tcmullet(2008) final thesis
TRANSCRIPT
EVALUATION OF TWO GIS HABITAT MODELS AND INITIAL
CHARACTERIZATION OF NESTING AND BREEDING-SEASON ROOSTING
MICROHABITAT FOR MEXICAN SPOTTED OWLS
IN THE GUADALUPE MOUNTAINS
A Thesis
Presented to the
School of Arts and Sciences
Sul Ross State University
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
by
Timothy Carl Mullet
December 2008
UMI Number: 1462869
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EVALUATION OF TWO GIS HABITAT MODELS AND INITIAL
CHARACTERIZATION OF NESTING AND BREEDING-SEASON ROOSTING
MICROHABITAT FOR MEXICAN SPOTTED OWLS
IN THE GUADALUPE MOUNTAINS
Approved:
Christophi Ph
6 Martin Terry, Ph.D.
s P. Ward, Jr., VI
Approved:
ase, PhD., Dean of Arts and Sciences
ABSTRACT
The Mexican spotted owl (Stix occidentalis lucida) is a federally threatened
species inhabiting mixed conifer forests and canyon systems throughout the southwestern
United States and Mexico. This subspecies has been found in steep-walled canyons and
less frequently in mixed-conifer forests of the Guadalupe Mountains of West Texas and
Southeast New Mexico. Prior to this study, no quantitative study of spotted owl habitat
in this region had been conducted. The purpose of this study was to characterize and
quantify the breeding-season habitat of Mexican spotted owls at two spatial scales based
on their occupancy in the Guadalupe Mountains. I determined the distribution of high-
quality habitat at a landscape scale by assessing the predictive ability of two existing
GIS-based habitat models initially designed from data outside this region. I quantified 21
microhabitat features surrounding known nest and roost sites to characterize the site-
specific conditions within canyon habitats.
I found Mexican spotted owls utilizing steep, narrow canyons with strong
vegetative components. The overlapping high-quality habitat predicted by both models
had the strongest association to known nest and roost sites and higher occupancy
estimates compared to the high-quality habitat predicted by either model alone, making it
the most efficient description of Mexican spotted owl breeding-season habitat at a
landscape-scale. Canopy-cover, saplings, and rocky debris were significant microhabitat
characteristics of nest and roost sites within this region. Canyon morphology, species
composition, and ground cover vegetation at nest and roost sites were homogenous
compared to random canyon sites.
iii
This study was the first attempt to quantify and describe the breeding-season
habitat of Mexican spotted owls using the predictions of GIS-based habitat models and
quantitative sampling methods in the Guadalupe Mountains. This study reaffirms the
utility of GIS-based habitat models as an effective means for predicting Mexican spotted
owl breeding-season habitat and the importance of steep, cool canyons for nesting and
roosting sites in the Guadalupe Mountains.
IV
ACKNOWLEDGEMENTS
This project was funded by the Chihuahuan Desert Research Network (CHDN),
National Park Service, United States Department of the Interior, through the Gulf Coast
Cooperative Ecosystem Studies Unit, Cooperative Agreement No. H5000 02 0271.
Additional support was provided through a teaching assistantship by the Sul Ross State
University Department of Biology. I owe thanks to Hildy Reiser and Tom Richie of
CHDN for their involvement and dedication to support this project. Sincerest thanks go
to Pat "Ranger" Ward for his significant guidance and his contributions to the
construction of project methodologies and data analysis, his donation of equipment, his
logistical and field support and friendship. He has been a mentor and role model, whose
advice will continue to influence my life in the future. Special thanks go to Chris Ritzi
for his contribution to project details and grant acquisition and his diligent support of my
academic and scientific pursuits. I have certainly gained life-improving experience
working with him. Additional thanks go to the staff of the Human Resources and
Granting Offices at Sul Ross State University. Their ability to adjust and maintain the
funding of the grant provided a great deal of support to accomplishing this project.
Special thanks go to Martin Terry for joining my committee at a late stage,
providing support, and contributing to the revisions of this thesis. I Thank Terry Johnson
for his willingness to discuss the application of his GIS habitat model for this study and
Dave Willey and Mike Zambon for providing interpretive support of their Utah model.
My greatest thanks go to Daniel Reed and Jared Grummer for their diligence and
excellent field work and for putting up with me, the lightning, bad food, and all that
comes with it. I couldn't have hired two better field technicians! I thank Fred Armstrong,
v
who introduced me to the rigorous terrain and beauty of the Guadalupe Mountains, for
teaching me the importance of maintaining professional relationships and a strong, moral
work ethic. Fred's advice and example gave me a positive frame of mind to keep me
going through that rugged landscape. I would like to acknowledge the volunteer help of
Michael Hayne and Joanne Kozuchowski. If they had known what they were getting
into, I doubt they would have volunteered. Nevertheless, their friendship and field
assistance were extremely helpful during a time period when I had no field assistance.
I would like to express my greatest thanks to Law Enforcement Rangers Iffy
Kahn, Peter Pappus, John Cwiklick, "Carver," and Jan Wobbenhorst, of Guadalupe
Mountains National Park for providing radios and extremely helpful safety support when
I traveled into the wild. My thanks go out to Larry "LP" Paul for his great sense of
humor and project support. I'd also like to thank LP for introducing my field tech,
Daniel, to the treachery of the Guadalupe Mountains on that fateful day a hiker had died
in the field. Daniel was scared to death from that moment on. I thank Renee West for
providing data and project support.
My expressed appreciation goes to Scott Schrader, Lori Manship, and Kevin
Urbanzcyk for their much-needed support with GIS. I would also like to thank Jonena
Hearst for her GIS support with the data of Guadalupe Mountains National Park and her
friendship. Most sincere thanks go to Bonnie Warnock for her statistical support and
encouragement. Special thanks go to Gary Roemer for his friendship and providing me a
place to stay while I was working at New Mexico State University. Gary's excellent
advice and great company were refreshing while I stayed at Dog Canyon. Thanks go to
John Karges and Colin Shackelford for sparking the idea that got this project off the
ground. I thank Sean Kyle, who probably provided some of the most insightful advice of
vi
my life. It really made a difference. Thanks go to Dan Leavitt, Tara Polosky, Robert
Hibbitts, and Ryan Welsh for their friendship and support. Their advice and company got
me through some very tough times.
Finally, I owe the most significant acknowledgement to my wife, Monica Mullet,
who saved my life and gave me a reason to pursue my dreams without hesitation. Her
unconditional love and support were the most significant contributions to this project
because without her I probably would not have been able to make it through the
challenges I faced. I also want to acknowledge the late Carl Theaker, whose love,
example, loyalty, and service to our country fueled my desire to do great things. Special
thanks go to Pauline Theaker for her unconditional love and support and the most helpful
advice I have received in my life. My greatest appreciation goes to Vaughn and Nancy
Mullet for their love and support. They never gave up on me, so I will never give up.
This thesis is dedicated to my two daughters, Thera Alexandria and Emma Therasia
Mullet. Here's to a better future!
vn
TABLE OF CONTENTS
Page
Abstract iii
Acknowledgements v
List of Tables x
List of Figures xi
List of Appendices xii
Chapter
I. Introduction 1
A. Study Area 9
B. Authorization 12
II. Evaluation of Two Models Used to Predict Mexican Spotted Owl Habitat
in the Guadalupe Mountains 13
A. Methods and Materials 16
1. Model Validation and Comparison Based on Historical Data . . . . 23
2. Estimating Occupancy and Detection Probabilities 27
3. Data Analysis 34
B. Results 46
1. Model Validation and Comparison Based on Historical Data . . . . 46
2. Estimating Occupancy and Detection Probabilities 47
C. Discussion 56
1. Model Validation and Comparison Based on Historical Data . . . . 58
2. Estimating Occupancy and Detection Probabilities 60
viii
Table of Contents, continued
III. Microhabitat Features of Mexican Spotted Owl Nest and Roost Sites in the
Guadalupe Mountains 65
A. Methods and Materials 66
1. Geomorphic Features 68
2. Vegetative and Surface Features 71
3. Data Analysis 76
B. Results 77
1. Geomorphic Features 78
2. Vegetative and Surface Features 79
C. Discussion 89
VI. Conclusion 95
V. Literature Cited 101
Appendices I l l
IX
LIST OF TABLES
Table Page
1. Interval Classes of the Southwestern Geophysical Habitat Model 17
2. Cumulative Interval Classes of the GHM 18
3. The Continuous Intervals of the Utah-based Habitat Model 21
4. Set of A Priori Hypothesis Models for Covariates Influencing Detection . . . . 40
5. Set of A Priori Hypothesis Models for Covariates Influencing Occupancy... 42
6. Total Number of Mexican Spotted Owl Nest and Roost Sites 47
7. Summary of Model-selection Procedure and Detection Probability 49
8. Factors Affecting the Occupancy (i|/) and Detection Probability (p) 50
9. Pearson Correlation Matrix of Predicted Mexican Spotted Owl Habitat 54
10. Proportion of Area Occupied (PAO) by Mexican Spotted Owls 55
11. List of Geomorphic Variables Measured 69
12. List of Vegetative and Surface Variables Measured 71
13. Summary of the Means and Standard Deviations of Geomorphic Features . . . 79
14. The Composition of Vegetative Species and the Number of Sample Sites . . . 82
15. Comparison of Tree Species Diameters 85
x
LIST OF FIGURES
Figure Page
1. Distribution of Spotted Owls in North America 2
2. Geographic Orientation of Study Area 11
3. Example of Johnson's (2003) Southwestern Geophysical Habitat Model . . . 19
4. Example of Willey et al.'s (2006) Utah-based Habitat Model 20
5. Distribution of Predicted High-quality Mexican Spotted Owl Habitat 25
6. Detailed Example of Historical Mexican Spotted Owl Nest and Roost Sites . 26
7. Placement of Sample Units Where Nighttime Surveys Were Conducted . . . . 31
8. Occupancy Estimates (\|/) for 25 Sample Units Surveyed 57
9. Comparison of Microhabitat Vegetative and Surface Features 81
10. Comparison of Heights from the Three Tallest Layers 87
XI
LIST OF APPENDICES
Appendix Page
Al. Nighttime Survey Datasheet I l l
A2. Data matrix for Detection Probability Covariates 113
A3. Data matrix of Occupancy Covariates 117
A4. Summary of Occupancy Estimates for Mexican Spotted Owls 122
A5. Habitat Sampling Datasheet 124
A6. Example of a Roost Site in the Guadalupe Mountains 127
A7. Example of a Random Up Canyon Sample Site 129
A8. Example of a Random Down Canyon Sample Site 131
A9. Partial View of GUM76 and GUM69 133
A10. View of GUM54 and GUM55 134
Al 1. Partial View of GRD47 135
A12. Distribution of Predicted High- and Low-quality Habitat in GUMO . . . 136
A13. Distribution of Predicted High- and Low-quality Habitat in CAVE . . . . 137
A14. Distribution of Predicted High- and Low-quality Habitat in GRD 138
xii
CHAPTER I
INTRODUCTION
The Mexican spotted owl (Strix occidentalis lucida) is one of three subspecies of
spotted owl endemic to North America. The other two subspecies are the California
spotted owl (S, o. occidentalis) and Northern spotted owl (S. o. caurina; Gutierrez et al.
1995). Unlike the Northern and California spotted owls, Mexican spotted owls occur
over a much larger, naturally fragmented range throughout the southwestern United
States and Mexico (Fig. 1; Ward et al. 1995). In the United States, Mexican spotted owls
are found commonly in rocky canyons and mountain ranges that support coniferous forest
in Arizona, New Mexico, central Colorado, southern Utah, and West Texas (Ward et al.
1995, Bryan and Karges 2001).
Surveys conducted from 1990 to 1993 found that 91% of Mexican spotted owls
known to exist in the United States occurred within U.S. Forest Service lands (Ward et al.
1995). The U.S. Fish and Wildlife Service (USFWS) concluded that methods of even-
aged silviculture and stand-replacing wildfires posed risks of substantial future losses and
degradation of nesting and breeding-season roosting habitat within these regions (USDI
1993). Consequently, Mexican spotted owls were listed as threatened on 15 April 1993
(USDI 1993). The Mexican Spotted Owl Recovery Plan was developed and approved
two years later with the purpose of providing information on all aspects of Mexican
spotted owl ecology and management (USDI 1995).
The first and most significant assumption made by the Recovery Plan was that the
geographical distribution of Mexican spotted owls is limited by the availability of
suitable nesting and breeding-season roosting habitat (USDI 1995). As a result,
1
Figure 1. Distribution of spotted owls in North America, including the Northern spotted
owl (Strix occidentalis caurina), California spotted owl (S. o. occidentalis), and Mexican
spotted owl (S. o. lucida; according to USDI1995 and Guti6rrez et al. 1995).
3
locating suitable breeding habitat and identifying the characteristics of nest and roost
sites are important steps towards making appropriate management decisions for Mexican
spotted owl recovery.
According to the Recovery Plan, Mexican spotted owls occur more readily in
"high elevation coniferous and mixed coniferous-broadleaved forests, often in canyons,"
(Ganey and Dick 1995: 2), with less emphasis on pine (Pinus)-oak (Quercus) and pinyon
(Pinus)-jumper (Juniperus) woodlands (Ganey and Dick 1995). It is therefore
appropriate to consider placing Mexican spotted owls into two generic categories: owls
nesting and roosting in mixed conifer forests and owls nesting and roosting in canyon
systems. Because the distribution of this subspecies and its associated ecosystems
encompass such a broad spatial scale, nesting and roosting habitat varies according to the
particular geographic region Mexican spotted owls inhabit.
Mixed-conifer forest is the primary habitat type used by Mexican spotted owls in
Arizona and New Mexico, where the highest densities of Mexican spotted owls have
been located (USDI 1995). In these regions, spotted owls often utilize mature or old-
growth stands of Douglas fir (Pseudotsuga menziesii), white fir (Abies concolor),
southwestern white pine (Pinus strobiformis), limber pine (Pinus flexilis), ponderosa
pine (Pinus ponderosa), and Gambel oak (Quercus gambelii; Ganey and Dick 1995).
Nesting and roosting sites are comprised of uneven-aged, multi-storied vegetation with
canopy cover typically shading more than 70% of the understory (Ganey and Balda
1989, Ganey and Dick 1995, Grubb et al. 1997, Ganey et al. 2000). Nests are usually
located on small stick platforms or in the cavities of large trees along northerly aspects
possessing slopes greater than 40 percent. Nest sites generally occur within a fairly
narrow band of elevation between 1,982 and 2,287 m (Ganey and Dick 1995). Roosts
are located upon the branches of both large and small trees within stands similar to those
used for nesting sites (as reviewed by Ganey and Dick 1995).
Contrary to spotted owls' extensive use of mixed-conifer forests, several studies
have discovered Mexican spotted owls utilizing canyons in northern Arizona (Willey et
al. 2001), southern Utah (Rinkevich and Gutierrez 1996, Willey 1998), central Colorado
(Johnson 1997), southeast New Mexico, and West Texas (Salas 1994, Kauffman 1994,
2001, 2002, 2005, Narahashi 1998, Williams 1999, Bryan and Karges 2001). Mexican
spotted owls in these regions have been found nesting and roosting on cliff-ledges, in
caves, and on tree branches along the northerly aspects of steep, narrow canyons.
Vegetation communities varied from Great Basin conifer woodlands and Mojave Desert
scrub in northern Arizona, southern Utah, and central Colorado (Rinkevich and Gutierrez
1996, Johnson 1997, Willey 1998, Willey et al. 2001), to mixed conifer forests, madrean
pine-oak woodlands, and Chihuahuan Desert scrub in southeast New Mexico and West
Texas (Salas 1994, Kauffman 1994, 2001, 2002, 2005, Narahashi 1998, Williams 1999,
Bryan and Karges 2001). Elevations of nest and roost sites generally ranged from 1,500
to 2,300 m. Based on the similarities between nesting and roosting habitat in mixed-
conifer forests and canyon systems, it is clear that vegetation, topography, and
geomorphology are important variables for characterizing Mexican spotted owl
breeding-season habitat in the United States.
Current Mexican spotted owl inventory and monitoring protocols are designed to
locate nest and roost sites and identify breeding-season habitat (USDI1993).
Knowledge of where habitat characteristics (like those mentioned above) are located
within the landscape allows researchers and resource managers to improve their
efficiency in finding nest and roost sites (Ward and Salas 2000). Until recently, analog
5
maps have been used exclusively to identify survey areas. With the advent of
Geographical Information Systems (GIS), researchers are now able to use the spatial data
of habitat variables for the development of predictive Mexican spotted owl habitat
models over a broad range of spatial scales.
In areas where previous knowledge of potential habitat is limited and large-scale
surveys are difficult to accomplish due to hazardous terrain, GIS-based habitat models
can predict the possible distributions of species-habitat relationships across the landscape
(Vogiatzakis 2003). Often times, models will assign a percentage of probability or
likelihood of locating a species within specific areas. These predictions enable managers
to prioritize survey efforts and create more effective sampling designs, ultimately
reducing obstacles caused by inaccessibility, insufficient funding, excessive survey
hours, and lack of adequate manpower.
A set of variables describing the direct interaction between the focal subject and
its environment must be established when developing a predictive habitat model
(Vogiatzakis 2003). The effectiveness of model development and application is
ultimately dependent on the availability and reliability of data used to predict potential
habitat. Where vegetative composition is known (e.g., U.S. Forest Service lands),
satellite imagery data (e.g., EMT+) that displays the distribution of vegetation on the
earth's surface can be used together with literature and topographic features to develop
an efficient model predicting the vegetative variables characteristic of Mexican spotted
owl breeding-season habitat. An example of this type of model is the ForestERA Data
Layer (ForestERA 2005). The ForestERA Data Layer was designed specifically for
predicting Mexican spotted owl nesting and roosting habitat in the mixed-conifer and
pine-oak forests of Arizona (ForestERA 2005). Recently, a more refined model
6
predicting Mexican spotted owl nesting and roosting habitat in the mixed-conifer forests
of the Jemez Mountains in northern New Mexico was developed (Hathcock and
Haarmann 2008). Unlike the ForestERA model, the Jemez Mountain model did not use
satellite imagery data, but rather, the site-specific vegetative characteristics of known
Mexican spotted owl nest and roost sites within the Jemez Mountains. Both models are
examples of how vegetative characteristics can be used to generate predictive maps of
potential Mexican spotted owl habitat with the appropriate availability of data.
Although prior studies have identified dependent vegetative variables for
Mexican spotted owl nesting and roosting habitat, they do not include a comprehensive
dataset encompassing the entire southwestern United States (Ganey and Dick 1995).
Therefore, a model predicting Mexican spotted owl breeding-season habitat based on
vegetative variables across its entire range is not available at this time.
Where vegetation data are incomplete or not as useful for predicting breeding-
season habitat, such as within canyon systems, models can be developed based on other
variables like topography, geomorphology, precipitation, and landscape-specific indices
(e.g., surface heat and soil-types). These data are more readily available and can provide
predictions at multiple scales across the landscape. Two models have been generated
from these types of data to predict Mexican spotted owl breeding-season habitat.
Johnson (2003) developed and validated the Southwestern Geophysical Habitat Model
(GHM) predicting Mexican spotted owl habitat throughout the southwestern United
States. A second model was generated and evaluated by Willey et al. (2006) predicting
Mexican spotted owl habitat in the canyons of southern Utah, also referred to as the
Utah-based Habitat Model (UBM). Each model projects a map of predicted breeding-
7
season habitat of the Mexican spotted owl using similar parameters. However, these two
models were generated and evaluated using slightly different methods.
The Guadalupe Mountains present a particularly interesting environment for
Mexican spotted owls in that both mixed-conifer forests and canyon systems are
available (Murphy 1984). Unfortunately, knowledge of Mexican spotted owl
distribution and breeding-season habitat has been slow to develop in this region. This
has been primarily due to the amount of hazardous and inaccessible terrain created by
steep, canyon slopes and lack of roads and trails (Narahashi 1998, Kauffman 2005). For
these reasons, the Guadalupe Mountains are an opportune study area for testing different
GIS predictive models like those of the GHM and UBM.
Previous surveys of the Guadalupe Mountains have been conducted in an attempt
to determine the distribution of nest and roost sites (Salas 1994, Kauffman 1994, 2001,
2002, 2005, Narahashi 1998, Williams 1999). However, when nest and roost sites have
not been located during daytime follow-ups, site occupancy has been based solely on the
detection of vocal responses of spotted owls during nighttime surveys. Conversely, areas
without a response were determined unoccupied (F. Armstrong, Guadalupe Mountains
National Park and L. Paul, Guadalupe Ranger District, Lincoln National Forest, pers.
comm.). These inferences can result in biased estimates of population densities, as well
as the distribution of breeding-season habitat. Compounding this issue is the fact that
quantitative data describing microhabitat variables of nest and roost sites are lacking in
areas where spotted owls have actually been located. Without adequate information
concerning the availability of breeding-season habitat, potential population densities, and
microhabitat selection of Mexican spotted owls in the Guadalupe Mountains, proper
8
management and recovery efforts for this area will be inadequate in this isolated part of
the spotted owl's range.
The purpose of this study was to provide a better understanding of Mexican
spotted owl breeding-season habitat in the Guadalupe Mountains. I accomplished this by
evaluating the application and utility of the GHM and UBM for predicting Mexican
spotted owl breeding-season habitat at a landscape scale (e.g., defining macrohabitat),
testing their effectiveness for estimating site occupancy from nighttime surveys, and
measuring and quantifying microhabitat features at Mexican spotted owl nest and roost
sites in the Guadalupe Mountains.
Accordingly, my objectives were to 1) validate and compare the predicted habitat
of the GHM and UBM using historical data of known nest and roost sites (1994 to 2006)
in the Guadalupe Mountains, 2) test the utility of the GHM's and UBM's habitat
predictions for estimating site occupancy in the Guadalupe Mountains based on the
results of nighttime surveys conducted during the 2007 breeding season (March through
August), and 3) sample, measure, and quantify select microhabitat features of Mexican
spotted owl nest and roost sites in the canyons of the Guadalupe Mountains using
quantitative sampling methods.
Results from this study will be useful for evaluating other strategies for inventory
and monitoring Mexican spotted owls within canyon systems, particularly where GIS
predictive habitat models are incorporated into the survey design. This study will also
provide baseline microhabitat data of nesting and roosting sites within canyons used by
Mexican spotted owls in the Guadalupe Mountains for comparison with nesting and
roosting sites in canyons of other regions.
9
I separated this thesis into two parts. The first part (Chapter II) focuses on the
effectiveness of the GHM and UBM at predicting known breeding-season locations and
estimating Mexican spotted owl occupancy. The second part (Chapter III) concentrates
on characterizing nest and breeding-season roost sites in the Guadalupe Mountains.
STUDY AREA
The Guadalupe Mountains are located in northern Culberson County of West
Texas and Otero and Eddy Counties of southeastern New Mexico. Field work was
conducted in the southern portion of the mountain range along the Texas-New Mexico
border. This region consisted of three federally administrative units including the
Guadalupe Mountains National Park, Carlsbad Caverns National Park, and the
Guadalupe Ranger District of the Lincoln National Forest (Fig. 2).
Guadalupe Mountains National Park (GUMO) is located immediately south of
the New Mexico border in Hudspeth and Culberson Counties, Texas. The 35,272 ha of
GUMO contains a diverse array of ecosystems conducive to rare and endemic species
(Murphy 1984). Elevations range from 1,104 to 2,584 m. Vegetation at lower elevations
is characteristic of the Chihuahuan Desert and contrasts sharply with the mesic
woodlands of intermittently, striated canyons and mixed-conifer forests of the higher
elevations. Eleven Protected Activity Centers (PACs) for Mexican spotted owls were
established in GUMO based on the results of a 2003-2005 survey (F. Armstrong pers.
comm.). Mexican spotted owls within this region inhabit steep, cool canyon systems
consisting of multi-layered, conifer-broadleaved vegetation (F. Armstrong pers. comm.).
Their nest sites have been located in the crevices and caves of north-facing canyon walls
10
(Kauffinan 2005, T. Mullet pers. obs.) with several unpaired males known to inhabit
mixed-conifer forest habitats (Armstrong 2000).
Known primarily for its impressive, karst cave systems, Carlsbad Caverns
National Park (CAVE) encompasses approximately 19,000 ha of wilderness in the
Guadalupe Mountains of Eddy County, New Mexico. The park preserves a variety of
plants and animals occupying the northernmost part of their geographic range (National
Park Service 2007). Although no formal surveys have been conducted to determine the
distribution and relative densities of Mexican spotted owls in this region, one confirmed
roosting pair was documented in July 2003, occurring within a steep canyon on the west
side of the park (R. West pers. comm.).
The Guadalupe Ranger District of the Lincoln National Forest (GRD) in Eddy
and Otero Counties, New Mexico, is bordered to the south by GUMO and to the east by
CAVE. The southern portion of the GRD consists of high-elevation, pine-oak
woodlands and steep canyon systems with an elevation band of 1,000 to 2,300 m. The
canyon systems within GRD are continuous from GUMO to CAVE, where 9 PACs have
been established within the district boundaries from data collected between 1994 and
2002 (Salas 1994, Kauffinan 1994, 2001, 2002, 2005, Narahashi 1998, Williams 1999).
Mexican spotted owls in this region occupy steep canyons, where they are known to nest
and roost along cliff ledges and within caves scattered on north-facing slopes (L. Paul
pers. comm.).
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12
AUTHORIZATION
Mexican spotted owls are listed as threatened by the USFWS under the
Endangered Species Act (50 CFR Part 17 RIN 1018-AB 56; USDI1993). This
subspecies is also listed as threatened by the states of Texas (Texas Parks and Wildlife
Department 2005) and New Mexico (New Mexico Department of Game and Fish 2003).
All materials and methods used in this study complied with state and federal laws
protecting threatened and endangered species under USFWS Threatened and Endangered
Species Permit TE149132-0, USDA, Forest Service Special Use Permit GRD111953,
and Scientific Research and Collecting Permits GUMO-2007-SCI-0004 and CAVE-
2007-SCI-0003 and were approved by the Sul Ross State University Animal Care and
Use Committee (SRSU-07001).
CHAPTER II
EVALUATION OF TWO MODELS USED TO PREDICT MEXICAN SPOTTED OWL
HABITAT IN THE GUADALUPE MOUNTAINS
Spotted owls are usually non-migratory, typically establishing and readily
defending territories that they remain faithful to for most, if not all of their lives (Forsman
et al. 1984, Gutierrez et al. 1995). It is evident that specific characteristics within a
landscape are preferred by Mexican spotted owls for nesting and breeding-season
roosting habitat (Ganey and Dick 1995). Slope, aspect, elevation, and vegetative
communities are a few of the variables indicative of Mexican spotted owl habitat and
territory locations (Ganey and Balda 1989, Ganey and Dick 1995, Grubb et al. 1997,
Ganey et al. 2000, Ward and Salas 2000). These characteristics can be readily mapped
and are often used to make initial assessments of landscapes potentially suitable for
supporting Mexican spotted owls. However, in landscapes like the Guadalupe
Mountains, dominated by rugged terrain, steep canyons, and lacking roads or trails,
inhibit the ability of surveyors to effectively verify potential territories (Narahashi 1998,
Kauffman 2005). With the availability of predictive habitat models such as the GHM and
UBM, surveys can be prioritized to target specific areas, and carried out with limited
funding, manpower, and field time by designing efficient methods to sample accessible,
predicted areas.
Johnson (2003) developed and validated the Southwestern Geophysical Habitat
Model (GHM) based on 626 daytime nesting and roosting locations (374 modeling
locations, 252 validation locations). Data were taken from surveys (up to 1994)
conducted during the breeding season (March through August) within Arizona, Colorado,
13
14
Colorado, New Mexico, Texas, and Utah. This model was designed to identify potential
Mexican spotted owl breeding-season habitat throughout the southwestern United States,
using variables derived from Universal Transverse Mercator (UTM) coordinates and 30-
m digital elevation model (DEM) data. These variables include longitude, latitude,
components of slope, local concavity and curvature, elevation, north-facing aspects,
long-term average summer and winter precipitation, pooling (intended to approximate
cool air and moisture in the landscape), and long-term average annual precipitation
(Johnson 2003).
The Utah-based Habitat Model (UBM) was developed to provide a defensible
habitat map depicting the extent of Mexican spotted owl habitat in southern Utah during
the breeding season (Willey et al. 2006). A set of a priori logistic regression models
predicting breeding-season habitat were developed from 30-m DEMs and remote sensing
imagery (Landsat 7 ETM+ sensor archives, June 2000) variables and then ranked for the
most parsimonious fit to the input data using Akaike's Information Criterion (AIC;
Burnham and Anderson 2002). Environmental associations between 30 occupied and 30
unoccupied habitats, taken from historical nighttime surveys, were compared to establish
habitat criteria. Akaike's Information Criterion weights were used to quantify the
relative importance of habitat variables, associations between variables, and identify
combinations of variables best suited for predicting Mexican spotted owl habitat (Willey
et al. 2006). These included: landscape ruggedness, slope, complexity, relative surface
heat and presence of cool zones, and a Modified Soil-Adjusted Vegetation Index
(MS AVI) for estimating vegetative cover. The model was then tested against 30 unique
Mexican spotted owl nighttime locations observed during a 2005 survey conducted in
southern Utah.
15
Although the GHM and UBM may be efficient tools for surveying potential
habitats in the Guadalupe Mountains, one must determine whether predictions made by
habitat models are effective within the region where they are being applied (Vaughn and
Ormerod 2003). By using more current data or datasets outside those used for model
development (e.g., known Mexican spotted owl nest and roost sites in the Guadalupe
Mountains), predictions can be tested to determine their effectiveness within the study
area of interest (Vaughn and Ormerod 2003). Sample results must also have the ability
to be extrapolated to other regions with similar predictions that were not included in the
sample design. This can be accomplished by designing a sampling procedure with a
randomized component within a target population and by using predicted habitat as a
means to estimate the probability of a site being occupied by a spotted owl. Occupancy
estimates determined within the parameters of the sample can then be inferred in other
areas of the Guadalupe Mountains under similar conditions (e.g., the rest of the target
population).
In this chapter, I compare the predictive efficiency of the GHM and UBM within
the Guadalupe Mountain range using 1) historical nest and breeding-season roost sites
(1994 to 2006) to test the percentages of breeding-season habitat predicted by both
models and 2) results of nighttime surveys conducted during the 2007 breeding season to
estimate occupancy as a function of predicted habitat. In the latter case, predictive
efficiency of the GHM and UBM was determined by developing a set of a priori
hypotheses (formalized as logistic-regression models), whereby the amount of habitat
predicted by either or both habitat models were treated as covariates. I used an
information theoretical approach to rank models according to the weight of supporting
evidence (Burnham and Anderson 2002, MacKenzie et al. 2006). This approach
16
generated estimates of detection probabilities and site occupancy for a single breeding
season in accessible regions of the Guadalupe Mountains. Combining these two
approaches of model evaluation also provided evidence of whether the GHM alone,
UBM alone, or both models together were more effective at predicting Mexican spotted
owl breeding-season habitat in the Guadalupe Mountains.
METHODS AND MATERIALS
I used the Southwestern Geophysical Habitat Model and Utah-based Habitat
Model to produce basic predictions of high- and low-quality habitat in the Guadalupe
Mountains. I used GIS software to manipulate each model's output according to its
suggested intervals of predicted habitat to produce a comparative map of these high- and
low-quality habitats. This habitat map allowed me to evaluate model predictions,
prioritize areas to conduct nighttime surveys, and quantify spatial data. I used
descriptive statistics to test model predictions and information criterion to determine
what model or combination of models was most effective for estimating Mexican spotted
owl occupancy.
The GHM is displayed as a grid-based raster image representing the distribution
of Mexican spotted owl breeding-season habitat within a landscape. Each grid cell is
assigned a number between 0 and 249 and partitioned into seven interval classes
representing a specific area of potential habitat (Johnson 2003; Table 1). Larger grid cell
numbers represent high-quality habitat and lower numbers represent low-quality or no
habitat. Each interval class is assigned a percentage of Mexican spotted owl nest and
roost sites expected to occur within the landscape. These percentages also imply an error
of omission. The error of omission is the percentage of nest and roost sites absent (i.e.,
17
omitted) from a predicted area. Errors are calculated as the difference between the
expected proportion of nest and roost sites and one (i.e., error of omission = 1 - p). For
example, the 210 to 249 interval predicts 30% of Mexican spotted owl nesting and
roosting locations (p = 0.3), it also implies a 70% error of omission (1 - 0.3 = 0.7; Table
1). Errors of omission can be calculated for any percentage assigned to predicted habitat.
Table 1. Interval classes of the Southwestern Geophysical Habitat Model (GHM),
including mapping colors and associated percentages of Mexican spotted owl nesting and
roosting sites expected to be present or absent (omitted; Johnson 2003).
Interval class
249-210 209-169 168-141 140-114 113-91 90-77 76-0
Color Red Orange Yellow Green Cyan Blue Gray
% expected 30% 30% 20% 10% 5% 2% 0%
% omitted 70% 70% 80% 90% 95% 98% 100%
Cumulative intervals can also be used to generate an optional index
characterizing a range of predicted Mexican spotted owl breeding habitat. When using
cumulative intervals, percentages of expected owl locations increase by increasing the
size of the interval (Johnson 2003; Table 2). Consequently, the area of predicted habitat
increases as more intervals are used to predict a larger percentage of owl locations. For
example, 80% of owl locations requires the cumulative interval 141 to 249, which
includes the total area predicted by the 210 to 249 (30%), 169 to 209 (30%), and 141 to
18
168 (20%) interval classes (Table 2 and Fig. 3). On a projected map, the GHM's interval
classes are symbolized as an array of seven colors (Fig. 3).
Table 2. Cumulative interval classes of the GHM representing predicted Mexican
spotted owl habitat with its associated percentage of owls expected to be present and
omitted from those intervals as generated and assigned by Johnson (2003).
Interval class
Cumulative 249-210 249-169 249-141 249-114 249-91 249-77 76-0
% expected 30% 60% 80% 90% 95% 97% 0%
% omitted 70% 40% 20% 10% 5% 2% 100%
The UBM is displayed as a raster image with continuous percentages of probable
Mexican spotted owl habitat assigned to every location. Willey et al. (2006) suggested
intervals of probabilities with an associated map displaying areas of predicted habitat
using five color-classes (Fig. 4). However, the continuous values enable the user to
select any range of percentages predicting the probability of Mexican spotted owl
19
210-249(30%)
169-209(30%)
141-168(20%)
114-140(10%)
91-113(5%)
77-90 (2%)
0-76 (not habitat)
Figure 3. Example of Johnson's (2003) Southwestern Geophysical Habitat Model
projection of predicted Mexican spotted owl habitat with assigned interval classes,
associated colors, and the percentage of Mexican spotted owls expected to be within
those interval classes (shown in parentheses).
20
Figure 4. Example of Willey et al.'s (2006) Utah-based Habitat Model projection of
predicted Mexican spotted owl habitat with selected intervals, colors, and associated
percentages of probability.
21
habitat along a scale between 0 and 100. These percentages also imply the same
calculated errors of omission explained earlier (Table 3).
Table 3. The continuous intervals of the Utah-based Habitat Model with their associated
color and percent probability of Mexican spotted owl habitat generated and assigned by
Willeyetal. (2006).
Interval class
Color Red Yellow Cyan Blue
% expected 100-91% 90-76% 75-51% 50-0%
% omitted 0-9% 10-24% 25-49% 50-100%
The UBM differs from the GHM in that it was developed using logistic-
regression, nighttime survey results, and MSAVI as an additional variable to topographic
and geomorphic data to predict Mexican spotted owl breeding-season habitat exclusively
in the canyons of southern Utah. Conversely, the GHM used site-specific records from
daytime locations throughout the southwestern United States and was based strictly on
topographic, geomorphic, and precipitation data (Johnson 2003, Willey et al. 2006).
Additionally, the UBM uses continuous percentages of probability of being Mexican
spotted owl habitat with an assigned confidence interval (95%), whereas the GHM's
percentages are assigned based on individual occurrence. This prevents the GHM's
percentages from being extrapolated to intervals other than the ones provided (Johnson
2003, Willey 2006).
The specific differences between the GHM's and UBM's assigned percentages
and their projections of predicted habitat required that model output be manipulated
22
according to their suggested intervals to make an appropriate comparison. To
standardize model output for comparison, I chose to separate model predictions into two
basic categories, high- and low-quality habitat. The GHM was used as a template for
defining high-quality habitat because it had an established percentage assigned to a
particular cumulative interval and became more inclusive as percentages increased.
Initial trials to determine which GHM interval to use as high-quality habitat revealed that
cumulative intervals with percentages > 90% included nearly all predicted areas of the
UBM >20%, making it difficult to distinguish one model from the other. Since the UBM
allows the user to select any percentage class along a continuous scale and the 80%
interval class of the GHM presents a more comparable display of both models, a
cumulative interval class for the GHM of 141 to 249 (80%) and a selected probability
class of 80 to 100% for the UBM were designated to represent high-quality breeding-
season habitat. By contrast, the lower intervals of the GHM (0 to 140) provided the
remaining 20% probability class, allowing the selection of the 0 to 20% probability class
for the UBM to define low-quality habitat for comparison. The UBM's remaining
percentage classes (21 to 79%) were categorized as medium-quality habitat. The UBM's
medium-quality habitat was only included for spatial uniformity in order to account for
gaps in area-specific calculations.
Four layers (i.e., strata) of overlapping and non-overlapping predicted Mexican
spotted owl habitat were produced as a result of restructuring the GHM and UBM into
high- and low-quality habitat. These included: 1) high-quality habitat predicted by GHM
alone; 2) high-quality habitat predicted by UBM alone; 3) overlapping high-quality
23
habitat predicted by both models; and 4) low-quality habitat predicted by both models.
All subsequent analyses were based on these four strata, with a focus on high-quality
habitat.
Model Validation and Comparison Based on Historical Data
I hypothesized that the GHM and UBM could accurately predict Mexican spotted
owl nest and roost sites in the Guadalupe Mountains. To test my hypothesis, I compared
the expected percentages of high- and low-quality habitat projected by the GHM and
UBM against known locations of Mexican spotted owl nest and roost sites in the
Guadalupe Mountains. I used historical data from surveys conducted between 1994 and
2006 throughout the study area. These nest and roost sites were more current than owl
locations used to develop the GHM and also provided a dataset well outside the region of
the UBM. Consequently, the efficiency of each model's predictions was determined by
how close expected percentages were to the observed proportion of daytime locations
within each stratum. I also determined which stratum was more strongly associated with
nest and roost sites in the Guadalupe Mountains. These results provided evidence of
how well the GHM and UBM predicted percentages of nest and roost sites, and which
model was the most efficient at doing so.
I expected the high-quality habitat predicted by the GHM to be more effective at
predicting historical daytime locations in the Guadalupe Mountains than the UBM. This
expectation was based on the fact that the GHM was generated from daytime location
data and specifically predicts nesting and roosting habitat over a range of habitat types,
varying from mixed-conifer forests to canyon systems, all represented in the Guadalupe
Mountains (Murphy 1984, Johnson 2003), whereas the UBM used nighttime data to
24
predict high-quality habitat in canyon systems alone. Additionally, overlapping
predictions of high-quality habitat likely contain variables consistent with the Mexican
spotted owl's general breeding-season habitat and project a much smaller spatial area of
potential habitat. For this reason, I predicted that the overlapping areas of high-quality
Mexican spotted owl habitat projected by the GHM and UBM would be more efficient at
predicting historical daytime locations than either model alone. Finally, I expected areas
of low-quality habitat predicted by both models to have no historical daytime locations.
All four strata were delineated using ESRI's ArcMap Ver. 9.2 software (available
from http://www.esri.com/software/) and the spatial data were then overlaid and clipped
to a digital map of the study area (Fig. 5). All historical nest and roost site records of
Mexican spotted owls (1994 - 2006) were compiled from the Resource Management
Databases of GUMO, CAVE, and GRD. Datasheets, technical reports, and field notes
were carefully examined to distinguish reliable records. The UTM coordinates and all
significant metadata of each reliable location were compiled into a spreadsheet and
imported as point features into ArcMap.
To test my hypothesis, I overlaid the point features of all nest- and roost-site data
onto the digital displays of high- and low-quality habitat in ArcMap. A 200-m radius
buffer was generated for each point feature to account for errors in recording or plotting
owl locations (Fig. 6). Nest and roost sites were selected by location in ArcMap to
determine the number of sites located within each of the four strata. The total number of
owl locations was used as the denominator, and the selected number of owl locations was
used as the numerator to give a percentage of daytime locations selected in each stratum.
I 1 Study area • § GHM • • U B M • • HQO LQO MQH
Figure 5. Distribution of predicted high-quality Mexican spotted owl habitat (£: 80%)
throughout the study area of the southern portion of the Guadalupe Mountains projected
by Johnson's (2003) Geophysical Habitat Model (GHM), Willey et al.'s (2006) Utah-
based Habitat Model, low-quality habitat (^ 20%) predicted by both models, and
medium-quality habitat (21-79%) predicted by the UBM.
0 0.
8 1.
6 ki
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1 1
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re 6
. D
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ites
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27
I calculated the area (km2) of each stratum within the 200-m radius buffer of each
nest and roost site to compare model predictions. The proportion of predicted habitat
within each buffer was calculated by dividing the amount of predicted habitat within
each buffer by the total area of the buffer (0.126 km2). All proportions of strata for nest
and roost sites were respectively added together, providing the weighted proportion of
predicted habitat projected by each stratum. These weighted proportions were then
compared to determine what proportion of each stratum had the strongest association to
nest and roost sites in the Guadalupe Mountains.
Estimating Occupancy and Detection Probabilities
Mexican spotted owls are nocturnal raptors known to defend their territory and
communicate using several types of vocalizations (Ganey 1990). According to methods
described for conducting nighttime inventories for Mexican spotted owls, a vocal
response indicates an individual is present within that particular area (USDA 1991).
MacKenzie et al. (2006) referred to this interpretation as evidence of a species' use of a
specific resource unit, which could be used to infer occupancy of a particular site. More
specifically, the history of detections observed within a sample unit over repeated
surveys can be used to determine the probability that a particular sample unit will be
occupied (MacKenzie et al. 2006).
However, one major challenge of conventional surveys for Mexican spotted owls
is determining whether sites without a response are occupied by owls that were simply
undetected or whether those sites are actually unoccupied. The conventional approach to
increasing the accuracy of site-occupancy determination based on vocal detections is to
visit potential habitats a minimum of three times over the course of a single breeding
28
season for two consecutive years, at which point sites without detections are considered
to be unoccupied (USDA 1991). Unfortunately, these inferences are still based on the
underlying assumption that detection is perfect (i.e., equal to 1.0). Although repeated
surveys would increase the probability of detecting a spotted owl, these methods do not
account for variation in survey effort (e.g., observer error) and owl behavior, which may
reduce detection probabilities and, ultimately, bias inferences of Mexican spotted owl
occupancy (USDA 1991, Olsen et al. 2005).
MacKenzie (2006) and MacKenzie et al. (2006) described methods for estimating
site occupancy with an emphasis on accounting for imperfect detection, which allows for
temporal and spatial variation in occupancy parameters (e.g., predicted habitat). These
methods are particularly useful for Mexican spotted owls because of their similarities to
field survey methods described by the Inventory for Mexican Spotted Owls (USDA
1991, MacKenzie 2006). MacKenzie et al. (2003) and Olsen et al. (2005) have both
effectively applied occupancy estimation models to studies of northern spotted owls,
which suggest that these methods could also be applicable to Mexican spotted owls.
Lavier (2005) has also applied this method to estimating relationships among forested
habitat features and site occupancy by Mexican spotted owls in the Sacramento
Mountains, New Mexico. For these reasons, I incorporated occupancy estimation
modeling with the spatially explicit predictions of the GHM and UBM to provide a more
robust investigation of where Mexican spotted owls and their breeding-season habitat are
distributed throughout the Guadalupe Mountains. This particular approach allowed me
to examine the utility of GIS-based habitat modeling as a tool for estimating species
occupancy.
For this portion of the study, I conducted a nighttime survey of the Guadalupe
Mountains during the 2007 breeding season (May through August) by incorporating
methods outlined by the Inventory for Mexican Spotted Owls (USDA 1991) and
MacKenzie et al. (2006). I focused nighttime surveys and occupancy estimates within
200-ha (2-km2) sample units throughout the study area. Sample units were surveyed
repeatedly within a single breeding-season to ensure a level of precision for estimating
occupancy (MacKenzie et al. 2003, MacKenzie and Royle 2005, MacKenzie 2005,
MacKenzie 2006). The single-species, single-season model outlined by MacKenzie et
al. (2006) provides an efficient method for estimating the occupancy of sample units
within a single breeding season with non-biased inferences.
I defined a sample unit as an area within the Guadalupe Mountains likely to be
occupied and defended by a Mexican spotted owl during the breeding season (i.e., a
breeding-season territory) in which a response could be detected. The Mexican Spotted
Owl Recovery Plan defined an "Activity Center" as a nest site, a roost grove used during
the breeding season, or the best nesting/roosting habitat in areas where such information
is lacking (USDI1995). According to this definition, I assumed an "Activity Center" to
be a significant portion of an owl's territory. Because no previous study had been
conducted to determine the size of a spotted owl territory in the Guadalupe Mountains
and because I conducted surveys without prior knowledge of nest and roost sites, I used
the latter portion of the Recovery Plan's definition to designate potential owl territories
(i.e., sample units) based on the four strata of predicted habitat described above.
I based the size of sample units on the results of previous studies in Arizona, New
Mexico, and Utah, where Mexican spotted owl breeding-season territories have been
defined (USDI 1995, Willey 1998, Ganey and Block 2005). The Recovery Plan
30
recommended that 243 ha be delineated for activity centers throughout the range of the
Mexican spotted owl (USDI1995). Willey (1998) found that Mexican spotted owls in
the canyon systems of southern Utah had a mean activity center size of 279 ha. Ganey
and Block (2005) found Mexican spotted owls using approximately 200 ha of mesic,
mixed-conifer forests within the Sacramento Mountains of New Mexico during the
breeding season. Consequently, a sample unit size of 200 ha (2 km2) in the Guadalupe
Mountains was considered to be an adequate size to include an owl's activity center and
conducive for complete vocal and audible coverage of survey sites (P. Ward, Mexican
Spotted Owl Recovery Team, pers. comm.).
I overlaid a grid consisting of 2-km2 cells onto the predicted habitat and study-
area map in ArcMap. Cells were initially selected so that a) high-quality habitat cells
had > 80% of their area containing high-quality habitat predicted by one or both models
(strata 1 through 3), b) low-quality habitat cells had > 99% of their area containing low-
quality habitat (stratum 4), and c) all cells were within administrative boundaries. All
sample units fitting these criteria were then numbered consecutively. Thirty sample units
(25 high-quality habitat cells and five low-quality habitat cells) with adequate access
were then selected, using a stratified, random-sampling technique (Fig. 7). The predicted
habitat map of each stratum was then clipped to the 30 sample units to establish
nighttime surveys. The 25 high-quality habitat sample units served as locations for
estimating site occupancy and detection probabilities based on high-quality habitat
predictions made by the GHM and UBM.
31
10 kilometers
1 jSample units • § ( <HM • | U B M ••JHQO ^ H L Q O
Figure 7. Placement of sample units where nighttime surveys were conducted to detect
and estimate site occupancy of Mexican spotted owls in the Guadalupe Mountains during
the 2007 breeding season (11 May to 28 August).
32
Consequently, inferences concerning occupancy and detection probabilities were
made only in regard to accessible locations and areas with 80% or more of a 2-km2 area
consisting of high-quality habitat within the study area. Additionally, low-quality habitat
was selected based on different criteria and, therefore, was tested as a separate
component of model validation and not included for occupancy estimation.
Mexican spotted owls can be located by imitating various vocalizations (hooting)
followed by listening for a response from specific vantage points (i.e., call stations) in a
sample unit (Forsman 1983, USDA 1991). Call stations were digitized as point features
within assigned sample units overlaid onto digital USGS 7.5" topographic maps of Texas
and New Mexico (available through http://www.tnris.state.tx.us and http://rgis.unm.edu)
in ArcMap, based on their accessibility and coverage of sample units. Call stations were
placed a maximum of 0.8 km apart within accessible areas along roads, trails, ridge tops,
and canyon bottoms. I positioned the call stations in this manner so that 1) all predicted
habitats within a sample unit were vocally covered, 2) all calls had an equal likelihood of
being heard by an owl within the grid cell, and 3) any response would have as equal
likelihood of being heard by surveyors (USDA 1991). The UTM coordinates of each
call station were recorded in ArcMap and entered into hand-held GPS units. These
coordinates were used in conjunction with a compass and respective analog topographic
maps to locate call stations in the field.
Surveys were conducted between 11 May and 28 August 2007 during the first
two hours following dusk and the last two-hours prior to dawn whenever possible,
although surveys were conducted any time possible during the night when adverse
weather conditions occurred (Forsman 1983). Hooting sessions lasted a maximum of 20
minutes at each call station. Four calls (male and female four-note, contact whistle, and
33
agitation call) were vocally imitated for 30 to 40 seconds with at least 60 seconds
between calls to listen for a response (Forsman 1983). Technicians hired to assist with
surveys were given a thorough training period by certified personnel in accordance with
the Mexican Spotted Owl Inventory Protocol (USDA 1991) and by standard operating
procedures outlined by the USFWS Threatened and Endangered Species Permit
(TE149132-0).
Because environmental conditions vary and the precision of predicting the
probability of Mexican spotted owl occupancy is dependent on the history of detections
and non-detections within a given sample unit (USDA 1991, MacKenzie and Royle
2005, Mackenzie 2006, MacKenzie et al. 2006), sample units were visited three times
throughout the breeding season. Each surveyor was assigned to survey every sample
unit at least once during the course of the breeding season to minimize heterogeneity in
sampling effort (MacKenzie 2006, MacKenzie et al. 2006). Nocturnal owl locations
were determined by estimating the distance from the surveyor to the responding owl with
an accompanying compass bearing (Ganey and Balda 1989). Field surveys were
conducted without previous knowledge of site occupancy or detection and were carried
out with the assumption that each visitation and sample unit was independent of one
another (MacKenzie 2005). It was also assumed that the population being surveyed was
closed to local extinction and colonization during the 2007 breeding season. This is a
reasonable assumption for spotted owls given their site-fidelity and other natural history
traits (Gutierrez et al. 1995).
Survey data were collected on datasheets modified from the Coordinated
Management, Monitoring, and Research Program developed to survey a population of
Mexican spotted owls in the Sacramento Mountains, New Mexico (Ward and Ganey
34
2004; Appendix Al). Recorded field data consisted of a start, end, call-response time,
date, sample unit code, call station identification number, observer(s), UTM coordinates,
species and sex of the responding owl, compass bearing, approximate distance to the
owl, personal comments, and an attached topographic map with the approximate location
of the responding owl. Locations of responding owls were then digitized as point
features in ArcMap, based upon angular and distance calculations made by observers on
topographic maps accompanying the datasheets.
Data Analysis
I used a combination of descriptive statistics and information criteria to test how
efficient the GHM and UBM were at predicting known Mexican spotted owl nest and
roost sites and at estimating Mexican spotted owl occupancy in the Guadalupe
Mountains, respectively. I used descriptive statistics to assess each model's efficiency
for predicting known Mexican spotted owl nest and roost sites in the Guadalupe
Mountains and to determine which model had the strongest association to those sites. I
used an information-theoretic approach advocated by Burnham and Anderson (2002) to
test for the most parsimonious fit of a priori models hypothesized to describe the
variation in occupancy and detection-probability estimates based on the amount of
predicted habitat and survey design, respectively.
Model validation and comparison based on historical data. I used a chi-square
goodness-of-fit test to determine whether the number of observed owl locations was
significantly different from the expected value predicted by the GHM (Zar 1999). I used
Yate's correction for continuity to compensate for samples less than five (%2 = 3.8416; a
= 0.00833; Zar 1999). I reported the number of daytime locations observed within strata
35
2 through 4 as a percentage of the total number of daytime locations observed.
Statistical tests to determine the difference from observed locations and expected values
for strata 2 through 4 could not be conducted based on two factors: 1) the UBM is
displayed as continuous data and cannot be tested against discrete data (i.e., number of
individual daytime locations) using traditional goodness-of-fit tests (B. Warnock and P.
Harveson, Sul Ross State University, pers. comm.) and 2) the predicted overlapping
high- and low-quality habitat essentially created a new predictive model without
predetermined percentages of expected Mexican spotted owl locations, making
comparisons between observed and expected values impossible (T. Johnson pers.
comm.). However, determining the proportion of known Mexican spotted owl daytime
locations in the Guadalupe Mountains observed within strata 2 through 4 provides an
initial validation of these models' effectiveness for predicting Mexican spotted owl nest
sites and breeding-season roost sites in this study area.
In spite of the limitations brought on by the manipulation of models, I was able to
determine whether historical nest and roost sites were associated with predicted habitat
and which model provided the strongest association of the four strata to those sites
within a 200-m radius. For this analysis, I used Fisher's exact test to determine whether
the observed association between strata and daytime Mexican spotted owl locations was
statistically significant with the weighted proportions of each stratum within a 200-m
radius of known nest and roost sites. I then used a Tukey-type, multiple-comparison test
between proportions to determine what stratum had the strongest association to observed
owl locations (Zar 1999).
Estimating occupancy and detection probabilities. Detection probabilities (p) are
an essential component for estimating site occupancy (\|/) by accounting for imperfect
detection of individual spotted owls. There are a number of reasons as to why a site is
unoccupied or a species was not detected within a given sample unit and, therefore, a
number of candidate models for estimating p from covariates. Burnham and Anderson
(2002) suggest that one should carefully consider a set of a priori candidate models and
determine the justification of these models for explaining possible outcomes. Anderson
et al. (2000) state that statistical null hypothesis testing has relatively little use for model
selection. They proposed using Chamberlin's (1965,1890) multiple working hypothesis
testing in conjunction with an information theoretical approach to select the "best fit"
hypothesis model given the observed data (Anderson et al. 2000). I, therefore, made
several a priori hypothesis models using logistic regression, proposing several factors
(i.e., covariates) that would possibly influence detection probabilities and occupancy.
The first hypothesis of detection and occupancy being constant (i.e., not influenced
by covariation or (30),
expPo/1+exppo (1.1)
was set as a standard point of reference to other models possibly influenced by
covariates. If this model is weighted distinguishably higher than models using predicted
habitat as covariates, then this would indicate that predicted habitat would be a poor
predictor of Mexican spotted owl occupancy. This would also be true for the parameters
hypothesized to influence detection probabilities.
Forsman et al. (1984) has noted that the vocal activity patterns of spotted owls
decrease as the breeding season approaches September. I therefore predicted that
detection probabilities were negatively correlated with survey period, SVP (i.e., a time
period in which all sample units are visited once);
37
exp(p0 - PiSVP)/1+ exp(p0 - PiSVP), (1.2)
where detection would decrease for all sample units during subsequent survey periods.
Sample units were coded with the appropriate survey-period number so that all cells
surveyed the first round of visits were given a value of 1. Sample units visited during
round two were coded as 2, and so on.
Considering all sample units could not be surveyed in a single night, the detection
probability for each sample unit would also be influenced by the day they were visited
throughout the breeding season. Thus, I hypothesized that the probability of detection
would be negatively correlated with visitation day, VSD (i.e., Julian days starting with
01 January = day 1 and 31 December = day 365);
exp(p0 - PiVSD) /1+ exp(p0 - PiVSD), (1.3)
where detection is expected to decrease for each sample unit as visitation days approach
the end of the breeding season (31 August = day 243). Model covariates of both SVP
and VSD provide different interpretations of how time influences detection probabilities.
By outlining detection probabilities based on a constant time for all sample units given a
survey period, as well as an individual time for each sample unit given the visitation day,
a more refined consideration of how detection probabilities are affected by time can be
assessed. Essentially, the question being asked is whether detection probabilities are
constant for survey periods or do they vary according to the specific day they were
surveyed. My hypothesis simply states that both survey period and visitation day
negatively influence detection based on the behavioral findings of Forsman et al. (1984).
38
Vocal coverage and an observer's ability to hear a response within a sample unit
can be dependent on the number of call stations assigned to that area. Although vocal
and audio coverage of a sample unit can be variable within canyons, I assumed that more
call stations (CST) within a sample unit would increase the probability of detection;
exp(p0 + PiCSTV 1+ exp(p0 + piCST). (1.4)
This particular hypothesis might be refuted if an increase of call stations (and hence
surveyor presence) caused spotted owls to cease calling. Without limits, a positive linear
relationship would support this hypothesis and the interpretation would be that saturation
of a sample unit with call stations would assure detection. However, the placement,
number, and configuration of call stations are likely dependent on the availability of
locations where call stations can be placed within a sample unit (e.g., ridge tops).
Consequently, an increased number of call stations may not cover any more area than
would a smaller number of call stations. I chose this particular hypothesis because there
was variability in the number of call stations I could place within particular sample units,
which had the possibility of influencing detection probabilities within particular sample
units.
The probability of detecting a species is known to vary among observers and
influence the detection probabilities in the auditory surveys of other organisms, such as
passerines and anurans. In this case, individual surveyors can vary in their ability to
elicit and detect owl calls. Likewise, detection of a species has also been known to
increase with the number of observers within a survey area (Nichols et al. 2000,
Diefenbach et al. 2003, Alldredge et al. 2006, Duchamp et al. 2006, Kissling and Garton
2006) because there are more observers watching and listening. As a result, I
39
hypothesized that detection probability would be positively correlated with the number
of observers (OBS) at a given sample unit;
exp(Po + piOBS) /1+ exp(p0 + piOBS). (1.5)
Occupancy was assumed to be influenced by the availability of habitat predicted
by the GHM and UBM. Thus, I hypothesized that occupancy would be positively
correlated with sample units that had more predicted high-quality habitat from the GHM,
UBM, and overlapping projections of both models (HQO; strata 1 through 3);
exp(|3o + PiGHM) /1+ exp(Po + PiGHM), (1.6)
exp(Po + PiUBM) /1+ exp(p0 + piUBM), (1.7)
exp(Po + PiHQO) /1+ exp(p0 + PiHQO). (1.8)
Furthermore, occupancy would likely be positively correlated to the total inclusion of
both model predictions (GHM-UBM) and therefore,
exp(Po + piGHM-UBM) • 0.9)
1+ exp(p0 + piGHM-UBM)
Low-probability habitat was simply analyzed by the detection or non-detection of
Mexican spotted owls. I predicted the five, low-probability habitat sample units to have
no detections throughout the course of the study (Tables 4, 5).
Tab
le 4
. Se
t of a
pri
ori h
ypot
hesi
s m
odel
s fo
r co
varia
tes
poss
ibly
influ
enci
ng d
etec
tion
prob
abili
ties
(p) f
or M
exic
an s
potte
d ow
l
resp
onse
s du
ring
a gi
ven
surv
ey p
erio
d or
with
in a
spec
ific
sam
ple
unit
in th
e G
uada
lupe
Mou
ntai
ns d
urin
g th
e 20
07 b
reed
ing
seas
on
(Mar
ch to
Aug
ust).
K =
num
ber o
f par
amet
ers.
Mod
el
Para
met
er
Mod
el s
truct
ure
K
Hyp
othe
sis
Det
l
Det
2
Det
3
PC)
p(sv
p)
p(vs
d)
exp(
p 0)/l
+exp
(po)
exp(
p 0 -
PiSV
P)/l+
exp(
Po -
PiS
VP)
exp(
p 0 -
PiV
SD)/
l+ex
p(p 0
- pi
VSD
)
1 Pr
obab
ility
of d
etec
tion
is c
onst
ant a
cros
s su
rvey
s an
d
sam
ple
units
2 Pr
obab
ility
of d
etec
tion
is n
egat
ivel
y co
rrel
ated
with
surv
ey p
erio
d (s
vp)
whe
re d
etec
tion
will
dec
reas
e fo
r al
l
sam
ple
units
dur
ing
subs
eque
nt s
urve
y pe
riod
s
2 Pr
obab
ility
of d
etec
tion
is n
egat
ivel
y co
rrel
ated
with
visi
tatio
n da
y (v
sd) w
here
det
ectio
n w
ill d
ecre
ase
for
each
sam
ple
unit
as v
isita
tion
days
app
roac
h th
e en
d of
the
bree
ding
sea
son
(Jul
ian
day
60 to
day
243
)
o
Tab
le 4
. Se
t of
a pr
iori
hyp
othe
sis
mod
els
for
cova
riat
es in
flue
ncin
g de
tect
ion
prob
abili
ties
(p)
for
Mex
ican
spo
tted
owl
resp
onse
s
duri
ng a
giv
en s
urve
y pe
riod
or
with
in a
spe
cifi
c sa
mpl
e un
it in
the
Gua
dalu
pe M
ount
ains
dur
ing
the
2007
bre
edin
g se
ason
(M
arch
to
Aug
ust)
. K =
num
ber
of p
aram
eter
s -
cont
inue
d.
Mod
el
Para
met
er
Mod
el s
truc
ture
K
H
ypot
hesi
s
Det
4 p(
cst)
ex
p(Po
+ P
iCST
)/l+
exp(
Po +
PiC
ST )
2
Prob
abili
ty o
f de
tect
ion
is p
ositi
vely
cor
rela
ted
wit
h th
e nu
mbe
r
of c
all
stat
ions
(es
t) w
here
the
dete
ctio
n of
a re
spon
se i
ncre
ases
wit
h th
e nu
mbe
r of
cal
l st
atio
ns w
ithin
a g
iven
sam
ple
unit
Det
5 p(
obs)
ex
p(p 0
+ P
iOB
S)/
l+ex
p(p 0
+ p
iOB
S)
2 Pr
obab
ility
of
dete
ctio
n is
pos
itive
ly c
orre
late
d w
ith
the
num
ber
of o
bser
vers
(ob
s) w
here
the
dete
ctio
n of
a r
espo
nse
incr
ease
s
with
an
incr
ease
d nu
mbe
r of
obs
erve
rs (
1, 2
, or
3 ob
serv
ers)
for
a gi
ven
sam
ple
unit
Tab
le 5
. Se
t of
a pr
iori
hyp
othe
sis
mod
els
for
cova
riat
es i
nflu
enci
ng o
ccup
ancy
(y)
of
Mex
ican
spo
tted
owls
wit
hin
sam
ple
units
in
the
Gua
dalu
pe M
ount
ains
dur
ing
the
2007
bre
edin
g se
ason
(M
arch
to A
ugus
t).
Not
e th
at m
odel
s O
cc2
- O
cc5
repr
esen
t th
e fo
ur
stra
ta u
sed
to v
alid
ate
the
Sout
hwes
tern
Geo
phys
ical
and
Uta
h-ba
sed
Hab
itat
Mod
els.
K =
num
ber
of p
aram
eter
s.
Mod
el
Para
met
er
Mod
el s
truc
ture
K
H
ypot
hesi
s
Occ
l
Occ
2
Occ
3
VO
\j/(g
hm)
\|/(u
bm)
exp(
p 0)/
l+ex
p(po
)
exp(
p 0+
PiG
HM
)/l+
exp(
Po+
PiG
HM
)
exp(
p 0+
PiU
BM
)/l+
exp(
p 0+
piU
BM
)
1 O
ccup
ancy
is
cons
tant
acr
oss
sam
ple
units
and
not
a fu
nctio
n of
pre
dict
ed h
abit
at
2 O
ccup
ancy
is
posi
tivel
y co
rrel
ated
with
the
amou
nt o
f ar
ea (
ha)
of h
igh-
qual
ity h
abita
t
pred
icte
d by
the
Geo
phys
ical
Hab
itat
Mod
el
(GH
M)
with
in e
ach
sam
ple
unit
2 O
ccup
ancy
is
posi
tivel
y co
rrel
ated
wit
h th
e
amou
nt o
f ar
ea (
ha)
of h
igh-
qual
ity h
abita
t
pred
icte
d by
the
Uta
h-ba
sed
Hab
itat
Mod
el
(UB
M)
with
in e
ach
sam
ple
unit
Tab
le 5
. Se
t of
a pr
iori
hyp
othe
sis
mod
els
for
cova
riat
es in
flue
ncin
g oc
cupa
ncy
(\|/)
of
Mex
ican
spo
tted
owls
wit
hin
sam
ple
units
in
the
Gua
dalu
pe M
ount
ains
dur
ing
the
2007
bre
edin
g se
ason
(M
arch
to A
ugus
t).
Not
e th
at m
odel
s O
cc2
- O
cc5
repr
esen
t the
fou
r
stra
ta u
sed
to v
alid
ate
the
Sout
hwes
tern
Geo
phys
ical
and
Uta
h-ba
sed
Hab
itat M
odel
s. K
= n
umbe
r of
par
amet
ers
- co
ntin
ued.
Mod
el
Para
met
er
Mod
el s
truc
ture
K
H
ypot
hesi
s
Occ
4 v|
/(hqo
) ex
p(po
+ p
iHQ
O)/
l+ex
p(p 0
+ P
iHQ
O)
Occ
5 \|/
(ghm
,ubm
) ex
p(p 0
+ P
iGH
M-U
BM
)/l+
exp(
p 0 +
piG
HM
-UB
M)
Occ
upan
cy i
s po
sitiv
ely
corr
elat
ed
with
the
amou
nt o
f ar
ea (
ha)
of
over
lapp
ing
high
-qua
lity
habi
tat
(hqo
) pre
dict
ed b
y th
e G
HM
and
UB
M w
ithi
n ea
ch s
ampl
e un
it
Occ
upan
cy i
s po
sitiv
ely
corr
elat
ed
with
the
amou
nt o
f ar
ea (
ha)
pred
icte
d by
bot
h th
e G
HM
and
UB
M (
ghm
-ubm
) w
ithin
eac
h
sam
ple
unit
44
I tested these hypotheses based on the detection histories of each sample unit
observed during the nighttime surveys of the 2007 breeding season. The area of each
model within each sample unit (in hectares) was calculated in ArcMap using the Utility
tool in ArcToolbox. These calculations were used to determine how Mexican spotted
owl occupancy within each sample unit was influenced by the amount of area of each
stratum. Covariates of detection probability were calculated using a simple count of
survey period (1, 2, 3), Julian visitation day (11 May = 131 to 28 August = 240), number
of observers (1, 2, 3), and number of call stations (1, 2, 3, 4) for each sample unit.
I then entered numerical data into Program PRESENCE ver. 2.0 (Hines 2006) to
estimate detection probabilities and the proportion of area occupied (PAO) by Mexican
spotted owls. The proportion of area occupied was calculated using the following
equation:
PAO = [£xv + EpOiOxi. J In, (1.10)
where the sum of sample units (x) with confirmed occupancy (\|/) are added to the
occupancy estimate (p(\|/)) of sample units without confirmed occupancy (1-\|/) and
divided by the total number of sites sampled (n). These calculations were compared to
the naive estimate of Mexican spotted owl occupancy, typically estimated as the
proportion of sites with confirmed detections divided by the total number of sites
sampled.
I applied the single-species, single-season model with the incorporation of
covariates and detection histories (MacKenzie et al. 2006; Appendices A2, A3).
PRESENCE utilizes Akaike's Information Criterion (AIC) to determine the "best fit
model" for estimating occupancy and detection probabilities given the most
45
parsimonious data applied to the a priori hypothesis models explained above using the
following equation:
AIC =-21og (8) + 2K. (1.11)
Akaike's Information Criterion provides an estimation of the expected distance relative
to the fitted model (-21og[0]) and the unknown infinite parameters (2K) actually
generating the observed data (Burnham and Anderson 2002). I used the second order
variant of AIC (AICC) derived by Sugiura (1978 as cited by Burnham and Anderson
2002) to correct for small sample size as follows:
AIC. = AIC + 2K/K+1) . (1.12) n - K - 1
taking into account the number of parameters (K) of a given model with respect to the
sample size (n).
Before calculating a priori models for occupancy, I calculated covariates of
detection probabilities and AICC with constant occupancy (\|/(.)) to obtain the top 95%
AICC weights (w) of detection probability covariates. I then incorporated the top-
ranking, detection-probability models with occupancy covariates to determine w and the
"best fit" model representing the observed results of nighttime surveys. For models with
closely ranking AICC values (i.e., AAICC < 2.0), I conducted a two-tailed, Pearson
correlation coefficient (r; a = 0.01) post hoc to determine whether the correlation
between the areas of predicted habitat with model covariates influenced the outcome of
AICC weights.
RESULTS
Model Validation and Comparison Based on Historical Data
A total of four nest sites and 27 roost sites (n = 31) were identified as reliable
historical daytime records. The high-quality habitat predicted by the GHM identified 25
(81%) nest and roost sites, which was not significantly different (given a = 0.05) from
the expected percentage of nest and roost sites predicted for the 80% interval class (%2 =
0.00, P = 1.00, df = 1). The high-quality habitat predicted by the UBM identified 18
(58%) nest and roost sites while projections of overlapping, predicted high-quality
habitat identified 15 (48%) nest and roost sites. Low-quality overlapping habitat
projected by both models identified only one roost site (3%). Two roost sites (6%) were
excluded from all high- and low-quality habitat predictions, but were identified by the
medium-quality habitats projected by the UBM.
The total areas of high-quality habitat predicted by the GHM, UBM, and
overlapping predictions (i.e., strata 1 through 3) within the study area were 160, 255, and
82 km2, respectively. The high-quality overlap had slightly greater relative densities of
nest-roost locations (0.18/km2) than the GHM (0.16/km2) or the UBM (0.07/km2).
According to Fisher's exact test, historical Mexican spotted owl nest and roost
sites were significantly associated with the proportion of predicted high-quality habitat
present within 200 m of historical locations (P < 0.0001, a = 0.05; Table 6). When all
four categories were compared, the proportion of high-quality overlapping habitat had a
significantly stronger association with historical nest and roost sites than all other
categories (P < 0.05). The q-value (derived from the Tukey-type test comparing stratum
1 to stratum 2) was 6.32 (qo.os, 5 = 6.29), indicating that the associations of these two
models to historical nest and roost sites were only slightly different from one another.
47
The proportion of low-quality habitat predicted by both models had the weakest
association to historical sites than all other categories (P > 0.05).
Table 6. Total number of Mexican spotted owl nest and roost sites (n = 31) in the
Guadalupe Mountains completely within the predicted high-quality habitats predicted by
the GHM, UBM, and high-quality overlapping predicted habitat projected by both
models (HQO), including the total area (km2) of predicted high-quality habitat, relative
density of nest and roost sites (n/km ), total area (km ) of predicted high-quality habitat
within a 200-m radius buffer surrounding nest and roost sites, and the weighted
proportion of habitat.
Nest/roost sites present
Total area
Relative density/km
Total area/200-m radius
Weighted proportion
GHMa
25
160
0.16
0.97
0.25
UBMa
18
255
0.07
0.40
0.10
HQOb
15
82
0.18
2.19
0.44
Models were not mutually exclusive;
b Sites also predicted by both GHM and UBM.
Estimating Occupancy and Detection Probabilities
A total of 142 survey nights were accomplished across three survey periods in 51
days between 11 May and 28 August 2007. Fourteen (56%) of the 25 high-quality
habitat sample units surveyed had one or more detections of Mexican spotted owls. Of
these sample units, seven (50%) had one or more owls recorded during all three survey
periods, three (21%) had one or more owls recorded during two of the three survey
periods, and four (29%) sample units had one or more owls recorded during only one
survey period. Mexican spotted owls were not detected within any of the five low-
quality habitat sample units during any of the three survey visits.
The probability of detecting Mexican spotted owls within sample units ranged
between 0.5 - 1.0. The greatest evidence (AICC weight = 0.33) was for the simpler
model that the probability of detection was constant during all survey visits and was
estimated to be 0.72 (Table 7). There was also some evidence that detection
probabilities decreased for all sample units as survey period (SVP) and individual
visitation day (VSD) approached September and that the probability of detection also
decreased with increasing number of observers (OBS) and call stations (CST) within
sample units. When calculated for parsimony, there was little distinction between the
AICC weights of constant detection probability and covariates of SVP, VSD, and CST
(AAICC < 2.00; Table 7). Additionally, model covariates of SVP and VSD had equal
AICC weights. The covariate OBS was the lowest weighted model and ranked
distinctively less (AAIQ < 2.00) than all other covariate models for detection
probability.
49
Table 7. Summary of model-selection procedure and detection probability estimates
with constant occupancy for factors hypothesized to affect the detection of Mexican
spotted owls within predicted habitat during the 2007 breeding season in the Guadalupe
Mountains. The factors considered for detection probabilities were visitation day (vsd),
survey period (svp), number of call stations (est), and number of observers (obs), and a
constant detection probability (p(.)). Reported is the relative difference in AICC values
compared to the top-ranked model (AAICC), AIC weights (w), number of parameters (K),
negative log-likelihood {-21), range of detection probability estimates (p), and the logistic
regression coefficient values (P) and standard errors (a).
Model AAIQ w K (-21) p Po(a) pi (a)
\|/(.),p(.) 0.00 0.33 2 82.05 0.72 0.96(0.37)
\|/(.),p(svp) 1.00 0.20 3 80.45 0.62-0.83 2.10(1.05) -0.54(0.44)
y(.),p(vsd) 1.00 0.20 3 80.45 0.56-0.88 4.16(0.82) -0.02(0.00)
\|/(.),p(cst) 1.25 0.18 3 80.70 0.45-0.80 1.91(0.94) -0.53(0.46)
y(.),p(obs) 2.42 0.10 3 81.87 0.65-0.75 1.37(1.05) -0.25(0.58)
The top ranking occupancy models were constant occupancy, UBM, GHM-
UBM, and HQO, all with constant detection probabilities, respectively. However,
constant occupancy carried only 30% of all AICC weights, whereas, the UBM made up
24% and the GHM-UBM and HQO respectively made up only 19% and 17% of all
50
model AICC weights. The GHM had the lowest cumulative weight (cumulative w = 0.10)
and differed markedly from the top ranking model (AAICC > 2.00; Table 8).
Table 8. Factors affecting the occupancy (\|/) and detection probability (p) of Mexican
spotted owls in the Guadalupe Mountains (2007), with constant model (i.e., v|/(.),p(.)).
The factors considered for occupancy are the proportion of high-quality habitat (80%
probability) predicted by GHM, UBM, overlapping high-quality habitat predicted by
both models (HQO), and by both habitat models (GHM-UBM). Factors considered for
detection probabilities are number of call stations (est), number of observers (obs), and
visitation day (vsd). Reported is the relative difference in AICC values compared to the
top-ranked model (AAICC), AICC model weights (w), the number of parameters (K), and
the negative log-likelihood (-21).
Model AAICc w K -21
\|/(.),p(.) 0.00 0.10 2 79.70
\Kubm),p(.) 0.25 0.09 3 82.05
y(ghm-ubm),p(.) 0.75 0.07 3 78.11
\|/(hqo),p(.) 0.96 0.06 3 78.23
\|/(.),p(svp) 1.00 0.06 3 78.27
\|/(.),p(vsd) 1.00 0.06 3 80.41
51
Table 8. Factors affecting the occupancy (i|/) and detection probability (p) of Mexican
spotted owls in the Guadalupe Mountains (2007), with constant model (i.e., \|/(.),p(.)).
The factors considered for occupancy are high-quality habitat (80% probability)
predicted by GHM, UBM, overlapping high-quality habitat predicted by both models
(HQO), and a combination of both habitat models (GHM-UBM). Factors considered for
detection probabilities are number of call stations (est), number of observers (obs), and
visitation day (vsd). Reported is the relative difference in AICC values compared to the
top-ranked model (AAICC), AIC model weights (w), the number of parameters (K), and
the negative log-likelihood (-21) - continued.
Model AAICc w K -21
\|/(.),p(cst) 1.25 0.05 3 80.45
y(ubm),p(svp) 1-51 0.05 4 80.45
\Kubm),p(vsd) 1.63 0.04 4 80.70
y(ubm),p(cst) 1.67 0.04 4 78.82
\|/(ghm-ubm),p(svp) 2.00 0.04 4 78.93
iKghm),p(.) 2.09 0.03 3 79.04
2.12 0.03 4 79.38 \|/(ghm-ubm),p(vsd)
\|/(ghm-ubm),p(cst) 2.12 0.03 4 79.57
Table 8. Factors affecting the occupancy (\|/) and detection probability (p) of Mexican
spotted owls in the Guadalupe Mountains (2007), with constant model (i.e., \|/(.),p(.)).
The factors considered for occupancy are high-quality habitat (80% probability)
predicted by GHM, UBM, overlapping high-quality habitat predicted by both models
(HQO), and a combination of both habitat models (GHM-UBM). Factors considered f<
detection probabilities are number of call stations (est), number of observers (obs), and
visitation day (vsd). Reported is the relative difference in AICC values compared to the
top-ranked model (AAICC), AIC model weights (w), the number of parameters (AT), and
the negative log-likelihood (-21) - continued.
Model AAICC w K -21
y(hqo),p(svp)
2 33 \|>(hqo),p(vsd)
\|/(.),p(obs) 2.42
y(hqo),p(cst) 2.44
\|/(ubm),p(obs) 2.97
\|/(ghm),p(svp) 3.35
v|/(ghm),p(vsd) 3.38
\|/(ghm-ubm),p(obs) 3.44
0.03 4 81.74
0.03 4 77.79
0.03 3 77.86
0.03 4 81.87
0.02 4 79.95
0.02 4 77.95
0.02 4 79.98
0.02 4 80.18
53
Table 8. Factors affecting the occupancy (\|/) and detection probability (p) of Mexican
spotted owls in the Guadalupe Mountains (2007), with constant model (i.e., \|/(.),p(.)).
The factors considered for occupancy are high-quality habitat (80% probability)
predicted by GHM, UBM, overlapping high-quality habitat predicted by both models
(HQO), and a combination of both habitat models (GHM-UBM). Factors considered for
detection probabilities are number of call stations (est), number of observers (obs), and
visitation day (vsd). Reported is the relative difference in AICC values compared to the
top-ranked model (AAICC), AIC model weights (w), the number of parameters (K), and
the negative log-likelihood (-21) - continued.
Model AAICo w K -21
\|/(ghm),p(cst) 3.58 0.02 4 80.26
\|/(hqo),p(obs) 3.66 0.02 4 79.25
\|/(ghm),p(obs) 4.76 0.01 4 81.36
Pearson's Correlation revealed that GHM-UBM was significantly positively
correlated with GHM (P = 0.000, a = 0.01), UBM (P = 0.001, a = 0.01), and HQO (P =
0.00, a = 0.01; Table 9). As well, HQO was significantly positively correlated with the
GHM (P = 0.002) and the UBM (P = 0.000). Because the UBM, HQO, and GHM-UBM
were not distinguishable from one another (AAICC < 2.00), I reported values of PAO and
individual sample unit occupancy based on the top ranking models for each covariate of
predicted high-quality habitat.
54
Table 9. Pearson correlation matrix of predicted high-quality Mexican spotted owl
habitat projected within 25 sample units surveyed in the Guadalupe Mountains during
the 2007 breeding season (May to August). Results apply to a two-tailed, normal
distribution.
Model GHM UBM HQO GHM-UBM
GHM 1.00 0.12 0.84** 0.77**
UBM 1.00 0.59 0.63
HQO 1.00 0.82**
GHM-UBM 1.00
** - Correlation is significant at the 99% confidence interval.
The total area of high-quality habitat across the total number of sample units was
5,305 ha. The UBM consisted of 2,834 ha of the sample unit area, while the GHM
consisted of the remaining 2,471 ha. Overlapping high-quality habitat consisted of 1,441
ha of the total area among sample units. The proportion of area occupied (PAO) by
Mexican spotted owls within accessible regions of predicted high-quality habitats in the
Guadalupe Mountains did not vary considerably between the top covariate models of
predicted habitat (Average PAO = 0.80; SD = 0.01). However, the PAO for each
covariate were considerably higher than the naive estimate of 0.56 (Table 10).
Tab
le 1
0. P
ropo
rtion
of a
rea
occu
pied
(PA
O) b
y M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe M
ount
ains
acc
ordi
ng to
cov
aria
tes
of
pred
icte
d hi
gh-q
ualit
y br
eedi
ng h
abita
t ran
ked
by d
iffer
ence
in
AIC
C (A
AIC
C) a
nd A
ICC w
eigh
t (w
). A
lso
repo
rted
are
the
num
ber
of
cova
riate
par
amet
ers
(K),
tota
l sam
ple
unit
area
(ha)
, the
sta
ndar
d er
ror
(SE
) of P
AO
, and
logi
stic
regr
essi
on c
oeff
icie
nt v
alue
s
(P) w
ith a
ssoc
iate
d st
anda
rd e
rror
s. N
aive
est
imat
e fo
r oc
cupa
ncy
was
0.5
6.
Mod
el
AA
IQ
w
K
PAO
(SE
) po
(SE
) Pi
(SE
)
V(u
bm),p
(.)
0.25
0.
09
3 0.
79(0
.02)
-2
.44(
0.95
) 0.
02(0
.01)
vKgh
m,u
bm),p
(.)
0.75
0.
07
3 0.
79(0
.03)
-3
.59(
0.82
) 0.
03(0
.01)
\|/(h
qo),p
(.)
0.96
0.
06
3 0.
80(0
.03)
-0
.72(
0.83
) 0.
02(0
.01)
y(gh
m),p
(.)
2.09
0.
03
3 0.
81 (
0.03
) -0
.43
(0.7
7)
0.01
(0.0
1)
56
Occupancy estimates for each sample unit increased as the respective proportions
of area of each predicted high-quality habitat model increased (Fig. 8; Appendix A4).
Therefore, occupancy estimates were positively correlated with larger amounts of
predicted high-quality habitat. The GHM had higher estimates of occupancy than the
UBM in sample units with less than 120 ha of high-quality habitat predicted by either
model, respectively. Conversely, the UBM had higher occupancy estimates than the
GHM for sample units where the amount of high-quality habitat was greater than 120 ha
per model. More importantly, sample units with larger amounts of high-quality
overlapping habitat (HQO) had higher occupancy estimates than sample units with larger
amounts of the GHM or UBM alone (Fig. 8).
DISCUSSION
This study was the first attempt to locate Mexican spotted owls and determine
their site occupancy in the Guadalupe Mountains using predictions of potential breeding-
season habitat generated by GIS-based habitat models. Additionally, no previous
attempts have been made to compare and contrast the efficiency of two models designed
to predict Mexican spotted owl breeding-season habitat in this region. The results of this
study show that: 1) the GHM's and UBM's high-quality habitat were effective at
locating a majority of known Mexican spotted owl nest and roost sites in the Guadalupe
Mountains, 2) predictive habitat models can be useful tools to inventory this threatened
species and estimate its occupancy based on model predictions, and 3) the overlapping
predictions of the GHM and UBM provide the most efficient model for predicting
Mexican spotted owl habitat in the Guadalupe Mountains.
&
0.9-
1
0.8-
0.7
0.6-
0.5-
§ 0.
4 H
B
n 0.
3 H
0.2
H
o.H
• +
• •
• ..
...
>. i
....
.
• H
QO
U
BM
• G
HM
Con
stan
t \|/
25
50
75
100
125
150
175
200
Pred
icte
d A
mou
nt o
f H
igh-
qual
ity H
abita
t (h
a)
Figu
re 8
. O
ccup
ancy
est
imat
es (v
y) fo
r 25
sam
ple
units
(200
ha/
sam
ple
unit)
sur
veye
d fo
r M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe
Mou
ntai
ns b
etw
een
Mar
ch a
nd A
ugus
t 200
7 co
mpa
red
to th
e ar
ea (h
a) o
f hig
h-qu
ality
bre
edin
g-se
ason
hab
itat p
redi
cted
by
the
GH
M,
UB
M, a
nd h
igh-
qual
ity o
verla
ppin
g ha
bita
t pre
dict
ed b
y bo
th m
odel
s (H
QO
).
58
Model Validation and Comparison Based on Historical Data
As predicted, the 141-249 (80%) interval class of the GHM was more efficient at
predicting individual daytime nest and roost sites than was the UBM's 80-100%
probability class. However, the overlap of both high-quality habitat strata was the most
efficient in predicting locations of nest and roost sites when the amount of area
encompassed by predicted habitat was considered.
Three important points may explain these results. First, Johnson (2003) used a
broader dataset of 626 daytime locations throughout the southwestern United States,
which likely provided a more uniform compilation of variables specifically describing
Mexican spotted owl nesting and roosting sites in the Guadalupe Mountains. Willey et al.
(2006) used 30 nighttime locations specific to the canyons of southern Utah, possibly
making the UBM's predictions of the owl's daytime roosting and nesting locations in the
Guadalupe Mountains less effective. Second, the GHM utilized eight locations known
prior to 1994 in the Guadalupe Mountains as part of the dataset used to generate and
validate model predictions (T. Johnson pers. comm.). Although eight is a small number
of locations compared to the total of 626 sites used to create the GHM, the use of
previously known locations in the Guadalupe Mountains may have increased the GHM's
effectiveness to predict nest and roost sites discovered in the study area between 1994
and 2006. Finally, the area of overlapping, predicted habitat combines the daytime-
based variables used to generate the GHM with the nighttime-based variables of the
UBM into one habitat map. The combination of the GHM and UBM may have provided
a more efficient model because of the additional information of spatial variables that
were more characteristic of Mexican spotted owl breeding-season habitat in the canyons
of the Guadalupe Mountains and because surveys conducted near dusk can elicit
responses of Mexican spotted owls when they are near their roosts or nests.
Both the GHM and UBM alone and the combined, non-overlapping high-quality
habitat (i.e., GHM-UBM) predicted a much larger area of potential spotted owl breeding-
season habitat than what is presently known of this species' distribution in the Guadalupe
Mountains. This suggests three general possibilities: 1) predicted areas may have more
nest and roost sites than what are currently known, 2) predicted locations are not
currently being used by spotted owls but additional habitat may be available for
dispersing offspring, or 3) predicted areas without known owl locations are not nesting
and roosting habitats.
Because of the ruggedness of the terrain and the lack of road and trail access in
the Guadalupe Mountains, previous surveys to locate nest and roost sites based on
nighttime responses have been unable to locate all daytime locations associated with
those responses. Consequently, the likelihood that there are more nest and roost sites is
evident, but whether these unknown locations are situated in areas displayed by the
GHM and UBM remains to be seen.
The places where nest and roost sites have been located in the Guadalupe
Mountains do have some distinct characteristics (see Chapter III), and it is known that
Mexican spotted owls, in general, display a preference for cool microclimates with
thermal cover provided by canyon walls and overstory tree canopy (Rinkevich and
Gutierrez 1996, Willey 1998, Ganey 2004). With the exception of a few areas, much of
the habitat predicted by the GHM consisted of large, exposed canyons, while the UBM,
on the other hand, predicted a large amount of open area along the eastern and western
escarpments of the Guadalupe Mountains range (T. Mullet pers. obs.). Both landscapes
60
are exposed to high ambient temperatures and strong winds. Based on these
observations, it is likely that a majority of the areas predicted by the GHM and UBM are
not specifically suitable for nesting and roosting habitat. Areas that are most suitable are
likely those immediately adjacent to or within areas of predicted as high-quality
overlapping habitat.
Overlapping high-quality habitat displayed by the intersection of both models
projected the smallest amount of area with relatively higher densities of daytime
locations and a stronger association with nest and roost sites. These results provide
supporting evidence of my initial prediction that overlapping high-quality habitat is more
efficient for predicting Mexican spotted owl daytime locations in the Guadalupe
Mountains than the GHM or UBM alone. These results also suggest that there may be a
mathematical algorithm that can be used to directly model and display the high-quality
overlapping habitat predicted by both models. Defining that algorithm was beyond the
scope of this study but if it can be developed, it may provide a more efficient tool
compared to projecting two separate models.
Estimating Occupancy and Detection Probabilities
The probability of detecting Mexican spotted owls in the Guadalupe Mountains
during the 2007 breeding season was < 1.0, providing evidence that there was imperfect
detection of vocal responses by Mexican spotted owls during nighttime surveys.
Although I attempted to distinguish some of the factors that could have influenced
variability in p, AICC weights for survey period (SVP), visitation day (VSD), and number
of call stations (CST) indicated that their effect on detection probabilities were similar.
61
The number of observers (OBS) ranked relatively lower (AAIC > 2.00) than other
covariates and therefore, possessed the least effect on detection probability.
Detection probabilities were negatively correlated with survey period and
visitation day. However, they both had an identical AICC value, which simply suggests
that the probability of detecting Mexican spotted owls generally decreased as surveys
approached the end of the breeding season. These results support the findings of
Forsman et al. (1984), who discovered that activity patterns of spotted owls (e.g., call
responses) increased in March and declined as the season approached October,
presumably with less need to defend an activity center or territory.
Conversely, detection probabilities also decreased with an increased number of
observers and call stations within sample units. Essentially, sample units with two to
three observers or three to four call stations had relatively lower detection probabilities
than sample units with one observer and one or two call stations. Although these results
contradict my original hypotheses, I believe the outcome can be explained by the terrain
of the areas sampled. Sample units where Mexican spotted owls were detected
possessed extremely steep canyons with very limited access. Bowden et al. (2003) found
a similar relationship between rough, roadless terrain and numbers of Mexican spotted
owls. Another possible explanation is that increased call stations and increased numbers
of observers may have swamped the survey period with too much stimulus, effectively
intimidating owls and inhibiting them from responding to vocal imitations given by
observers. Consequently, the number of accessible areas to establish call stations was
limited to only one or two vantage points on top of ridges. For logistical reasons, I
attempted to maximize manpower and the number of sample units surveyed within a
single night by assigning sample units with one or two call stations to a single individual.
62
Also, in many of these areas spotted owls vocalized so aggressively towards observers, a
single call station was able to elicit a response within the first 20-min time interval for
calling. I believe the most favorable explanation is related to rugged terrain and lack of
access.
Previous studies have reported increases in audio detections of focal species by
increasing the number of observers (Nichols et al. 2000, Diefenbach et al. 2003,
Alldredge et al. 2006, Duchamp et al. 2006, Kissling and Garton 2006). It is also
intuitive that increasing the number of call stations would increase vocal and audio
coverage of sample units, possibly increasing the probability of detection. Because
fewer stations and observers can reach less accessible areas, roughness of terrain may
also explain why my detection probabilities for CST and OBS were negatively
correlated.
Occupancy models, consisting of covariates of UBM, GHM-UBM, and HQO,
were not distinguishably better than one another (AAICC < 2.00) or the constant (no-
habitat-covariate) model. However, the GHM was distinctively less suitable for
explaining occupancy estimates (AAICC > 2.00). The results suggest that the UBM,
GHM-UBM, and HQO are better estimates of occupancy than the GHM but were
somewhat inconclusive as to what specific effect these models have on occupancy.
Logistic regression coefficient values for habitat model covariates (specifically Pi)
indicated that occupancy was positively correlated with predicted habitat. Also,
estimates of \\i were clearly correlated with each of the habitat covariates, indicating that
the predicted habitat amounts provided useful information about \|/. This is in explicit
conflict to the results suggesting that the constant model was equally informative.
However, the constant model explained less variation in the data and was likely weighted
63
slightly higher than the covariate models because of the need to estimate fewer
parameters from the same data.
Given the similarities between top ranking models with predicted-habitat
covariates, some correlation between the habitat amounts and AICC values may explain
the similarity in model weights. In fact, the amount of predicted habitat of the UBM was
significantly positively correlated with HQO and GHM-UBM. These results are
intuitive because both HQO and GHM-UBM consist of components specific to the
UBM. This correlation likely explains why models were ranked so similarly using AIC,
and, unlike the constant model, all of the top-weighted, habitat-covariate models had the
same number of parameters.
Although AIC provided supporting evidence of how predicted high-quality
habitat likely influenced occupancy estimates, the results did not explain which model
(GHM, UBM, or HQO) was most efficient for estimating Mexican spotted owl
occupancy in the Guadalupe Mountains. To answer this question, it was important to
consider the proportion of area occupied to the amount of habitat predicted by the GHM,
UBM, and HQO used to obtain those proportions.
The proportion of area occupied by Mexican spotted owls was similar across all
three high-quality habitat models providing evidence that all three models were effective
estimators of occupancy. However, given the area of predicted habitat, high-quality
overlapping habitat was the most efficient model for estimating Mexican spotted owl
occupancy in the Guadalupe Mountains, with an overall estimate of 0.80. This estimate
was larger than the nai've estimate of 0.56, indicating that 24% of sample units without
detections were actually occupied. Accounting for the imperfect detection of spotted owl
responses during nighttime surveys clearly increases the level of occupancy expected.
64
In summary, I found reasonable evidence supporting that the overlapping area
portrayed by the intersection of the Southwestern Geophysical Habitat Model and the
Utah-based Habitat Model provided the most efficient prediction of Mexican spotted owl
habitat in the Guadalupe Mountains. This overlapping prediction of high-quality habitat
was suitably associated with known locations of historical daytime roosts and nests used
by these owls and with the probability of site occupancy. The overall proportion of
accessible, predicted habitat, as displayed by the overlapping high-quality habitat model,
was 80% occupied during the 2007 breeding season. These results provided a
foundation for future survey and conservation efforts in the Guadalupe Mountains and,
possibly, for predicting habitat in other portions of the Mexican spotted owl's range.
CHAPTER III
MICROHABITAT FEATURES OF MEXICAN SPOTTED OWL
NEST AND ROOST SITES IN THE GUADALUPE MOUNTAINS
A majority of studies describing Mexican spotted owl habitat use and selection
throughout their range have focused on mixed-conifer forests (Ganey and Dick 1995).
Less attention has been concentrated on habitat characteristics in canyon systems
(Rinkevich and Gutierrez 1996, Johnson 1997, Willey 1998). This is largely due to the
urgency of and emphasis on understanding the impacts of timber harvest on forested
habitat of spotted owls in National Forests and because nest and roost sites within canyon
systems are inherently difficult to access (Rinkevich and Gutierrez 1996, Willey 1998).
As more spotted owls are being found in canyon systems, an understanding of their
habitat preferences in this type of environment will provide more comprehensive
knowledge of habitat selection and use toward conservation planning.
Hall et al. (1997) referred to microhabitat as finer-scaled features used by a
species within a landscape. Microhabitat is viewed as a relative term dependent on the
species of interest and should be defined explicitly for proper reference (Hall et al. 1997).
For this study, I defined microhabitat as the specific location used by Mexican spotted
owls for nesting and roosting. The features of these microhabitats would thus be focused
on the fine-scaled components of the landscape that elicit a settling response for nesting
and roosting behavior. Although locations of nest and roost sites of Mexican spotted
owls in the Guadalupe Mountains have been documented, site-specific, microhabitat
features that characterize these locations have not been collected and quantified.
Anecdotal descriptions have provided evidence of Mexican spotted owls nesting
65
66
nesting exclusively within caves and along cliffs of steep-walled canyons and roosting in
trees, cliffs, and caves within cool, shaded areas similar to those used for nesting sites
(Kauffman 1994, 2001, 2002, 2005, Narahashi 1998). More importantly, microhabitat
features at nest and roost sites in the canyons of the Guadalupe Mountains appear to be
unique in terms of their canyon morphology and vegetative structure compared to
locations up and down canyon (T. Mullet pers. obs.). Collection and analysis of
microhabitat features of nest and roost sites will help quantify and distinguish differences
in local microhabitat conditions and enable comparisons to other studies conducted in
canyon regions that have been carried out using similar methods.
My purpose for this portion of my study was to provide an initial description of
the microhabitat characteristics of Mexican spotted owl nest and breeding-season roost
sites within the Guadalupe Mountains. I accomplished this by quantifying the fine-
scaled microhabitat characteristics at an accessible sample of nest, roost, and associated
random sites up and down canyon. Additionally, I compared my results to previous
studies that used compatible methods for describing nesting and roosting habitat of
Mexican spotted owls dwelling in canyons systems in other geographic regions
(Rinkevich and Gutierrez 1996, Johnson 1997, Willey 1998, Willey et al. 2001).
METHODS AND MATERIALS
All historical nest and roost site records of Mexican spotted owls (1994 to 2005)
were compiled from the Resource Management Databases of GUMO, CAVE, and GRD.
Datasheets, technical reports, and field notes were carefully examined to compile reliable
records. Universal Transverse Mercator (UTM) coordinates of spotted owl nest and
roost locations and all relevant metadata of each reliable record were imported as point
features into ArcMap. The point features (i.e., nest and roost sites) were then overlaid
onto digital USGS 7.5" topographical maps of Texas and New Mexico in the same
coordinate system (North American Datum 1927) that nest and roost sites were
originally recorded in the field.
Initially, I determined access to nest and roost sites by the distance of points from
trails and roads (all of which were within 2.5 to 3.0 km), but it soon became apparent
that access to most sites was greatly influenced by the ruggedness of the terrain. An
additional review of field notes and personal communication with resource managers (F.
Armstrong; L. Paul; and R. West) revealed that many sites were only accessible by
means of rappelling or technical rock climbing. I eliminated these sites from sampling
because the training and safety requirements exceeded my experience. Additionally, I
eliminated nest and roost sites < 200 m from the most current record from sampling to
reduce pseudo-replication of multiple sites that may have been used by the same
individual owl or pair. I entered UTM coordinates of the remaining nest and roost sites
into a hand-held Geographical Positioning System (GPS) and used that information in
combination with USGS 7.5" topographic maps to locate nest and roost sites for field
sampling.
I measured microhabitat variables and recorded their values on datasheets at
accessible nest and roost sites, as well as at coinciding, random sites established up and
down canyon from each sampled nest or roost site (Appendix A5). Because nest sites
were located along cliff ledges above the canyon bottom and reported roost trees lacked
both sufficient site descriptions and GPS coordinates (due to canyon geomorphology
inhibiting satellite imagery), I conducted sampling from the canyon bottom to the nearest
nest and roost location indicated by the GPS coordinates. I located random sites by
68
pacing random distances up and down canyon to the nearest 10 m, selected from a
random numbers table (50 to 100 m), and sampled those sites the same day as their
coinciding nest or roost site.
I recorded two separate data sets of microhabitat features for all sites. These
included 1) geomorphic features describing the surface of the Earth associated with nest
and roost sites and 2) vegetative and surface features describing the vegetative
community structure, ground-cover types, and species associated with nest and roost
sites. Variables measured for each data set were based on similar variables recorded by
Johnson (1997).
Geomorphic Features
I sampled eight geomorphic variables at all accessible nest, roost, and random
sample sites to describe the concavity and curvature of canyons where spotted owls have
been known to nest or roost (Table 11). These variables included the width and depth of
the canyon, aspect of the sample site and the canyon drainage, and elevation of the
canyon bottom.
69
Table 11. List of geomorphic variables measured at nest, breeding-season roost, and
random-sample sites up and down canyon in an attempt to describe Mexican spotted owl
microhabitat in the canyons of the Guadalupe Mountains.
Sample Variable Description Measuring Method
Canyon width
Canyon bottom Width of the canyon drainage channel in
meters at canyon bottom
24-m contour Width of canyon in meters surrounding
sample plots at the 24-m contour
41 -m contour Width of canyon in meters surrounding
sample plots at the 41-m contour
Canyon depth Average of canyon wall heights in meters
measured from canyon bottom to the rim of
the canyon's walls to the left and right of the
sample site. Heights were calculated by
subtracting the elevation at canyon rim by
that of the canyon rim.
Meter measuring
tape
Measuring tool and
digital USGS 7.5"
topographic maps
Measuring tool and
digital USGS 7.5"
topographic maps
in ArcMap
Measuring tool,
digital elevation
models, digital
USGS 7.5"
topographic maps
in ArcMap
70
Table 11. List of geomorphic variables measured at nest, breeding-season roost, and
random-sample sites up and down canyon in an attempt to describe Mexican spotted owl
microhabitat in the canyons of the Guadalupe Mountains - continued.
Sample Variable Description Measuring Method
Aspect The aspect the nest, roost, or up and down
canyon sample plot is facing in radian
degrees
Aspect of Aspect of the canyon drainage at canyon
canyon drainage bottom in radian degrees facing up and
down canyon from sample site
Elevation Elevation at canyon bottom to the closest
40-ft contour (12 m) to each sample site's
UTM, rounded to the nearest meter
Compass bearing
Compass bearing
Taken from digital
USGS 7.5"
topographic maps
I recorded the width of the canyon drainage at canyon-bottom to the nearest
meter in the field with a 50-m measuring tape. I measured canyon width above the
canyon bottom to the nearest meter using the linear map distance between contour lines
at 24 m (80 feet) and 41 m (160 feet) above the canyon bottom for each sampled site
using the measuring tool on digital topographic maps in ArcMap. I estimated canyon
depths of nest, roost, and random sample sites to the closest meter by measuring the
heights of the canyon walls to the left and right of sample sites from the canyon bottom
using digital topographic maps and digital elevation models (DEMs) in ArcMap and then
averaging between both sides. I used a compass to record aspect (to the nearest radian
71
degree) of the canyon wall associated with the nest and roost site, as well as, the aspects
of the canyon drainage facing up and down canyon from sample sites. I used digital
topographic maps in ArcMap to measure the elevations of nest, roost, and random
sample sites to the closest 40-ft contour (12 m) at each sample site's UTM location,
rounded to the nearest meter.
Vegetative and Surface Features
I measured thirteen vegetative and surface variables at all accessible nest, roost,
and random sample sites to quantitatively describe the vegetative community structure,
ground-cover types, and species present at sample sites (Table 12). These variables were
sampled within a 0.01-ha plot established 5 m from the edge of the canyon drainage in
the direction of the nest or roost site. I used this point as the plot center. I applied the
same method to random plots up and down canyon for consistency.
Table 12. List of vegetative and surface variables measured at nest, breeding-season
roost, and random sample sites up and down canyon, intended to characterize Mexican
spotted owl microhabitat in the canyons of the Guadalupe Mountains.
Sample Variable Description Measuring Method
Canopy cover Presence of canopy cover provided by tree Percent cover on
foliage or canyon walls line-point intercept
Sapling Presence of tree saplings with Percent cover on
diameters < 10 cm line-point intercept
72
Table 12. List of vegetative and surface variables measured at nest, breeding-season
roost, and random sample sites up and down canyon, intended to characterize Mexican
spotted owl microhabitat in the canyons of the Guadalupe Mountains - continued.
Sample Variable Description Measuring Method
Shrub Presence of shrubs with
diameters < 10 cm and heights < 3 m
Herbaceous Presence of forbs, grass, lichen, or
moss at each meter
Canyon wall Presence of canyon wall intersecting or
obstructing sample plot
Rocky debris Presence of large boulders or small rocks
Woody debris Presence of fallen snags or branches
Bare ground Presence of exposed soil, duff, or leaf-litter
Water Presence of a stream or a significant
pool of water
Percent cover on
line-point intercept
Percent cover on
line-point intercept
Percent cover on
line-point intercept
Percent cover on
line-point intercept
Percent cover on
line-point intercept
Percent cover on
line-point intercept
Percent cover on
line-point intercept
73
Table 12. List of vegetative and surface variables measured at nest, breeding-season
roost, and random sample sites up and down canyon, intended to characterize Mexican
spotted owl microhabitat in the canyons of the Guadalupe Mountains - continued.
Sample Variable Description Measuring Method
Layer height
Layer 1
Layer 2
Layer 3
Tree species
diameter
Tallest visible layer of vegetation
Second tallest layer of vegetation
distinguishably lower than Layer 1
Third tallest layer of vegetation
distinguishably lower than Layer 2
Diameter (cm) at breast height (dbh) for
commercial valued trees3 or root-collar
diameter (red) for non-commercial valued
trees
Height relative to
observer (~ 1.8 m)
Height relative to
observer (~ 1.8 m)
Height relative to
observer (~ 1.8 m)
Diameter tape
a Includes Douglas fir, southwestern white pine, ponderosa pine, and Gambel oak
Includes western hop-hornbeam, chinkapin oak, big-toothed maple, Juniperus sp., etc.
A review of studies reported in the Recovery Plan suggested that vegetative
layering is likely an important characteristic for eliciting a settling response in spotted
owls (Ganey and Dick 1995) Therefore, the heights of the three tallest vegetative layers
(>1 m) were estimated to the nearest meter by comparing the relative height of each layer
to my own height (approximately 1.8 m tall). I defined vegetative layer height at each
site independent from that of other sample sites using the following criteria: Layer 1 =
the tallest visible layer of vegetation, Layer 2 = the second-tallest layer of vegetation
distinguishably lower than Layer 1, and Layer 3 = the third-tallest layer of vegetation
distinguishably lower than Layer 2. In other words, the height of Layer 2 was dependent
on the height of Layer 1 and the height of Layer 3 was dependent on that of Layer 2. No
layer where vegetation was < 1 m tall.
Consequently, I anticipated several combinations of layer height. For example,
Site A could have a layer combination of Layer 1 = the tallest trees, Layer 2 = saplings,
and Layer 3 = shrubs; Site B contains no trees but has saplings as the tallest visible layer
and therefore has a combination of Layer 1 = the tallest saplings, Layer 2 = shrubs, and
Layer 3 is absent. I used this method of assigning layer heights relative to the vegetation
present at sample sites because of the anticipated variability in sample site vegetation. It
also enabled flexibility in evaluating the differences or similarities of vegetative layering
among nest, roost, and random sites up and down canyon. Additionally, I identified and
recorded plant species of each layer using Powell (1998).
Vegetation can be directly measured with a high level of accuracy and precision
using the line-intercept method (Higgins et al. 1996). Point-intercepts are typically used
along a transect and provide rapid assessment of vegetative cover with comparable
estimates to that of the line-intercept method (Higgins et al. 1996). Incorporating both
methods (i.e., line-point intercept) enables a time-efficient and precise method for
estimating ground and canopy cover (Floyd and Anderson 1987). Consequently, I used
the line-point intercept method to measure the presence (1) and absence (0) of
vegetative- and surface-cover classes at 1-m intervals along 2-perpendicular, 10-m
transects intersecting the center of the 0.01-ha sample plot. I established the first transect
75
line along a randomly selected compass bearing. I offset the second transect line by 90°
from the randomly selected compass bearing.
Vora (1988) recommended that ocular methods of estimating canopy cover be
used versus that of a densitometer when understory vegetation is clumped or if available
field time limits sample size. Considering access to sample sites was largely a time-
limiting factor and that vegetative cover was expected to be clumped due to the
geomorphology of canyons (T. Mullet pers. obs.), I used an ocular method for estimating
the presence of canopy cover. I accomplished this by placing a 1.5-mm radius PVC
tube, approximately 13 cm long, over each meter interval and peering upwards through
the tube at a 90° angle from the transect. I fashioned cross-hairs by overlaying two cords
of waxed hemp over the opening of the tube at 90° angles with a pendulum hanging from
the middle. I was able to peer through the tube at a precise angle when the pendulum
aligned with the cross-hairs.
Ground cover variables included saplings (trees < 10-cm diameter), shrubs,
herbaceous cover (e.g., forbs, grasses, lichens, or mosses), canyon wall (sheer cliff-face),
rocky debris (e.g., boulders and smaller rocks), woody debris (e.g., fallen snags and dead
branches), bare ground (e.g., exposed soil, duff, or leaf-litter), and water. I recorded
presence of ground cover variables by visually observing a variable overlapping the
transect meter-mark at 1 -m intervals.
Additionally, I recorded the composition of tree species with diameters > 10 cm
and their associated layer within each 0.01-ha plot. I measured diameter at breast height
(dbh) for all timber valued species (e.g., Douglas-fir, southwestern white pine, ponderosa
pine, and Gambel oak) and snags, while root-collar diameter (red) was measured for non-
timber valued trees (e.g., western hop-hornbeam [Ostrya knowltonii], big-toothed maple
76
[Acer grandidentatum], chinkapin oak [Quercus muehlenbergii], Juniperus sp., etc.).
Finally, I took 4 digital photographs (sample plot, up canyon, cross canyon, and down
canyon) of each sampled site from the canyon bottom for pictorial description
(Appendices A6, A7, A8).
Data Analysis
I calculated descriptive statistics (e.g., mean, standard deviation, and range) for
all variables to describe the general characteristics of nest, roost, and random sites up and
down canyon. I calculated percent cover as the cumulative number of each variable
present along both 10-m transects within the 0.01-ha sample plot divided by 19 (because
the fifth intercept of both transects had the same value). I then averaged these
percentages across nest, roost, and random sample sites up and down canyon for
comparison. I calculated all descriptive statistics using Microsoft Office Excel 2003 and
SPSS 15.0 for Windows.
I combined nest and roost sites for comparison to random-sample sites up and
down canyon. I calculated inferential statistics to determine whether the means of
measured variables at nest and roost sites were significantly different from those of
random-canyon sites. I used a Kolmogrov-Smirnov test to determine whether sampled
data existed within a normal distribution (Zar 1999). I compared and tested normally
distributed data using a parametric one-way ANOVA to determine whether nest and
roost sites were significantly different from random sample sites (Zar 1999). I analyzed
data that were not normally distributed using a non-parametric Kruskall-Wallis ranked
test to determine significance among data (Zar 1999). In situations where the data were
77
significant (p < 0.05) I used a Mann-Whitney test to determine the significant difference
between nest and roost sites from random-sample sites up and down canyon (Zar 1999).
I calculated the mean aspects (i.e., mean angle) of nest, roost, and associated
random-samples sites, as well as, canyon drainage aspects - facing up and down canyon
- using methods outlined by Zar (1999). I compared the means of aspects distributed
normally using a parametric Watson-Williams test for multiple samples (Zar 1999) to
determine the differences between nest and roost sites and random sites up and down
canyon. I compared mean aspects that were not normally distributed with a non-
parametric Watson's two-sample U test, where I compared aspects of nest and roost
sites to up canyon and down canyon sample sites separately because a multi-sample test
could not be used for samples with tied data (Zar 1999).
I calculated differences in layer-species composition of nest and roost sites
sampled from 0.01-ha plots by comparing the total number of each species present
within each layer to the corresponding layers of random sites using non-parametric
statistics (i.e., Kruskal-Wallis ranked test). I also calculated the evenness of species
observed within each layer of sample sites using the Shannon-Weaver's index (Zar
1999). I calculated inferential statistics using Microsoft Office Excel 2003 and SPSS
15.0 for Windows.
RESULTS
I identified a total of four nest and 27 roost sites (n = 31) as reliable historical
records. Of these sites, 13 were considered accessible after careful review of digital
topographic maps. However, field attempts later revealed that only eight of these sites
were accessible by foot and consequentially sampled. I encountered and sampled two
78
additional roosts with owls present in the field, resulting in a total sample size of 2 nest
and 8 roost sites (n = 10). The total number of random sites sampled included 10 up
canyon and 10 down canyon, each of which were paired with a given nest or roost site.
The distance between random sites and nest or roost sites ranged from 50 paced-m to 100
paced-m.
I visited all nest, roost, and random sample sites between April and October
2007. Because of the predominance of evergreen species and the manner in which data
were collected, I assumed that variations in foliage over this time period did not
significantly alter the outcome of the data (e.g., canopy and ground cover).
Geomorphic Features
Average canyon width of nest and roost sites at canyon bottom was 9.5 m (SD =
5.06). Canyon width at the 24- and 41-m contours averaged 65 (SD = 26.8) and 139 m
(SD = 49.2), respectively. Canyon depth (from canyon-bottom to the rim) ranged from
52 to 400 m (Mean = 172 m, SD = 110.1). The average aspect of nest and roost sites was
154° (SD = 62°). Nest- and roost-site elevations at canyon bottom ranged from 1,512 to
2,486 m (Mean = 1,897 m, SD = 279.5). Aspects of the canyon drainage for nest and
roost sites facing up canyon averaged 96° (SD = 73°), while aspects facing down canyon
averaged 169° (SD = 74°; Table 13). There were no significant differences (P < 0.05)
between nest/roost sites and random sites up and down canyon when geomorphic
features were compared.
Table 13. Summary of the means and standard deviations (in parentheses) of
geomorphic features measured at historical Mexican spotted owl nest and roost sites and
random sites up (« = 10) and down canyon (n = 10 for each sample) in the Guadalupe
Mountains of southeastern New Mexico and West Texas. None of the differences were
statistically significant (P > 0.05).
Sample variable ~ Nest/roost T T Down canyon . Up canyon
sues
Canyon width (m) at 24-m contour 84(48.0) 65(26.8) 54(16.3)
Canyon width (m) at 41-m contour 153(66.0) 139(49.2) 115(26.9)
Canyon width (m) at bottom
Canyon depth (m)
Elevation (m)
Aspect (°)
Aspect of drainage facing
up canyon (°)
Aspect of drainage facing
down canyon (°)
9.5(6.11) 9.50 (5.06) 7.5 (3.50)
184(119.5) 172(110.1) 168(116.2)
1,882 (286.0) 1,897 (279.5) 1,905 (284.8)
100(79)
15 (76)
115(79)
154(62)
96 (73)
169 (74)
42 (66)
346 (69)
107 (72)
Vegetative and Surface Features
Canopy cover sheltered approximately 75% of the understory at nest and roost
sites (Mean = 74.7% SD = 43.57) and more than half the understory was covered by
saplings (Mean = 62.6%, SD = 48.51) and rocky debris (Mean = 58.4%, SD = 49.42).
Bare ground covered 50.5% (SD = 50.13) of nest and roost sites while woody debris
80
(Mean = 13.7%, SD = 34.46), shrubs (Mean = 8.4%, SD = 27.8), and herbaceous cover
(Mean =11.6%, SD = 32.08) made up less than 25% of the understory. Sheer canyon
walls made up only 2.6% (SD = 16.05) of nest and roost sites (Fig. 9).
Layer 1 of nest and roost sites consisted of seven tree species with diameters > 10
cm (Mean = 21.4 cm, SD = 10.45, species evenness = 0.95) and heights ranging from 2
m to 15 m (Mean = 7 m, SD = 4.5). Layer 2 was comprised of six species including
three Rocky Mountain junipers with diameters > 10 cm (Mean red = 14.0 cm, SD < 0.01)
and a single snag (dbh = 25.0 cm). The average height of Layer 2 was 4 m (SD = 2.7),
with an evenness of 0.83. Big-toothed maple was the predominant species among all
sample sites in Layer 2. Layer 3 consisted of six species of plants with only a single
individual of Chinkapin oak with a diameter > 10 cm (red = 23.0 cm). Average height
was 2 m (SD = 1.7) and had a species evenness of 0.98 (Tables 14,15; Fig. 10).
90%
Per
cent
co
ver
£3 D
own
cany
on
•Nes
t an
d ro
ost s
ites
•Up
cany
on
Can
opy
cove
r Sa
plin
gs
Shru
bs
Her
bace
ous
Can
yon
wal
l R
ocky
deb
ris
Woo
dy d
ebris
B
are
grou
nd
cove
r S
ampl
e va
riab
le
Figu
re 9
. C
ompa
riso
n of
mic
roha
bita
t ve
geta
tive
and
surf
ace
feat
ures
pre
sent
at n
est,
roos
t, an
d ra
ndom
0.0
1-ha
sam
ple
plot
s in
the
cany
ons
of th
e G
uada
lupe
Mou
ntai
ns.
Let
ters
at b
ars
indi
cate
sig
nifi
cant
dif
fere
nces
(P
< 0
.05)
. 0
0
Tab
le 1
4. T
he c
ompo
sitio
n of
veg
etat
ive
spec
ies
and
the
num
ber
of s
ampl
e si
tes
whe
re th
ey w
ere
obse
rved
wit
hin
the
thre
e ta
lles
t
laye
rs o
f M
exic
an s
potte
d ow
l nes
t and
roo
st s
ites
and
rand
om s
ampl
e si
tes
up a
nd d
own
cany
on i
n th
e G
uada
lupe
Mou
ntai
ns.
Lay
er 1
L
ayer
2
Lay
er 3
Dow
n N
est/
Up
Dow
n N
est/
U
p D
own
Nes
t/
Up
Spec
ies
Can
yon
Roo
st
Can
yon
Can
yon
Roo
st
Can
yon
Can
yon
Roo
st
Can
yon
Ace
r gr
andi
dent
atum
2
2 3
56
33
2
Cel
tis
laev
igat
a va
r. r
etic
ulat
a 3
1
Cer
coca
rpus
mon
tanu
s
Das
ylir
ion
whe
eler
i 1
Juni
peru
s de
ppea
na
1 1
Juni
peru
s sc
opul
orum
1
1
Ost
rya
know
lton
ii
14
2 2
3 3
1 1
Tab
le 1
4. T
he c
ompo
sitio
n of
veg
etat
ive
spec
ies
and
the
num
ber
of s
ampl
e si
tes
whe
re th
ey w
ere
obse
rved
wit
hin
the
thre
e ta
llest
laye
rs o
f M
exic
an s
potte
d ow
l nes
t and
roos
t si
tes
and
rand
om s
ampl
e si
tes
up a
nd d
own
cany
on i
n th
e G
uada
lupe
Mou
ntai
ns -
cont
inue
d.
Spec
ies
Lay
er 1
L
ayer
2
Lay
er 3
Dow
n N
est/
Up
Can
yon
Roo
st
Can
yon
Dow
n N
est/
Up
Can
yon
Roo
st
Can
yon
Dow
n N
est/
Up
Can
yon
Roo
st
Can
yon
Pin
us p
onde
r osa
1
1
P. s
trob
ifor
mis
Pri
mus
ser
otin
a
Pse
udot
suga
men
zies
ii
Que
rcus
gam
beli
Q.
gris
ea
00
Tab
le 1
4. T
he c
ompo
sitio
n of
veg
etat
ive
spec
ies
and
the
num
ber
of s
ampl
e si
tes
whe
re th
ey w
ere
obse
rved
with
in th
e th
ree
talle
st
laye
rs o
f M
exic
an s
potte
d ow
l nes
t and
roo
st s
ites
and
rand
om s
ampl
e si
tes
up a
nd d
own
cany
on i
n th
e G
uada
lupe
Mou
ntai
ns -
cont
inue
d.
Spec
ies
Lay
er 1
L
ayer
2
Lay
er 3
Dow
n N
est/
U
p
Can
yon
Roo
st
Can
yon
Dow
n N
est/
U
p
Can
yon
Roo
st
Can
yon
Dow
n N
est/
U
p
Can
yon
Roo
st
Can
yon
Q. m
uehl
enbe
rgii
1
3 1
1 1
1
Ugn
adia
spe
cios
a
Vit
is a
rizo
nica
Snag
00
Tab
le 1
5. C
ompa
riso
n of
tree
spe
cies
dia
met
ers
pres
ent
with
in th
e tw
o ta
llest
veg
etat
ive
laye
rs o
f ne
st, r
oost
, and
ran
dom
sam
ple
site
s
in th
e ca
nyon
s of
the
Gua
dalu
pe M
ount
ains
. L
ayer
3 w
as c
ompr
ised
of
a si
ngle
indi
vidu
al o
f Q
uerc
us m
uehl
enbe
rgii
(re
d =
23.
0 cm
)
at r
oost
site
and
a s
ingl
e in
divi
dual
of
Pse
udot
suga
men
zies
ii (
dbh
= 1
1.0
cm)
at a
ran
dom
site
up
cany
on.
The
re w
ere
no s
igni
fica
nt
diff
eren
ces
in tr
ee d
iam
eter
whe
n co
mpa
red
acro
ss la
yers
or
sam
ple
site
s (P
> 0
.05)
.
Lay
er 1
L
ayer
2
Spec
ies
Dow
n ca
nyon
Mea
n SD
Nes
t an
d R
oost
Mea
n SD
Up
cany
on
Mea
n SD
Dow
n ca
nyon
N
est
and
Roo
st
Up
cany
on
Mea
n SD
M
ean
SD
Mea
n SD
Ace
r gr
andi
dent
atum
20
.0
5.66
19
.6
4.12
14
.6
4.30
20
.3
7.83
12
.3
1.86
Juni
peru
s sc
olop
urus
J. d
eppe
ana
10.0
0.
00
14.0
0.
00
13.0
0.
00
Ost
rya
know
lton
ii
14.5
3.
54
20.5
11
.79
17.5
0.
71
10.7
0.
58
17.0
0.
00
16.0
5.
29
Pin
us p
onde
rosa
42
.0
0.00
40
.0
14.1
4 66
.5
2.12
12
.0
0.00
P. s
trob
ifor
mis
17
.0
0.00
37
.0
0.00
32
.5
0.71
12
.0
0.00
Tab
le 1
5. C
ompa
rison
of t
ree
spec
ies
diam
eter
s pr
esen
t with
in th
e tw
o ta
llest
veg
etat
ive
laye
rs o
f nes
t, ro
ost,
and
rand
om s
ampl
e si
tes
in th
e ca
nyon
s of
the
Gua
dalu
pe M
ount
ains
. L
ayer
3 w
as c
ompr
ised
of a
sin
gle
indi
vidu
al o
f Q
uerc
us m
uehl
enbe
rgii
(re
d =
23.
0 cm
)
at ro
ost s
ite a
nd a
sing
le in
divi
dual
of P
seud
otsu
ga m
enzi
esii
(db
h =
11.
0 cm
) at a
rand
om s
ite u
p ca
nyon
. T
here
wer
e no
sig
nific
ant
diff
eren
ces
in tr
ee d
iam
eter
whe
n co
mpa
red
acro
ss la
yers
or s
ampl
e si
tes
(P >
0.0
5) -
cont
inue
d.
Lay
er 1
L
ayer
2
Dow
n ca
nyon
N
est a
nd R
oost
U
p ca
nyon
D
own
cany
on
Nes
t and
Roo
st
Up
cany
on
Spec
ies
Mea
n SD
M
ean
SD
Mea
n SD
M
ean
SD
Mea
n SD
M
ean
SD
Pse
udot
suga
men
zies
ii
19.0
0.
00
18.0
6.
56
23.0
0.
00
11.0
0.
00
Que
rcus
gam
beli
14
.0
0.00
14
.0
0.00
12
.5
2.65
26
.0
0.00
Q.
gris
ea
13.0
0.
00
Q.
mue
hlen
berg
ii*
22.0
2.
83
34.0
12
.73
28.8
10
.21
17.0
0.
00
Snag
36
.0
0.00
13
.0
0.00
19
.0
0.00
25
.0
0.00
29
.5
7.78
* D
iam
eter
mea
sure
d at
root
-col
lar
(red
). A
ll ot
hers
mea
sure
d as
dia
met
er a
t bre
ast h
eigh
t (db
h).
87
Height (m)
I Down canyon
I Nest and roost sites
I Up canyon
Figure 10. Comparison of vegetative community heights from the three tallest layers
among nest, roost, and random sample sites up and down canyon. There were no
significant differences of heights within layers when compared (P > 0.05).
88
Species within all three layers and across all random-sample sites were
homogenous, with a narrow evenness range of 0.81 to 0.96. Big-toothed maple was
among the most prevalent species present among all 10 nest and roost sites and 26 of all
30 sites sampled. Western hop-hornbeam was present at seven nest/roost sites (17 sites
total), making it the second most prevalent species encountered. Typical tree species
indicative of spotted owl territories throughout New Mexico (e.g., Douglas fir,
southwestern white pine, ponderosa pine, and Gambel oak) were present at less than half
of nest and roost sites and less than one-third of all sample sites combined.
Canopy-cover was significantly more prevalent at nest and roost sites than down-
canyon sites (P = 0.007, a = 0.05) but not significantly different from up-canyon sites (P
= 0.259, a = 0.05; Fig. 10). Saplings and rocky debris were significantly more abundant
at nest and roost sites than random sites down (P < 0.001 and 0.014, respectively; a =
0.05) and up canyon (P = 0.006 and 0.005, respectively; a = 0.05).
Vertical canyon walls were significantly more prevalent at down canyon sites
than at nest/roost sites (P = 0.001, a = 0.05), while presence of shrubs, herbaceous
vegetation, and woody debris were not statistically significant (P > 0.05) among nest,
roost, and random sample sites (Fig. 10). Water was not encountered within any 0.01 ha
sample plot, although 7 out of 10 sites were within 600 m of annual springs or pools of
water collected within concave rock surfaces. Layer heights, tree species composition,
and tree diameters at nest and roost sites were not significantly different (P > 0.05) when
compared to random sites up and down canyon.
The two roost sites discovered while hiking into canyons for sampling occurred
in Upper Pine Springs Canyon, GUMO in April 2007 and in a secondary canyon north of
West Slaughter Canyon, CAVE in September 2007. The Upper Pine Springs roost
89
contained a single adult owl roosting approximately 3-m above the canyon bottom in a
southwestern white pine on a south-facing slope. This area of Pine Springs Canyon has
been known to be occupied by a pair since 2003, although no evidence has been recorded
of nesting behavior for these owls since that time despite subsequent reproductive
monitoring efforts (F. Armstrong pers. comm., T. Mullet unpubl. data).
The roost north of West Slaughter Canyon contained a pair of adult spotted owls
perched approximately 5-m above the canyon bottom in a big-toothed maple on a north-
facing slope. A known pair was recorded roosting approximately 800-m up canyon from
this site in 2003 (R. West pers. comm.). It is unknown if the pair observed in 2007 was
the same pair recorded in 2003 but, given the proximity of the two sites (approximately
500 m), it is likely that both sites are within the same territory.
DISCUSSION
All Mexican spotted owl nest and roost sites were found in steep, mesic canyons
consisting predominantly of big-toothed maple, western hop-hornbeam, and to a lesser
extent, Douglas fir, southwestern white pine, and Gambel oak. These habitats are
comparable to those of southern Utah, Colorado, and the Grand Canyon of northern
Arizona in that spotted owls tend to dwell in canyon systems more than in mixed-conifer
forests (Rinkevich and Gutierrez 1996, Johnson 1997, Willey 1998, Willey et al. 2001).
The fact that canopy cover was significantly more abundant at nest and roost sites
when compared to random sites down canyon is consistent with studies that have
identified canopy cover as an important microhabitat characteristic in both mixed-conifer
forests (Ganey and Balda 1989, Ganey and Balda 1994, Ganey and Dick 1995, Ganey et
al. 2000, May et al. 2004) and canyon environments (Ganey and Dick 1995, Rinkevich
90
and Gutierrez 1996, Johnson 1997, Willey 1998). Ganey (2004) found evidence that
canopy cover, provided by overhead vegetation, played an important role in regulating
ambient temperatures within nest and roost sites of mixed-conifer forests. Rinkevich and
Gutierrez (1996) and Willey (1998) discovered that Mexican spotted owls in southern
Utah roosted primarily along cliff ledges in canyons with little or no tree cover. Owls in
these canyon systems were protected from direct sunlight and high, diurnal temperatures
due to the complex geomorphology of the canyon walls surrounding the roosts
(Rinkevich and Gutierrez 1996, Willey 1998). Johnson (1997) also revealed a positive
relationship between owls roosting in canyons and high percentages of canopy cover
provided by tree cover. With this supporting evidence, canopy cover is evidently an
important variable required for sufficient nesting and roosting habitat in canyon systems
and mixed-conifer forests in general, although, it is not as clear in canyon systems if
owls are selecting cool microsites that have the growing conditions to produce taller,
more complex vegetation or whether they are selecting the vegetation. Both conditions
seemed to be present at the nest and roost sites that I sampled.
The ecological implication that rocky debris and saplings were significantly more
abundant at nest and roost sites than random sites could be linked to the availability of
prey. Woodrats (Neotoma sp.) are considered to be a vital prey source for Mexican
spotted owls throughout their range (Ward and Block 1995, Johnson 1997, Ward 2001,
Block et al. 2005). Serrentino and Ward (2003) reported that over 40% of prey biomass
consumed by Mexican spotted owls (n = 2) in the Guadalupe Mountains (GRD)
consisted of white-throated and Mexican woodrats (N. leucodont and N. mexicana,
respectively). Both species of woodrat are known to occupy rocky crevices and have
91
been positively correlated with rocky debris associated with spotted owl territories (Ward
and Block 1995, Ward 2001, Serrentino and Ward 2003, Schmidly 2004).
Saplings have been thought to be important understory cover for Mexican
woodrats (Finely 1958, Howe 1978, Comely and Baker 1986). Ward (2001) confirmed
that the diversity of saplings was positively correlated to the number of Mexican
woodrats captured within Mexican spotted owl territories in the Sacramento Mountains
of New Mexico. Sureda and Morrison (1998) found that Mexican woodrats in southern
Utah were exclusively captured in canyons where their associated habitat was correlated
with the foraging habitats of Mexican spotted owls observed by Willey (1992 unpubl.
data). However, Sureda and Morrison (1999) found that the presence of Mexican
woodrats within these canyons was negatively associated with canopy cover and
positively correlated with pinyon-juniper and cacti. This habitat type was not
encountered at nest and roost sites specifically, but it was prevalent along the ridges
above the canyon where sites were sampled and spotted owls have been known to forage
(T. Mullet pers. obs.). Although no formal study has been conducted to compare
Mexican spotted owl prey selection to surrounding habitat in the Guadalupe Mountains,
my results suggest that there could be a correlation between habitat conditions at nest
and roost sites and cover for woodrats.
The finding of sheer canyon walls being more common at down canyon sites
compared to nest and roost sites differed from the findings of Willey (1998), who found
Mexican spotted owls present in the canyons of southern Utah surrounded by vertical
walls and virtually no vegetation. However, my findings are similar to those of Johnson
(1997), who used similar plot sampling methods along the vegetated canyon bottoms in
Colorado. Roost sites in the Guadalupe Mountains did possess sheer canyon walls > 5 m
92
from the edge of the canyon drainage (T. Mullet pers. obs.). Additionally, both nest
sites sampled in this study were reported to be within small caves located along sheer
canyon walls above the canyon bottom (Kauffman 2005).
Because the presence of canyon walls were only recorded within a 0.01-ha plot
only 5 m from the edge of the canyon drainage, microhabitat sample sites did not
account for canyon walls present outside sample plots. More importantly, these results
indicate that vegetation and ground cover are more abundant along the canyon bottoms
in the Guadalupe Mountains than those canyons identified by Willey (1998).
Canyon widths within the Guadalupe Mountain were narrower at the 24-m
contour than canyon widths in Colorado (Johnson 1997). Considering the canyon widths
of roost sites in Colorado were significantly narrower than random sites up and down
canyon, canyon width in the Guadalupe Mountains were more uniform than sampled
canyons in Colorado, and therefore not as effective for delineating habitat as it is in
Colorado. Additionally, this indicates that finer-scale variables such as canopy cover,
saplings, and rocky debris are more important variables for identifying sites used by
Mexican spotted owls for roosting or nesting in the Guadalupe Mountains.
The elevations of canyon roost sites in Zion National Park of southern Utah
averaged 1,829 m (Ganey and Dick 1995), which is comparable to that of roost sites in
the Guadalupe Mountains. In contrast, average elevations of nest and roost sites in
Colorado were 2,309 m, considerably higher than the average elevation of sites in the
Guadalupe Mountains. Additionally, the aspects of sites in Utah canyons were
predominantly north-facing, with south-facing slopes documented to a lesser extent
(Ganey and Dick 1995). These aspects are similar to the results presented in this study.
93
Canyon depth and concavity have been used by Johnson (2003) as a component
to generate a GIS model predicting Mexican spotted owl nesting and breeding-season
roosting habitat throughout the southwestern United States (see Chapter II). Initial
validations have found significant correlations between the locations of nest and roost
sites and these variables (Johnson 2003).
Johnson (1997) had sampled ground-cover variables similar to those recorded in
this study. His results were similar to those found in this study in that no significant
differences were evident between nest and roost sites and random sites up and down
canyon. This lack of significant difference between samples and studies indicates that
ground cover variables (other than those mentioned as significant) are not as important
for identifying the microhabitat characteristics at Mexican spotted owl nest and roost
sites with suitable canyons.
A reanalysis of studies conducted by SWCA (1992) and Zhou (1994) by Ganey
and Dick (1995) suggested that Mexican spotted owls dwelling within mixed conifer
forest nested in areas with multiple layers of structurally complex vegetation.
Furthermore, Ganey and Dick's (1995) review of D. W. Willey's (Montana State
University, Bozeman MT) unpublished data from Canyonlands, Manti La Sal, and Zion
National Parks indicated that, among these regions, roost tree heights had variable
averages of 7 m (SD = 3), 21 m (SD = 11) and 12 m (SD - 8), respectively. These
heights were comparable to those of this study. The lack of significant differences in
layer heights among sample sites in the Guadalupe Mountains suggests that these areas
were largely homogenous.
Vegetative species within the canyons of the Guadalupe Mountains were similar
across all sample sites. The presence of these species were similar to those reported by
94
Ganey and Dick (1995) and Johnson (1997), which included Douglas fir, ponderosa pine,
maple, and juniper species. Gambel oak has been documented as a significant tree
species for roosting in mixed-conifer forest habitats (Ganey and Dick 1995), although no
records of their significance in canyon systems has been noted. Southwestern white pine
was present at most sites and was also reported as the roost tree for the Upper Pine
Springs Canyon owl. This conifer species is evidently a common roost tree for spotted
owls in the Sacramento Mountains (J. Ward pers. comm.). The only exceptions to these
findings were the presence of chinkapin oak and western hop-hornbeam in the canyons
of the Guadalupe Mountains. Chinkapin oaks were present in relatively low numbers but
were one of the largest species recorded. One also served as a known roosting tree in
West Slaughter Canyon. Western hop-hornbeam was well represented among sample
sites. I observed a female roosting with her two fledglings in a western hop-hornbeam in
2006 within the vicinity of one of the sampled nest sites, although this was not observed
again in 2007. The significance of these tree species with regard to microhabitat is not
known, but they appear to be common within the canyons of the Guadalupe Mountains
and serve as important roosting structures during the breeding season.
CHAPTER IV
CONCLUSION
An understanding of the distribution and habitat characteristics of Mexican
spotted owls in the Guadalupe Mountains has been slow to develop. Complete surveys
have yet to be conducted for Carlsbad Caverns National Park as of 2008, resulting in an
incomplete representation of spotted owl occurrence in this region. Aside from
conventional surveys and anecdotal descriptions, information concerning occupancy and
habitat selection of spotted owls in this region is currently limited or virtually
nonexistent. This has been largely due to the ruggedness of the terrain and lack of
significant funding. In the preceding chapters, I have presented methodologies that
provide a more efficient use of available information to produce results that lead to a
better understanding of Mexican spotted owl distribution and habitat conditions in the
Guadalupe Mountains.
As with many species, survey effort has been the leading method for determining
where Mexican spotted owls are located. These surveys have been based on previous
knowledge of geomorphology and vegetative structure to improve their efficiency of
locating potential, spotted owl territories. Now, with the advent of GIS-based habitat
models, like those generated by Johnson (2003), ForestERA (2005), Willey et al. (2006),
and Hathcock and Haarmann (2008), resource managers have the means to prioritize
survey effort to a more defined area of interest, given a variety of spatially explicit
variables.
For this study, model predictions of high-quality habitat using the 80%
cumulative interval class projected by Johnson's (2003) Southwestern Geophysical
95
96
Habitat Model and the 80 - 100% probability class of Willey et al.'s (2006) Utah-based
Habitat Model proved effective for predicting the distribution of Mexican spotted owls in
the Guadalupe Mountains, agreeing with my overall hypothesis. It is clear that the
spatial variables used to generate the predictions of the GHM and UBM are important
habitat components for describing spotted owl breeding-season habitat selection in this
region. More importantly, the overlapping high-quality habitat predicted by both models
provided the most efficient predictions overall by minimizing the total area of potential
habitat.
Although the Utah-based Habitat Model was designed strictly from nighttime
data recorded in the canyonlands of southern Utah (Willey et al. 2006), this model
performed well for the Guadalupe Mountains, where Mexican spotted owls are known to
utilize canyon habitats (Narahashi 1998, Kauffinan 1994, 2001, 2002, 2005). It was
evident that the UBM performed relatively better for estimating the occupancy of spotted
owls during nighttime surveys than it did at predicting specific daytime nest and roost
sites. Comparatively, the GHM was relatively more effective at locating specific nest
and roost sites than estimating Mexican spotted owl occupancy based on nighttime
surveys. These results are good examples of how important the type of data used for
model development is when applying predictions out of their original context.
An important observation made during this study was the fact that overlapping
high-quality habitat predicted areas with steep canyons more readily than the GHM and
UBM did alone (Appendices A9, A10, Al 1). These regions also had a high proportion
of occupancy and correlation with nest and roost sites. My microhabitat data confirm
that nest and roost sites and surrounding habitats possessed narrow and deeply concave
canyons, indicative of overlapping high-quality habitat. Furthermore, specific site
97
locations were relatively homogenous with respect to vegetation, consisting of large trees
providing significant amounts of canopy cover. It is evident that Mexican spotted owls
in the Guadalupe Mountains are selecting breeding-season habitat based on these
characteristics which likely provide cool microclimates conducive for roosting and
nesting conditions.
The selection of canyon habitats by Mexican spotted owls has been well
documented but not thoroughly studied (Ganey and Dick 1995). Rinkevich and
Gutierrez (1996) and Willey (1998) suggested that Mexican spotted owls in the canyons
of southern Utah were protected from high diurnal temperatures and direct sunlight by
steep, narrow canyon walls and large trees. Willey (1998) went on to mention that these
structures likely provided spotted owls a means to thermoregulate their body
temperature. Ganey (2004) found that Mexican spotted owl nest sites in Northern
Arizona were significantly cooler than random sites, likely a result of the greater amount
of canopy cover's tendency to reduce solar radiation and increase local humidity,
ultimately creating cooler ambient temperatures. Based on my personal observations,
nesting and roosting habitats were noticeable cooler than surrounding areas due to the
combined cover provided by vegetation and steep, narrow canyons. The occurrence of
Mexican spotted owls in steep, cool canyons in the Guadalupe Mountains gives evidence
of why high-quality overlapping habitat was so effective at predicting breeding-season
habitat.
The relationship between the GHM and UBM creating the high-quality overlap is
likely based on a combination of variables that provide a more refined characterization of
Mexican spotted owl breeding-season habitat, as mentioned earlier. Previous studies
have found that a combination of steep slopes, northerly aspects, an elevation range
98
between 1,500 and 2,300 m, complex canyon geomorphology, and vegetation
characterize Mexican spotted owl breeding-season habitat more effectively than
topographic or vegetation data alone (Ganey and Balda 1989, Ganey and Dick 1995,
Grubb et al. 1997, Ganey et al. 2000, Ward and Salas 2000). Johnson (2003) and Willey
et al. (2006) both incorporated variables of slope, north-facing aspects, elevation, and
local concavity and curvature in the landscape to build their predictions. However, each
model used a different set of variables to better describe the possible vegetation and
microclimate conditions of Mexican spotted owl breeding-season habitat. The GHM
used topographic data combined with latitude, longitude, and precipitation, while the
UBM incorporated topography with relative surface heat (to identify the presence of cool
zones) and a Modified Soil-Adjusted Vegetation Index. I believe that when the variables
used by each model were arbitrarily combined (i.e., overlaid in ArcMap), a very distinct
interaction between the quantities of some of these variables produced a more refined
interpretation of Mexican spotted owl breeding-season habitat in the Guadalupe
Mountains. Unfortunately, identifying the specific variables and their quantities would
take an exceptional amount of arithmetic to isolate.
MANAGEMENT IMPLICATIONS
Resource managers have been charged with the recovery of Mexican spotted
owls throughout the southwestern United States (USDI1995). Key components for this
recovery includes understanding the relationship between Mexican spotted owls and
their preferred habitat, the availability of those habitats, and how readily those habitats
are occupied (USDI 1995).
99
As technology progresses, strategies for managing species-habitat relationships in
a digital environment will, undoubtedly, become more prevalent. Geographical
Information Systems continue to prove their effectiveness as tools for understanding
ecological relationships and designing management strategies. It is the application of
these tools, with the appropriate methodology, that enable accurate inferences to be made
concerning species distribution, habitat availability, and site occupancy. However,
without significant field validations of digital projections, the accuracy and precision of
those predictions lack true significance. This study provides resource managers in the
Guadalupe Mountains with essential baseline information of Mexican spotted owl
occupancy and the distribution of likely nesting-roosting habitat.
By using the GHM and UBM, I was able to produce a more effective map of
potential Mexican spotted owl breeding-season habitat in the Guadalupe Mountains
(Appendices A12, A13, A14). My findings indicate that all accessible areas of the
Guadalupe Mountains within overlapping high-quality habitat have an 80% probability
of being occupied by Mexican spotted owls.
Presently, there is no means of isolating the overlapping high-quality habitat.
Therefore, application of this model still requires the added expense (or benefit) of both
the GHM and UBM. It would be advantageous to managers and researchers to isolate
the overlapping high-quality habitat to make more efficient use of time and resources
when conducting inventory and monitoring programs. I recommend that the occupancy
surveys used in this study be repeated for at least two consecutive years to obtain more
substantial estimates based on broader temporal variation. I also recommend that
managers consult the predicted habitat of the GHM, UBM, and overlap as a template for
shaping PAC boundaries (USDI1991) and designing fire prescription/suppression plans,
100
additional inventory projects, and implementing recovery efforts in the Guadalupe
Mountains.
Although the microhabitat description was based on sites that were collected over
a 10 year span, thus allowing for temporal variation, the sample size was limited and
restricted to accessible sites. A fuller understanding of and, perhaps, greater
predictability for microhabitat characteristics may be gained by visiting and measuring
the remaining but less-accessible sites.
I also suggest a few changes to the microhabitat sampling methodology, in order
to determine at what point significant differences can be detected using these canyon
variables. Sampling techniques could also be modified to include more canyon area.
For instances, random-sample sites could be sampled at further distances up and down
canyon in increments of > 100 m, and the 0.01-ha sample plot could possibly be enlarged
to 0.02-ha plots. However, these suggestions may create greater difficulties in placement
and sampling strategies due to the steepness of the terrain, than what were encountered
using smaller plots. This should be additional plots rather than substitutions to maintain
comparability with the findings from the 10 sites studied herein.
The results of this study reaffirm the utility of GIS-based habitat models as
effective tools for predicting Mexican spotted owl breeding-season habitat and the
importance of steep, cool canyons for nesting and roosting sites in the Guadalupe
Mountains. It is my hope that the methods outlined in this study and the results herein,
will contribute to the management of Mexican spotted owls in the Guadalupe Mountains
and add an additional step towards their recovery.
Chapter V
LITERATURE CITED
ALLDREDGE, M.W., K.H. POLLOCK, AND T.R. SIMONS. 2006. Estimating detection
probabilities from multiple observer point counts. The Auk 123: 1172-1182.
ANDERSON, D.R., K.P. BURNHAM, AND W.L. THOMPSON. 2000. Null hypothesis testing:
problems, prevalence, and an alternative. Journal of Wildlife Management 64:
912-923.
ARMSTRONG. F. 1999-2000 Mexican spotted owl inventory report for the Bush Mountain
Survey Units: Guadalupe Mountains National Park, Salt Flat (TX): Resource
Management Office, Guadalupe Mountains National Park (US); 2000. Available
from: Resource Management Office, Guadalupe Mountains National Park, Salt
Flat, TX.
BLOCK, W.M., J.L. GANEY, P.E. SCOTT, AND R. KING. 2005. Prey ecology of Mexican
spotted owls in pine-oak forests of northern Arizona. Journal of Wildlife
Management 69: 618-629.
BOWDEN, D.C., G.C. WHITE, A.B. FRANKLIN, AND J.L. GANEY. 2003. Estimating
population size with correlated sampling unit estimates. Journal of Wildlife
Management 67: 1-10
BRYAN, K.B. AND J.P. KARGES. 2001. Recent bird records in the Davis Mountains. Texas
Birds 3: 40-53.
BURNHAM, K.P. AND D.R. ANDERSON. 2002. Model selection and multimodel inference:
a practical information-theoretic approach. 2nd ed. New York, NY: Springer-
Verlag. 488 p.
101
CHAMBERLIN, T.C. 1965. 1890. The method of multiple working hypotheses. Science
148: 754-759.
CORNELY, J.E., AND R.J. BAKER. 1986. Neotoma mexicana. Mammalian Species 262:1-
7.
DiEFENBACH, D.R., D.W. BRAUNING, AND J.A. MATTICE. 2003. Variability in grassland
bird counts related to observer differences and species detection rates. The Auk
120: 1168-1179.
DUCHAMP, J.E., M. YATES, R. MUZIKA, AND R.K. SWIHART. 2006. Estimating
probabilities of detection for bat echolocation calls: an application of the double-
observer method. Wildlife Society Bulletin 34: 408-421.
FiNLEY, R.B., JR. 1958. The wood rats of Colorado: distribution and ecology. University
of Kansas Publications, Museum of Natural History 10: 213-552.
FLOYD, D.A. AND J.E. ANDERSON. 1987. A comparison of three methods for estimating
plant cover. Journal of Ecology 75: 221-228.
FORESTERA. 2005. ForestERA data layer overview - Mexican spotted owl management
guidelines. Forest Ecosystem Restoraton Analysis Project. Available at:
http://forestera.nau.edu/detail_mso_guidelines.htm. (Accessed: November 12,
2006).
FORSMAN, E.D. 1983. Methods and materials for locating and studying spotted owls.
General Technical Report PNW-162. Portland, Oregon: USDA Pacific Northwest
Experimental Station. 8 p.
, E.C. MESLOW, AND H.M. WIGHT. 1984. Distribution and biology of the spotted
owl in Oregon. Wildlife Monographs 87: 1-64.
GANEY, J.L. 1990. Calling behavior of spotted owls in Northern Arizona. The Condor
92: 485-490.
. 2004. Thermal regimes of Mexican spotted owl nest stands. The Southwestern
Naturalist 49: 478-486
AND R.P. BALDA. 1989. Distribution and habitat use of Mexican spotted owls in
Arizona. Condor 91: 355-361.
AND . 1994. Habitat selection by Mexican spotted owls in northern Arizona.
The Auk 111: 162-169.
AND J. A. DICK, JR. 1995. Habitat relationships of Mexican spotted owls: current
knowledge. In: United States Department of Interior, Fish and Wildlife Service,
Recovery Plan for the Mexican spotted owl (Strix occidentalis lucida), Volume
II. Technical supporting information. Chapter 4:1-42. United States Fish and
Wildlife Service, Albuquerque, NM. Available at http://mso.fws.gov/recovery-
plan.htm.
AND W.M. BLOCK. 2005. Winter movements and range use of radio-marked
Mexican spotted owls: an evaluation of current management recommendations.
Gen. Tech. Rep. RMRS-GTR-148-WWW. Fort Collins, CO: U.S. Department of
Agriculture, Forest Service, Rocky Mountain Research Station. 9 p.
, , AND R.M KING. 2000. Roost sites of radio-marked Mexican spotted owls in
Arizona and New Mexico: sources of variability and descriptive characteristics.
Journal of Raptor Research 34: 270-278.
; ? j .P. WARD, JR., AND B.E. STROHMEYER. 2005. Home range, habitat
use, survival, and fecundity of Mexican spotted owls in the Sacramento
Mountains, New Mexico. Southwestern Naturalist 50: 323-333.
GRUBB, T.G., J.L. GANEY, AND S.R. MASEK. 1997. Canopy closure around nest sites of
Mexican spotted owls in northcentral Arizona. Journal of Wildlife Management
61:336-342.
GUTIERREZ, R.J., A.B. FRANKLIN, AND W.S. LAHAYE. 1995. Spotted owl (Strix
occidentalis). In: A. Poole and F. Gill, editors. The birds of North America,
Number 179. Academy of Natural Sciences, Philadelphia, PA, and American
Ornithologists' Union, Washington, D.C. 28 p.
HALL, L.S, P.R. KRAUSMAN, AND M.L. MORRISON. 1997. The habitat concept and a plea
for standard terminology. Wildlife Society Bulletin 25: 173-182.
HATHCOCK, CD. AND T.K. HAARMANN. 2008. Development of a predictive model for
habitat of the Mexican spotted owl in northern New Mexico. The Southwestern
Naturalist 53: 34-38.
HIGGINS, K.F., J.L. OLDEMEYER, K.J. JENKINS, G.K. CLAMBY, AND R.F. HARLOW. 1996.
Vegetation sampling and measurement. In: T.A. Bookhout, ed. Research and
management techniques for wildlife and habitats. 5 ed., rev. Bethesda, MD: The
Wildlife Society, p 567-591.
HlNES, J.E. 2006. PRESENCE2 - software to estimate patch occupancy and related
parameters. USGS-PWRC. Available at:
http://www.mbr-pwrc.usgs.gov/software/presence.html. (Accessed: March 17,
2007)
HOWE, R.J. 1978. Agonistic behavior of three sympatric species of woodrats (Neotoma
mexicana, N. albigula, and N. stephensi). Journal of Mammalogy 59: 180-786.
JOHNSON, C.L. 1997. Distribution, habitat, and ecology of the Mexican spotted owl in
Colorado. M.S. Thesis. Greeley: University of Northern Colorado. 78 p.
JOHNSON, T. 2003. Geophysical spotted owl habitat model for the southwestern United
States. Final Report to U.S. Geological Survey, PO 00CRSA0718.
KAUFFMAN, D.R. 1994. Final report Guadalupe spotted owl survey (NM): The Kauffman
Group, Geo-Marine Inc. (US); 12 August 1994. Available from: Resource
Management Office, Guadalupe Ranger District, Lincoln National Forest, NM.
. 2001. Partial report [Guadalupes only] of work conducted during 2001 under
permit TE040346-0: The Kauffman Group, Geo-Marine Inc. (US); 17 October
2001. Available from: Resource Management Office, Guadalupe Ranger District,
Lincoln National Forest, NM.
. 2002. Report of work conducted during 2002 under permit TE040346-2: The
Kauffman Group, Geo-Marine Inc. (US); lOAugust 2002. Available from:
Resource Management Office, Guadalupe Ranger District, Lincoln National
Forest, NM.
. 2005. Final report for MSO consulting agreement 10489-2cn04-D, project
10070.0081.01. Mountain Park (NM): The Kauffman Group, Geo-Marine Inc.
(US); 19 July 2005. Consulting agreement: 10489-2cn04-D, Project
10070.0081.01. Available from: Resource Management Office, Guadalupe
Mountains National Park, Salt Flat, TX.
KISSLING, M.L. AND E.O. GARTON. 2006. Estimating detection probability and density
from point-count surveys: a combination of distance and double-observer
sampling. The Auk 123: 735-752.
LAVIER, R.A. 2005. Effects of weather and habitat on site occupancy in the Sacramento
Mountains, New Mexico. M.S. Thesis. Fort Collins: Colorado State University.
114 p.
MACKENZIE, D.I. 2005. What are the issues with presence-absence data for wildlife
managers? Journal of Wildlife Management 69: 849-860.
. 2006. Modeling the probability of resource used: the effect of, and dealing with,
detecting a species imperfectly. Journal of Wildlife Management 70: 367-374.
. AND A. ROYLE. 2005. Designing occupancy studies: general advice and allocating
survey effort. Journal of Applied Ecology 42: 1105-1114.
, J.D. NICHOLS, G.B. LACHMAN, S. DROEGE, J.A. ROYLE AND C.A. LANGTIMM.
2002. Estimating site occupancy rates when detection probabilities are less than
one. Ecology 83: 2248-2255.
1 ? J .A. ROYLE, K.H. POLLOCK, L.L. BAILEY, AND J.E. HINES. 2006.
Occupancy estimation and modeling: inferring patterns and dynamics of species
occurrence. San Diego: Elsevier Inc., Academic Press. 343 p.
, , J.E. HINES, M.G. KNUTSON, AND A.B. FRANKLIN. 2003. Estimating site
occupancy, colonization, and local extinction when a species is detected
imperfectly. Ecology 84: 2200-2207.
MAY, C.A., M.L. PETERSON, AND R.J. GUTIERREZ. 2004. Mexican spotted owl nest- and
roost-site habitat in northern Arizona. Journal of Wildlife Management 68: 1054-
1064.
MURPHY, D. 1984. The Guadalupes. Guadalupe Mountains National Park. NM: Carlsbad
Caverns and Guadalupe Mountains Association.
NARAHASHI, T. 1998 Guadalupe Mexican spotted owl survey final report (NM),
Guadalupe Ranger District, Lincoln National Forest (US); September 1998.
Available from: Resource Management Office, Guadalupe Ranger District,
Lincoln National Forest, NM.
107
NATIONAL PARK SERVICE. 2007. Nature and Science, Carlsbad Caverns. Available at:
http://www.nps.gov/cave/naturescience/index.htm. (Accessed: September 07,
2007).
NEW MEXICO DEPARTMENT OF GAME AND FISH. 2003. New Mexico species of concern.
Available at: http://www.wildlife.state.nm.us/conservation/habitat_handbook/
documents/wildlifeofconcern.pdf (Accessed: June 7, 2007).
NICHOLS, J.D., J.E. HINES, J.R. SAUER, F.W. FALLON, J.E. FALLON, AND P.J. HEGLUND.
2000. A double-observer approach for estimating detection probability and
abundance from point counts. The Auk 117: 393 - 408.
OLSEN, G.S., R.G. ANTHONY, E.D. FORSMAN, S.H. ACKERS, P.J. LOSCHL, J.A. REID,
K.M. DUGGER, E.M. GLENN, AND W.J. RIPPLE. 2005. Modeling of site
occupancy dynamics for northern spotted owls, with emphasis on the effects of
barred owls. Journal of Wildlife Management 69: 918 - 932.
POWELL, A.M. 1998. Trees and shrubs of the Trans-Pecos and adjacent areas. 1st edition.
Austin: University of Texas Press. 498 p.
RINKEVICH, S.E. AND R.J. GUTIERREZ. 1996. Mexican spotted owl habitat characteristics
in Zion National Park. Journal of Raptor Research 30: 74-78.
SALAS, D. Sacramento division and Guadalupe Ranger District Mexican spotted owl
annual survey and inventory report, (NM): United States Forest Service, Lincoln
Federal Building (US); 1994. Available from: Resource Management Office,
Guadalupe Ranger District, Lincoln National Forest, NM.
SCHMIDLEY, D.J. 2004. The mammals of Texas. Rev ed. Austin: Texas Parks and
Wildlife Press. 501 p.
108
SORRENTINO, G., AND J.P. WARD, JR. 2003. Analysis of Mexican spotted owl food habits
gathered from two caves in the Guadalupe Mountains, Lincoln National Forest,
New Mexico. Unpublished Technical Report, USDA Forest Service, Rocky
Mountain Research Station, Alamogordo, New Mexico. 7 p.
SUREDA, M. AND M.L. MORRISON. 1998. Habitat use by small mammals in southeastern
Utah, with reference to Mexican spotted owl management. Great Basin Naturalist
58:76-81.
SWCA, INC. 1992. Nest tree and nest tree habitat characteristics of Mexican spotted owl
in Arizona and New Mexico. Final Report submitted to U.S. Forest Service,
Southwestern Region,. Albuquerque, New Mexico 31 p.
TEXAS PARKS AND WILDLIFE DEPARTMENT. 2005. Mexican spotted owl (Strix
occidentalis lucida). Available at:
http://tpwd.state.tx.us/huntwild/species/mexowl/ (Accessed: January 11, 2006).
USDA NATIONAL FOREST SERVICE. 1991. Inventory for Mexican spotted owls. Interim
Directive No. 2, USDA Forest Service Technical Report. Albuquerque, New
Mexico.
USDI FISH AND WILDLIFE SERVICE. 1993. Endangered and threatened wildlife and
plants: final rule to list the Mexican spotted owl as a threatened species. Federal
Register 58: 14248-14270.
. 1995. Recovery plan for the Mexican spotted owl: Vol. I. Albuquerque, NM.
VAUGHN, LP. AND S.J. ORMEROD. 2003. Improving the quality of distribution models for
conservation by addressing shortcomings in the field collection of training data.
Conservation Biology 17: 1601-1611.
109
VOGIATZAKIS, I.N. 2003. GIS-based modeling and ecology: a review of toolsand
methods. Geographical Paper No. 170. Department of Geography, The University
of Reading, Whiteknights, United Kingdom.
VORA, R.S. 1988. A comparison of the spherical densitometer and ocular methods of
estimating canopy cover. Great Basin Naturalist 48: 224-227.
WARD, J.P. JR. 2001. Ecological responses by Mexican spotted owls to environmental
variation in the Sacramento Mountains, New Mexico. Ph.D. dissertation. Fort
Collins: Colorado State University. 423 p.
AND W.M. BLOCK. 1995. Mexican spotted owl prey ecology. In: Recovery plan for
the Mexican spotted owl Vol. II. Albuquerque, NM: USDI Fish and Wildlife
Service p 1-48.
AND D. SALAS. 2000. Adequacy of roost locations for delineating buffers around
Mexican spotted owl nests. Wildlife Society Bulletin 28(3):688-698.
AND J. L. GANEY. 2004. Evaluating effects of forest treatments on Mexican
spotted owls in the Sacramento Mountains: a coordinated management,
monitoring and research program. Unpublished Study Plan, USDA Forest
Service, Rocky Mountain Research Station, Alamogordo, New Mexico.
, A.B. FRANKLIN, S.E. RINKEVICH, AND F. CLEMENTE. 1995. Distribution and
abundance of Mexican spotted owls. In: United States Department of Interior,
Fish and Wildlife Service, Recovery Plan for the Mexican spotted owl (Strix
occidentalis lucida), Volume II. Technical supporting information. Albuquerque,
NM: United States Fish and Wildlife Service. Available at
http://mso.fws.gov/recovery-plan.htm.
110
WiLLEY, D.W. 1998. Movements and habitat utilization by Mexican spotted owls within
the canyonlands of Utah. Unpublished Dissertation. Northern Arizona University,
Arizona. 57-67 p.
, R.V. WARD, AND D. SPOTSKY. 2001. Surveys for Mexican spotted owls within the
interior canyonlands of Grand Canyon National Park. Annual report of
accomplishment 2001 field season. Available at:
http://www2.nature.nps.gov/views/Sites/GRCA/Media/PDF/MSO_2001rpt.pdf.
(Accessed: January 11, 2006)
, M. ZAMBON, AND R. WEBER. 2006. Predicting Mexican spotted owl habitat in
Utah. Annual Progress Report. Utah Division of Wildlife Resources. Salt Lake
City, Utah.
WILLIAMS, J. Mexican spotted owl survey report 1999 (NM); Smokey Bear Ranger
District (US); 1999. Available from: Resource Management Office, Guadalupe
Ranger District, Lincoln National Forest, NM.
ZAR, J.H. 1999. Biostatistical analysis. 4th ed. Upper Saddle River, NJ: Princeton Hall.
664 p.
ZHOU, B. 1994. Quantifying Mexican spotted owl habitat with the Johnson system of
distributions. M.S. Thesis. Flagstaff: Northern Arizona University. 169 p.
APPENDIX Al
Nighttime Survey Data Sheet
ME
XIC
AN
SPO
TT
ED
OW
L S
UR
VE
Y F
OR
M—
GU
AD
AL
UPE
MO
UN
TA
INS
(Cre
ated
21
Mar
ch 2
007)
Res
earc
h si
te n
ame:
N
IGT
TIM
E D
AT
ASH
EE
T
Dat
e:
(GU
M, G
RD
, or
CA
V w
/ gri
d ce
ll #)
da
y/m
onth
/yea
r
Surv
ey S
tart
Su
rvey
End
O
bser
verf
s):
Tim
e:
Tim
e:
24 h
r 24
hr
CA
LL
PO
INT
INFO
RM
AT
ION
*CaI
l poi
nt
USG
S qu
ad
Cal
ling
bega
n C
allin
g en
d R
espo
nse
Sex
Tim
e of
re
spon
se
Com
pass
be
arin
g
Dis
tanc
e fr
om
poin
t
Ow
l U
TM
E
(6 d
igit
s)
Ow
l U
TM
N
(7di
gits
)
* G
rid
cell
# w
/ res
earc
h si
te 3
-4 l
ette
r co
de f
ollo
wed
by
the
call
stat
ion
num
ber.
Res
pons
e-ye
s/no
; Age
: A=a
dult,
S=
suba
dult,
J=
juve
nile
, or U
= u
nkno
wn;
Sex
: M=M
ale,
F=F
emal
e;
App
endi
x A
l. N
ight
time
surv
ey d
atas
heet
.
APPENDIX A2
Data Matrix for Detection Probability Covariates
App
endi
x A
2. D
ata
mat
rix fo
r de
tect
ion
prob
abili
ty c
ovar
iate
s an
d th
eir
valu
es b
y su
rvey
sam
ple
unit
for
each
of t
hree
sur
vey
visi
ts
of M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe M
ount
ains
, sou
thea
ster
n N
ew M
exic
o an
d W
est T
exas
. A
bbre
viat
ions
; CA
V =
Car
lsba
d
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger D
istri
ct, G
UM
= G
uada
lupe
Mou
ntai
ns N
atio
nal P
ark,
SV
P =
sur
vey
perio
d,
VSD
= v
isita
tion
day,
OB
S =
num
ber
of o
bser
vers
, CST
= n
umbe
r of
cal
l sta
tions
.
Sam
ple
unit
SVP1
SV
P2
SVP3
V
SD1
VSD
2 V
SD3
OB
S1
OB
S2
OB
S3
CST
1 C
ST2
CST
3
CA
V12
]
CA
V13
GR
D7
1
GR
D10
GR
D18
GR
D26
GR
D27
GR
D32
GR
D33
L
2
I 2
I 2
I 2
I 2
I 2
I 2
I 2
I 2
3 3 3 3 3 3 3 3 3
176
176
163
163
196
150
191
190
149
208
208
197
197
219
191
219
218
195
236
236
221
221
239
219
239
240
220
3 3 2 1 2 2 2 1 2
1 1 2 1 1 3 2 1 3
2 2 2 1 2 2 2 2 2
2 2 2 3 2 2 2 1 4
2 2 1 3 2 1 2 1 3
2 2 2 3 2 2 2 1 1
App
endi
x A
2. D
ata
mat
rix fo
r de
tect
ion
prob
abili
ty c
ovar
iate
s an
d th
eir v
alue
s by
surv
ey s
ampl
e un
it fo
r ea
ch o
f thr
ee s
urve
y vi
sits
of M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe M
ount
ains
, sou
thea
ster
n N
ew M
exic
o an
d W
est T
exas
. A
bbre
viat
ions
; CA
V =
Car
lsba
d
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger D
istri
ct, G
UM
= G
uada
lupe
Mou
ntai
ns N
atio
nal P
ark,
SV
P =
sur
vey
perio
d,
VSD
= v
isita
tion
day,
OB
S =
num
ber o
f obs
erve
rs, C
ST =
num
ber
of c
all s
tatio
ns -
cont
inue
d.
Sam
ple
unit
SVP1
SV
P2
SVP3
V
SD1
VSD
2 V
SD3
OB
S1
OB
S2
OB
S3
CST
1 C
ST2
CST
3
GR
D34
1
GR
D35
1
GR
D39
1
GR
D42
]
GR
D43
1
GR
D45
GR
D47
1
GR
D48
GU
M50
[ 2
[ 2
I 2
I 2
L
2
I 2
I 2
L
2
I 2
3 3 3 3 3 3 3 3 3
190
148
165
165
143
142
141
169
189
218
190
198
198
189
189
189
200
217
240
218
226
226
217
217
217
227
238
1 2 1 2 2 2 2 2 1
2 1 1 2 1 1 1 2 1
2 2 2 2 1 2 1 2 2
2 3 3 2 2 2 1 3 1
1 2 3 2 1 1 1 3 2
2 2 1 1 2 2 1 2 2
App
endi
x A
2. D
ata
mat
rix fo
r de
tect
ion
prob
abili
ty c
ovar
iate
s an
d th
eir
valu
es b
y su
rvey
sam
ple
unit
for
each
of t
hree
sur
vey
visi
ts
of M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe M
ount
ains
, sou
thea
ster
n N
ew M
exic
o an
d W
est T
exas
. A
bbre
viat
ions
; CA
V =
Car
lsba
d
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger
Dis
trict
, GU
M =
Gua
dalu
pe M
ount
ains
Nat
iona
l Par
k, S
VP
= s
urve
y pe
riod,
VSD
= v
isita
tion
day,
OB
S =
num
ber
of o
bser
vers
, CST
= n
umbe
r of
cal
l sta
tions
- co
ntin
ued.
Sam
ple
unit
SVP1
SV
P2
SVP3
V
SD1
VSD
2 V
SD3
OB
S1
OB
S2
OB
S3
CST
1 C
ST2
CST
3
GU
M54
]
GU
M55
]
GU
M59
GU
M60
]
GU
M69
GU
M76
GU
M93
1 2
1 2
I 2
1 2
I 2
1 2
I 2
3 3 3 3 3 3 3
170
132
131
161
173
172
173
200
188
188
194
204
204
201
227
216
216
220
234
234
228
2 1 1 3 2 3 1
2 1 2 1 1 2 2
2 1 2 1 2 2 1
2 4 1 2 2 2 1
2 3 1 2 1 2 2
2 2 1 2 1 2 1
APPENDIX A3
Data Matrix for Occupancy Covariates
App
endi
x A
3. D
ata
mat
rix fo
r oc
cupa
ncy
cova
riate
s of
hig
h-qu
ality
pre
dict
ed h
abita
t, th
eir
amou
nts
(ha)
by
sam
ple
unit
(200
ha)
and
the
dete
ctio
n (1
) and
non
-det
ectio
n (0
) for
thre
e su
rvey
vis
its o
f Mex
ican
spo
tted
owls
in th
e G
uada
lupe
Mou
ntai
ns, s
outh
east
ern
New
Mex
ico
and
Wes
t Tex
as.
Abb
revi
atio
ns; C
AV
= C
arls
bad
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger
Dis
tric
t,
GU
M =
Gua
dalu
pe M
ount
ains
Nat
iona
l Par
k, G
HM
= S
outh
wes
tern
Geo
phys
ical
hab
itat m
odel
, UB
M =
Uta
h-ba
sed
habi
tat m
odel
,
HQ
O =
hig
h-qu
ality
ove
rlapp
ing
pred
icte
d ha
bita
t, G
HM
-UB
M =
com
bine
d ar
ea o
f GH
M a
nd U
BM
min
us h
alf t
he a
rea
of H
QO
.
Cel
l Num
ber
Vis
it 1
Vis
it 2
Vis
it 3
GH
M 8
0%
UB
M>8
0%
HQ
O
GH
M+U
BM
CA
V12
CA
V 1
3
GR
D10
GR
D18
GR
D26
GR
D27
GR
D32
GR
D33
0 0 0 0 1 0 1 0
0 0 0 0 1 0 1 0
0 0 0 0 0 0 1 1
95.4
5
127.
16
73.3
2
78.2
8
81.8
7
78.3
6
80.7
0
95.3
6
85.7
9
109.
15
102.
95
55.3
7
125.
35
102.
83
82.3
8
106.
01
52.7
5
69.1
6
34.4
3
25.9
2
56.8
8
43.6
9
38.0
8
52.8
8
128.
49
167.
15
141.
84
107.
73
150.
33
137.
50
125.
00
148.
48
App
endi
x A
3. D
ata
mat
rix fo
r oc
cupa
ncy
cova
riate
s of
hig
h-qu
ality
pre
dict
ed h
abita
t, th
eir
amou
nts
(ha)
by
sam
ple
unit
(200
ha)
and
the
dete
ctio
n (1
) and
non
-det
ectio
n (0
) for
thre
e su
rvey
vis
its o
f Mex
ican
spo
tted
owls
in th
e G
uada
lupe
Mou
ntai
ns, s
outh
east
ern
New
Mex
ico
and
Wes
t Tex
as.
Abb
revi
atio
ns; C
AV
= C
arls
bad
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger D
istr
ict,
GU
M =
Gua
dalu
pe M
ount
ains
Nat
iona
l Par
k, G
HM
= S
outh
wes
tern
Geo
phys
ical
hab
itat m
odel
, UB
M =
Uta
h-ba
sed
habi
tat m
odel
,
HQ
O =
hig
h-qu
ality
ove
rlapp
ing
pred
icte
d ha
bita
t, G
HM
-UB
M =
com
bine
d ar
ea o
f GH
M a
nd U
BM
min
us h
alf t
he a
rea
of H
QO
-
cont
inue
d.
Cel
l Num
ber
GR
D34
GR
D35
GR
D39
GR
D42
GR
D43
GR
D45
GR
D47
Vis
it 1
0
Vis
it 2
0 0 1 1 1 0 1
Vis
it 3
0 0 1 1 1 0 1
GH
M 8
0%
32.6
6
43.9
8
128.
75
109.
71
133.
28
119.
66
159.
28
UB
M >
80%
130.
48
116.
23
104.
87
135.
16
147.
47
118.
97
157.
57
HQ
O
21.8
3
24.1
7
62.1
3
85.3
7
92.0
8
81.8
8
123.
31
GH
M+
UB
M
141.
31
136.
04
171.
50
159.
49
188.
67
156.
75
193.
54
App
endi
x A
3. D
ata
mat
rix fo
r oc
cupa
ncy
cova
riate
s of
hig
h-qu
ality
pre
dict
ed h
abita
t, th
eir
amou
nts
(ha)
by
sam
ple
unit
(200
ha)
and
the
dete
ctio
n (1
) an
d no
n-de
tect
ion
(0) f
or th
ree
surv
ey v
isits
of M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe M
ount
ains
, sou
thea
ster
n N
ew
Mex
ico
and
Wes
t Tex
as.
Abb
revi
atio
ns; C
AV
= C
arls
bad
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger D
istri
ct,
GU
M =
Gua
dalu
pe M
ount
ains
Nat
iona
l Par
k, G
HM
= S
outh
wes
tern
Geo
phys
ical
hab
itat m
odel
, UB
M =
Uta
h-ba
sed
habi
tat m
odel
,
HQ
O =
hig
h-qu
ality
ove
rlapp
ing
pred
icte
d ha
bita
t, G
HM
-UB
M =
com
bine
d ar
ea o
f GH
M a
nd U
BM
min
us h
alf t
he a
rea
of H
QO
-
cont
inue
d.
Cel
l Num
ber
Vis
it 1
Vis
it 2
Vis
it 3
GH
M 8
0%
UB
M>8
0%
HQ
O
GH
M+U
BM
1 76
.40
126.
66
46.6
8 15
6.38
0 82
.08
74.7
8 37
.83
119.
04
0 51
.11
112.
04
21.8
3 14
1.31
0 11
.85
128.
89
4.27
13
6.47
0 13
7.31
15
4.05
99
.07
192.
29
0 13
1.44
15
1.76
10
4.93
17
8.28
1 18
3.96
13
0.09
11
5.35
19
8.70
GR
D48
GR
D7
GU
M50
GU
M54
GU
M55
GU
M59
GU
M60
1 0 1 0 0 0 1
1 0 0 0 0 0 1
App
endi
x A
3. D
ata
mat
rix fo
r oc
cupa
ncy
cova
riate
s of
hig
h-qu
ality
pre
dict
ed h
abita
t, th
eir
amou
nts
(ha)
by
sam
ple
unit
(200
ha)
and
the
dete
ctio
n (1
) an
d no
n-de
tect
ion
(0) f
or th
ree
surv
ey v
isits
of M
exic
an s
potte
d ow
ls in
the
Gua
dalu
pe M
ount
ains
, sou
thea
ster
n N
ew
Mex
ico
and
Wes
t Tex
as.
Abb
revi
atio
ns; C
AV
= C
arls
bad
Cav
erns
Nat
iona
l Par
k, G
RD
= G
uada
lupe
Ran
ger D
istri
ct,
GU
M =
Gua
dalu
pe M
ount
ains
Nat
iona
l Par
k, G
HM
= S
outh
wes
tern
Geo
phys
ical
hab
itat m
odel
, UB
M =
Uta
h-ba
sed
habi
tat m
odel
,
HQ
O =
hig
h-qu
ality
ove
rlapp
ing
pred
icte
d ha
bita
t, G
HM
-UB
M =
com
bine
d ar
ea o
f GH
M a
nd U
BM
min
us h
alf t
he a
rea
of H
QO
-
cont
inue
d.
Cel
l Num
ber
Vis
it 1
Vis
it 2
Vis
it 3
GH
M 8
0%
UB
M>8
0%
HQ
O
GH
M+U
BM
GU
M69
1
1 0
90.4
8 97
.95
33.2
3 15
5.20
GU
M76
0
0 0
168.
08
47.1
1 42
.73
172.
46
GU
M93
0
1 1
100.
76
130.
33
70.0
6 16
1.04
APPENDIX A4
Appendix A4. Summary of occupancy estimates for Mexican spotted owls within each
sample unit designated by predicted habitat and surveyed during the 2007 breeding
season (May to August) in the Guadalupe Mountains.
Sample unit v|/(ubm) v(hqo) \|/(ghm) \|/(ghm-ubm)
CAV12
CAV13
GRD07
GRD10
GRD18
GRD26
GRD27
GRD32
GRD33
GRD34
GRD35
GRD39
GRD42
GRD43
GRD45
GRD47
GRD48
0.41
0.55
0.35
0.52
0.25
0.65
0.52
0.39
0.53
0.68
0.60
0.53
0.70
0.76
0.61
0.80
0.65
0.56
0.63
0.49
0.48
0.44
0.58
0.52
0.49
0.56
0.42
0.43
0.60
0.69
0.72
0.68
0.82
0.53
0.57
0.62
0.54
0.53
0.54
0.54
0.54
0.54
0.57
0.45
0.47
0.63
0.59
0.63
0.61
0.68
0.53
0.42
0.65
0.36
0.50
0.30
0.55
0.47
0.39
0.54
0.50
0.46
0.68
0.61
0.77
0.59
0.79
0.59
122
123
Appendix A4. Summary of occupancy estimates for Mexican spotted owls within each
sample unit designated by predicted habitat and surveyed during the 2007 breeding
season (May to August) in the Guadalupe Mountains - continued.
Sample unit
GUM50
GUM54
GUM55
GUM59
GUM60
GUM69
GUM76
GUM93
\|/(ubm)
0.57
0.67
0.79
0.78
0.67
0.49
0.22
0.67
\|/(hqo)
0.42
0.35
0.74
0.76
0.80
0.47
0.51
0.63
\j/(ghm)
0.49
0.42
0.64
0.63
0.72
0.56
0.69
0.58
\)/(ghm-ubm)
0.50
0.47
0.78
0.72
0.81
0.58
0.68
0.62
APPENDIX A5
Habitat Sampling Datasheet
124
DA
TE
:
AS
SO
CIA
TE
D O
WL
LOC
AT
ION
:
SIT
E (
CA
NY
ON
UT
M-E
:
) NA
ME
:
CA
NY
ON
WID
TH
(M
):80
16
CH
AN
NE
L/S
TR
EA
M W
IDT
H (
M):
ELE
VA
TIO
N:
5-M
RA
DIU
S C
IRC
ULA
R P
LO
T
WO
OD
Y P
LAN
T L
AY
ER
GU
AD
AL
UP
E M
OU
NT
AIN
S M
SO
CA
NY
ON
HA
BIT
AT
FO
RM
PLO
T:
UP
N
/R
DN
O
BS
ER
VE
RS
:
AS
PE
CT
:
0
UT
M-N
:
QU
AD
:
ZO
NE
:
CA
NY
ON
WA
LL H
EIG
HT
(MV
. /
AS
PE
CT
OF
CA
NY
C
LAY
ER
1 H
EIG
HT
:
SP
EC
IES
:
SP
EC
IES
D
iam
(C
M)
LYR
LAY
ER
2 H
EIG
H
SP
EC
IES
:
SP
EC
IES
T:
Dia
m (
CM
) LY
R
DN
:
BE
AR
ING
S
1ST
LIN
E:
LAY
ER
3 H
EIG
HT
:
SP
EC
IES
: SP
EC
IES
/
2ND
LIN
E:
Dia
m (
CM
) LY
R
App
endi
x A
5. H
abita
t sam
plin
g da
tash
eet,
(pag
e 1
of 2
).
ASS
OC
IAT
ED
OW
L
LO
CA
TIO
N:
PLO
T: U
P N
/R
DN
LIN
E E
NT
ER
CE
PT
LIN
E 1/2
1 2 3 4 5 6 7 g 9 10
SUM
TR
EC
AN
SA
PLN
G
/ / / / / / / / / /
SHR
UB
S
/ / / / / / / / / /
HE
RB
AC
/ / / / / / / / / /
WA
LL
/ / / / / / / / / /
RO
CK
/ / / / / / / / / /
LG
LO
G
/ / / / / / / / / /
BA
RE
/ / / / / / / / / /
WA
TE
R
/ / / / / / / / / /
KE
Y:
TR
EC
AN
= t
ree
cano
py;
SAPL
ING
= t
ree
sapl
ing;
SH
RU
BS
= s
hrub
; H
ER
BA
C =
non
-woo
dy p
lant
s or
mos
s; W
AL
L =
roc
k -w
all;
RO
CK
= l
arge
roc
k or
bou
lder
; L
GL
OG
=
larg
e lo
g or
woo
dy d
ebri
s; B
AR
E =
dir
t or
leaf
litt
er;
WA
TE
R =
int
erm
iten
t or
per
man
ent
wat
er
App
endi
x A
5. H
abita
t dat
ashe
et (p
age
2 of
2).
APPENDIX A6
Example of a Roost Site in the Guadalupe Mountains
127
App
endi
x A
6. E
xam
ple
of a
roos
t site
in th
e G
uada
lupe
Mou
ntai
ns.
Clo
ckw
ise f
rom
top
left:
fac
ing
roos
t, fa
cing
up
cany
on, f
acin
g
dow
n ca
nyon
, fac
ing
cros
s ca
nyon
.
APPENDIX A7
Example of a Random Up-canyon Sample Site
129
App
endi
x A
7. E
xam
ple
of a
rand
om u
p ca
nyon
sam
ple
site
in th
e G
uada
lupe
Mou
ntai
ns.
Clo
ckw
ise
from
top
left:
fac
ing
roos
t-si
de,
faci
ng u
p ca
nyon
, fac
ing
dow
n ca
nyon
, fac
ing
cros
s ca
nyon
. o
APPENDIX A8
Example of a Random Down-canyon Sample Site
App
endi
x A
8. E
xam
ple
of a
rand
om d
own
cany
on s
ampl
e si
te in
the
Gua
dalu
pe M
ount
ains
. C
lock
wis
e fr
om to
p le
ft: fa
cing
roo
st-
side
, fac
ing
up c
anyo
n, fa
cing
dow
n ca
nyon
, fac
ing
cros
s ca
nyon
.
APPENDIX A9
SfeK$*>
, *-*«!l&^,% 3 ^
Appendix A9. Partial view of GUM76 and GUM69 from a call station at GUM69.
GUM76 had a larger amount of high-quality habitat predicted by the GHM (120/200 ha).
GUM69 had equal amounts of GHM and UBM (90 and 95 ha, respectively). Lines are
approximate boundaries of sample units.
133
APPENDIX A10
-A. K
' , v i- • .msr
Appendix A10. View of GUM54 and GUM55 from Permian Reef trail, New Mexico
side. GUM54 was predominantly high-quality habitat predicted by UBM (117/200 ha).
GUM55 had relatively similar amounts of GHM and UBM (137 and 154 ha,
respectively). Lines are approximate boundaries of sample units.
134
APPENDIX Al l
Appendix A l l . Partial view of GRD47 from a call station. GRD47 had a predominant
amount of high-quality overlapping habitat (123/200 ha). Lines are approximate
boundaries of the sample unit.
135
APPENDIX A12
Legend
I | Uuadalupe Mountains NP
Low-quality habitat
Appendix A12. Distribution of predicted high- and low-quality habitat in GUMO,
including the GHM, UBM, and overlap (HQO).
136
APPENDIX Al 3
Legend I I Carlsbad Caverns NP
| HQO
| UBM
|GHM
Low-quality habitat
Appendix A13. Distribution of predicted high- and low-quality habitat in CAVE
including the GHM, UBM, and overlap (HQO).
137
APFJEND1X A14
N
s
Legend
I I Guadalupe Ranger District
HUHQO • • UBM • i CUM
1 uw-quality habitat
Appendix A14. Distribution of predicted high- and low-quality habitat in GRD,
including the GHM, UBM, and overlap (HQO).
138