

the practical needs of the involved management and funding 

agencies in scheduling treatments.

Proposed Study Methods

In this study, we expand on the conventional paired 

watershed study to evaluate the full water balance within 

watersheds. After the end of each water year (October 1–

September 30), data will be summarized, and the annual water 

balance will be determined. Point-based measurements will be 

conducted at sites that are representative of each watershed in 

terms of pretreatment structure and treatment intensity. 

Observation length, in years

Variance of regression residuals

(s2xy) of runoff, in millimeters

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

2 4 6 8 10 12

Mean

95% confidence interval

14 16

EXPLANATION

Figure 2. Estimated variance of regression residuals (s2

xy) of runoff 

versus length of calibration period. Mean (solid line) and bootstrapped 95-percent-confidence intervals (dashed lines) are based 

on 1,000 simulations for estimating variance of regression residual 

of regression relationship derived from calibration period data. Data 

from sites 8 and 13 of the Beaver Creek Experimental Watershed.

Biophysical Monitoring

To evaluate pretreatment and posttreatment forest 

conditions that may affect hydrologic-response variables, 

overstory and understory vegetation will be measured to 

determine the effect of treatment and to monitor recovery. The 

biophysical environment will be characterized before and after 

treatment, largely through monitoring conducted by the U.S. 

Forest Service but also through biologic surveys completed by 

the Ecological Restoration Institute (ERI) at Northern Arizona 

University. Understory vegetation will be monitored by using 

field plots, based on a sampling design typically used by ERI 

(Stoddard and others, 2011; McGlone and others, 2012). 

Overstory measurements in plots will quantify percentage 

of tree-canopy cover, tree density, and leaf-area index and 

determine the distributions of tree heights, ages, species, 

crown base heights, and canopy volumes. The previous 

impacts of fire will be estimated as percentage of crown 

scorch and percentage of bole char. From the overstory 

measurements, basal area, crown bulk density, and canopy fuel 

load will be derived. Understory measurements will include 

frequency and cover of species; tree regeneration by species; 

shrub density, species distribution, and size-class distribution; 

and fractional substrate cover. Finally, the surface fuel loading 

by size class and forest-floor depth will be measured to 

quantify surface fuels.

Precipitation and Weather

Each watershed will have one research-grade meteorological station located in the upper part of the watershed, with 

all-season precipitation gauges placed at the meteorological 

station site and in the lower watershed at or near the watershed outlet. The weather stations will measure the following 

parameters at 15-minute intervals: precipitation (rain and snow 

depth), windspeed and wind direction, solar-radiation flux, 

temperature, relative humidity, and barometric pressure.

Snow

Snowmelt from high-elevation forested watersheds is 

a major source of runoff for most rivers in the southwestern 

United States (Gottfried and others, 2003), and so measurements of snow-water equivalent (SWE) are required. Snow 

depth and SWE will be measured in each watershed by using 

two snow courses, a set transect along which snow depth and 

SWE are measured at weekly to monthly intervals, and one 

snow pillow, an automated system that measures and logs 

SWE. 

Snow-course transects will be oriented in straight lines 

perpendicular to the slope and, if possible, on slopes with a 

northern aspect. At evenly spaced data-collection points, snow 

depth and SWE will be measured with a Mount Rose-type 

sampler. Snow courses, which capture spatial variations in 

snowpack, will be surveyed monthly. To characterize temporal 
Understanding Forest Restoration Effects on Water Balance: Study Design for the Four Forest Restoration Initiative 83

variations, one snow pillow will be installed in each watershed 

near the snow course and the weather station to measure snow 

depth and SWE hourly for December through April. 

Soil Water Storage

Soil water content, or soil moisture, is a fundamental 

determinant of many ecologic processes, particularly in 

semiarid ecosystems (Zou and others, 2008). Traditional soil 

moisture sensors, such as time domain reflectometry (TDR) 

sensors, are generally inexpensive to purchase but can require 

high labor costs for installation, maintenance, data downloads, 

and interpolation of data from a few points to broader areas. 

The recently developed COsmic-ray Soil Moisture Observing 

System (COSMOS) probe detects cosmic-ray neutrons that 

are an ever-present component of the land-surface radiation 

environment and have an intensity that is inversely correlated 

with water in any form (Desilets and others, 2009), although 

subsurface and surface contributions are distinguishable. The 

probe is capable of measuring average soil water content over a 

circular footprint of 34 hectares (ha) and depths of as much as 

50 cm (Zreda and others, 2012), and so it can characterize soil 

water content across heterogeneous areas.

One COSMOS probe will be placed in each watershed as 

resources allow. Each probe will be placed such that the entire 

measurement footprint is contained within the central part 

of the watershed. Measurements will be recorded every few 

seconds and transmitted several times per day. An array of three 

sets of TDR sensors will be placed in each watershed, within 

the footprint of COSMOS data collection where applicable. 

The TDR sensors will be placed at multiple depths in shallow 

(<40 cm) and deep (>40 cm) rooting zones and will measure 

soil moisture at 15-minute intervals. The two methods will 

serve as a check on each other, but the TDR probes will also 

characterize soil water storage in the deep rooting zone of 

mature ponderosa trees, a depth to which the COSMOS probe 

does not consistently reach. A pilot study is currently underway 

using a portable TDR instrument to characterize the spatial 

heterogeneity of soil moisture in ponderosa pine forests. The 

findings from this study will inform the placement of TDR 

probes to provide the best possible representation of the spatial 

variation in soil water storage.

Evapotranspiration

ET will be monitored by using a combination of 

measurements, including sap flux, soil water content, and 

eddy-covariance (EC) flux. EC flux, in which fast fluctuations 

of vertical windspeed are correlated with fast fluctuations in 

atmospheric water-vapor density, is the most direct method 

of measuring ET at the ecosystem scale. Owing to high costs 

relative to other measurements, it is feasible to install EC 

towers in only a maximum of six watersheds. The preliminary 

selection of the six watersheds includes two controls (MS1, LM-1, fig. 1), two medium-intensity treatments (MS-2, 

LM-5), and two high-intensity treatments (MS-3, LM-4). 

Overstory and understory transpiration will be measured 

at all watersheds and scaled up to ecosystem ET, with EC 

measurements used for validation.

Overstory transpiration will be measured by using Graniertype heat-dissipation sap-flux sensors (Lu and others, 2004), 

based on the detection of convective heat transport with the 

sap stream. Two sensors are inserted into the sapwood ~4 cm 

apart vertically. The upper sensor is heated with constant power, 

and the sap-flow velocity is calculated from the temperature 

difference between the two needles. Sap-flux sensors will 

be installed in trees representing all diameter classes, and 

information on the density and size distribution of overstory trees 

(see subsection above entitled “Biophysical Monitoring”) will be 

used to determine ecosystem-level overstory transpiration flux. 

Following Simonin and others (2007), the sum of understory 

transpiration and soil evaporation will be estimated as the rate 

of change of shallow (<40 cm) soil moisture during rain-free 

periods when soil moisture is below field capacity (that is, when 

gravity-driven drainage is negligible).

Groundwater Recharge

Groundwater recharge will be measured by using chloride 

mass balance, a common approach in arid and semiarid 

regions (Allison and others, 1994; Guan and others, 2010). 

Atmospheric input of chloride in precipitation, as well as dry 

deposition, is concentrated in the soil water by ET, which does 

not remove chloride. By measuring chloride concentration in 

precipitation, runoff, and soil water over a depth profile, the 

rate of deep drainage below the root zone can be determined. 

Although determination of water transport below the root zone 

is complicated by the regional geology, we assume that water 

will either infiltrate into aquifers or discharge as baseflow 

at downstream sites, thus contributing to regional water 

resources. A pilot study is currently underway to determine 

the frequency of sampling and the number of sample points 

needed to accurately characterize groundwater recharge in 

ponderosa pine forests.

Streamflow

Parshall flumes will be used to measure streamflow at 

the outlet of each watershed. Flumes of a size that provide 

good precision for low-flow data will be fitted into steelplate weirs designed to traverse the width of the stream. 

Each flume will be equipped with a built-in stilling well with 

a self-contained pressure transducer to measure depth. For 

higher flows that overtop the flume, crest-stage gauges will 

be installed. 

The installation of Parshall flumes requires extensive 

planning, and permitting is likely to delay the start of 

calibration-period data collection. For initial data collection, 

a new method utilizing time-lapse cameras, which we refer 

to as “flowtography,” will be used to estimate streamflow. 
84 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

The channel cross section and profile are surveyed by using 

a laser level and rod, bankfull width is flagged, and channel 

roughness is estimated. A calibrated T-post with reflective 

markings at 6-inch intervals is driven into the channel at the 

deepest point on the cross section. A time-lapse wildlifetracking camera is mounted in a tree and focused on the 

T-post, and photographs are taken day and night at 15-minute 

intervals (fig. 3). Water height is determined visually from 

the photographs and used to calculate streamflow from 

Manning’s equation:

 V = k

n

Rh

2/3

S1/2

, (2) 

where V is the cross-sectional average velocity, k is a 

conversion factor, n is the unitless Manning coefficient for 

surface roughness, Rh is the hydraulic radius of the channel, 

and S is the slope of the water surface. After flumes are 

installed, the cameras and T-post gauges will be left in service 

for a period of at least 1 year to determine how closely this 

method estimates streamflow. 

Water Quality

Because forested watersheds provide a considerable 

percentage of drinking water to cities in Arizona, there is 

concern about the potential positive or negative effects on 

water quality of forest restoration treatments. Event-driven 

water-quality samples will be collected with an ISCO automatic sampler prompted by the streamflow gauge’s pressure 

transducer. Samples will be collected hourly until the flow 

event ends or all the autosampler bottles are full. The main 

parameters of concern will be suspended-sediment concentration (SSC; to gauge sediment discharge, see next subsection), 

total organic carbon content, and chloride concentration (for 

use in the chloride mass-balance calculation), but other basic 

water-quality parameters may also be analyzed (pH, conductivity, salinity, isotopes, and so on).

Figure 3. Typical photograph from a time-lapse “flowtography” 

camera aimed at a calibrated T-post to estimate water depth. 

Photo provided by Salt River Project.

Sediment Yield

Soil erosion and sediment yield in forest systems have 

significant economic and environmental effects (Ward 

and Baker, 1984). In this study, silt fences will be used to 

determine natural and management-induced hillslope-erosion 

rates in the paired watersheds (Robichaud and Brown, 2002). 

A silt fence is a synthetic geotextile fabric with small (0.3–

0.8millimeter) openings that pass water but not sediment; it 

is anchored in place by wiring it to wooden or metal posts. A 

minimum of 4 silt fences will be installed in each watershed.

For estimating SSC in streams, turbidity is generally a 

much better predictor than water discharge (Lewis, 1996). 

With frequent calibration, the relation of turbidity to SSC 

can be used to estimate suspended-sediment loads efficiently. 

At each streamflow gauge, an in situ turbidimeter will 

be installed to measure and record turbidity at 15-minute 

intervals and compared with laboratory measurements of 

total suspended solids (TSS) in water collected by the ISCO 

autosampler to establish a turbidity-SSC relationship. Annual 

sediment yield will be calculated for comparison between 

catchments.

Data Analysis and Modeling

Our proposed analyses fall into two categories. (1) 

Hypothesis testing seeks to determine whether forest 

restoration treatment has a statistically significant effect on 

the parameters of interest that are being measured directly 

in the watersheds: runoff, ET, SWE, soil moisture, water 

quality, and groundwater recharge. (2) Model development 

and validation uses the collected data to test and improve 

the ability of hydrologic models to represent the effects of 

forest restoration. These analytical methods are preliminary, 

and more specific methods will be developed adaptively 

during the early stages of data collection.

Hypothesis Testing

 A paired watershed study with both calibration and 

treatment observations is an example of a before/after, controlimpact paired-series (BACIPS) experimental design. The 

effect of treatment that is quantified by the linear regression 

approach described in the subsection entitled “Determination 

of Treatment Effect and Study Duration” can be tested for 

statistical significance by using standard hypothesis-testing 

methods. If the assumptions are met, the greatest statistical 

power for determining whether the effect of treatment 

is statistically significant in a BACIPS study is given by 

analysis of variance (ANOVA), with the difference between 

measurements in paired treatments as the dependent variable 

and treatment type and observation period (pretreatment or 

posttreatment) as the independent variables (Smith, 2006). In 
Understanding Forest Restoration Effects on Water Balance: Study Design for the Four Forest Restoration Initiative 85

the case of paired watersheds, the absolute difference between 

the treatment and control measurement of most variables of 

interest will be influenced by climate (Baker, 1986). Therefore, 

we will use analysis of covariance (ANCOVA), with annual or 

winter precipitation as a covariate. 

Model Development and Testing

Paired watershed experiments are commonly criticized 

for having little or no replication, owing to physical 

constraints. Their value lies largely in their utility for 

calibrating and testing hydrologic models and not in 

statistical inference (DeFries and Eshleman, 2004). This 

study will be especially useful for modeling applications 

because it measures several components of the water 

budget, unlike previous paired watershed studies that 

measured only precipitation and runoff. These additional 

measurements, including ET, groundwater recharge, SWE, 

and soil moisture, will enable verification of the internal 

components of process-based hydrologic models. Modeling 

efforts associated with the paired watershed project will 

be framed by the identification and parametrization of a 

basin-scale water balance model. Several existing models 

will be tested for their ability to accurately represent the 

effects of restoration on the water balance, including the 

U.S. Forest Service’s Forest Vegetation Simulator-Water 

Resources Evaluation of Non-point Silvicultural Sources 

(FVS-WRENSS) and models from academia, such as the 

topographic model (TOPMODEL) and the triangulatedirregular-network (TIN)- based Real-time Integrated Basin 

Simulator (tRIBS). The goal of this future modeling work 

will be to extrapolate the results of the paired-watershed 

study to larger catchments, such as the Salt and Verde River 

watersheds, where forest restoration could impact the water 

resources of major metropolitan areas.

Acknowledgments

This study design was developed with the help of W. 

Wallace Covington and David W. Huffman of the Ecological 

Restoration Institute. The report was reviewed by Mark 

Hubbel and Tim Skarupa of the Salt River Project, Dan Neary 

and Bill Elliott of the U.S. Forest Service’s Rocky Mountain 

Research Station, Pete Wolgemuth of the U.S. Forest 

Service’s Pacific Southwest Research Station, Roy Jemison 

of U.S. Forest Service Region 3, Dino De Simone of the U.S. 

Department of Agriculture’s Natural Resources Conservation 

Service, and Wonsook Ha of the Northern Arizona University 

School of Earth Science and Environmental Sustainability. 

It was prepared in partial fulfillment of Salt River Project 

Contract 12.0356. 

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Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter J

Long-Term Post-Wildfire Correlates with Avian Community 

Dynamics in Ponderosa Pine Forests 

By Jamie S. Sanderlin,1 William M. Block,1 and Brenda E. Strohmeyer1

Abstract

We used a 10-year data set to illustrate the longterm correlates of wildfire on avian species richness in 

the ponderosa pine (Pinus ponderosa) forests of northern 

Arizona. This study was conducted in the vicinity of the 

Horseshoe and Hochderffer Fires, which occurred in 1996, 

and sampling began 1 year after the fires. Using pointcount data from breeding seasons, we described how avian 

species richness, local colonization, and local extinction 

changed over time following the wildfires. We used 

Bayesian hierarchical models to describe occupancy as a 

function of burn severity (severe, moderate, or unburned), 

years since wildfire, and species, while accounting for 

variables that influenced detection probability, such as 

species and sampling effort. The avian species from our 

study followed general patterns predicted from changes in 

vegetation structure in response to fire based on foraging and 

nesting requirements. Our results indicated that landscape 

heterogeneity from a mixture of fire severities and time since 

fire increased overall species richness across the landscape. 

Avian species in pine forests evolved with fire. Given that 

some species demonstrated an affinity to severely burned 

forests suggests that similar conditions existed historically. 

Introduction

Disturbance plays important roles for the conservation 

of birds by influencing habitat distribution and landscape 

heterogeneity. Increasing landscape heterogeneity often leads 

to increased overall species richness with patches having 

different species compositions (Brawn and others, 2001; 

Platt and Connell, 2003). Species richness is a common 

indicator of management success (for example, Russell and 

others, 2009). Within pine forests in the southwestern United 

States fire is a common natural disturbance (Covington 

and Moore, 1994; Moir and others, 1997), and some avian 

species have evolved as fire specialists (Hutto and others, 

2008). Historically, fires in these pine forests tended to be 

low-severity surface fires. Within the past 20 years, however, 

fire regimes have changed and have become high-severity, 

stand-replacing events. An increase in the frequency of large, 

severe wildfires in the southwestern United States can be 

attributed to increases in fuel loads and continuity as the 

result of livestock grazing, logging, and fire suppression 

(Allen and others, 2002; Covington and Moore, 1994; Moore 

and others, 1999; Swetnam and others, 1999).

Most information about avian response to wildfire 

is short-term (<5 years) (Saab and Powell, 2005; Saab 

and others, 2005). As changes in forest structure occur 

from fire, we would expect some bird species to respond 

differently depending on time since fire and the fire regime 

of the area. Foraging method, nest type, and location all 

influence a species response to fire (Saab and Powell, 2005). 

In addition to time since fire, fire severity influences bird 

response to fire (Kotliar and others, 2002; Smucker and 

others, 2005). Further, relatively little is known about avian 

response to southwestern fires extending the need to improve 

management strategies for avian species (Bock and Block, 

2005). One of the primary management tools in southwestern 

ponderosa pine forests is prescribed fire, so we need to 

understand how birds respond to wildfire at different burn 

severities and if we can mimic the historical fire regime with 

the frequency and severity of prescribed fire.

Our objectives were to quantify wildfire correlates with 

avian community metrics in Northern Arizona following the 

Hochderffer and Horseshoe Fires of 1996. Specifically, we 

were interested in relations of fire burn severity and time 

since wildfire with species occupancy, species richness, and 

local colonization and local extinction. We used multispecies 

occupancy models (Dorazio and others, 2006; MacKenzie 

and others, 2006) to estimate community metrics, while 

accounting for imperfect detection. Imperfect detection is 

important to include in analyses, especially with rare or 

elusive species or when trying to assess change over time. 

Estimates of community metrics could be biased if species 

occupy an area but were never detected during a survey or 

multiple surveys and could lead to biased study conclusions 

and management actions. 1

U.S. Forest Service Rocky Mountain Research Station
90 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Methods

Study Area

Two wildfires (Horseshoe Fire and Hochderffer Fire) 

occurred in May and June 1996 in the Flagstaff Ranger 

District of the Coconino National Forest in north-central 

Arizona. The region of the fires is shown in figure 1. 

The Horseshoe Fire encompassed about 3,500 hectares 

(ha) and the Hochderffer fire encompassed more than 6,600 ha 

adjacent to the Horseshoe Fire. The predominant vegetation 

type was ponderosa pine forest. Other tree species included 

Gambel oak (Quercus gambelii), piñon pine (Pinus edulis), 

and alligator (Juniperus deppeana) and one-seeded junipers 

(J. monosperma). Understory vegetation included buckbrush 

(Ceanothus fendleri), rabbitbrush (Chrysothamnus nauseosa), 

Oregon grape (Mahonia repens), and various grasses and forbs. 

Burn severity in these areas ranged from high to moderate 

to low. We used three broad categories of severity (high, 

moderate, unburned) originally defined by Dwyer and Block 

(2000) within our analyses. We used the following ranges 

of the delta normalized burn ratio (dNBR) generated from 

a comparison of Landsat Thematic Mapper (TM) imagery 

recorded before and after wildfire (Eidenshink and others, 

2007; also see http://www.mtbs.gov/) to define the burn 

severity categories for each transect. Raw dNBR values were 

compiled at a 30×30meter (m) resolution, and a mean dNBR 

was calculated for a 100-m-radius neighborhood centered on 

the point count station. Our high severity category represented 

average dNBR of all points per transect ≥150, moderate 

severity was <150 and ≥65, and unburned was <65. We used 

unburned areas for contrasts because pre-wildfire data were 

unavailable. Unburned areas were similar to burned areas with 

respect to pre-burn stand structure and species composition but 

may have deviated from pre-settlement ponderosa pine forest 

conditions described by Covington and others (1997).

0

0

1 2

1 2 3

MILES 

KILOMETERS 

A

B

C

D E

F G

H

I

J

K

N

X

R

Z Colorado R.

ARIZONA

Study

area

N

Figure 1. Map showing the avian community study area after the Hochderffer and Horseshoe wildfires that 

occurred in 1996 in northern Arizona. All 15 avian transects are labeled on the topographic map using different 

colors to easily distinguish the different transects: blue circles (transects A, C, E, G, I, K, R, Z), red circles (transects 

B, D, F, H, J, N, X). (Topographic map from Esri ArcGIS Resources online, copyright 2013 National Geographic 

Society and y

i-cubed.). 
Long-Term Post-Wildfire Correlates with Avian Community Dynamics in Ponderosa Pine Forests 91

Field

We sampled birds starting 1 year after the fires using the 

variable-radius point-count method (Reynolds and others, 

1980). Sampling occurred during the breeding season over a 

period of 10 years (primary periods), where each season had as 

much as three secondary periods (visits). Each transect route 

consisted of an average of 10 points (range 3–20) spaced at 

200 m intervals. The 15 transect lines were categorized as high 

severity (7transects with 49 points total), moderate severity 

(4transects with 50 points total), and unburned (4 transects with 

50 points total). We sampled each point three times during the 

summer breeding season to sample variation in bird distribution 

and detectability. Counts began within 30 minutes of sunrise 

and were completed no later than 4 hours after sunrise to sample 

points during periods of high bird activity. Observers remained 

still for 2 minutes after reaching a point to allow birds to resume 

normal activity patterns after disturbance. The actual point count 

lasted 8 minutes. The observer recorded weather, windspeed, 

and temperature in addition to species, age, and sex of all birds 

detected, mode of detection (audio or visual), and estimated 

horizontal distance to the bird.

Analytical

We used Bayesian hierarchical models (Gelman and 

others, 2004) to model detection and occupancy (MacKenzie 

and others, 2006; Royle and Dorazio, 2008, p. 380–399) and 

to estimate species richness N. We assumed occupancy states 

of species could change with local extinction and colonization 

between years, but not during each summer season of each year. 

We condensed data across points within each transect to increase 

species detections per year and to reduce model complexity. 

We used a three-dimensional data matrix y, where element yijt

was a sum of binary indicators for species detection of species 

i (i=1,...,N) at transect j (j=1,...,15) during primary sampling 

occasion year t (t=1,..., 10), rather than the binary indicators of 

species detection typically used in single-species or multispecies 

occupancy models. When binary indicator of species detection 

xijt=1, we detected species i at transect j during primary sampling 

occasion year t. We used the sum of all binary species i

detections over all secondary sampling occasions at each

transect, where yijt y = ijt xijts

s=1

3

and ={0, 1, 2, 3}. We used species, 

time since wildfire, and burn severity to model probability of 

occupancy, and we used species and effort to model detection probability. For numerical reasons, we use standardized 

covariates of time since wildfire and effort. We defined effort 

as number of points in each transect (table 1).

Model

Because we were interested in the whole avian 

community, not just the species we observed (nobs), we used 

Bayesian hierarchical models with unknown species richness 

Table 1. Number of bird-count points sampled per transect 

at different burn severities (high, moderate, unburned) after 

the 1996 Hochderffer and Horseshoe wildfires that occurred in 

northern Arizona.

Transect Burn severity Number of pointcount stations

A High 14

B Moderate 20

C High 10

D1 High 10

E Unburned 10

F High 3

G High 4

H Moderate 15

I High 9

J Unburned 15

K Unburned 15

N High 4

R Moderate 7

X High 5

Z Moderate 8

N High 4

1

Transect D was classified as unburned for the first 3 years after 

wildfire. The Pumpkin Fire went through this area in 2000, so for years 

4 through 10 it was classified as high burn severity.

(Royle and Dorazio, 2008, p. 384–387). We defined wi as 

a latent Bernoulli random variable, with probability Ω, 

indicating whether species i from the supercommunity was 

available for sampling during all primary sampling seasons:

 

 [wi |Ω] ~ Bern(Ω). (1)

This assumed all of our avian community species were 

part of the same supercommunity, or regional species pool. To 

model community change, we assumed a Markovian process 

described the current occupancy state, whereby probability of 

occupancy at time t was partially dependent on the probability 

of occupancy at time t–1. We modeled the probability of 

Bernoulli latent variable zijt for occupancy given probability of 

occupancy ψijt and wi as:

 

 [zijt |ijt , wi] ~ Bern(ijt wi), (2)

where probability of occupancy ψijt was a function of 

covariates (indicator of moderate burn severity [mburn], 

indicator of high burn severity [hburn], time since wildfire 

[time], time since wildfire squared [time2]) and the previous 

occupancy state with zij0 ~ Bern(ai1(0)):
92 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

logit( ijt)=

ai 0 + ai 1 zij 0 + ai 2 mburn j + ai 3 hburn j + ai 4 time 0 + ai 5 time 0

2 +

ai 6 mburn j time 0 + ai 7 hburn j time 0 + ai 8 mburn j time 0

2

ai 9 hburn j time 0

2

,

t = 1

ai 0 +a i 1 zij ,t 1 + ai 2 mburn j + ai 3 hburn j + ai 4 timet + ai 5 timet

2 +

ai 6 mburn j timet + ai 7 hburn j timet + ai 8 mburn j timet

2

ai 9 hburn j timet

2

.

t >1

Value ai1 describes a species-specific normal random effect 

for local probability of survival. These terms also describe 

species-specific normal random effects: ai0 (intercept), ai2 

(moderate burn severity), ai3 (high burn severity), ai4 (time 

since wildfire), ai5 (time since wildfire squared), ai6 (interaction 

between time since wildfire and moderate burn severity), ai7

(interaction between time since wildfire and high burn severity), 

ai8 (interaction between time since wildfire squared and 

moderate burn severity), and ai9 (interaction between time since 

wildfire squared and high burn severity). We included quadratic 

effects of time since wildfire to capture nonlinear responses of 

species. Occupancy parameter estimates for each species were 

random variables governed by community-level parameters.

We modeled the probability of observation of species i at 

transect j during primary period t, yijt, given secondary periods 

K, probability of detection pij, and occupancy latent variable zijt

using a binomial distribution with K trials and probability of 

success pij×zijt: 

[ yijt | pij ,zijt]~Bin(K, pij zijt). (4)

We used species and sampling effort (eff) as covariates to 

model probability of detection p:

 

 logit( pij)= bi 0+b1 eff j

. (5)

Covariate bi0 was a species-specific normal random effect, 

whereas b1 was a fixed effect. Detection parameter estimates 

for each species were random variables governed by a 

community-level parameter. We modeled heterogeneity 

among species using a covariance term (ρ) between species 

intercepts of occurrence (ai0) and detection probability 

(bi0) (Royle and Dorazio, 2008, p. 391). We assumed a 

multivariate logit scale normal distribution, where the 

only nonzero off-diagonal elements of the variancecovariance matrix with occupancy and detection parameters 

were between ai0 and bi0. This covariance term, our data 

augmentation step, and the assumption that covariates of 

species were random variables from a common distribution 

allowed us to make inferences on occupancy and detection 

probability of species that we never detected. 

We calculated the number of species, species richness, 

at each transect j and year t: 

Njt = zijt

i=1

M

. (6)

We also calculated local species colonization, (γ), probability 

that a species selected at random from the community was 

not present in the community at time t–1, and local species 

(3)

extinction (ε), or probability that a species selected at random 

from the community was present at time t-1 but not at time t 

(Williams and others, 2002):

 

jt =

(zijt ) (1 zij,t 1

) i=1

M

zij,t 1

i=1

M

 

jt =

(1 zijt) (zij,t 1

) i=1

M

zij,t 1

i=1

M .

 

Inference

We used Bayesian hierarchical models (Gelman and 

others, 2004) in R2WinBUGS (Sturtz and others, 2005), an 

R package (R Development Core Team, 2011) that interfaces 

with WinBUGS (Lunn and others, 2000) to obtain parameter 

estimates. We used independent non-informative priors 

because we had no prior knowledge about the parameters: 

Ω ~ Uniform(0,1), expit(μa1(0)) ~ Uniform(0,1), σa1(0) ~ 

Uniform(0,10), expit(μa0) ~ Uniform(0,1), σa0 ~ Uniform(0,10), 

ai1(0) ~ Uniform(0,1), μa2 ~ Normal(0,1), σa2 ~ Uniform(0,10), 

μa3 ~ Normal(0,1), σa3 ~ Uniform(0,10), μa4 ~ Normal(0,1), σa4

~ Uniform(0,10), μa5 ~ Normal(0,1), σa5

 ~ Uniform(0,10), μa6 

~ Normal(0,1), σa6 ~ Uniform(0,10), μa7 ~ Normal(0,1), σa7 ~ 

Uniform(0,10), μa8 ~ Normal(0,1), σa8 ~ Uniform(0,10), μa9 ~ 

Normal(0,1), σa9 ~ Uniform(0,10), expit(μb0) ~ Uniform(0,1), 

σb0 ~ Uniform(0,10), b1 ~ Normal (0, 1), ρ ~ Uniform(–1,1). We 

used data augmentation (Dorazio and others, 2006) to extend 

inference to the entire community. We used a sufficiently large 

supercommunity size M, so the posterior distribution of Ω was 

centered well below its upper limit (Ω≤0.5). We augmented 

the species observation matrix y with (M–nobs) rows of zeros 

for all transect×primary period sampling sessions. We ran four 

parallel chains (length 50,000 it, burn-in 25,000 it, thinning 

20 it) to estimate the posterior distribution median of model 

parameters and 95-percent Bayesian Credible Intervals (BCI). 

We determined statistical significance when 95-percent BCIs 

did not overlap zero. Convergence was reached ( Rˆ =1.0–1.1; 

Brooks and Gelman, 1998). We assessed goodness-of-fit (GOF) 

using two statistics (deviance [–2×log-likelihood], squared 

and (7)

(8)
Long-Term Post-Wildfire Correlates with Avian Community Dynamics in Ponderosa Pine Forests 93

loss i=1

N

j=1

15

t=1

10

Yexpected,ijt Yijt

2

), 

where Yijt is observed data for species i, at transect j, during 

year t for a Bayesian p-value (Gelman and others, 2004, 

p.162). The Bayesian p-value is the proportion of replicateddata summary values (deviance, squared loss) that were 

greater than or equal to the observed value (Gelman and 

others, 2004, p. 162).

Results

Data Summary

We detected 107 bird species (table 2; refer to this table 

for scientific names of birds discussed in text) over the study 

period. Total number of species observed on all transects 

was comparable across years (nobs,yr=[59, 54, 57, 57, 60, 

72, 60, 60, 63, 65]). There was evidence of overdispersion 

(Bayesian p-value deviance=0.7112, Bayesian p-value 

squared loss=1.0). Histograms of median posterior estimates 

of detection probabilities were bimodal and detection 

probabilities varied by species, but histograms were similar 

for all transects (fig.2). Detection probabilities increased with 

increased sampling effort (median 1

ˆ

b =0.4424, 95-percent 

BCI=0.3938–0.4893).

Table 2. Avian species detected during summer breeding 

seasons after the 1996 Hochderffer and Horseshoe wildfires of 

northern Arizona.

Common species name Scientific name

Wild turkey Meleagris gallopavo

Turkey vulture Cathartes aura

Cooper’s hawk Accipiter cooperii

Northern goshawk Accipiter gentilis

Sharp-shinned hawk Accipiter striatus

Red-tailed hawk Buteo jamaicensis

Swainson’s hawk Buteo swainsoni 

Ferruginous hawk Buteo regalis 

Peregrine falcon Falco peregrinus 

Merlin Falco columbarius 

American kestrel Falco sparverius

Prairie falcon Falco mexicanus 

Band-tailed pigeon Patagioenas fasciata

Mourning dove Zenaida macroura

Great horned owl Bubo virginianus

Northern pygmy-owl Glaucidium gnoma

Long-eared owl Asio otus 

Flammulated owl Otus flammeolus

Lesser nighthawk Chordeiles acutipennis

Common nighthawk Chordeiles minor

Common species name Scientific name

Common poor-will Phalaenoptilus nuttallii

Black-chinned hummingbird Archilochus alexandri

Blue-throated hummingbird Lampornis clemenciae

Broad-tailed hummingbird Selasphorus platycercus

Rufous hummingbird Selasphorus rufus

Northern flicker Colaptes auratus

Acorn woodpecker Melanerpes formicivorus

Lewis’s woodpecker Melanerpes lewis

Downy woodpecker Picoides pubescens 

American three-toed woodpecker Picoides dorsalis 

Hairy woodpecker Picoides villosus

Red-naped sapsucker Sphyrapicus nuchalis

Williamson’s sapsucker Sphyrapicus thyroideus 

Olive-sided flycatcher Contopus cooperi

Western wood-pewee Contopus sordidulus

Hammond’s flycatcher Empidonax hammondii

Dusky flycatcher Empidonax oberholseri

Cordilleran flycatcher Empidonax occidentalis

Gray flycatcher Empidonax wrightii

Ash-throated flycatcher Myiarchus cinerascens

Cassin’s kingbird Tyrannus vociferans

Western kingbird Tyrannus verticalis

Warbling vireo Vireo gilvus

Hutton’s vireo Vireo huttoni

Plumbeous vireo Vireo plumbeous

Pinyon jay Gymnorhinus cyanocephalus 

Western scrub-jay Aphelocoma californica

Stellar’s jay Cyanocitta stelleri

Mexican jay Aphelocoma wollweberi

Clark’s nutcracker Nucifraga columbiana

American crow Corvus brachyrhynchos 

Common raven Corvus corax

Horned lark Eremophila alpestris 

Violet-green swallow Tachycineta thalassina

Mountain chickadee Poecile gambeli

Bushtit Psaltriparus minimus

Red-breasted nuthatch Sitta canadensis

White-breasted nuthatch Sitta carolinensis

Pygmy nuthatch Sitta pygmaea

Brown creeper Certhia americana

Rock wren Salpinctes obsoletus

House wren Troglodytes aedon

Blue-gray gnatcatcher Polioptila caerulea

Ruby-crowned kinglet Regulus calendula

Golden-crowned kinglet Regulus satrapa

Mountain bluebird Sialia currucoides

Western bluebird Sialia mexicana

Townsend’s solitaire Myadestes townsendi

Red-breasted nuthatch Sitta canadensis

White-breasted nuthatch Sitta carolinensis

Pygmy nuthatch Sitta pygmaea

Brown creeper Certhia americana

Rock wren Salpinctes obsoletus

Table 2.—Continued
94 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Species Richness

Avian species richness, on average, increased over the 

10-year time period for transects, although 95-percent BCIs 

overlapped for these transects (fig. 3). 

Although patterns were not significant because the 

95-percent BCIs overlapped, we describe general patterns of 

the average posterior median species richness estimates for the 

transects at different severities below. The average posterior 

median species richness estimate of moderate severity 

transects ( ˆ

SR = 64 median, avg , ˆ

SR =5 median, sd ) was greater than high

severity transects ( ˆ

SR =55 median, avg , ˆ

SR =5 median, sd ) by 9 species

over the 10-year period and greater than unburned transects 

( ˆ

SR =50 median, avg , ˆ

SR =5 median, sd ) by 14 species over the 10-year

period. The average posterior median species richness estimate 

of high-severity transects was greater than unburned transects 

by 5 species over the 10-year period. One particular example 

(unburned for first 3 years and high burn severity for last 

7 years for transect D versus unburned for transect E) is of 

interest. Posterior median estimates of species richness were 

similar for the first 3 years when transects D and E were both 

Common species name Scientific name

House wren Troglodytes aedon

Blue-gray gnatcatcher Polioptila caerulea

Ruby-crowned kinglet Regulus calendula

Golden-crowned kinglet Regulus satrapa

Mountain bluebird Sialia currucoides

Western bluebird Sialia mexicana

Townsend’s solitaire Myadestes townsendi

Hermit thrush Catharus guttatus

American robin Turdus migratorius

Northern mockingbird Mimus polyglottos

American pipit Anthus rubescens 

Cedar waxwing Bombycilla cedrorum

Olive warbler Peucedramus taeniatus

Red-faced warbler Cardellina rubrifrons

Yellow-rumped warbler Setophaga coronata

Grace’s warbler Setophaga graciae

Black-throated gray warbler Setophaga nigrescens

Townsend’s warbler Setophaga townsendi

Orange-crowned warbler Oreothlypsis celata

Virginia’s warbler Oreothlypsis virginiae

Green-tailed towhee Pipilo chlorurus

Spotted towhee Pipilo maculatus

Rufous-crowned sparrow Aimophila ruficeps

Black-throated sparrow Amphispiza bilineata

Lark sparrow Chondestes grammacus

Brewer’s sparrow Spizella breweri 

Chipping sparrow Spizella passerina

Vesper sparrow Pooecetes gramineus 

Grasshopper sparrow Ammodramus savannarum 

Song sparrow Melospiza melodia 

Dark-eyed junco Junco phaeonotus

Hepatic tanager Piranga flava

Western tanager Piranga ludoviciana

Summer tanager Piranga rubra

Black-headed grosbeak Pheucticus melanocephalus

Eastern meadowlark Sturnella magna

Western meadowlark Sturnella neglecta

Brewer’s blackbird Euphagus cyanocephalus 

Brown-headed cowbird Molothrus ater

Bullock’s oriole Icterus bullockii

Table 2.—Continued

Median posterior estimate of detection probability

Number of species

60

45

30

15

0

60

45

30

15

0

60

45

30

15

0

0 0.5 1 0 0.5 1 0 0.5 1 0 0.5 1 0 0.5 1

Hburn: A Hburn: C Hburn: D Hburn: F Hburn: G

Hburn: I Hburn: N Hburn: X Mburn: B Mburn: H

Mburn: R Mburn: Z Uburn: E Uburn: J Uburn: K

Figure 2. Histograms of median posterior estimates of detection 

probabilities for avian species at the 15 avian transects (fig. 1) 

for 10 years after the 1996 Hochderffer and Horseshoe wildfires 

that occurred in northern Arizona. Subplots are labeled with burn 

severity and transect name. Note that transect D was classified 

as unburned for the first 3 years after wildfire and then high 

severity afterwards. Hburn, high burn severity; Mburn, moderate 

burn severity; Uburn, unburned.
Long-Term Post-Wildfire Correlates with Avian Community Dynamics in Ponderosa Pine Forests 95

unburned; however, median posterior estimates of species 

richness increased for the high-severity transect D with time, 

while the unburned transect E stayed relatively constant. 

Local Colonization and Local Extinction

We found no clear patterns in local colonization and local 

extinction over all 10 years for unburned, moderate-burn, and 

high-burn severity transects (fig. 4) because all 95-percent 

BCIs overlapped. 

In general, posterior median estimates of local 

colonization were greater than posterior median estimates 

of local extinction of avian species, which is consistent with 

posterior median estimates of species richness increasing over 

the 10-year time period for all transects. Local-colonization 

posterior median estimates tended to be greater than local 

extinction estimates for the last 5 years, indicating that 

local colonization was increasing with time, although not 

significantly. 

Responses of Individual Species

Occupancy responses of individual species to time and 

burn severity were not the same (table 3). Nine species showed 

an increase in the probability of occupancy (for example, 

95-percent BCI did not include zero) over time. One species, 

western kingbird, exhibited a significantly negative response 

to the quadratic form of time, indicating that probability of 

occupancy was greater in the beginning and ending part of 

the 10-year time period. Six species had significantly greater, 

and one species, Townsend’s solitaire, had significantly lower 

probability of occupancy in moderate burn severity. Seven 

species had significantly greater and 14 species had significantly 

lower probability of occupancy in high burn severity. Three 

species (American kestrel, rock wren, western meadowlark) 

had significantly higher and one species had significantly 

lower (Townsend’s solitaire) probability of occupancy for both 

moderate and high burn severity transects. The only significant 

interaction between burn severity and time or burn severity and 

the quadratic form of time was with the American kestrel, which 

had a positive interaction with high-burn severity and time for 

probability of occupancy.

Discussion

Burn Severity and Species Richness

Our results indicate that fire severity is an important 

driver of species richness, supporting research that fire creates 

landscape heterogeneity (Brawn and others, 2001). Median 

posterior estimates for avian species richness on transects that 

had fire were greater than unburned transects over the 10-year 

period, and species richness median posterior estimates for 

moderate severity transects were greater than high severity 

transects, although the credible intervals did overlap. Similarly, 

Bock and Block (2005) reported more species in severely 

burned (45) and moderately burned areas (41) than on 

unburned areas (31) 3 years post-fire on the same transects. 

Although the numbers of species detected increased over time 

for all transects, burned areas still exhibited more species than 

unburned areas 10 years after fire. Other studies (for example, 

Fontaine and others, 2009) have documented decreased avian 

^

SR

100

80

60

40

20

100

80

60

40

20

100

80

60

40

20

Hburn: G Hburn: I Hburn: N Hburn: X

Mburn: B Mburn: H Mburn: R Mburn: Z

Uburn: D Uburn: E Uburn: J Uburn: K

Unburned

EXPLANATION

Moderate High

100

80

60

40

20

Years since fire

0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10

Hburn: A Hburn: C Hburn: D Hburn: F

Figure 3. Diagram showing avian community species richness 

(ŜR) posterior estimates and 95-percent Bayesian credible intervals 

over different burn severity (unburned, white box; moderate burn, 

box with an x; high burn, black box) transects (fig. 1) for 10 years 

after the 1996 Hochderffer and Horseshoe wildfires in northern 

Arizona. Subplots are labeled with burn severity and transect name. 

Hburn, high burn severity; Mburn, moderate burn severity; Uburn, 

unburned.
96 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Years since fire

Parameter estimate

0.6

0.5

0.4

0.3

0.2

0.1

0

Hburn: A Hburn: C Hburn: D Hburn: F

Hburn: G Hburn: I Hburn: N Hburn: X

Mburn: B Mburn: H

Uburn: D Uburn: E Uburn: J Uburn: K

Local extinction Local colonization

EXPLANATION

0.6

0.5

0.4

0.3

0.2

0.1

0

0.6

0.5

0.4

0.3

0.2

0.1

0

0.6

0.5

0.4

0.3

0.2

0.1

0

Mburn: R Mburn: Z

2 4 6 8 10 2 4 6 8 10 2 4 6 8 10 2 4 6 8 10

Figure 4. Diagram showing avian community local extinction 

(black circle) and local colonization (white box) posterior 

estimates and 95-percent Bayesian credible intervals for all 15 

avian transects (fig. 1) for 10 years after the 1996 Hochderffer and 

Horseshoe wildfires in northern Arizona. Subplots are labeled 

with burn severity and transect name. Hburn, high burn severity; 

Mburn, moderate burn severity; Uburn, unburned.

densities with high-severity fire but not necessarily changes 

in species richness. Hurteau and others (2008) did not detect 

differences in species richness over different experimental fuelreduction treatments (thin to reduce stem density and basal area 

only, prescribed burn only, and thin and prescribed burn). 

Local Colonization and Local Extinction

In general, local colonization of avian species was greater 

than local extinction over the 10-year period for all transects, 

which is consistent with our result of species richness 

increasing over the 10-year time period for all transects. 

This could be the result of other environmental variables not 

included in this analysis (that is, rainfall, temperature) or even 

broader regional trends not specific to our study area.

Burn Severity and Time Since Fire for Individual 

Species

Species that showed significant responses to fire 

within our study followed the general predictions of Saab 

and Powell (2005) (table 4). As changes in forest structure 

occur from fire, we would expect some species to respond 

differently depending on time since fire. Our study includes 

how species respond to fire with regards to probability of 

occupancy; however, in the other studies we reference below, 

it is important to note that species response could be based 

on occupancy, counts not adjusted for detection probability, 

abundance, density, or nest density. Early postdisturbance 

vegetation structure after fire includes snags and groundcover 

from herbs. Aerial and ground insectivores, bark and wood 

foragers, and cavity nesters tend to favor burned habitats. 

Examples of aerial insectivores that positively responded 

to fire from our study and other studies included Lewis’s 

woodpecker (Hutto and others, 2008; Saab and Dudley, 

1998; Saab and others, 2007) and the olive-sided flycatcher 

(Kalies and others, 2010; Kotliar and others, 2002; Lowe and 

others, 1978; Raphael and others, 1987; Smucker and others, 

2005). The common nighthawk, also an aerial insectivore, 

responded positively to fire in other studies (Bock and Bock, 

1978; Kalies and others, 2010) but responded negatively to 

fire in our study. The Cassin’s kingbird and western kingbird 

are also aerial insectivore species that we would expect to 

positively respond to fire (Bock and Bock, 1978; Kirkpatrick 

and others, 2006), but we did not detect a significant 

response to fire in our study. Cassin’s kingbird occupancy 

positively increased and western kingbird occupancy 

negatively decreased with time for all transects, suggesting 

that factors not accounted for in our study influenced 

these species instead. Examples of ground insectivores 

that positively responded to fire from our study and other 

studies included house wren (Blake, 1982; Kirkpatrick and 

others, 2006; Kotliar and others, 2007; Raphael and others, 

1987), mountain bluebird (Bock and Bock, 1983; Dieni and 

Anderson, 1999; Kotliar and others, 2002; Lowe and others, 
Long-Term Post-Wildfire Correlates with Avian Community Dynamics in Ponderosa Pine Forests 97

Table 3. Wildfire responses by individual avian species following 

the 1996 Hochderffer and Horseshoe wildfires in northern Arizona. 

[All significant positive (+) and negative (-) for covariates time, time2, 

moderate burn severity (Mburn), high burn severity (Hburn), and 

interactions were noted. Species not listed did not exhibit significant 

relationships with fire severity, time, or their interaction. There were no 

significant relations for the interactions of burn severity and time2. Scientific 

names of species are in table 2]

Common species 

name

time time2 Mburn Hburn Mburn

× time

Hburn

× time

Red-tailed hawk +

American kestrel + + +

Common nighthawk -

Broad-tailed 

hummingbird

+

Lewis’s woodpecker +

Olive-sided 

flycatcher

+

Ash-throated 

flycatcher

+

Cassin’s kingbird +

Western kingbird -

Warbling vireo + -

Plumbeous vireo -

Mountain chickadee -

Red-breasted 

nuthatch

+

White-breasted 

nuthatch

-

Pygmy nuthatch + -

Brown creeper -

Rock wren + + +

House wren +

Mountain bluebird +

Townsend’s solitaire - -

Olive warbler -

Yellow-rumped 

warbler

-

Grace’s warbler -

Virginia’s warbler -

Lark sparrow +

Chipping sparrow + +

Western tanager -

Western meadowlark + +

Brown-headed 

cowbird

+ -

1978; Raphael and others, 1987; Smucker and others, 2005), 

and rock wren (Blake, 1982; Kotliar and others, 2002). We 

expected broad-tailed hummingbirds to respond positively 

to fire due to an increase in flowers as in other studies (Bock 

and Block, 2005; Kalies and others, 2010; Kotliar and others, 

2007), but we did not detect a positive response to fire in 

our study. We did, however, have a significant increase in 

occupancy of broad-tailed hummingbirds with time across 

all transects, suggesting that factors not accounted for in our 

study influenced this species. The only significant interaction 

between burn severity and time was with the American 

kestrel, which had a positive interaction with high-burn 

severity and time for probability of occupancy, indicating as 

time increases after fire probability of occupancy increases. 

This is likely the result of increased prey abundance, 

availability of nest cavities, and open habitat structure 

after fire. Similar patterns have been documented with the 

American kestrel in other studies (Kalies and others, 2010; 

Lowe and others, 1978; Saab and Dudley, 1998; Saab and 

others, 2007).

As time progresses, snags fall and the understory 

develops with more shrubs. We would expect omnivores and 

shrub nesters to do well when this happens. One example of 

an omnivore that responded positively to fire in our study 

and other studies was the chipping sparrow (Bock and Bock, 

1983; Lowe and others, 1978; Raphael and others, 1987; 

Smucker and others, 2005). Finally, as the forest matures, the 

structure is characterized by a closed canopy with few snags 

and low understory development, whereas old-growth forests 

include snags, an open canopy, and an understory that starts to 

develop. Saab and Powell (2005) found that foliage gleaners 

preferred unburned habitats, which was true with all our 

species with significant negative responses to fire. Examples 

of foliage insectivores with negative responses to fire in our 

study and other studies included warbling vireo (Dieni and 

Anderson, 1999; Kotliar and others, 2002) 3 to 6 years after 

fire (Bagne and Purcell, 2011; Kirkpatrick and others, 2006; 

Kotliar and others, 2007), yellow-rumped warbler (Kotliar 

and others, 2007; Raphael and others, 1987; Smucker and 

others, 2005), western tanager (Raphael and others, 1987; 

Smucker and others, 2005), and plumbeous vireo (Kotliar 

and others, 2002). Examples of secondary cavity-nesting 

species that had negative responses to fire in our study and 

other studies included mountain chickadee (Dwyer and Block, 

2000; Hurteau and others, 2008; Kotliar and others, 2002; 

Kotliar and others, 2007; Lowe and others, 1978; Saab and 

Powell, 2005), white-breasted nuthatch (Blake, 1982; Dwyer 

and Block, 2000), pygmy nuthatch (Blake, 1982; Dickson 

and others, 2009; Dwyer and Block, 2000; Lowe and others, 

1978), and brown creeper (Kotliar and others, 2002; Lowe and 

others, 1978; Raphael and others, 1987). 

We detected only one species that had a mixed response 

(positive with moderate burn and negative with high severity 

burn) to wildfire, the brown-headed cowbird. Dieni and 

Anderson (1999) also found a similar mixed response for 

the brown-headed cowbird. This is not entirely surprising, 
98 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 4. Foraging mode, nest layer, and nest type for species, 

adapted from criteria of Saab and Powell (2005), with significant 

responses to wildfire in our study in northern Arizona following 

the 1996 Hochderffer and Horseshoe wildfires. 

[Response to fire from our study: positive (+), negative (-), or both. Foraging 

mode: AI=aerial insectivore, BI=bark insectivore, FI=foliage insectivore, 

GI=ground insectivore, CA=carnivore, OM=omnivore, and SI=shrub 

insectivore. Nest layers: GR=ground, SH=shrub, and CA= subcanopy to 

canopy. Nest types: O=open, C=closed, and P=parasitic. Scientific names of 

species are in table 2]

Common species name

Response

to fire

(our study)

Forage 

mode

Nest 

layer

Nest 

type

Red-tailed hawk + CA CA O

American kestrel + CA CA C

Common nighthawk - AI GR O

Lewis’s woodpecker + AI CA C

Olive-sided flycatcher + AI CA O

Warbling vireo - FI CA O

Plumbeous vireo - FI CA O

Mountain chickadee - FI CA C

White-breasted nuthatch - BI CA C

Pygmy nuthatch - BI CA C

Brown creeper - FI/BI CA C

Rock wren + GI GR C

House wren + GI CA C

Mountain bluebird + GI/AI CA C

Townsend’s solitaire - AI GR O

Olive warbler - FI CA O

Yellow-rumped warbler - FI CA O

Grace’s warbler - FI CA O

Virginia’s warbler - SI SH O

Chipping sparrow + OM SH O

Western tanager - FI CA O

Western meadowlark + GI GR O

Brown-headed cowbird both OM - P

depending on what host species the brown-headed cowbird is 

targeting for parasitizing. Brown-headed cowbird hosts in the 

western United States include the plumbeous vireo, western 

tanager, and warbling vireo (for example, Goguen and others, 

2009; Marvil and Cruz, 1989; Ortega and Ortega, 2003), which 

had negative responses with high-severity burn in our study, 

mirroring the negative response of high-severity burn with the 

brown-headed cowbird. Similarly, the chipping sparrow, also a 

brown-headed cowbird host (for example, Ortega and Ortega, 

2001), had a positive response to moderate-severity burn in 

our study, mirroring the positive response to moderate severity 

burn with the brown-headed cowbird.

Historical conditions in ponderosa pine forests of northern 

Arizona were characterized by an open park-like structure 

(Covington and others, 1997). The unburned areas used as 

contrasts in our study exhibited higher tree densities than found 

historically in ponderosa pine forests as the result of livestock 

grazing, logging, and fire suppression (Allen and others, 2002; 

Covington and Moore, 1994; Moore and others, 1999; Swetnam 

and others, 1999). We sampled birds within unburned forest 

to contrast with areas of moderate and high burn severity. The 

unburned areas did not represent historical forest conditions.

Our study demonstrates the importance of fire for 

providing and maintaining habitat for many species. Not all 

species responded similarly, as some species showed affinity 

to severely burned forests, whereas others favored moderately 

burned or unburned forest. We need to fine tune this work by 

incorporating covariates that may better explain bird-habitat 

relations. Future research with this study will include the 

influence of burn severity and patch size (Saab and others, 

2005), using dNBR at points instead of broad fire-severity 

categories and including weather and vegetation covariates. 

These additions will aid resource managers in characterizing 

the forest vegetation structure for desired management actions.

Management Implications

Our results indicate the importance of monitoring avian 

species for longer periods of time following wildfire. Many 

studies are restricted to 2 to 3 years post-fire, and the results 

from such studies may not capture long-term effects. At the 

landscape scale, overall species richness can be maximized 

by increasing landscape heterogeneity from different 

fire severities. These patches will have different species 

compositions, leading to an increase in species richness at 

the landscape scale. Avian species in pine forests evolved 

with fire. Given that some species demonstrated an affinity to 

severely burned forests suggests that similar conditions existed 

historically. Given behavior of contemporary fire, we envision 

ample severely burned forest in the foreseeable future. 

Unknown, however, is information that outlines the amount 

and distribution of patches across the landscape. Research is 

needed that addresses the temporal and spatial aspects of fire 

and fire severity including the size, shape, juxtaposition, and 

interspersion of patch sizes across the landscape. 
Long-Term Post-Wildfire Correlates with Avian Community Dynamics in Ponderosa Pine Forests 99

Acknowledgments

We thank Jill Dwyer, Laura Doll, Laura Dickson, 

Laura McGrath, Terry Pope, Gabe Martinez, Jose Iniguez, 

Brenda Strohmeyer, and Scott Vojta for collecting avian and 

vegetation data. We also thank the Coconino National Forest, 

Flagstaff Ranger District; National Fire Plan for funding (J. 

Sanderlin); J. Iniguez for assistance with maps; J.G. Dudley 

for calculations of delta normalized burn ratio (dNBR); C. 

Sieg for assistance with computing power; and E. Kailes 

and L.S. Baggett and two anonymous reviewers for helpful 

comments during review.

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Kanab Basin looking south. Photograph by Robert J. Hart, U.S. Geological Survey.
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter K

Hunting Methods and Harvest Demographics for Black 

Bears in Arizona, 1981–2011 

By Brian F. Wakeling1

Abstract

Harvest characteristics can be useful data in the evaluation 

of hunt structures for managed predator species, yet little 

information is available to assist with the interpretation of 

harvest characteristics of the American black bear (Ursus 

americanus). I examined age and sex characteristics obtained 

from 6,553 harvest reports and 2,910 teeth submitted for 

cementum aging between 1981 and 2011 in Arizona. I found 

no evidence that hunters without hounds (boot hunters) or 

hunters using hounds (hound hunters) were more selective in 

black bear harvests. I also found no difference in the mean age 

of male black bears harvested by hound hunters (4.7 years) and 

boot hunters (4.6 years), nor between the mean age of female 

black bears harvested by hound hunters (6.5 years) and boot 

hunters (5.7 years). However, female black bears harvested 

by both methods were older on average than were male black 

bears. Although bear harvest in Arizona currently does not 

appear excessive, should the need for greater restrictions 

become necessary in the future, season length, season timing, 

and greater restrictions on female harvests are likely to be more 

effective than restricting use of hounds in hunting.

Introduction

Predator species harvest management is dependent, 

in part, on harvest vulnerabilities. For instance, in Arizona, 

mountain lion (Puma concolor) harvests are accomplished 

predominately (65 percent of total harvest) with the use of 

hounds (Zornes and others, 2006). Hound hunters harvest 

more male (about 60 percent of their total take) than female 

mountain lions, whereas hunters that use calling, observation 

with binoculars, and stalking (boot hunters) typically harvest 

more female (about 60 percent of their total take) than male 

mountain lions (Zornes and others, 2006). Hound hunters also 

harvest older male mountain lions, on average, than do boot 

hunters in Arizona (Wakeling and others, 2015). The effect of 

these harvest demographics is that, although hound hunters 

harvest more mountain lions overall than do boot hunters, their 

harvest is more selective and less likely to negatively influence 

the mountain lion population. This knowledge may be useful 

when formulating hunting regulations.

Less information is currently available regarding the 

harvest of black bears (Ursus americanus) in Arizona. 

Black bear harvest is regulated in Arizona to maintain a 

median harvest age of female black bears of >5 years, in 

conjunction with a maximum harvest of <10 percent of 

the total estimated adult female segment of the population. 

Although population estimates of black bears in Arizona are 

imprecise, management units where these estimates have been 

verified through the use of genetic mark-recapture studies 

have proven them to be conservative (for example Atwood 

and others, 2011; Cunningham and Wakeling, 2011). Black 

bear hunting regulations have been increasingly restrictive 

over time with the intent of limiting harvest. A permit has 

been required to hunt black bears since 1954, and they have 

been classified as big game by State statute since 1968 

(Cunningham and Wakeling, 2011). Restrictions on the use 

of baiting were first implemented in 1982 and soon expanded 

statewide. Spring hunts were first authorized in 1985, but 

the use of hounds during these seasons was precluded. In 

1992, Arizona first implemented female harvest limits, which 

if reached in the harvest, would close the season to further 

take of bears in that area during the hunt. Cunningham and 

Wakeling (2011) reported that between 1998 and 2002, the 

mean ages of harvested males and females were 5.9 and 

6.2 years, respectively, yet they acknowledge that age and 

sex composition of the harvest may vary substantially among 

years. For instance, they report that yearlings composed 

42 percent of the harvest between 1998 and 2000, whereas 

they only composed 3 percent during 2001 and 2002. Still, 

little has been published regarding the demographics of black 

bear harvest with respect to harvest method when compared 

with mountain lion harvests.

Arizona implemented a mandatory black bear harvest 

reporting and checking procedure in 1981. I analyzed age 

and sex of the harvested animals as documented through 

these mandatory checks to determine if differences existed in 

harvest characteristics by harvest methods. This information 

can be useful in promulgating future regulation if needed. 1

Nevada Department of Wildlife, Game Division
104 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Study Area

 Black bears occupy about 32,000 square kilometers (km2) 

in Arizona, and inhabit Madrean evergreen, mixed-conifer, 

spruce (Picea spp.), fir (Abies spp.), ponderosa pine (Pinus 

ponderosa), and piñon (Pinus edulis)-juniper (Juniperus spp.) 

woodlands, as well as chaparral communities (Cunningham and 

Wakeling, 2011). Black bears are found at elevations ranging 

from about 900 to 2,750 meters (m). In Arizona, black bears 

are rarely observed in the southwestern deserts or north of the 

Colorado River. Lawful hunting seasons have been established 

throughout most occupied range.

Methods

In Arizona, lawfully harvested black bears must 

be physically inspected by the Arizona Game and Fish 

Department. Each black bear is inspected to verify sex and 

extract a premolar for cementum aging (Dimmick and Pelton, 

1996). Because regulations have changed over the years, I 

compared the number of black bears harvested by method 

(boot hunting and hound hunting) in three decades (1981–

1990, 1991–2000, 2001–2011) with a chi-square contingency 

table analysis (Zar, 1984). I similarly compared the number of 

female black bears harvested by method. I also individually 

compared the mean age of black bears by sex to determine if 

changes occurred over time. Finally, I used analysis of variance 

to determine if differences existed by age among sex and 

harvest classes, and used Tukey’s honest significant difference 

mean separation test to determine if individual classes differed 

(Zar, 1984); I considered all differences significant if P < 0.05.

Results

I analyzed age data from 2,910 black bears that were 

physically inspected between 1981 and 2011, and from which 

premolars could be successfully extracted and age determined. 

During this time period, 6,553 black bears were physically 

inspected, but premolars could not always be extracted or 

usable cememtum age data could not be obtained due to poor 

sample collection.

During the 1980s and 1990s, only 18 percent of the black 

bears harvested were taken with the aid of hounds. Since 

2000, this percentage has increased to about 23 percent (X2

= 32.285, df = 2, P < 0.001; table 1). Similarly, although the 

amount of total female black bear harvest by hound hunters 

averaged about 18 and 19 percent during the 1980s and 

1990s, respectively, that percentage increased to 25 percent 

since 2000 (X2

 = 15.892, df = 2, P < 0.001; table 2). Yet the 

proportion of the total harvest of female black bears by all 

hunters has decreased from 40–42 percent during the 1980s 

and 1990s to about 37 percent since 2000 (X2

 = 12.75, df = 2, 

P < 0.005; table 3). This shift was not observed in harvest by 

hound hunters (X2

 = 2.43, df = 2, not statistically significant; 

table 4), but was observed in the harvest by boot hunters (X2 = 

11.435, df = 2, P < 0.005; table 5). Since 2000, hound hunters 

have harvested a larger proportion of the total bear harvest 

(table 1), although both the total harvest (from >0.400 prior 

to 2000 to <0.370 after 2000; table 3) and the harvest by boot 

hunters (from >0.400 prior to 2000 to <0.370 after 2000; 

table 5) includes a smaller proportion of female black bears.

The age of harvested black bears showed additional 

correlations with harvest trends. The mean age of female black 

bears seemed to trend upward regardless of method of take 

(table 6), as did the mean age of male black bears (table 7). Male 

black bears harvested by both hunting methods were younger, 

on average, than were female black bears; however, there was 

no difference in mean age of either male or female black bears 

between methods (P < 0.001, 3 df, F = 28.03; table 8).

Discussion

Although less information regarding the relation between 

harvest statistics and level of exploitation exists for black bear 

than for mountain lion, Bunnell and Tait (1985) suggested that 

harvests composed of equal proportions of male and female 

bears were indicative of overharvest. During the last decade 

in Arizona (2001–11), about 63 percent of the total harvest 

was male and 64 percent of the harvest by boot hunters was 

male. About 62 percent of hound hunter harvests were male, 

but harvest by hound hunters only composed 24 percent of 

the total harvest. The total harvest has shifted from 60 percent 

male during 1981–1990 and 58 percent male during 1991–

2000, to a slightly larger proportion (63 percent) since then. 

There has been no detectable shift in harvest by hound hunters; 

that pattern in harvest characteristics is similar although the 

magnitude is somewhat less.

Inferences about effects of harvest can be difficult 

without knowledge of the age structure of the black bear 

population itself. For instance, mean age in heavily exploited 

black bear populations was determined to be 4.5 years in 

north-central Arizona above the Mogollon Rim (LeCount, 

1987) and 4.9 years in the White Mountains of Arizona 

(LeCount, 1990), whereas the mean age in a lightly exploited 

population was 8.1 years in central Arizona (LeCount, 1982). 

LeCount (1990) recommended implementing management 

strategies that yielded greater proportions of males in the 

harvest and harvests of black bears that were ≥6 years of 

age on average. Arizona harvests appear to be meeting these 

objectives, but changes over time seem to be most influenced 

by adaptive regulations (for example, eliminating the use 

of baits and season timing). For instance, spring harvests in 

Arizona are primarily scheduled to end before most females 

abandon winter dens, therefore spring harvests are dominated 

by males. Moreover, spring harvests account for <10 percent 

of the total annual take.
Hunting Methods and Harvest Demographics for Black Bears in Arizona, 1981–2011 105

Table 1. Time period and number (proportion) of black bears 

taken by hunting method in Arizona.

Time period Number (proportion) of harvested black bears

By boot hunters By hound hunters

1981–1990 2003 (0.818) 447 (0.182)

1991–2000 1650 (0.820) 363 (0.180)

2001–2011 1927 (0.762) 603 (0.238)

X2 = 32.285, df = 2, P < 0.001

Table 2. Time period and number (proportion) of female black 

bears taken by hunting method in Arizona.

Time period

Number (proportion) of 

female black bears harvested 

By boot hunters By hound hunters

1981–1990 777 (0.816) 175 (0.184)

1991–2000 673 (0.811) 157 (0.189)

2001–2011 684 (0.748) 231 (0.252)

X2 = 15.892, df = 2, P < 0.001

Table 3. Time period and number (proportion) of male and female 

black bears harvested by all methods in Arizona.

Time period Number (proportion) of black bears harvested

Male Female

1981–1990 1417 (0.598) 952 (0.402)

1991–2000 1166 (0.584) 830 (0.416)

2001–2011 1583 (0.634) 914 (0.366)

X2 = 12.75, df = 2, P < 0.005

Table 4. Time period and number (proportion) of male and female 

black bears harvested by hound hunters in Arizona.

Time period

Number (proportion) of black bears

harvested by hound hunters

Male Female

1981–1990 269 (0.606) 175 (0.394)

1991–2000 206 (0.567) 157 (0.433)

2001–2011 373 (0.618) 231 (0.382)

X2 = 2.433, df = 2, NS

Table 5. Time period and number (proportion) of male and 

female black bears harvested by boot hunters in Arizona.

Time period

Number (proportion) of black bears

harvested by boot hunters

Males Females

1981–1990 1148 (0.596) 777 (0.404)

1991–2000 960 (0.588) 673 (0.412)

2001–2011 1210 (0.639) 684 (0.361)

X2 = 11.435, df = 2, P < 0.005

Table 6. Time period and mean age of harvested female black 

bears by hunting method in Arizona.

Time period Mean age of female black bears harvested

By boot hunters By hound hunters

1981–1990 5.3 5.7

1991–2000 6.1 6.4

2001–2011 6.1 6.2

Table 7. Time period and mean age of harvested male black 

bears by hunting method in Arizona.

Time period Mean age of male black bears harvested

By boot hunters By hound hunters

1981–1990 4.4 4.9

1991–2000 4.9 4.5

2001–2011 5.4 5.1

Table 8. Mean black bear age by sex and method of take in 

Arizona, 1981–2011.

Method of take Male Female

Hound hunting 4.7a 6.5b

Boot hunting 4.6a 5.7b

P < 0.001, 3 df, F = 28.03

a,b Means with the same letter do not differ using Tukey’s HSD at P = 0.05
106 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Although current data do not suggest that Arizona black 

bear harvests are excessive, should more conservative harvests 

ever become necessary, I see no evidence to suggest that boot 

hunters or hound hunters are more selective. Boot hunters 

seem to consistently harvest greater proportions of males than 

do hound hunters, although not on a biologically meaningful 

scale. Consequently, restricting one method over another is 

unlikely to be effective. The preponderance of males in the 

harvest and the younger age of harvested male black bears are 

both likely tied to the greater home ranges and movements 

of male black bears when compared with female black 

bears (Bunnell and Tait, 1985; LeCount and Yarchin, 1990), 

and hence their greater vulnerability. Harvests may best be 

reduced, if needed, by shortening seasons or structuring dates 

to occur when females are likely to be in dens.

Acknowledgments

I would like to thank Frances Peck for frequent queries 

into historical databases to provide for the necessary analyses. 

Amber Munig, Larissa Harding, and Kirby Bristow provided 

useful reviews that improved the analyses and manuscript; 

errors that remain are my own.

References Cited

Atwood, T.C., Young, J.K., Beckmann, J.P., Breck, S.W., Fike, 

J., Rhodes, O.E., Jr., and Bristow, K.D., 2011, Modeling 

connectivity of black bears in a desert sky island archipelago: Biological Conservation, v. 144, no. 12, p. 2851–2862, 

accessed September 5, 2013, at http://dx.doi.org/10.1016/j.

biocon.2011.08.002.

Bunnell, F.L., and Tait, D.E.N., 1985, Mortality rates of North 

American bears: Artic Institute of North America, v. 38, no. 

4, p. 316–323, http://dx.doi.org/10.14430/arctic2151.

Cunningham, S., and Wakeling, B.F., 2011, Black bear, in 

McKinney, T., deVos, J.C., Jr., and Wakeling, B.F., eds., 

Mammalian predators in Arizona: Phoenix, Arizona Game 

and Fish Department, p. 5–28.

Dimmick, R.W., and Pelton, M.R., 1996, Criteria of sex and 

age, in Bookhout, T.A., ed., Research and management 

techniques for wildlife and habitats (5th ed.): Bethesda, 

Md., The Wildlife Society, p. 169–214.

LeCount, A.L., 1982, Characteristics of a central Arizona 

black bear population: The Journal of Wildlife 

Management, v. 46, no. 4, p. 861–868, http://www.jstor.org/

stable/3808218.

LeCount, A.L., 1987, Characteristics of a northern Arizona 

black bear population—Final report: Phoenix, Arizona 

Game and Fish Department, Federal Aid in Wildlife 

Restoration, project W-76-R, 26 p.

LeCount, A.L., 1990, Characteristics of an east-central 

Arizona black bear population: Arizona Game and Fish 

Department, Technical Report 2, 32 p., http://www.azgfd.

gov/w_c/documents/TR2CHARACTERISTICSOFANEASTCENTRALARIZONABLACKBEARPOPULATION.pdf.

LeCount, A.L., and Yarchin, J.C., 1990, Black bear habitat 

use in east-central Arizona—A final report: Arizona Game 

and Fish Department, Federal Aid in Wildlife Restoration 

Project, project W-78-R, Technical Report 4, 42 p., http://

www.azgfd.gov/w_c/documents/TR4BLACKBEARHABITATUSEINEASTCENTRALARIZONA.pdf.

Wakeling, B.F., Day, R.L., Munig, A.A., and Childs, 

J.L., 2015, Age and sex composition of harvest and 

timing of birth frequency for Arizona mountain lions, 

in Huenneke, L.F., van Riper, C., and Hays-Gilpin, 

K.A., eds., The Colorado Plateau VI—Science and 

management at the landscape scale: Tucson, Univerisity 

of Arizona Press, p. 95–101, http://muse.jhu.edu/

books/9780816502356?auth=0.

Zar, J.H., 1984, Biostatistical analysis (2d ed.): Englewood 

Cliffs, N.J., Prentice-Hall, Inc., 718 p.

Zornes, M.L., Barber, S.P., and Wakeling, B.F., 2006, Harvest 

methods and hunter selectivity of mountain lions in Arizona, 

in Cain, J.W., III, and Krausman, P.R., eds., Managing 

wildlife in the southwest: Tucson, Ariz, Southwest Section 

of the Wildlife Society, p. 85–89.
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter L

Visitor-Use Impacts and Habitat Associations of the 

Avifauna Occupying the Colorado River Corridor in Grand 

Canyon National Park 

By Valerie J. Horncastle,1 Brett G. Dickson,1,2 and Todd A. Chaudhry3

Abstract 

The 2006 Colorado River Management Plan (CRMP) 

is a visitor-use management plan that specifies actions to 

conserve natural and cultural resources, while enhancing visitor 

experience and river running recreational opportunities along 

the Colorado River in Grand Canyon National Park (GCNP). 

We examined the effects of the 2006 CRMP visitor use limits 

on the avifaunal community by analyzing monitoring data 

collected from 2007 to 2011. To answer key management 

questions, we used occupancy estimation techniques to quantify 

relations among avian species or guilds and multiple habitat 

and environmental variables. In addition, we used communitylevel estimators to quantify relations with different hydrological 

zones, use levels, and control and campsite locations. A total of 

615 point-count surveys were conducted, and 72 species from 

10 different dietary guilds were detected. Although numerous 

species were detected, only four species and two guilds had a 

sufficient number of detections to generate reliable estimates 

of occupancy. Blue-gray gnatcatcher (Polioptila caerulea) 

and black-chinned hummingbird (Archilochus alexandri)

exhibited higher occupancy downstream, whereas Bewick’s 

wren (Thryomanes bewickii) exhibited lower occupancy. 

Geomorphic reach was a common, strong predictor of high 

occupancy for these species. Total vegetation volume also 

influenced occupancy, with higher rates associated with greater 

vegetation volume for both the blue-gray gnatcatcher and 

yellow-breasted chat (Icteria virens). Campsite use level did not 

appear to influence occupancy for any of the species or guilds 

we evaluated. Patterns of avian richness and diversity were 

significantly different across visitor-use levels and hydrological 

zones, and total vegetation volume was a positive predictor of 

species richness. Relative abundance of omnivores and insect 

aerial foragers was greater in campsites compared to control 

sites, whereas nectivores were less abundant at campsites. Our 

results can be used to inform and update the CRMP and assist

in guiding future management actions related to river visitor use 

activities and the conservation of natural resources in GCNP.

Introduction 

Throughout the Southwestern United States, riparian 

areas support a disproportionately high density and diversity 

of bird species compared to adjacent nonriparian habitats 

(Rosenberg and others, 1982). Although riparian areas 

comprise less than 1 percent of the Western landscape (Knopf 

and others, 1988) they provide critical resources for both 

breeding and migrating birds (Skagen and others, 1998). 

Southwestern riparian woodlands may support as many as 10 

times the number of bird species per hectare when compared 

to adjacent upland habitats primarily due to greater availability 

of food and cover (Szaro and Jakle, 1985). The Colorado 

River corridor through the Grand Canyon, in particular, 

provides important habitat for wintering, migrant, and 

breeding birds (Spence, 2006). 

 Grand Canyon National Park (GCNP) is a popular 

tourist area and visitor-use effects (recreational, research, 

and administrative) from backpackers and boat trips on the 

river are potentially influencing individual species, as well as 

decreasing overall avifaunal community structure. Usually 

as a result of direct disturbance or noise, human presence has 

been shown to reduce richness and abundance of avifauna 

(Fernández-Juricic, 2000; Schlesinger and others, 2008). 

In addition, visitor use at campsites along the river corridor 

can lead to increased trash, trails, and damage and loss of 

vegetation, thereby affecting avifaunal species (Zachmann 

and others, 2012). Several studies have documented mixed 

effects of pedestrians and trails on avifaunal communities, 

with species and guilds varying in their sensitivity to this type 

of disturbance (Fernández-Juricic, 2000; Miller and Hobbs, 

2000; Miller and others, 2003).

GCNP developed the Colorado River Management 

Plan (CRMP) (National Park Service, 2006) which specifies 

1

Landscape Conservation Initiative, Northern Arizona University

2

Conservation Science Partners

3

Colorado Plateau Cooperative Ecosystem Studies Unit, National Park 

Service—Intermountain Region
108 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

actions to conserve natural and cultural resources, as well 

as visitor experience in GCNP, while enhancing riverrunning recreational opportunities on the Colorado River. To 

determine and address effects of the CRMP visitor-use limits 

and launch patterns on park resources, the National Park 

Service developed a Research, Monitoring, and Mitigation 

Program (RM&MP). The key objectives of the RM&MP 

were to determine the status and trends of key resources 

(that is, vegetation, soil, recreation, and avifauna), identify 

and understand meaningful resource condition changes 

associated with river use, identify appropriate mitigation and 

management actions, and assess the efficacy of such actions 

within an adaptive management framework (National Park 

Service, 2006). 

The primary goal of this project was to analyze and 

interpret avifaunal data collected from 2007 to 2011 to answer 

the park’s key management questions. General management 

questions included whether species occupancy, community 

richness, and diversity were negatively associated with visitoruse level. Specifically, our research had three main objectives—

(1) estimate annual occupancy and detection probabilities for 

avifaunal species and guilds and relate these parameters to 

several different habitat, environmental, or detection variables, 

(2) estimate and compare avifaunal community parameters 

(that is, diversity and richness) among different hydrological 

zones and visitor-use levels, and (3) compare relative abundance 

(bird counts) between campsites and control sites for general 

bird groups (that is, residents, nonresidents, and guilds) 

when occupancy modeling was not possible. Specifically, we 

addressed whether riparian nesting-bird species, nonresidents, 

and different dietary guilds had higher relative abundance at 

control sites versus campsites. For community metrics, we 

predict greater species richness and diversity at control sites 

versus high-use campsites. We hypothesize that different dietary 

guilds would respond differently to visitor use. Specifically, we 

predict that ground and foliage insectivorous and omnivorous 

species would have greater abundance at campsites compared 

to control sites as a result of an increase in forage (that is, 

food scraps and ants), whereas nectivores would have lower 

abundance. 

Methods

Study Area

The Colorado River in GCNP flows 478 kilometers (km) 

through Arizona, starting in the northeast corner of the park 

downstream of Glen Canyon Dam and stretching westward 

to Lake Mead. The elevation of the river drops a total of 590 

meters (m) from the dam (955 m) to Lake Mead (365 m). 

Dominant vegetation communities range from Great Basin 

desert scrub at the upper reaches of the Colorado River to 

a Sonoran desert scrub at the lower reaches. The post-dam 

vegetation zone, or new high-water zone (NHWZ), adjacent 

to the river consists mostly of tamarisk (Tamarix chinensis), 

coyote willow (Salix exigua), rushes and grasses. The pre-dam 

vegetation zone, or old high-water zone (OHWZ), consists 

of a variety of species, including Apache plume (Fallugia 

paradoxa), net-leaf hackberry (Celtis reticulata), mesquite 

(Prosopis glandulosa), and catclaw (Acacia greggii).

Surveys

Avifaunal survey sites were located between Lee’s 

Ferry and Diamond Creek along the Colorado River corridor 

(fig. 1). Field crews conducted point-count surveys once 

a year during spring (April-May) 2007–2011 at campsites 

and nearby paired control sites (mean distance=800 m). To 

measure the effects of different levels of camping use on 

bird occurrence, point counts were taken in the NHWZ at 

each campsite and control site and in OHWZ patches at sites 

where they were present. One or two point counts were made 

at each site depending on site size. Typically, sites that had 

>150 m of shoreline were surveyed with two point counts per 

zone, whereas sites <150 m received only one point count. 

Surveys were conducted as soon as it was light enough to see 

a distance of at least 200 m and ended at no later than 10 a.m. 

Point counts were not conducted if wind strength was 4 or 

greater on the Beaufort scale or during periods of heavy rain 

or snow to minimize the effects of environmental variables on 

detectability. Each point count was conducted for a total of 10 

minutes. Data were collected on all bird species observed by 

both auditory and visual cues, and distances to detections were 

estimated within three distance categories (<25 m, 25–50 m, 

>50 m). Estimates of total vegetation volume also were 

measured at all bird point-count locations, including campsites 

and control sites, following Mills and others (1991).

Analyses

Few efforts have been made to inventory avifauna in 

GCNP that would also permit statistical estimation of key 

population parameters. Thus, we attempted to take advantage 

of a survey effort that could permit statistical inferences 

about avifaunal populations occupying the Colorado River 

corridor. The CRMP avifaunal sampling effort and associated 

data analysis described here was originally designed within 

a distance-based framework (Buckland and others, 2001). 

However, the sampling protocol recorded all audio and 

visual observations whether or not they were on site or off 

site; therefore, we dropped all observations >50 m from 

the analysis to reduce the possibility of including off-site 

observations in the site-level analysis. With only two effective 

distance categories, (<25 m, 25–50 m), we could not reliably 

estimate a detection probability for each species using program 

DISTANCE (Thomas and others, 2010). After discussions 

with GCNP staff and ecological statistician D. MacKenzie 

(oral commun., 2012), we determined the avifaunal data would 
Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the Colorado River Corridor 109

be more amenable to an occupancy-modeling framework 

using a spatial replication approach (MacKenzie and 

others, 2006). A multiseason occupancy modeling approach 

permits simultaneous estimation of detection probability and 

occupancy, as well as colonization and local extinction rates 

(MacKenzie and others, 2003). In addition, occupancy models 

that account for imperfect detection and include covariates 

can be less biased than models that disregard imperfect 

detection and improve estimates by accounting for among-site 

variation (MacKenzie and others, 2004; Guillera-Arroita and 

others, 2014). It is important to note that a spatially replicated 

sampling approach (without replacement) can be vulnerable to 

positive bias in occupancy estimators when some individual 

sampling locations are unoccupied at the time of sampling 

(Kendall and White, 2009; Guillera-Arroita, 2011). Because 

many of our species were highly mobile and samples were 

collected sequentially, some of this bias may have been 

mitigated (Kendall and White, 2009; Guillera-Arroita, 2011). 

Our survey sites were only 150 m apart, whereas several of 

the bird species in our study area typically have home ranges 

varying from 0.4 to 1.6 hectares (ha) (Schoener, 1968). We 

acknowledge that some of these species could have moved 

between or among survey sites, although observers attempted 

to minimize the influence of this behavior on their surveys. 

Occupancy Estimates

We used a “spatial replication” approach, where the 

second point count done at some of the campsites was treated 

as the “repeat survey,” providing two surveys per year for 

some of the sites (MacKenzie and others, 2006). If a site did 

not have a second point count, it was considered to have a 

missed (second) visit. Occupancy models were run for each 

species and guild for which we had sufficient data. Sites with 

only 1 year of detection data or without measurement of 

habitat variables were excluded from our analysis. 

GRAND CANYON

NATIONAL PARK

Lees Ferry

Diamond

Creek

NAVAJO

INDIAN

RESERVATION

KAIBAB

NATIONAL

FOREST

HUALAPAI

INDIAN

RESERVATION KAIBAB NATIONAL FOREST

HAVASUPAI

INDIAN

RESERVATION

Avifaunal survey sites

EXPLANATION

Colorado River

Grand Canyon National Park Boundary

ARIZONA

Study area

0

0

10 20 30

10 20 30 40

MILES 

KILOMETERS 

1

2

3

4

5

6

7

8 9

10

11

N

Figure 1. Map showing locations of all campsites sampled under the National Park Service Research, Monitoring, and 

Mitigation Program avifaunal surveys in Grand Canyon National Park, Arizona, 2007–2011. Geomorphic reaches of the Colorado 

River below Lees Ferry (1–11) are denoted with brackets and labeled. Note that not every site was sampled in every year of the 

monitoring program. 
110 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 1. Description of response variables used in the analysis of the 2007–2011 avifaunal data in Grand Canyon National Park, Arizona. 

[Variables are denoted as (ψ) if they were used to describe occupancy and (p) for detectability in the models. Cfs, cubic feet per second; m, meters]

Variable Variable type1 Variable description

Recreational use (ψ) Discrete The estimated frequency of campsite occupancy based on administrative records, a survey of 

commercial river guides, staff knowledge, and ancillary data. 0=none, 1=low, 2=medium, 3=high.

Impact (ψ,p) Discrete Indicates whether a point count was located in a control site or a campsite. 0=control, 1 =camp.

Hydrologic zone (ψ) Discrete Indicates whether the vegetation and soil/substrate transect was located in the new high-water 

zone=35 (that is, 35,000 cfs) or the old high water zone=90 (that is, 90,000 cfs).

Total vegetation volume (ψ,p) Continuous The average vegetation volume of all species occurring at the survey point across all years. 

Geomorphic reach (ψ,p) Discrete Geomorphic reaches defined by Schmidt and Graf (1990). 1=upstream most, to 11=downstream 

most. This variable was also used to indicate changes in vegetation type and canyon width as one 

moves downstream. 

River noise (p) Discrete Refers to the distance the point count was to the closest river rapid. 0<50 m, 1=50–100 m, 

2=100–200 m, 3>200 m.

Amount of trash1 (ψ) Continuous Number of pieces of trash picked up during the campsite assessment. Includes food scraps and 

feminine products. “Micro” and “macro” litter counts were added together from the original data to 

form the new variable “litter.”

Backpacker accessibility (ψ) Discrete 0=site is not accessible by hikers, 1=site is accessible by hikers.

Year (ψ,p) Discrete Year that data was collected (2007–2011).

Time of day (p) Discrete Time of day that the avifaunal point count was conducted. 0=before 0700, 1=0700–0900, 2=after 

0900.

Observer (p) Discrete 0=less experienced observer, 1=more experienced observer.

1

Variables were treated as a continuous predictor variable in the occupancy modeling and the variable importance modeling.

We used nine covariates of occupancy or detection—

visitor-use level (“control,” low, moderate, and high), impact 

(control and campsite), hydrologic zone (OHWZ and NHWZ), 

total vegetation volume, river noise (distance to rapids), 

amount of trash, backpacker accessibility, geomorphic reach, 

and year (table 1). Although vegetation data were collected 

during each survey, we considered it a site-specific variable 

as opposed to a survey-specific variable, because vegetation 

data were not collected in 2011 or in the same location each 

year. For each site, we calculated the average total vegetation 

volume across all years and used this value in our modeling. 

Survey-specific variables included observer and time of day 

and were hypothesized to influence detectability. Observer 

performance varies among and within individuals and is 

strongly influenced by training, experience, hearing acuity, 

eyesight, and motivation (Rosenstock and others, 2002). To 

account for some of this variability, a survey was sent to all 

observers that rated the experience level (less experience 

versus more experience) of the other observers. Time of day 

can affect detectability because the singing rate for most 

species is usually highest before or near official sunrise and 

then declines slowly for the next 4 hours. Therefore, we 

included a three-category covariate for time of day in our 

models (before 7 a.m., 7–9 a.m., and after 9 a.m.).

For each species and guild with sufficient total 

detections (typically >50) we fit multiseason occupancy 

models in program PRESENCE, version 4.1 (Hines, 2006). 

We derived estimates of occupancy for 2007, the initial year 

of the study, and year-specific estimates for 2008–2011 by 

modeling probabilities of colonization and local extinction 

(MacKenzie and others, 2003). For species without enough 

detections to run multiseason analysis (typically >30 and <80 

detections), we used the single-season occupancy estimation 

feature in program PRESENCE, version 4.1 (Hines, 2006), 

but pooled detections across years for each site. For both the 

multiseason and single-season analyses, we derived estimates 

for each parameter separately based on “global” models that 

simultaneously included the most parsimonious model for the 

other parameters (Dickson and others, 2009). We used Akaike’s 

Information Criterion (AIC) (Burnham and Anderson, 2002) 

to identify the “best” model(s) among a candidate set of nested 

models that each represented a priori-determined combinations 

of the variables defined above. We considered candidate 

models with AIC difference (ΔAIC) values <4.0 as those that 

best approximated the data. We also included null models of 

occupancy and detection probability (denoted by “dot” models)

within each candidate set to evaluate the performance (and fit) 

of the best model(s) (Anderson, 2008). These null models held 
Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the Colorado River Corridor 111

occupancy, colonization, extinction and detectability constant 

across sites and surveys. 

We computed model-averaged parameter estimates and 

unconditional standard errors for variables included in models 

with ΔAIC values <4.0 (Burnham and Anderson, 2002). Using 

model-averaged regression estimates and unconditional standard 

errors, we computed a Z-statistic to estimate the magnitude 

and rank the relative importance of each variable (Gotelli and 

Ellison, 2004). We considered absolute values of Z>2.0 to be 

indicative of a reasonably strong predictor variable. Because we 

used an information-theoretic approach to model selection and 

model-based inference, we did not compute p-values.

Before implementing our occupancy models, we 

computed a Spearman correlation coefficient to determine if 

univariate correlations between continuous variables were 

present or problematic (that is, >0.70). Because no variables 

had a correlation coefficient >0.30, none were eliminated 

from our analyses. 

Community Estimates

We used EstimateS (Colwell, 2006) to calculate estimates 

of avifaunal diversity and richness for each hydrological zone, 

use level, year, and type of site (control site versus campsite). 

Lande and others (2000) recommend that a measure of 

Simpson diversity, as well as species richness, should be used 

when comparing communities to account for discontinuities in 

evenness amongst assemblages. For simplicity, we only report 

and discuss the results for Simpson diversity and the jackknife 

(Jack1) richness estimator (Burnham and Overton, 1978, 

1979). We chose the Simpson’s index because its performance 

is well understood, it is intuitively meaningful, and it is one 

of the most robust diversity measures available (Magurran, 

2004). Simpson’s index provides a good estimate of diversity 

at relatively small sample sizes and will rank assemblages 

consistently. The jackknife richness estimator was selected 

because it has been found to be least biased for smaller sample 

sizes (Palmer, 1990; Hellmann and Fowler, 1999) and also 

accommodates data on abundance (Gotelli and Colwell, 2011).

We used Welch’s t-test (test statistic=t, α=0.05) (Ruxton, 

2006) to statistically compare and contrast differences in 

diversity and richness among hydrologic zones, control sites 

and campsites, and use levels, because it allowed us to compare 

samples with unequal variance. There were relatively few sites 

in our dataset that were categorized as high or moderate use 

levels, so these categories were combined to increase sample 

size and are labeled as high use in our results below. High 

use and low use were each compared to no-use sites. A oneway ANOVA (test statistic=F, α=0.05) (Kruskal and Wallis, 

1952) was also used to test for differences in diversity and 

richness among all 5 years. If the ANOVA indicated significant 

differences among years, a Tukey (HSD) test (test statistic=q, 

α=0.05) was used to determine which years were significantly 

different from others (Tukey, 1953; Kramer, 1956). We used a 

linear mixed model to evaluate the relation between observed 

species richness and total vegetation volume for transects 

(n=115) located within camp or control sites. We treated the 

interaction between site (n=39) and site type (control versus 

camp) as a subject-level (nested) random effect and total 

vegetation volume (standardized and rescaled) as a fixed effect. 

Because the true covariance structure in our response variable 

(observed rather than estimated species richness) was not 

known, we used an empirically based method to calculate the 

standard errors of the fixed effect parameters (Williams, 2000). 

We evaluated model fit and performance using AIC and, by 

convention, considered a ΔAIC value between our model and 

an intercept-only (that is, null) model of >4.0 to be acceptable 

for inference (Anderson, 2008). We determined statistical 

significance of total vegetation volume variable using a twotailed t-test (test statistic=t, α=0.05). We implemented these 

models using the MIXED procedure in SAS (version 9.2, SAS 

Institute, Inc., Cary, North Carolina).

To address our third objective, we used a Wilcoxon signed 

rank test for paired data (Wilcoxon, 1945; test statistic=Z, 

α=0.05) to test for differences in relative abundance between 

camp and control sites for different guilds, riparian nesting 

bird species, and resident bird species. We paired camp and 

control samples within the same site and year such that a given 

site could be represented by as many as five paired samples, 

resulting in 334 paired samples for the 153 sites. This analysis 

was limited to guilds or groups that had >10 detections.

Results

A total of 615 point counts were conducted from 2007–

2011 along the Colorado River in GCNP, with the greatest 

number of counts (n=131) in 2009 and the fewest (n=114) in 

2011. Seventy-two species from 10 different dietary guilds 

were detected. The most common species were blue-gray 

gnatcatcher (Polioptila caerulea), common yellowthroat 

(Geothlypis trichas), house finch (Carpodacus mexicanus), 

Lucy’s warbler (Oreothlypis luciae), and yellow warbler 

(Dendroica petechia) (appendix A).

Occupancy Estimates

Among the 153 sites sampled, only 48 of these sites 

received two point counts in a given year. Although we lacked 

sufficient data to model occupancy for most species detected 

in the river corridor, we were able to implement multiseason 

occupancy models for 3 species and 2 avifaunal dietary guilds 

(table 2)—black-chinned hummingbird (Archilochus alexandri), 

Bewick’s wren (Thryomanes bewickii), blue-gray gnatcatcher 

(Polioptila caerulea), the nectivore guild, and the seed groundforager guild. The number of detections for species varied with 

blue-gray gnatcatcher having the most detections (160), followed 

by black-chinned hummingbird (82), and Bewick’s wren (58). 

For black-chinned hummingbird and Bewick’s wren, the best 

occupancy model included geomorphic reach and hydrologic 

zone (table 2). For both species, geomorphic reach was the 
112 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 2. Multiseason occupancy model selection results for two avifaunal dietary guilds and three species in Grand Canyon National 

Park, Arizona, 2007–2011. 

[We considered candidate models with Akaike’s Information Criterion difference (ΔAIC) values <4.0 as those that best approximated the data. Modeled effects 

include use level (n=4; U), year (n=5; Y), impact (campsite versus control; I), zone (old high water zone versus new high water zone; Z), noise (distance to 

rapids; N), total vegetation volume (V), amount of trash (T), reach number (R), backpacker site (H), time (TM), and observer (O). Constant model denoted by 

“dot.” Models that did not converge are not presented and were not used for inference. See appendix A for scientific species names]

Model –2l

1 k2 ΔAIC3 w4

Nectivore5

Ψ(R,Z) 498.75 19 0 0.485

Ψ(R,Z,V) 498.04 20 1.29 0.255

Ψ(R,Z,V,U) 497.68 21 2.93 0.112

Ψ(R,Z,V,U,I) 496.32 22 3.57 0.081

Ψ(R) 506.01 18 5.26 0.035

Ψ(R,Z,V,U,I,T) 496.20 23 5.45 0.032

Ψ(.) 522.32 17 19.57 0

Seed ground forager6

Ψ(U,Z,T,H) 666.16 15 0 0.403

Ψ(U,Z,T) 669.46 14 1.30 0.210

Ψ(U,I,Z,T,H) 665.94 16 1.78 0.166

Ψ(U,I,Z,T,H,V) 665.56 17 3.40 0.074

Ψ(U,I,Z,T,H,V,R) 664.25 18 4.09 0.052

Ψ(U,Z) 675.13 13 4.97 0.034

Ψ(U) 677.19 12 5.03 0.033

Ψ(.) 679.40 11 5.24 0.073

Black-chinned hummingbird7

Ψ(R,Z) 404.43 17 0 0.412

Ψ(R,Z,V) 403.06 18 0.63 0.301

Ψ(R,Z,V,U) 402.79 19 2.36 0.127

Ψ(R) 409.38 16 2.95 0.094

Ψ(R,Z,V,U,I) 402.72 20 4.29 0.048

Ψ(R,Z,V,U,I,T) 402.72 21 6.29 0.018

Ψ(.) 423.17 15 14.74 0.001

Model –2l

1 k2 ΔAIC3 w4

Bewick’s wren8

Ψ(Z,R) 302.93 13 0 0.467

Ψ(R) 306.43 12 1.50 0.221

Ψ(Z,V,R) 302.47 14 1.54 0.216

Ψ(Z,V,R,H) 303.16 15 4.23 0.056

Ψ(U,Z,V,R,H) 303.15 16 6.22 0.021

Ψ(U,Z,V,T,R,H) 301.37 17 6.44 0.019

Ψ(.) 329.69 11 22.76 0

Blue-gray gnatcatcher9

Ψ(U,V,R) 551.74 20 0 0.429

Ψ(U,V,R,I) 550.71 21 0.97 0.264

Ψ(V,R) 556.42 19 2.68 0.112

Ψ(U,V,R,I,H) 550.73 22 2.99 0.096

Ψ(U,V,R,I,H,T) 549.60 23 3.86 0.062

Ψ(R) 560.67 18 4.93 0.037

Ψ(.) 582.73 17 24.99 0

1

Twice the negative log-likelihood value. 

2

Total number of model parameters. 

3

AIC difference value. 

4

AIC model weight. 

5

γ(U,I,V,R)ε(V,R)p(Y,V,R,O). 

6

γ(V)ε(I)p(Y,R). 

7

γ(V,R)ε(V)p(Y,I,N,R,O). 

8

γ(.)ε(.)p(Y,N,R,O). 

9

γ(I,R,Z)ε(I,Z,V,T,H)p(I,V,R,TM,O).

best predictor of occupancy (table 3), with black-chinned 

hummingbird and Bewick’s wren positively and negatively 

associated with geomorphic reach, respectively. Annual 

estimates of occupancy for black-chinned hummingbird were 

relatively constant during 2007–2011, ranging from 0.48 to 0.57 

(fig. 2). Candidate model results and estimates for black-chinned 

hummingbird were similar to the nectivore guild, because 

detections within this guild consisted mostly of this one species. 

There were only two other species—broad-tailed hummingbird 

(Selasphorus platycercus) and Costa’s hummingbird (Calypte 

costae)—in this guild and they had less than 14 detections each. 

Annual estimates for Bewick’s wren were more variable. Years 

2007, 2008, 2010, and 2011 produced relatively low estimates 

(<0.33) and indicated a slight downward trend in occupancy, 

although occupancy increased (0.54) in 2009 (fig. 2). The best 

model for blue-gray gnatcatcher included geomorphic reach, 

total vegetation volume, and visitor-use level. This species 

was positively associated with total vegetation volume and 

geomorphic reach. Visitor-use level was not a strong predictor of 

occupancy (table 3) even though it appeared in the best model. 
Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the Colorado River Corridor 113

Blue-gray gnatcatcher exhibited relatively low but constant 

annual estimates of occupancy, ranging from 0.06 to 0.08 (fig. 2), 

although such small estimates should be interpreted with caution.

For the nectivore guild, the best model of initial occupancy 

included geomorphic reach and hydrologic zone (table 2). The 

best predictor of occupancy for nectivores was geomorphic 

reach with occupancy greater downstream (table 3). Annual 

estimates varied across years with 2008 (0.49) and 2011 (0.40) 

having lower estimates than 2007, 2009, and 2010, which 

had occupancy estimates between 0.6 and 0.7 (fig. 3). The 

best model for the seed ground forager included visitor-use 

level, hydrologic zone, amount of trash, and backpacker 

accessibility; however, none of these variables were strong 

predictors of occupancy. Annual estimates for the seed 

foraging guild peaked in the middle of the study, with lower 

estimates in 2007 (0.60) and 2011 (0.39) and the highest 

estimates in 2009 (0.76) (fig. 3). 

There was only one species, yellow-breasted chat (Icteria 

virens), with a sufficient number of detections to produce 

reliable estimates of occupancy using the single-season 

Table 3. Standardized model-averaged regression coefficients ( b

~

), unconditional standard errors (SE), and Z-statistics (Z) for 

variables included in the best models of occupancy (2007) for two guilds and four species in Grand Canyon National Park, Arizona. 

[Variables were ranked only for guilds whose best models were >4 ΔAIC (Akaike’s Information Criterion difference) units from the null model (tables 2 and 4), 

indicating that these variables were approximating the data well. Z-values >2.0 are indicative of reasonably strong predictor variable and denoted in bold. NE, 

variable was not estimable. See appendix A for scientific species names]

Variable b

~ SE Z b~ SE Z b~ SE Z

Nectivore guild Seed ground forager Blue-gray gnatcatcher

Hydrologic zone -2.54 1.37 1.86 -1.46 1.04 1.41 NE NE NE

Use level 0.03 0.15 0.21 -0.65 0.42 1.53 0.72 0.54 1.35

Vegetation volume 0.01 0.003 0.63 0.001 0.001 0.43 0.02 0.005 2.96

Geomorphic reach 0.53 0.15 3.66 -0.005 0.01 0.45 0.62 0.14 4.46

Amount of trash 0.001 0.002 0.25 0.06 0.04 1.25 0.003 0.01 0.42

Impact -0.21 0.43 0.48 0.16 0.42 0.38 -0.61 1.00 0.61

Backpacker accessibility NE NE NE -1.09 1.02 1.07 0.22 0.45 0.50

Black-chinned hummingbird Bewick’s wren Yellow-breasted chat

Hydrologic zone -1.86 1.06 1.76 -2.62 2.43 1.08 -0.001 0.05 0.01

Use level 0.03 0.12 0.21 0.01 0.02 0.25 -0.04 0.09 0.39

Vegetation volume 0.004 0.005 0.78 0.002 0.004 0.57 0.01 0.003 4.80

Geomorphic reach 0.63 0.17 3.75 -2.38 1.05 2.26 -0.04 0.08 0.54

Amount of trash 0 0.001 0 -0.001 0.002 0.44 NE NE NE

Impact 0.03 0.15 0.22 NE NE NE NE NE NE

Backpacker accessibility NE NE NE 0.20 0.38 0.52 NE NE NE

1.0

0.8

0.6

0.4

0.2

0

Occupancy probability

2007 2008 2008 2009 2011

Year

Blue-gray gnatcatcher

Bewick’s wren

Black-chinned hummingbird

EXPLANATION

Figure 2. Graph showing annual mean (± unconditional standard 

errors, SE; see gray vertical bars) estimates of occupancy 

for blue-gray gnatcatcher, Bewick’s wren, and black-chinned 

hummingbird in Grand Canyon National Park, Arizona, 2007–2011. 

See appendix A for scientific species names.

1.0

0.8

0.6

0.4

0.2

0 2007 2008 2008 2009 2011

Occupancy probability

Year

Nectivore

EXPLANATION

Seed ground forager

Figure 3. Graph showing annual mean (± unconditional standard 

errors, SE; see gray vertical bars) estimates of occupancy for 

the nectivore and seed ground forager guilds in Grand Canyon 

National Park, Arizona, 2007–2011. 
114 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Table 4. Single-season occupancy model selection results 

for the yellow-breasted chat (Icteria virens) detected in Grand 

Canyon National Park, Arizona, 2007–2011. 

[We considered candidate models with Akaike’s Information Criterion difference (ΔAIC) values <4.0 as those that best approximated the data. Modeled 

effects include use level (n=4; U), year (n=5; Y), impact (campsite versus 

control; I), zone (old high water zone versus new high water zone; Z), noise 

(distance to rapids; N), total vegetation volume (V), amount of trash (T), 

reach number (R), backpacker site (H), time (TM), and observer (O). Constant 

model denoted by “dot.” Models that did not converge are not presented and 

were not used for inference]

Model –2l

1 k2 ΔAIC3 w4

Yellow-breasted chate

Ψ(V) 182.54 5 0 0.542

Ψ(V,R) 181.87 6 1.33 0.279

Ψ(V,R,U) 181.43 7 2.89 0.128

Ψ(V,R,U,Z) 181.43 8 4.89 0.047

Ψ(.) 193.89 4 9.35 0.005

1

Twice the negative log-likelihood value. 

2

Total number of model parameters. 

3

AIC difference value. 

4

AIC model weight. 

5

p(N,TM). 

Table 5. Wilcoxon signed rank test (α=0.05) comparing 

abundance of seven avifaunal guilds among control and 

campsites in Grand Canyon National Park, Arizona, 2007–2011. 

[Significant values denoted in bold. Negative Z values indicate a negative relation between abundance and campsite. Point counts were separated by site and 

year in the analysis. P, p-value]

Guilds Number of 

sites Z P

Insect aerial forager 334 2.21 0.027

Insect foliage gleaner 334 –0.79 0.428

Insect ground forager 334 –1.03 0.304

Nectivore 334 –3.10 0.002

Omnivorous ground forager 334 2.53 0.011

Seed foliage gleaner 334 –1.84 0.066

Seed ground forager 334 0.01 0.989

Table 6. Wilcoxon signed rank test (α=0.05) comparing 

abundance of avifaunal groups between control and campsites in 

Grand Canyon National Park, Arizona, 2007–2011. 

[Negative Z values indicate a negative relation between abundance and campsite. Point counts were separated by site and year in the analysis P, p-value]

Guilds Number of 

sites Z P

Resident 392 0.42 0.678

Nonresident 392 –0.72 0.470

Riparian nester 392 0.34 0.731

Nonriparian nester 392 –0.10 0.922

analysis (table 4). However, model selection uncertainty was 

high and only total vegetation volume emerged as a strong 

positive predictor of occupancy (table 3).

Community Estimates

We performed Wilcoxon tests on seven different avian 

guilds (table 5). Three dietary guilds exhibited significant 

differences in abundance between control sites and campsites. 

Both the insect aerial forager (Z=2.21, n=334, P=0.027) and 

the omnivorous ground forager (Z=2.53, n=334, P=0.011) 

guild had greater abundance in campsites, whereas the 

nectivore guild (Z=–3.10, n=334, P=0.002) had greater 

abundance in control sites. There was no difference in 

abundance for resident, nonresident, riparian, or nonriparian 

species among camp and control sites (table 6).

Our model of observed species richness, conditioned only 

on total vegetation volume, was 5.7 AIC units “better” (that 

is, lower) than the null model, suggesting acceptable model 

performance. After controlling for heterogeneity in the sitelevel random effect, total vegetation volume was a statistically 

significant and positive predictor of observed species richness 

(estimate=0.01; unconditional standard errors, SE=0.01; 

t=2.73; P=0.01).

Avian diversity and richness did not differ between 

control sites and campsites, yet there were differences among 

visitor-use levels (table 7). Richness was significantly less in 

high-use sites compared to no-use sites (t=13.55, P <0.05); 

however, there were no significant differences in diversity. 

When comparing low-use sites to no-use sites, there were 

no significant differences in richness or diversity. Mean 

richness (t=20.87, P <0.05) and diversity (t=12.32, P <0.05) 

were greater in NHWZs compared to OHWZs. Diversity (F 

(4,266)=65.96, P <0.001) and richness (41.99, P <0.001) 

differed significantly among the 5 years. Tukey post-hoc 

comparisons of the 5 years indicated that diversity in 2007 and 

2008 was significantly greater than in all other years. Diversity 

in 2010 was greater than 2009 (q=11.34, P <0.001) and 

2011 (q=7.95, P <0.001). Richness in 2010 was significantly 

greater than all other years (table 7). Richness in 2008 was 
Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the Colorado River Corridor 115

Table 7. Species richness estimates for each hydrological zone, use level, year, and type of 

site (control site versus campsite) in Grand Canyon National Park, Arizona, 2007–2011. 

[Bold t-values indicate statistically significant based on Wilcoxon signed rank test (α)=0.05 and two-tailed 

|t-crit| of 1.96. Bold F-values indicate statistically significant based on α=0.05 and |F-crit| of 3.09. OHWZ, old 

high-water zone. NHWZ, new high-water zone. SE, standard error]

Variable Number

of sites

Number 

of species 

observed

jack1

mean

jack1

SE t Simpson mean

Simpson

SE t

OHWZ 22 47 48.51 4.60 20.87 7.12 0.83 12.32

NHWZ 89 68 70.58 3.73 9.42 0.56

Control site 86 56 60.17 3.51 -1.51 8.83 0.6 -0.30

Campsite 86 63 68.08 3.90 9.10 0.67

High use 47 50 51.07 3.33 13.55 8.78 0.95 0.26

Low use 39 55 59.73 4.74 0.43 9.25 0.83 0.42

No use 86 56 59.37 3.46 8.82 0.58

2007 56 37 38.60 2.58 F=41.99 8.66 0.66 F=65.96

2008 51 39 40.11 2.88 8.39 1

2009 58 35 36.59 2.11 6.74 0.47

2010 56 41 42.33 2.62 7.88 0.71

2011 50 36 37.02 3.28 7.05 0.89

significantly greater than 2007 (q=4.09, P <0.001), 2009 

(q=9.6, P <0.001), and 2011 (q=8.13, P=0.03). Richness in 

2007 was significantly greater than 2009 (q=5.62, P <0.001) 

and 2011 (q=4.25, P=0.02).

Discussion

Occupancy Estimates

For 4 of the 6 species and guilds that had reliable 

estimates of occupancy, geomorphic reach explained 

occupancy estimates the best. The nectivore guild, bluegray gnatcatcher, and black-chinned hummingbird all had 

higher occupancy downstream, whereas Bewick’s wren had 

lower occupancy downstream. Spence (2006) also found 

that species presence varied along the Colorado River 

corridor. He found similar results with Bewick’s wren, which 

was most common in the upper and middle reaches of the 

river, whereas black-chinned hummingbird and blue-gray 

gnatcatcher were found only in reaches with suitable habitat 

that generally occurred further downstream. The importance 

of geomorphic reach that we observed is probably due 

to the large changes in habitat, elevation, and vegetation 

composition as one moves down the Colorado River. Total 

vegetation volume (averaged across years) was the only 

variable that strongly influenced occupancy but for only two 

species. Occupancy estimates for blue-gray gnatcatcher and 

yellow-breasted chat increased with total vegetation volume.

Similarly, Sogge and others (1998) and Spence, 2006, found 

that bird abundance, species richness, and diversity were 

positively associated with the volume of large, woody plant 

species.

No species or guild reflected increasing or decreasing 

trends over the 5-year analysis period. Relatively small 

numbers of detections each year, few repeat surveys, and 

high variability in detection or presence among years 

precluded our finding statistically reliable trends. Some of 

the variability in occupancy estimates across years, and the 

relatively large associated standard errors, are explained by 

high interannual variability in the number of detections, as 

well as sampling methodology issues. For the seed foraging 

guild, the majority of detections occurred in 2007 (n=46) 

and the fewest in 2011 (n=16). In addition, at the majority 

of sites that received two annual point counts, many species

were detected at one point count station but not the other, 

creating a low estimate of detectability and therefore greater 

occupancy within sites sampled twice per year (Kendall and 

White, 2009). Additional replication visits at every site and 

over a longer time period would help to decrease some of 

the bias and variability (Guillera-Arroita and others, 2014). 

Spence (2006), through power analyses, also determined 

that more than 5 years of surveys were necessary to 

determine any reliable trend for individual avifaunal species 

in the Grand Canyon and that the number of surveys varied 

for each species. 
116 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Although many of our results were similar to those of 

other studies, our models of occupancy (among species and 

guilds) typically exhibited low model weights, indicating that 

the variables we used were not highly informative. These 

results also could be partially attributed to the small number of 

detections obtained for each species. There were several factors 

involved in the collection of the avifaunal data that likely 

contributed to a relatively small total number of detections and 

a lack of precision in our estimates. The acoustic characteristics 

of Grand Canyon, coupled with noise from the Colorado River 

itself (although included as a detectability covariate), could 

have led to misidentification of bird calls and fewer detections 

overall, especially for inexperienced observers. Sites were 

sampled only once per year, and a majority of sites were 

sampled only 2 to 3 times during the entire study period, due to 

a range of logistical, resource, and environmental constraints. In 

addition, because the repeat surveys (that is, detection histories) 

we used in our models were derived from a single visit to a 

site, they were not truly independent in space or time. This 

unavoidable situation increased the likelihood that estimators 

and inferences about the factors that influence either occupancy 

or detection were biased. Lack of independence for detectability 

among sites typically results in occupancy being overstated 

(MacKenzie and others, 2006) and this should be considered 

when interpreting our results. However, our repeat surveys did 

have the benefit of minimizing the time over which closure 

was assumed for the occupancy models (Rota and others, 

2011). An increased and well-distributed sample of sites, more 

precisely determined or measured variables, and more surveys 

throughout the course of a season would allow GCNP to gain 

a better understanding of these interactions and improve model 

performance at the species and guild levels. 

Community Estimates

In our study, avian species richness appeared to be 

most influenced by vegetation volume and visitor-use levels. 

Specifically, observed species richness was positively associated 

with total vegetation volume and greater in areas with no visitor 

use. Species sensitive to disturbance by humans may avoid 

areas where human activity is common or may occur in reduced 

abundance decreasing the likelihood of that species being 

observed during a point-count survey. Decreases in richness in 

high visitor-use areas may have been due to lower vegetation 

volume at campsites or changes in vegetation structure or type 

(Wolf and others, 2013). Other researchers have also found a 

negative relation between recreation intensity and abundance 

of vegetation on bird species richness (Miller and others, 2003; 

Steven and others, 2011; Wolf and others, 2013). Schlesinger 

and others (2008) found that disturbance from human activity 

might be the most important factor for richness, surpassing 

even habitat loss from development. Additional information on 

vegetation type or other site-level information may lead to a 

better understanding of differences in use level. For example, a 

particular vegetation type or structure may be present in high- or 

low-use sites but not in control sites. 

Consistent with our hypothesis, relative abundance of 

some avifaunal guilds was associated with visitor activity but 

not always in the way we predicted. The omnivore ground 

foragers had higher abundance at campsites, however, we did 

not see the increase in insect ground foragers that we expected. 

Schlesinger and others (2008) also found that omnivore 

abundance was positively related to recreational activity and 

other urban stressors. Omnivores have greater dietary flexibility, 

enabling their success in environments with altered food 

resources (Chace and Walsh, 2006). Although we did not see a 

difference between insect foliage gleaners and ground foragers, 

we did see a greater abundance for insect aerial foragers in 

campsites compared to control sites. Aerial insect foragers may 

have been more abundant in campsites because they are more 

open and would allow for better visibility of flying insects and 

easier aerial maneuvers when foraging (Eckhardt, 1979). The 

nectivore guild was the only guild that had significantly lower 

abundance at campsites compared to control sites. The nectivore 

guild may have had higher abundance in control sites because 

the black-chinned hummingbird (a dominant species in the 

nectivore guild in our study area) nests almost exclusively in 

tamarisk in the NHWZ vegetation (Brown, 1992) which was 

typically more abundant at control sites. Schlesinger and others 

(2008) also found changes in abundance for certain dietary 

guilds, specifically, a dramatic decrease in richness of dietary 

specialists, principally insectivores, granivores, and nectivores, 

with increased development and recreation.

The differences we detected in richness and diversity 

between the OHWZ and NHWZ should be interpreted 

cautiously. Although we used Welch’s t-test to account for 

uneven sample sizes, the difference in the number of sites was 

so great that unbalanced sample size could have influenced our 

results in undesirable ways. In addition, Hellmann and Fowler 

(1999) found that the jack1 estimator of richness is negatively 

biased for small sample sizes and positively biased for large 

sample sizes, thereby creating a greater difference in richness 

among these sites than really existed. However, Spence (2006) 

also found that the majority of species were detected in the 

NHWZ. We speculate that the differences in the number of 

detections between the two water zones could be related to 

changes in vegetation community. The OHWZ vegetation 

community consists of a variety of xeric species, whereas the 

NHWZ consist mostly of riparian species. Thus, fundamental 

between-zone differences in vegetation and distance to water 

are probably influencing species diversity and abundance.

Although we were unable to discern a difference between 

lower amounts of vegetation volume and visitor use on 

avifaunal communities, both factors are probably playing a role 

in community structure. Decreases in avifaunal richness in highuse areas could be attributed to lesser amounts of vegetation 

volume and visitor-use impacts. It is likely that human activity 

directly affects behavior of individual bird species (Boyle and 

Sampson, 1985; Blumstein and others, 2005) by flushing them 

from nesting or foraging sites and reducing time and energy 

available for other important activities (Fernández-Juricic, 

2000). These changes in behavior can lead to the extirpation 
Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the Colorado River Corridor 117

of sensitive and vulnerable species, such as the reduction in 

richness and abundance of nectivores that we observed.

For any future efforts to sample avifauna in the Colorado 

River corridor, we would suggest that GCNP alter its sampling 

design and consider approaches to avifaunal sampling and 

demographic parameter estimation that are based on an 

occupancy framework, rather than a distance framework. An 

occupancy-based approach (for example, after MacKenzie 

and others, 2002, 2006) (1) relies on fewer assumptions 

than estimates of density or abundance derived in a distance 

framework; (2) is more often robust to low detection; (3) 

permits estimation of proportion of area occupied by a species, 

which can be a more informative statistic to managers than 

density or abundance; (4) allows estimation of multiseason 

or “dynamic” occupancy parameters, including colonization 

and extinction rates; and (5) provides a more flexible means of 

comparing community composition and diversity among groups 

(that is, species or guild-specific occurrence, richness, and 

evenness). By sampling each site twice a year and collecting 

data for 5 to 10 years, the park would be able to implement a 

multiseason occupancy approach allowing them to account for 

imperfect detection and determine trends in occupancy. 

For the purpose of estimating demographic parameters 

(that is, occupancy, density) conditioned on habitat 

covariates, we recommend adding additional habitat or 

environmental variables that are measured at survey locations 

(directly) or using remotely sensed data (indirectly). These 

variables could include dominant vegetation type and patch 

size, slope, and elevation, among others that are hypothesized 

to predict occupancy for one or more target species. Indeed, 

Sogge and others (1998) found that patch area and volume 

of woody plant species in the Grand Canyon were the best 

predictors of bird species abundance and richness, with area 

alone accounting for about 65 percent of the variation in their 

data. In addition, if site-level inference conditioned on habitat 

covariates is desirable, then it is important that bird detections 

are made within a survey-specific area and precisely recorded.

Acknowledgments

We would like to acknowledge staff from Grand Canyon 

National Park who were involved in the design, implementation, 

and data entry for the avifauna and vegetation surveys, 

including R.V. Ward, Jeremy White, Janice Stroud-Settles, Rosa 

Palarino, Rachel Bennett, Brian Gatlin, Jane Rodgers, Cecilia 

Leumas, Ed Kulinski, Emily Slayton, Eric Krouse, Hattie 

Oswald, Jason Lovelady, Jean Lawrence, Jeremy Russell, Kevin 

Payne, Lydia Smith, Sarah Sells, Allison Clark, and Rebecca 

Kohler. We would like to further acknowledge Janice StroudSettles for her assistance and guidance in preparing data for 

analysis, Adia Sovie for data control, and Santiago Garcia and 

Jesse Anderson for data management and queries. Finally, we 

would like to thank Darryl MacKenzie for helpful advice on our 

occupancy modeling approach. 

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Appendix
Visitor-Use Impacts and Habitat Associations of the Avifauna Occupying the Colorado River Corridor 121

Appendix A. Results of bird species point counts from 2007–2011 along the Colorado River in Grand Canyon National Park.

[Data from the 48 of these sites that received two point counts in a given year. Seventy-two species from 10 different dietary guilds were detected. pt1, point 

count 1; pt2, point count 2; NA, not applicable]

Common name Scientific name

2007 2008 2009 2010 2011

Total

pt1 pt2 pt1 pt2 pt1 pt2 pt1 pt2 pt1 pt2

Lucy’s warbler Oreothlypis luciae 69 31 57 20 67 34 73 27 49 17 444

House finch Carpodacus mexicanus 40 13 28 7 23 11 30 3 14 6 175

Yellow warbler Setophaga petechia 4 2 32 11 29 13 30 12 24 13 170

Blue-gray gnatcatcher Polioptila caerulea 20 9 23 7 24 13 28 10 21 5 160

Common yellowthroat Geothlypis trichas 5 4 11 4 15 7 26 9 12 5 98

Black-chinned hummingbird Archilochus alexandri 17 5 19 6 3 1 16 6 7 2 82

Canyon wren Catherpes mexicanus 13 9 14 6 19 5 5 0 4 2 77

Ash-throated flycatcher Myiarchus cinerascens 0 0 13 2 20 9 13 2 13 4 76

Bell’s vireo Vireo bellii 10 4 11 2 17 6 12 3 5 4 74

Bewick’s wren Thryomanes bewickii 16 4 5 1 12 8 6 1 3 2 58

Black-throated sparrow Amphispiza bilineata 8 2 5 2 15 3 13 0 5 3 56

Say’s phoebe Sayornis saya 11 4 4 4 9 5 4 1 6 2 50

Lincoln’s sparrow Melospiza lincolnii 34 8 2 1 1 0 1 1 0 0 48

White-crowned sparrow Zonotrichia leucophrys 33 10 4 0 0 0 0 1 0 0 48

Rock wren Salpinctes obsoletus 15 5 4 3 6 4 1 0 3 3 44

Violet-green swallow Tachycineta thalassina 6 2 12 10 0 0 0 0 0 0 30

Yellow-breasted chat Icteria virens 0 0 7 3 4 3 3 4 4 2 30

Western tanager Piranga ludoviciana 0 0 3 2 5 0 11 5 1 1 28

Wilson’s warbler Cardellina pusilla 0 0 7 2 4 1 5 2 2 1 24

Black phoebe Sayornis nigricans 2 0 5 0 4 1 4 2 3 1 22

Mourning dove Zenaida macroura 3 1 1 2 2 2 6 2 3 0 22

Lesser goldfinch Spinus psaltria 4 0 0 0 2 1 6 0 4 1 18

Marsh wren Cistothorus palustris 1 0 0 0 10 5 0 0 0 0 16

Blue grosbeak Passerina caerulea 0 0 1 1 4 3 0 0 3 1 13

Costa’s hummingbird Calypte costae 4 1 0 0 0 0 1 0 5 2 13

Yellow-rumped warbler Setophaga coronata 1 0 1 0 0 0 7 3 0 1 13

Lazuli bunting Passerina amoena 0 0 5 3 0 0 1 2 0 0 11

Western wood pewee Contopus sordidulus 0 0 2 0 0 0 7 0 1 1 11

Cactus wren Campylorhynchus brunneicapillus 4 0 0 0 2 2 0 0 1 0 9

Summer tanager Piranga rubra 0 0 0 1 3 2 1 1 0 0 8

Black-headed grosbeak Pheucticus melanocephalus 0 0 2 1 0 0 1 2 1 0 7

Blue grouse Dendragapus obscurus 0 0 0 0 0 1 4 2 0 0 7

Ruby-crowned kinglet Regulus calendula 6 1 0 0 0 0 0 0 0 0 7

Broad-tailed hummingbird Selasphorus platycercus 2 0 0 0 1 1 0 0 1 1 6

Song sparrow Melospiza melodia 2 0 1 0 0 0 1 1 1 0 6

Dark-eyed junko Junco hyemalis 3 1 1 0 0 0 0 0 0 0 5

Warbling vireo Vireo gilvus 0 0 1 0 0 0 3 1 0 0 5

Chipping sparrow Spizella passerina 3 0 0 1 0 0 0 0 0 0 4

Hooded oriole Icterus cucullatus 0 0 1 1 0 2 0 0 0 0 4

MacGillivray’s warbler Geothlypis tolmiei 0 0 0 0 1 1 1 1 0 0 4
122 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Appendix A.—Continued

Common name Scientific name

2007 2008 2009 2010 2011

Total

pt1 pt2 pt1 pt2 pt1 pt2 pt1 pt2 pt1 pt2

Northern mockingbird Mimus polyglottos 2 1 0 0 0 0 1 0 0 0 4

Brown-crested flycatcher Myiarchus tyrannulus 0 0 0 0 2 0 0 0 1 0 3

Indigo bunting Passerina cyanea 0 0 1 0 0 0 2 0 0 0 3

Olive-sided flycatcher Contoopus cooperi 0 0 0 0 2 1 0 0 0 0 3

Rose-breasted grosbeak Pheucticus ludovicianus 0 0 0 0 0 0 2 1 0 0 3

Sage sparrow Artemisiospiza nevadensis 0 0 1 0 1 1 0 0 0 0 3

Black-chinned sparrow Spizella atrogularis 0 0 2 0 0 0 0 0 0 0 2

Brewer’s sparrow Spizella breweri 2 0 0 0 0 0 0 0 0 0 2

Bullock’s oriole Icterus bullockii 0 0 0 0 0 0 0 1 1 0 2

Great tailed grackle Quiscalus mexicanus 0 0 0 0 1 0 0 0 1 0 2

House sparrow Passer domesticus 1 0 1 0 0 0 0 0 0 0 2

Loggerhead shrike Lanius ludovicianus 0 0 0 0 1 0 1 0 0 0 2

Magnolia warbler Setophaga magnolia 0 0 0 0 0 2 0 0 0 0 2

Rufous-crowned sparrow Aimophila ruficeps 0 0 0 0 1 1 0 0 0 0 2

Scott’s oriole Icterus parisorum 0 0 0 0 0 0 2 0 0 0 2

Spotted sandpiper Actitis macularius 0 0 0 0 0 0 0 0 1 1 2

Southwestern willow flycatcher Empidonax traillii extimus 0 0 0 0 0 0 1 0 1 0 2

Brewer’s blackbird Euphagus cyanocephalus 0 0 0 0 0 0 0 1 0 0 1

Coopers hawk Accipiter cooperii 1 0 0 0 0 0 0 0 0 0 1

Greater roadrunner Geococcyx californianus 1 0 0 0 0 0 0 0 0 0 1

Green-tailed towhee Pipilo chlorurus 1 0 0 0 0 0 0 0 0 0 1

Hammond’s flycatcher Empidonax hammondii 0 0 0 0 0 0 0 0 1 0 1

Hermit warbler Setophaga occidentalis 1 0 0 0 0 0 0 0 0 0 1

Peregrine falcon Falco peregrinus 0 0 1 0 0 0 0 0 0 0 1

Red-winged blackbird Agelaius phoeniceus 0 0 0 0 0 0 0 0 0 1 1

Tree swallow Tachycineta bicolor 1 0 0 0 0 0 0 0 0 0 1

Turkey vulture Cathartes aura 0 0 0 0 0 0 0 1 0 0 1

White-throated sparrow Zonotrichia albicollis 1 0 0 0 0 0 0 0 0 0 1

Dietary guild

Insect foliage gleaner NA 80 32 74 23 77 36 85 32 66 23 528

Insect ground forager NA 70 22 29 10 46 16 22 6 14 5 240

Seed ground forager NA 47 13 30 8 24 12 34 5 16 6 195

Insect aerial forager NA 17 6 33 14 30 12 25 5 25 6 173

Nectivore NA 24 7 19 6 5 4 23 7 16 5 116

Omnivorous ground forager NA 4 4 0 1 1 0 5 2 1 2 20

Seed foliage gleaner NA 4 0 0 0 2 1 6 0 4 1 18

Fruit foliage gleaner NA 0 0 0 0 4 1 0 0 1 2 8

Carnivore soarer NA 1 0 1 0 0 0 0 1 0 0 3

Small animal prober NA 0 0 0 0 0 0 0 0 1 1 2
Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

Edited by Barbara E. Ralston

U.S. Geological Survey Scientific Investigations Report 2015–5180

Chapter M

Bat Surveys in Pipe Spring National Monument and 

Ensuing Interpretive Programs 

By Cameron Jack,1* John R. Taylor,1 Andrea Bornemeier,2 and Amber Van Alfen2

Abstract

Located in northern Arizona, springs emerge within 

Pipe Spring National Monument and provide one of the few 

stable water sources in the desert between the Grand Canyon 

and Zion National Park. Wildlife, Paiute Indians, Mormon 

pioneers, and modern-day visitors have all used this oasis 

as a life-sustaining rest area. Winsor Castle, a fort built 

over the main spring, and two adjacent developed ponds all 

needed maintenance, which necessitated that bat surveys be 

conducted so as to minimize the effect of construction on 

the wildlife that depends on the ponds. Here we discuss how 

the results of acoustic surveys and mist-netting events led 

to an understanding of the importance of the springs to bat 

populations and how this research has sparked a successful 

string of interpretive programs for the monument.

Introduction

Pipe Spring National Monument and the Kaibab Paiute 

Indian Reservation are located on the Arizona Strip, the area of 

Arizona north of the Grand Canyon and spanning the UtahArizona border. During the late 19th and early 20th centuries, 

Pipe Spring was the main water source for Mormon settlers 

traveling a primitive wagon road known as the Honeymoon 

Trail. Today the monument is 40 acres (16.19 hectares) 

and is surrounded by the reservation. The water from the 

natural springs continues to be stored in two ponds near the 

monument’s historical structures. These ponds are a contributing 

resource to the historical district and cultural landscape. There 

are two springs at the monument emerging from beneath the 

fort. The springs are fed by the Navajo Sandstone aquifer to the 

northwest, by way of the Sevier Fault (McKoy, 2000).

The ponds continue to provide a constant supply of water 

for livestock and irrigation for the gardens and fruit trees that 

reflect the park’s rich history. These open water sources are 

also beneficial to local wildlife. A mammal survey conducted 

by the U.S. Geological Survey in 2001 documented that at 

least 21 species use the area. Squirrels, rats, shrews, and mice 

are food sources for coyotes, bobcats, badgers, and foxes (S. 

Haymond, written commun., 2003). Red-tailed hawks and 

great horned owls also spend time in the trees surrounding the 

ponds in hopes of gaining an easy meal, and bats rely on the 

ponds as a place to hunt insects and drink.

Over time the stone masonry of the nearby ponds, 

constructed by Mormon ranchers in the 1870s, has deteriorated 

and is in need of repair. Leaks in the clay-bottom pond basins 

have led to water loss in the surrounding soil, leaving less 

water for garden and orchard maintenance. To make the 

necessary repairs, the ponds would need to be drained. Though 

repairs would be easiest to make in the summer, draining the 

ponds during that time could be devastating to the wildlife that 

depend on the water for survival. Park staff decided to review 

options for timing of the construction to minimize the effects it 

would have on wildlife.

Of all the animals that depend on the ponds, bats are the 

only ones that need an open water source with a calm surface. 

While a small squirrel or fox may be able to obtain water from 

a puddle or stream, bats require a water source that will allow 

them to drink on the wing. Furthermore, the bat diversity at 

Pipe Spring National Monument is represented by some of 

the largest and smallest bats in Utah and Arizona. Much like 

airplanes, big bats require a larger flyway when drinking, 

while the smaller, more agile bats can often drink from small 

cattle troughs. Park officials decided that if the ponds required 

draining for maintenance, the repairs should be made when 

bats’ use of the ponds is at its lowest level for the year.

Methods

Bat surveys at the monument have been going on for 

more than 30 years, but nearly all of this work has taken place 

from June through August, the peak time for bat activity (J. 

Tyburec, written commun., 2001; H.S. Kim and S. Johnson, 

written commun., 2004; S. Johnson, written commun., 2005). 

Currently, 9 of the 14 species that have been captured at Pipe 

Spring National Monument are considered Species of Special 

Concern by the U.S. Fish and Wildlife Service (Hinman and 

Snow, 2003). It has been suggested by previous studies that at 

1

Southern Utah University

2

National Park Service 

*

Now at University of Florida 
124 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

least 17 species may be present in the area, but the additional 

3 species not yet captured have only been included by 

association with the habitat. 

Our surveys began in March 2011 and ended in October 

2012. Over this time we made acoustic recordings of bat activity 

at the ponds for one night every two weeks. This technique 

involves coupling a full-spectrum ultrasonic bat detector 

(Pettersson D240X, Pettersson Elektronick) to an H2 (Zoom) 

digital voice recorder. We placed the detector 3 m (9 ft) above 

ground by strapping it to a large cottonwood tree and facing it 

toward the ponds. It was protected from the elements by a PVC 

housing constructed from a large electrical junction box. The 

audio signal was routed through a 4-meter-long (12 ft) audio 

cable to the recorder, which was housed in a weather-resistant 

toolbox at the base of the tree. This setup allowed both the 

recorder and the detector to run off of a stable power source and 

facilitated data downloading and periodic changing of digital 

storage media (16 GB flash memory) used by the recorder.

The bat detection system was turned on at sunset and 

run continuously through the night to provide approximately 

12 hours of monitoring. Analysis of the data was achieved 

using SonoBat version 3.03 software. Before analysis, all files 

were parsed and scrubbed using SonoBat. This relatively quick 

process separated short snippets of audio calls that did not 

contain obvious bat calls. This process significantly reduced 

processing time. After scrubbing, all remaining recordings were 

subjected to automated analysis using the species classification 

feature of the software. For a call to receive a bat species 

classification consensus, it had to have a discreet probability 

of likelihood of at least 0.90 and a minimum acceptable 

call quality of 0.80. These user-controlled settings are the 

software’s default settings and were used for all call analyses.

Using SonoBat U.S. West Summarizer, reports were 

generated to summarize individual monitoring nights. These 

reports include consensus counts, corrected counts, and the 

estimated likelihood of presence (ELP) for each individual 

species. The consensus count is a species-specific tally of 

consensus decisions generated by the software. The corrected 

count considers a species respective call ambiguity and reduces 

the consensus count to minimize misclassifications and 

provides a more confident and conservative count. The ELP is a 

probabilistic estimate based on the known ambiguity and overlap 

of each particular species (SonoBat version 3.2). A species with 

a very distinctive call will therefore require fewer recordings 

to generate a high ELP. Due to temporal variation of species 

compositions, and to limit false positives, summary reports were 

generated from single night, single site data (Hayes, 1997).

We note that acoustic identification such as this is 

probabilistic, and not as reliable as identifying bats through 

morphological or genetic methods. For this reason, we also 

conducted mist-netting events each month, as close to the new 

moon as possible. A typical survey consisted of deploying three 

nets, 6–9 m (19–30 ft) in length, in the following array: one net 

on the sidewalk passing between the two ponds and two nets 

around the pond perimeter (fig. 1). Nets were opened at sundown 

and closed three hours later. During this time, handlers carefully 

Figure 1. An aerial photograph of Winsor Castle and the adjacent 

ponds. The white lines depict the typical netting arrangement 

during bat surveys. Sites 1 and 2 were always in place, with sites 3 

and 4 as optional.

removed bats caught in the nets, took measurements, weighed 

and identified their species, examined them for parasites, and 

released them. Bats were also inspected for signs of white-nose

syndrome, a fungal infection that is sweeping across the nation 

and reducing bat populations. The bat handlers took precautions 

to guard against spreading disease between bats by using 

disposable gloves for each capture. Furthermore, all researchers 

were vaccinated for rabies and were trained in bat handling.

Results

Most of the calls detected by the Pettersson D240x were 

from bat species that had already been identified in previous 

studies. The most common bats netted in the area were the 

western pipistrelle (Parastrellus hesperus), which comprised 

25.2 percent of all captures, the fringed myotis (Myotis 

thysanodes), 20.3 percent, and the California myotis (Myotis 

californicus), 12.9 percent. In comparison, a few bats were 

only detected acoustically, specifically the long-legged myotis 

(Myotis volans), the western red bat (Lasiurus blossevillii), 

long-eared myotis (Myotis evotis), and the western mastiff bat 

(Eumops perotis) (table 1).

June through September exhibited the greatest species 

richness. Two time periods, late May/early June and late 

September/early October, both resulted in more than 15 of the 

17 possible species being detected acoustically. Though these 

two time periods were well represented by different species, 
Bat Surveys in Pipe Spring National Monument and Ensuing Interpretive Programs 125

Table 1. Listing of the number of species netted and acoustically detected 

over the course of the study in comparison to past studies. 

[Acoustic numbers represent the conservative corrected count as determined by the SonoBat 

software. The letter “X” indicates presence and the letter “P” indicates that the species is 

possibly present as determined by the respective authors] 

Species

This study Communication

Netted Acoustic Bogan 

(2005)

Johnson 

(2005)

Tyburec 

(2013)

Antrozous pallidus 20 7 X X

Corynorhinus townsendii 1 16 X X X

Eptesicus fuscus 14 1 X X

Euderma maculatum 0 3 P X

Eumops perotis 0 3 X

Idionycteris phyllotis* 7 0* X X X

Lasionycteris noctivagans 0 3 X X

Lasiurus blossevillii 0 1 P

Lasiurus cinereus 3 7 X X X

Myotis californicus 23 178 X X X

Myotis ciliolabrum 4 28 X X X

Myotis evotis 0 1 X

Myotis lucifugus 2 1 X X X

Myotis thysanodes 40 64 X X X

Myotis volans 13 1 X X

Myotis yumanensis 9 8 X X X

Parastrellus hesperus 50 740 X X X

Tadarida brasiliensis 9 569 X X X

*

Idioycleris phyllotis does not currently have a reference call library in SonoBat. These 

calls were most likely detected but not classified by the software. 

Figure 2. Andrea Bornemeier, Chief of Interpretation at Pipe 

Spring National Monument, shows a bat to eager participants 

during one of the educational outreach programs.

most of the summer month time periods routinely resulted in 

more than 10 species being detected (fig. 2).

The spotted bat (Euderma maculatum), E. perotis, L. 

blossevillii, and M. evotis are bats that have been detected 

acoustically but have never been caught at Pipe Spring 

National Monument. The silver-haired bat (Lasionycteris 

noctivagans) was also detected acoustically, but has not been 

caught at the monument since 2004. There were no bat species 

caught during the two-year study that had not been caught in 

prior studies (table 1).

Taking into account this study and all previously known 

studies conducted at Pipe Spring National Monument, we 

suggest that there may be as many as 18 species of bats in 

residence in the area. However, of these 18, E. maculatum, 

E. perotis, L. blossevillii, and M. evotis have only been 

documented acoustically. At the time of publication, an 

extensive study has been initiated to build a statistically strong 

bat-call reference library for E. perotis, as this species may be 

more prevalent in the area than previously thought.

Netting generated a fair amount of excitement among 

park staff and visitors. It quickly became apparent these 
126 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

periodic events could serve as a foundation for new interpretive 

programs to increase public awareness of and appreciation for 

bats. The park’s interpretive team quickly went about creating 

posters and sending out e-mails advertising these public events.

In the survey’s first year, a group of college students who 

were visiting as part of the Partners in Parks program attended 

one of the mist-netting events. A number of Boy Scouts 

participated in the evening activities to fulfill requirements 

for the mammal study merit badge, and introductory biology 

students from Southern Utah University (SUU) benefited from 

engaging in the scientific research, despite varying career 

interests. Other participants also served as interns under the 

SUU–National Park Service Intergovernmental Internship 

Cooperative. We soon expanded these public netting events 

to include other topics, such as “Bats and the Night Sky,” 

which paired an evening of astronomy with bat natural history. 

“Bats and Bugs” soon followed, allowing the public to view 

insects caught at the same time as bats. Finally, “Bats and 

Salamanders” allowed participants to net salamanders from 

the ponds and learn about their life histories.

In all, approximately 600 participants enjoyed an 

evening under the stars learning about bats and a variety of 

other topics. We drew participants from nearly 100 miles 

(161 kilometers) away, not to mention travelers who just 

happened to be at the monument and decided to wait for the 

evening programs. These engaging experiences help visitors 

connect to the monument in a very personal way (fig. 2).

Discussion

Several bat species deemed sensitive by the U.S. Fish and 

Wildlife Service were either netted or acoustically detected at 

Pipe Spring: the Yuma myotis (Myotis yumanensis), M. evotis, 

M. thysanodes, M. volans, the western small-footed myotis 

(Myotis ciliolabrum), the spotted bat (E. maculatum), the 

Allen’s big-eared bat (Idionycteris phyllotis), the Townsend’s 

big-eared bat (Corynorhinus townsendii), and the western 

mastiff bat (E. perotis) (Hinman and Snow, 2003).

E. perotis and E. maculatum were the only two sensitive 

species not caught on site during the two-year study, though 

we are certain both were recorded acoustically many times 

in 2011. In addition, C. townsendii, M. evotis, E. perotis, and 

E. maculatum were recorded in 2011, but not 2012. Perhaps 

only a small population of these sensitive species live at Pipe 

Spring, although this hypothesis could be affected by the 

smaller sample size in 2012. 

One of the strengths of acoustic monitoring is the 

tremendous amount of data gathered over many sampling 

hours through very little effort. It provides a relatively easy 

way to monitor the park’s bats throughout the year, potentially 

spotting interesting patterns that may not be elucidated by 

mist-netting alone. One such pattern can be found in table 2: 

half of the species detected in this study were both captured 

and acoustically detected and appear to be present in area for 

much of the year. Of these, P. hesperus and T. brasiliensis seem 

perennial to the area (table 2; see also table 1). The other nine 

species proved difficult to capture or acoustically detect each field 

season, suggesting that they may be in the area only sporadically, 

possibly using the area as a migratory stop or wintering location. 

It is also highly likely that species like E. maculatum, E. perotis, 

T. brasiliensis, and Lasiurus sp. could be either migratory and 

(or) flying over the area during feeding bouts.

We found that bats’ use of the ponds in Pipe Spring 

National Monument is steady from May through October. Bat 

activity and diversity drop off sharply by mid-November and 

stay low until spring (fig. 3). Pipe Spring National Monument 

appears to be an important water and food source for bats and 

should be treated with care. Maintenance that requires pond 

drainage should occur in November, when daytime temperatures 

average 13 °C (56 °F) and nighttime lows are around freezing, 

and should be avoided from May through late October, 

especially during birthing periods for particular wildlife species. 

In the summer of 2012, the rock walls surrounding the ponds 

were excavated, reinforced, and reconstructed, but that work did 

not require the ponds to be drained. 

The National Academy of Sciences states in their 

Framework for K-12 Science Education that an understanding 

of science and engineering is essential for every American 

citizen (Quinn and others, 2012). Furthermore, they suggest 

that these scientific principles and practices should be made 

on a personal level in a meaningful and relevant way. This 

study initially focused solely on collecting bat data, but quickly 

adapted to provide the meaningful and relevant scientific 

perspective, delivered at the personal level: what began as a 

straightforward research project blossomed into the synergistic 

development of engaging interpretive programs. 

One college student who attended the bats and salamanders 

program wrote a paper explaining the “profound effect” the 

experience had had on him. It helped him decide that a career in 

science may be exactly what he has been searching for. Another 

student wrote to say she had never seen a bat before and loved 

learning about them and listening to their calls. By the end of 

the night she had found herself referring to them as “cute little 

guys” and realized that, as she said, “this is exactly why I’ve 

come to college . . . to explore new things.” The possibilities 

of expanding interpretive programs in conjunction with park 

research are being realized in Pipe Spring National Monument 

and are a bright and refreshing way to engage the childlike 

curiosity in all park visitors.

The excitement generated by this program has spread to 

other nearby parks, which also view this format as offering 

great possibilities. The 2013 field season brought bat inventory 

work and similar interpretive programs at nearby Zion and 

Bryce Canyon National Parks and Cedar Breaks National 

Monument. Unlike Pipe Spring, those parks experience much 

higher visitation, which necessitates limiting the number 

of participants. Nevertheless, the goal is the same: to help 

visitors connect physically and emotionally with the public 

lands they love. We hope these connections will last a lifetime, 

influencing participants to continue to protect and care for their 

national parks. 
Bat Surveys in Pipe Spring National Monument and Ensuing Interpretive Programs 127

Table 2. Estimated likelihood of presence as determined by SonoBat. 

[Summary of all acoustic data (2011 and 2012 combined). Each month was divided in half to provide greater resolution. Estimated likelihood of presence data is given 

in percent likelihood (%), but the netted numbers are individuals of each species caught. M. californicus and M. ciliolabrum hand identifications may be inaccurate; we 

used the latest keys available to us through the Western Bat Working Group, but DNA was not used for identification purposes] 

Species

March April May June July

1–15 16–31 Netting 1–15 16–30 Netting 1–15 16–31 Netting 1–15 16–31 Netting 1–15 16–31 Netting

Antrozous pallidus 1 24% 4 100% 23% 6 24% 62% 6

Corynorhinus townsendii 25% 25% 99% 1

Eptesicus fuscus 23% 5 9% 7 23% 23%

Euderma maculatum 86% 86%

Eumops perotis 99% 99%

Idionycteris phyllotis 1 1 4

Lasionycteris noctivagans 23% 100%

Lasiurus borealis

Lasiurus cinereus 33% 86% 1 39% 1 67% 33% 1

Myotis californicus 98% 100% 79% 100% 19% 46% 6 100% 100% 4 53% 92% 3

Myotis ciliolabrum 20% 71% 20% 100% 100% 3 97% 95% 1

Myotis evotis 24%

Myotis lucifugus 20% 100%

Myotis thysanodes 100% 1 100% 100% 13 100% 100% 8 100% 100% 4

Myotis volans 7 19% 1 2

Myotis yumanensis 10% 34% 19% 2 39% 58% 1

Parastrellus hesperus 100% 100% 1 20% 40% 1 20% 100% 8 100% 100% 9 100% 100% 19

Tadarida brasiliensis 86% 33% 3 33% 1 33% 100% 100% 1 100% 33%

Species August September October November

1–15 16–31 Netting 1–15 16–30 Netting 1–15 16–31 Netting 1–15 Netting

Antrozous pallidus 24% 2 24% 48% 1

Corynorhinus townsendii 100% 74%

Eptesicus fuscus 23% 1 23% 23% 1

Euderma maculatum 86%

Eumops perotis

Idionycteris phyllotis 1

Lasionycteris noctivagans 23% 67%

Lasiurus borealis 38% 19%

Lasiurus cinereus 33% 78% 78% 33% 55% 65% 32%

Myotis californicus 98% 100% 3 19% 100% 3 99% 99% 7 99%

Myotis ciliolabrum 76% 20% 1

Myotis evotis 20%

Myotis lucifugus 1 1

Myotis thysanodes 100% 100% 5 99% 99% 7 99% 3

Myotis volans 19% 1 2

Myotis yumanensis 19% 3 27% 1 19% 19% 1 8% 1

Parastrellus hesperus 100% 100% 9 100% 100% 3 100% 100% 1 99%

Tadarida brasiliensis 100% 97% 97% 100% 2 33% 100% 3 33%
128 Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau

16

14

12

10

8

6

4

2

0

Mar

Captured

Detected acoustically

Month

Apr May

Total number of species

Jun Jul

EXPLANATION

Aug Sep Oct Nov

Figure 3. Graph comparing the total number of different species 

captured and acoustically detected each month within the 2011 

and 2012 field seasons. Acoustic data is based on consensus 

count as determined by SonoBat software.

References Cited

Hayes, J.P., 1997, Temporal variation in activity of bats and 

the design of echolocation-monitoring studies: Journal of 

Mammalogy, v. 78, no. 2, p. 514–524.

Hinman, K.E., and Snow, T.K., eds., 2003, Arizona bat 

conservation strategic plan: Arizona Game and Fish 

Department Technical Report 213. [Also available online 

at http://www.azgfd.gov/pdfs/w_c/bat/NGTR%20213%20

-%20Arizona%20Bat%20Conservation%20Strategic%20

Plan.pdf.]

McKoy, K.L., 2000, Cultures at a crossroads—An 

administrative history of Pipe Spring National Monument: 

National Park Service, Cultural Resources Selections no. 

15, 793 p. [Also available online at http://npshistory.com/

series/archeology/rmr/15/index.htm.]

Quinn, H., Schweingruber, H., and Keller, T., eds., 2012, 

A framework for K-12 science education—Practices, 

crosscutting concepts, and core ideas: Washington, D.C., 

The National Academies Press, 400 p.
Chevlon Creek at Old Territorial Road. Photograph by Robert J. Hart, U.S. Geological Survey.
Ralston and others—Proceedings of the 12th Biennial Conference of Research on the Colorado Plateau—Scientific Investigations Report 2015–5180

ISSN 2328-0328(online)

http://dx.doi.org/10.3133/sir20155180