1. 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
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3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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 Ill
IX
11. 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
12. 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
13. LIST OF APPENDICES
Appendix Page
Al. Nighttime Survey Datasheet Ill
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
14. 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
15. 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).
16. 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 (Pinusflexilis), 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
17. 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
18. 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
19. 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-
20. 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
21. 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.
22. 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
23. 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.).
25. 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).
26. 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
27. 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
(MSAVI) 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.
28. 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
29. 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.,
30. 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
31. 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
32. 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).
33. 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.
34. 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
35. 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
36. 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
37. 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.
38. 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.
40. 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
41. 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.
42. 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
43. 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.
44. 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).
45. 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
46. 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
47. 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
48. 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
49. 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);
50. 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).
51. 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
52. 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).
57. 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
58. 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 apriori 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.
59. 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.
60. 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
61. 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.
62. 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
63. 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
64. 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
65. 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
66. 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.
67. 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).
69. 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.
71. 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
72. 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
73. 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.
74. 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.
75. 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
