National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Prairie Falcon Monitoring Protocol 

for Pinnacles National Monument 

Version 2.2 

Natural Resource Report NPS/SFAN/NRR—2010/216 
ON THE COVER 

Juvenile prairie falcon. 

Photograph by: Gavin Emmons 
 
Prairie Falcon Monitoring Protocol for Pinnacles 

National Monument 

Version 2.2 

Natural Resource Report NPS/SFAN/NRR—2010/216 

Gavin Emmons 

Jim Petterson 

National Park Service 

Pinnacles National Monument 

Research and Resource Management 

5000 Highway 146 

Paicines, CA 95043 

Marcus Koenen 

David Press 

National Park Service 

Golden Gate National Recreation Area 

Fort Cronkhite, Bldg 1063 

Sausalito, CA 94965 

June 2010 

U.S. Department of the Interior 

National Park Service 

Natural Resource Program Center 

Fort Collins, Colorado
 ii 

The National Park Service, Natural Resource Program Center publishes a range of reports that 

address natural resource topics of interest and applicability to a broad audience in the National 

Park Service and others in natural resource management, including scientists, conservation and 

environmental constituencies, and the public. 

The Natural Resource Report Series is used to disseminate results of scientific studies in the 

physical, biological, and social sciences for both the advancement of science and the 

achievement of the National Park Service mission. The series provides contributors with a forum 

for displaying comprehensive data that are often deleted from journals because of page 

limitations. 

All manuscripts in the series receive the appropriate level of peer review to ensure that the 

information is scientifically credible, technically accurate, appropriately written for the intended 

audience, and designed and published in a professional manner. This report received formal, 

high-level peer review based on the importance of its content, or its potentially controversial or 

precedent-setting nature. Peer review was conducted by highly qualified individuals with subject 

area technical expertise and was overseen by a peer review manager. 

Views, statements, findings, conclusions, recommendations, and data in this report are those of 

the author(s) and do not necessarily reflect views and policies of the National Park Service, U.S. 

Department of the Interior. Mention of trade names or commercial products does not constitute 

endorsement or recommendation for use by the National Park Service. 

This report is available from the San Francisco Bay Area Network Inventory and Monitoring 

Program (http://science.nature.nps.gov/im/units/sfan/index.cfm) and the Natural Resource 

Publications Management website (http://www.nature.nps.gov/publications/NRPM). 

Please cite this publication as: 

Emmons, G., J. Petterson, M. Koenen, and D. Press. 2010. Raptor monitoring protocol for 

Pinnacles National Monument: narrative - version 2.2. Natural Resource Report 

NPS/SFAN/NRR—2010/216. National Park Service, Fort Collins, Colorado. 

NPS 114/104176, June 2010 

 
 iii 

Revision History Log 

Prev. 

Version # 

Revision 

Date 

Author Changes Made Reason for Change New 

Version 

# 

NA Sept. 26, 

2003 

G. Emmons, 

A. Fesnock 

1st draft sent for 

review 

 NA 

NA June 

2005 

G. Emmons, 

A. Fesnock. 

Refined and 

augmented contents 

Respond to internal 

review 

1.02 

1.02 May 

2008 

G. Emmons, 

D. Press, J. 

Pettersen, 

M. Koenen 

Clarifications, 

largely to data 

analysis section 

Respond to peerreview comments 1.1 

1.1 May 

2009 

M. Koenen, 

G. Emmons, 

P. Johnson, 

D. Adams 

Clarifications, 

largely to data 

analysis; inclusion 

of SOPs in main 

document 

Respond to peerreview comments 2.1 

2.1 Mar 

2010 

M. Koenen Added final 

changes to 

sampling design 

and data analysis 

To incorporate findings 

from Starcevich and 

Steinhorst (2010) 

2.2 

 v 

CONTENTS

Page 

Figures.......................................................................................................................................... viii

Tables ............................................................................................................................................. ix

Appendixes ..................................................................................................................................... x

Executive Summary ....................................................................................................................... xi

Acknowledgements ...................................................................................................................... xiii

Acronyms ..................................................................................................................................... xiv

1 - Background and Objectives ....................................................................................................... 1

1.1 Issue Being Addressed and Rationale for Monitoring Raptor Populations ...................... 1

1.2 Monitoring Questions ....................................................................................................... 2

1.3 Monitoring Objectives ...................................................................................................... 3

1.4 Management Objectives .................................................................................................... 3

1.5 Other Raptor Species ........................................................................................................ 3

1.6 Trigger for Management Activities................................................................................... 5

2 - Sampling Design........................................................................................................................ 6

2.1 Population Being Monitored ............................................................................................. 6

2.2 Sampling Frequency and Replication ............................................................................... 8

2.3 Sample Size and Statistical Power .................................................................................... 8

2.4 Sample Design Summary ................................................................................................ 13

3 - Field Methods .......................................................................................................................... 15

3.1 Field Season Preparations ............................................................................................... 15

3.2 Field Season Surveys ...................................................................................................... 15

3.3 Ancillary Data ................................................................................................................. 16

3.4 End of the Field Season .................................................................................................. 17
 vi 

4 - Data Management .................................................................................................................... 19

4.1 Management of Statistical Data ...................................................................................... 19

4.2 Overview of Database Design......................................................................................... 19

4.3 Data Entry and Verification ............................................................................................ 19

4.4 Metadata and Data Archival Procedures ......................................................................... 20

5 - Analysis and Reporting ........................................................................................................... 23

5.1 Reporting Schedule and Formats .................................................................................... 23

6 - Operational Requirements ....................................................................................................... 27

6.1 Roles and Responsibilities .............................................................................................. 27

6.2 Budget ............................................................................................................................. 28

6.3 Qualifications and Training ............................................................................................ 29

6.4 Annual Workload and Field Schedule ............................................................................ 29

6.5 Facility and Equipment Needs ........................................................................................ 29

6.6 Permit Procedures ........................................................................................................... 30

6.7 Procedures for Revising the Protocol ............................................................................. 30

7 - Glossary ................................................................................................................................... 31

8 - Literature Cited ........................................................................................................................ 37

1. INTRODUCTION ................................................................................................................... 47

2. GENERAL ANALYSIS APPROACH ..................................................................................... 47

2.1 Occupancy and trend modeling ................................................................................. 47

2.2 Fecundity and trend modeling ....................................................................................... 48

2.3 Power approach ............................................................................................................. 49

2.4 Revisit designs ................................................................................................................ 49

3. PRAIRIE FALCON POWER ANALYSES ............................................................................ 51

3.1 Survey design ................................................................................................................. 51
 vii 

3.2 Occupancy analysis ........................................................................................................ 51

3.3 Fecundity power analysis ............................................................................................... 56

3.4 Conclusions .................................................................................................................... 60

4. SPOTTED OWL POWER ANALYSES ................................................................................. 61

4.1 Sampling design .............................................................................................................. 61

4.2 Occupancy analysis ........................................................................................................ 63

4.3 Fecundity analysis .......................................................................................................... 69

4.4 Conclusions .................................................................................................................... 73

5. CONCLUSIONS....................................................................................................................... 74

6. REFERENCES ........................................................................................................................ 75
 viii 

Figures 

Page 

Figure 1. Historical territories near climbing routes (core) and throughout the rest of the 

park (non-core). ............................................................................................................................... 4 

Figure 2. GIS map depicting potential nesting areas at PINN with >40 degree slopes. ................. 7 
 ix 

Tables 

Page 

Table 1. Power to detect trend for different sample sizes and revisit designs. ............................ 11 

Table 2. Power to detect trend for non-core sites only. ............................................................... 11 

Table 3. Power to detect a 50% decline in the number of hatchlings. ......................................... 12 

Table 4. Power to detect a 50% decline in the number of known fledglings. ............................. 12 

Table 5. Power to detect trend in the binomial probability for hatchlings for a census of 

core sites and a sample of non-core sites. ..................................................................................... 12 

Table 6. Power to detect trend in the binomial probability for fledglings for a census of 

core sites and a sample of non-core sites. ..................................................................................... 13 

Table 7. Power to detect trend in the binomial probability for hatchlings for non-core 

sites only ....................................................................................................................................... 13 

Table 8. Power to detect trend in the binomial probability for fledglings for non-core 

sites only ....................................................................................................................................... 13 

Table 9. Summary of reporting and communication products. ................................................... 24 

Table 10. Estimated annual budget for FY09. ............................................................................. 29 

Table 11. Summary of annual work schedule (Jan–Aug). ........................................................... 30 

 
 x 

Appendixes 

Page 

Appendix A. Analysis of Power to Detect Trends in Occupancy and Fecundity in 

Prairie Falcon and Spotted Owl Populations ................................................................................ 43 

Appendix B. Raptor Monitoring Data Sheet ................................................................................. 88 

Appendix C. Raptor Monitoring Data Sheet: Example ................................................................ 91 
 xi 

Executive Summary 

This protocol and the attached standard operating procedures (SOP) detail the background, field 

methods, data management, annual and long-term reporting and analyses, and operational 

requirements to monitor prairie falcons (Falco mexicanus) at Pinnacles National Monument in 

California. Prairie falcons nesting at the monument are sensitive to human disturbances from 

climbing and hiking activities. 

Raptor monitoring began in 1984 with the negotiated settlement of a potential lawsuit. The 

lawsuit was threatened as a result of the monument’s desire to close off parts of the park 

including popular climbing routes to reduce access to breeding prairie falcon areas. The 

compromise negotiated required the park to monitor cliff nesting raptors annually to track 

breeding behavior and establish climbing advisories only in those areas where falcon nesting 

activity occurs. This has been largely perceived as a win-win by both the monument and the 

climbing community. 

Initially, raptor monitoring focused on minimizing disturbance in areas regularly visited by 

climbers (core areas). As time allowed, monitoring was also conducted in areas less accessible to 

the public (non-core areas). Expanding the monitoring area has allowed the park to evaluate 

long-term changes in population and fecundity. The monitoring described in this protocol is 

focused on the following objectives: 

1. Track changes in prairie falcon occupancy for all historically occupied territories. 

2. Track changes in prairie falcon fecundity success as measured by a. number of chicks/nest 

produced and b. number of chicks/nest fledged in historically occupied areas. 

The field season begins in January with the onset of the nesting season. The last nestlings 

typically fledge by mid July. Field surveys are conducted by one technician throughout the 

nesting surveys. All historic territories are visited a maximum of three times to determine 

occupancy. Territories are monitored up to 4 hours per visit to observe territorial behaviors such 

as defensive territorial displays, food exchanges between males and females, use of perches or 

night roosts. To determine fecundity, nests are visited frequently enough to positively identify 

nesting stage including territorial occupancy, courtship, incubation, rearing of nestlings, and 

fledging of young within a breeding season. 

Climbing advisories are posted at the beginning of the breeding season to restrict access to areas 

historically used by cliff nesting raptors. The advisories are lifted if there is no territorial cliff 

nesting raptor or when activity around a nest ceases (i.e. nest failed or chicks fledged). 

 

To date, more than 20 years of data show a relatively stable population in the monument. From 

1984 – 2009 there have been an average of 7.4 territorial pairs in the core area. There have been 

an average of 6.3 nesting pairs producing 19.3 nestlings per year. The number of fledglings per 

nest have ranged from 0 to 4.4 per year. Including all territories, the most nestlings were 

produced in 1994 (45) and the second most in 2009 (41). No nestlings were produced in 1998 

during a year of heavy rains which likely prevented nesting. 
 xii 

Ancillary data collected during the field investigations have helped document two other cliff 

nesting raptors (golden eagles [Aquila chrysaetus] and peregrine falcons [Falco peregrinus]). 

Five other raptors have been documented at the park including: American Kestrel (Falco 

sparverius), Cooper’s Hawk (Accipiter cooperii), Red-shouldered Hawk (Buteo lineatus), Redtailed Hawk (Buteo jamaicensis), and White Tailed Kite (Elanus leucurus). Documented owl 

species include Barn Owl (Tyto alba), Great Horned Owl (Bubo virginianus), Northern Pygmy 

Owl (Glaucidium californicum), and Western Screech Owl (Megascops kennicottii). The 

monument has been involved with the Condor Recovery Program since 2003. There are now 

more than 26 California Condors (Gymnogyps californianus) in the monument. In 2010, the first 

condors nested in the monument in over 100 years. 

 
 xiii 

Acknowledgements 

Previous versions of this protocol were written by Gavin Emmons (Raptor Biotech) and parts by 

Amy Fesnock (previous Wildlife Biologist of Pinnacles National Monument). Guidance during 

the protocol development was provided by Jim Petterson, Wildlife Biologist, Pinnacles National 

Monument. Jason Herynk developed a state of the art Microsoft Access database, which was 

improved by Dave Press. Stephen Skartvedt provided GIS support. Andrea Williams provided 

comments to an earlier draft of this protocol. Dawn Adams and Kris Freeman provided technical 

editing assistance. Drs. Gretchen LeBuhn and Ed Connor provided comments to an earlier 

version of this document. Drs. Leigh Ann Starcevich and Kirk Steinhorst provided assistance 

with a power analysis, overall sampling design, and data analyses sections. 
 xiv 

Acronyms 

AARWP Annual Administrative Report and Work Plan 

ANOVA Analysis of variance 

CDFG California Department of Fish and Game 

CNDDB California Natural Diversity Database 

ETA Estimated time of arrival 

FAB Front-end application builder 

FGDC Federal Geographic Data Committee 

FOP Friends of Pinnacles National Monument 

GOGA Golden Gate National Recreation Area 

JSA Job safety analysis 

MANOVA Multivariate analysis of variance 

MS Microsoft 

NPS National Park Service 

NRDT Natural Resource Database Template 

NRPM Natural Resource Publications Management 

PINN Pinnacles National Monument 

PTT Push to talk 

QA/QC Quality assurance / quality control 

RRM Research and Resource Management 

SCPGRG Santa Cruz Predatory Bird Research Group 

SFAN San Francisco Bay Area Inventory and Monitoring Network 

SOTP Standard operating procedure 

UTM Universal Transverse Mercator 

VWS Ventana Wildlife Society 

YOY Young of the year 
 1 

1 - Background and Objectives 

1.1 Issue Being Addressed and Rationale for Monitoring Raptor Populations 

Pinnacles National Monument (PINN) is a National Park Service (NPS) unit located in the 

Gabilan Mountains of central California, and provides a diverse habitat for cliff-nesting raptor 

species, including sensitive species such as prairie falcons (Falco mexicanus), peregrine falcons 

(Falco peregrinus), and golden eagles (Aquila chrysaetos). The park was specifically set aside to 

protect the rock cliffs and the caves underlying them for the scientific and public interest values, 

with specific note to not injure or destroy features and wildlife associated with the rocks and 

caves. Since the founding of the park unit in 1908, the dramatic landscapes, extensive trails, 

arrays of summits, and cliff-wall routes at PINN have also been used intensively for recreation 

by rock-climbers and hikers (Rubine 1995). 

Raptors are an important component of ecosystems, and their presence at high trophic levels in 

most food webs make them good indicators of the effects of local and regional changes in 

ecosystems (Marshall 1957; Newton 1979; Brown 1982; Stiles 1985; Brown et al. 1988; 

Steenhof 1998; Sodhi et al. 1990; Meyer 1995; Jaksic et al. 1996; Squires and Reynolds 1997; 

Mazur and James 2000; Haemig 2001; Parrish et al. 2001; Kochert et al. 2002; Roemer et al. 

2002; White et al. 2002; Lundberg and Moberg 2003; Croll et al. 2005; Preisser et al. 2005; 

Sekercioglu 2006a, 2006b). It has been suggested that management activities aimed at preserving 

habitat for bird populations can have the added benefit of preserving entire ecosystems and their 

attendant ecosystem services (Mitani et al. 2001; Millenium Ecosystem Assessment 2005). 

Moreover, raptors have a tremendous following among the public, and many parks provide 

information on the status and trends of birds in the park through their interpretive programs.

Concerns with nesting raptors being impacted by visitor activities associated with rock climbing 

and off-trail hiking were identified as early as the 1920’s, when the Balconies Cliffs were 

identified as a “Bird Sanctuary” and visitor use of these cliffs was prohibited (Rechtin 1992). 

Many scientific studies have documented the negative impacts of human disturbance of raptor 

nest and roost sites, and the resulting nest failures and territorial abandonment associated with 

these disturbances. Nesting raptor species at PINN that are sensitive to human disturbance 

include prairie falcons (Fyfe and Olendorff 1976; Ogden and Hornocker 1977; Harmata et al. 

1978; Sitter 1983; Steenhof 1998), peregrine falcons (particularly in remote locations: see 

Hickey 1942, 1969; Bond 1946; Steenhof 1998), golden eagles (Newton 1979, 1990; Scott 1985; 

Steidl et al. 1993; Steenhof et al. 1997; Watson 1997; Kochert et al. 1999), sharp-shinned hawks 

(Accipiter striatus; Delannoy and Cruz 1988), and long-eared owls (Asio otus; Marks 1986; 

Marti and Marks 1989; Bloom 1994). 

Prairie falcons nest in particularly high numbers at PINN, with an average of 9.6 nesting pairs 

per year documented from 1984–2006 throughout the park and 6.5 nesting pairs per year 

documented from 1984–2006 in core areas with historical or potential rock-climbing impacts 

(Emmons 2006; see Protocol Glossary for terminology definitions related to the raptor 

monitoring program and Figure 1 for a GIS map of core area locations). Studies of prairie falcon 

nest occupancy and productivity have also shown the species to be especially sensitive to human 

disturbance from recreation (Boyce 1982), climbing to nests (Ellis 1973, Kochert et al. 2002), 

mining (Becker and Ball 1981; Bednarz 1984), agriculture (U.S. Dept. of Interior 1979), habitat 
 2 

destruction and nest site limitation (Becker and Ball 1981; Steenhof et al. 1997), and proximity 

to major roadways (Platt 1974, Boyce 1982). 

The main sources of human disturbance of nesting raptors at PINN are visitors that are rockclimbing and hiking, both on- and off-trail in the park. Scientific studies have consistently 

suggested that these recreation activities can be balanced against raptor nesting by establishing 

closure or advisory areas that act as buffers between human activity and raptor nesting during the 

breeding season (Fyfe and Olendorff 1976; Olsen and Olsen 1978, 1980; Becker and Ball 1981; 

Suter and Joness 1981; Porter et al. 1987; Holthuijzen et al. 1990; Cade et al. 1996; White et al. 

2002). 

1.1.1 Historical Development of Breeding Raptor Monitoring in Pinnacles 

Raptors have been of interest at PINN since it was established. Park naturalists and local bird 

enthusiasts have documented nesting raptors since the late 1910s. In the 1970s California 

Department of Fish and Game (CDFG) conducted a series of surveys assessing habitat used by 

peregrine and prairie falcons. The Santa Cruz Predatory Bird Research Group (SCPBRG) 

approached the monument in 1982 with a proposal to assess the impact of climbing on cliffnesting raptors, and the project was funded and completed in 1984. The recommendation from 

this project was to continue monitoring breeding success of cliff nesting raptors (specifically 

prairie falcons and golden eagles; by this time peregrine falcons had been extirpated from 

PINN). 

In 1987, PINN initiated its long-term monitoring program, in part to avoid litigation threatened 

by visitor access advocates. Monitoring information was used to establish climbing/hiking 

advisories only around active nests. The climbing/hiking advisories are notices posted at PINN 

visitor centers and along trails leading to areas with cliff-nesting raptors. These advisories serve 

to inform hikers and climbers about cliff-nesting raptors and potential disturbances. While they 

are not forced closures, the advisories reduce potential disturbances by recommending and 

directing visitors to use other areas within the park. Advisories are lifted when monitoring 

determines that nesting activity has ceased. This monitoring and management approach 

represented a win for both managers wishing to protect nesting raptors and a win for climbers 

wishing access to the park during the peak climbing season. 

The field methods set forth in this protocol have been consistently used since 1987. During the 

early years, however, monitoring focused primarily on “core areas” of the park (Figure 1). “Core 

areas” include historic territories established in the vicinity of climbing routes. Nest surveys were 

outside of the core areas (non-core areas) were only conducted as time allowed. Starting in 2000 

the park made a commitment to monitor all known historic territories in the park (core and noncore areas). 

1.2 Monitoring Questions 

The long-term monitoring set out to answer the following questions: 

• Are the number of cliff-nesting raptor territories in the highly visited core climbing areas 

changing over time? Are the numbers changing in the rest of the monument? 
 3 

• Is productivity of cliff-nesting raptors (number of nestlings per nest that reach fledgling 

stage) changing over time in the core areas? Is productivity changing in the rest of the 

monument? 

While collecting information on trends, information will be gathered to help the park answer the 

following management-related question: 

• For which areas do climbing advisories need to be established in order to reduce potential 

disturbance? 

• Are there differences in prairie falcon productivity between the core and non-core areas? 

If population or productivity trends occur, research could be initiated to address the following 

question: 

• What is causing changes in the number of territories and/or productivity? 

1.3 Monitoring Objectives 

In order to meet park management objectives to reduce disturbance to nesting raptors and to 

track population changes over time, this protocol sets forth the following two measurable 

monitoring objectives: 

1. Track changes in prairie falcon occupancy for all historically occupied territories. 

2. Track changes in prairie falcon fecundity success as measured by a. number of chicks/nest 

produced and b. number of chicks/nest fledged in historically occupied territories. 

1.4 Management Objectives 

Because this protocol grew out of management needs, a clear management objective had been 

established at the onset of the program. That objective is to protect cliff-nesting raptors from 

human disturbance by establishing climbing/hiking advisories in core use areas at the beginning 

of the breeding season (January). These advisories remain in effect until two weeks after the last 

fledgling occurs (usually mid July). Advisories are also removed in areas where nesting is not 

confirmed after 3 site visits. 

1.5 Other Raptor Species 

While not a formal part of this protocol, ancillary data are collected by technicians as they travel 

through a variety of habitats on the way to and from the prairie falcon sites. During these efforts, 

for example, technicians gather presence/absence data and productivity for up to 15 other species 

of raptors (see Appendix D for full list). These data are of great interest to managers because 

many of these species are considered “species at risk” by the CDFG (2008). 
 4 

Figure 1. Historical territories near climbing routes (core) and throughout the rest of the park (non-core). 
 5 

Of the raptor species that occur at PINN, the species that are at greatest risk of being impacted by 

human presence and disturbance in riparian habitats include: Cooper’s hawks (Accipiter 

cooperii), sharp-shinned hawks, white-tailed kites (Elanus leucurus), and long-eared owls. While 

the lack of consistent nesting data for these species over the course of the 20-year raptor 

monitoring program precludes trend analysis on these species, the data provide park managers 

valuable information on the locations of important breeding areas. This information has been 

used for planning purposes relating to the revision of the PINN General Management Plan, 

guided timing of routine maintenance activities, as well as improved understanding of the 

diversity of breeding raptors at PINN. 

1.6 Trigger for Management Activities 

In addition to establishing and maintaining climbing advisories, raptor monitoring directly 

supports management activities. In past years, for example, data collection activities helped 

document illegal activities such as base jumping that caused disturbances to nesting falcons. In 

this example, the documentation informed law enforcement rangers and led to convictions of 

jumpers in a federal court (Emmons 2006). 

Over the long-term, the power analysis presented below, indicates that monitoring will allows us 

to determine a 50% decline in territorial occupancy, hatching, or fledgling rates over 10 years 

with relatively high precision. Because waiting for 10 years is not ideal, park managers have set 

intermediate assessment points that will serve as early warnings to avert an unrecoverable crisis 

(see Bennetts et al. 2007). The assessment points will trigger a review of the data to evaluate if a 

decline is likely to be occurring, if obvious causes of a decline can be discerned, and if 

management actions should be considered. The first assessment points is met after noting a 25% 

decline of territory occupancy, hatching, or fledgling rates after 5 years of monitoring. A second 

assessment point is met after a 35% decline occurs in 7 years. 

If declines are noted and they are likely due to disturbances from park visitors, management 

options may include (but are not limited to) additional visitor education, increased staffing to 

better monitor climbing and hiking activities in critical areas, or increased limitations to climbing 

and off-trail hiking during the raptor breeding season. If causes for declines are poorly 

understood, additional research may be triggered. The assessment points will help the park 

identify funding sources and implement research before a 50% decline over 10 years is realized. 

Research would be designed to discern cause and effect to identify appropriate management 

response. Food availability, for example, may play a significant role in understanding prairie 

falcon occupancy and reproductive success but at present, it is poorly understood (Buranek 

2006). 
 6 

2 - Sampling Design 

Raptors are notoriously difficult to monitor (Marzluff et al. 1994; Dunk 1995; McFadzen and 

Marzluff 1996; Marzluff et al. 1997; Steenhof 1998; Bildstein and Meyer 2000; Kochert et al. 

2002). Raptors are secretive and occur in low densities, making them difficult to monitor using 

typical methods like Variable Circle Plot Point Counts (Ralph et al. 1993). In general, the 

accepted methodology involves identifying suitable nesting areas and locating and observing 

nests (Steenhof 1987), with a minimum of two to three nest observation visits per breeding 

season (Fuller and Mosher 1981; Fraser et al. 1983; Steenhof 1987). With such a low sampling 

frequency, it is virtually impossible to attain reliable productivity information and essentially 

only allows for determining presence/absence of breeding pairs in a given area. For determining 

absence of prairie falcon occupancy within a given territory, two to three visits, scheduled to 

occur in different sequential months, were considered adequate. 

At PINN, however, raptor breeding activity and productivity is used to establish and evaluate 

climbing/hiking advisories every year (see Background and Objectives above). To meet this 

need, more frequent sampling is required. Frequent nest site observations also allow for more 

accurate estimates of feeding and copulation rates, and precise productivity and fledging dates. 

This section provides a general outline of the sampling strategy employed. 

2.1 Population Being Monitored 

Based on 20 years of monitoring, 36 potential prairie falcon territories have been identified at 

PINN. Of the 36 territories in the target population, 18 of these territories fall into the "core" 

area. The core area consists of territories that are more accessible to climbers and hikers. These 

areas have been surveyed more consistently and frequently over the last 22 years so that 

hiking/climbing pressure can be monitored during the breeding season. Another 18 non-core 

areas were identified and added to the sample over time. Due to the added monitoring 

importance of the core areas, the core and non-core areas will be treated as separate strata with 

core territories censused annually. 

2.1.1 Territories 

A falcon territory is defined as an area that contains, or has historically contained, at least one 

nest with a mated falcon pair. Within a territory, no more than one pair of a given species is 

known to have bred at one time. Falcons will defend their territory against the presence of 

conspecific individuals. Note that this definition is not synonymous with a nest site which is the 

specific location of a falcon nest, either current or historical. Each territory may contain multiple 

potential nest sites, but no more than one will be occupied each year. Surveying a territory entails 

watching for falcon activity in the area, and visually checking all known nest sites in the territory 

for occupancy. To ensure that all likely territories have been identified for monitoring, GIS was 

used to identify potential nest sites based on exposed substrates and slope (> 40 degrees; Figure 

2). The resulting map demonstrated that all potential nest sites had been previously searched and 

could be discounted as likely nest sites. In most cases, the potential nest site habitat identified in 

the map, does not provide suitable ledges for nesting. Periodic searches of some potential nest 

site habitat areas, however, may be warranted to determine if the population is expanding into 

unlikely or unexpected habitats. See Glossary for more detailed definitions of nest site and 

territories. 
 7 

All potential cliff-nest territories within the core area have been surveyed every year since 1987. 

All historical territories in core areas and a subset of territories in the non-core areas will be 

made annually. 

Figure 2. GIS map depicting potential nesting areas at PINN with >40 degree slopes. Field surveys, 

however, found that areas outside of core and non-core areas did not provide suitable nesting ledges. 
 8 

2.2 Sampling Frequency and Replication 

Occupancy and fecundity by territorial or nesting raptor pairs will be determined through an 

annual census of all core area territories and a sample of non-core areas. Sites are visited at least 

3 times each year, spaced 21-28 days apart, with visits lasting 1 to 4 hours. 

2.3.1 Sampling Frequency 

Sampling will be limited to the raptor-breeding season (January through July). Site visits begin 

in January and the fledglings leave the nest by July. 

Occupancy: To determine occupancy (presence of at least one prairie falcon), a maximum of 

three site visits will be made to historical nesting areas. Presence of a territorial prairie falcon on 

any of the three surveys confirms occupancy, and lack of presence after three surveys confirms 

lack of occupancy. In sites where prairie falcon detections are made, territorial occupancy is 

assessed by examining the behavior of each detected prairie falcon. Territorial behavior is 

verified by observing courtship or reproductive behavior or evidence of offspring. 

Fecundity: To determine fecundity, nests are visited frequently enough to positively identify 

nesting stage including territorial occupancy, courtship, incubation, rearing of nestlings, and 

fledging of young within a breeding season (Fuller and Mosher 1981; Fraser et al. 1983; 

Steenhof 1987). 

2.3.2 Intensified Sampling 

Additional site visits are made to the territories in the core area (at least one visit every 7–14 

days) to support establishment of advisory areas that protect breeding raptors from impacts of 

climbers and off-trail hikers at the park. To remain consistent, however, additional data on 

occupancy collected during these site visits are not included in comparisons made against noncore areas. These surveys are used only to inform management activities and interpret potential 

sources of nest failures/chick mortality during the nesting season. 

2.3.3 Length of Site Visit 

Three- to five-hour observation periods are commonly recommended to document territory 

occupancy of peregrine falcons and prairie falcons (U. S. Fish and Wildlife Service 1984; Cade 

et al. 1996; Smith and Hutchins 2006). Steenhof et al. (1999) employed 2-hour observation 

periods during point surveys to document territory occupancy of prairie falcons in the Snake 

River region of Idaho. To confirm absence of occupancy at potential falcon territories, we 

adopted a conservative standard of observing for at least 4 hours per visit. Site visits may be 

short (e.g., 1 hour) when breeding status can be quickly determined (e.g., when all nestlings at a 

nest site were clearly visible and easily aged) or they need to continue until at least 4 hours pass 

if territorial behaviors are not determined. 

2.3 Sample Size and Statistical Power 

The magnitude of levels of detectable trends in occupancy and fecundity metrics that are 

detectable are inextricably linked with the sample size precision of the estimator, the Type I error 

rate, and the statistical power considerations of the test used to assess the trend (Cohen 1988). 
 9 

The Type I error rate, designated as α, is the probability of rejecting a true null hypothesis. For 

two-sided tests of trend, a Type I error would mean that the population was found to be changing 

when it was not. A Type II error occurs by failing to reject a false null hypothesis and thus 

concluding no change has occurred. For long-term monitoring, the cost of a Type I error may be 

far less than the cost of a Type II error. Mistakenly rejecting a true null hypothesis (Type I error) 

may trigger a management conservation action that is not actually needed. However, failing to 

detect a significant trend may have deleterious effects that cannot be reversed by the time the 

trend is actually detected. A conservative approach is to use a larger α value for higher power 

and reduced probability of a Type II error (Buhl-Mortensen 1996; Gibbs, et al. 1998; Mapstone 

1995). For this power analysis, an α value of 0.20 is used. Power does not fall below the α value 

assumed for the trend test. 

Starcevich and Steinhorst (2010; Appendix A) analyzed data collected from 2008 to 2009 to 

evaluate occupancy sampling design to reflect the current conditions the power to detect trends 

of prairie falcon occupancy. Pilot data collected prior to 2008 excluded some territories for 

which no detection was made, so these data were not used for the power analysis. This issue did 

not affect the fecundity data, so a separate analysis was conducted on data collected from 2002-

2009 to evaluate fecundity sampling design. While fecundity data are available since 1984, the 

data collected since 2002 represent the current methodology and are thought to be more 

consistent. 

2.3.1 Occupancy Analysis 

Let yijk be the outcome for site i, year j, and visit k, and let yijk take a value of 1 if a prairie falcon 

is detected and 0 otherwise. Let ijk y be the number of detections made at site i and year j during 

the k

th visit. The zero-inflated binomial distribution is expressed as: 

( ) ()() ( ) 1

11 0 ijk ijk y y PY y p p I y ijk ij ij ij ij ijk π π −= = − +− = , 

where πj is the occupancy rate in year j and ij p is the detection rate for site i and year j. Assume 

that there are S sites, T years, and K visits to a site each year. Define the indicator function as 

( ) 0 ij I y = as 1 when 0 ij y = and 0 otherwise. This model assumes an equal number of visits to a 

site within a year, but K can vary among sites or among years. 

Occupancy (π) is modeled with logistic regression as a function of related covariates: 

0 1 log , 1

ij

ij

ij

x

π

γ γ

π

⎛ ⎞ ⎜ ⎟ = + ⎜ ⎟ − ⎝ ⎠

where ij x represents a covariate for the i

th site and the jth year. Multiple site-level or year-level 

covariates may be incorporated. To test for linear trend in the logged odds ratio of occupancy, 

the year covariate should be included as a predictor in the occupancy model. The probability of 

zero inflation (p) is simultaneously estimated by logistic regression: 
 10 

0 1 log 1

ij

ijk

ij

p

x

p

β β

⎛ ⎞

= + ′ ⎜ ⎟ ⎜ ⎟ − ⎝ ⎠

, 

where ijk x represents a covariate collected at the i

th site and kth visit during the jth year. 

The implicit dynamics occupancy model is assumed for this analysis. In contrast to the explicit 

dynamics model, estimates of colonization and local extinction are not explicitly measured in the 

implicit dynamics model (MacKenzie et al. 2006). The net effect of extinction and colonization 

rates on occupancy is monitored rather than estimating the parameters separately since 

monitoring net change over time is the primary goal. 

Maximum likelihood is used to estimate the regression coefficients from the models for 

occupancy and detection rates. Occupancy can be modeled at the site-by-year level so predictors 

should be collected at the site level, year level, or site-by-year level. Detection probabilities are 

allowed to vary at each visit for each site and year, so covariates are often environmental 

covariates that vary from visit to visit (MacKenzie et al. 2006). However, if detection rates are 

also changing over time, a model that includes a year covariate may be needed for accurate 

occupancy estimation. Model selection is conducted using the Akaike Information Criterion 

(AIC). Model output is examined to be certain that valid variance estimates are obtained. When 

valid estimates of the variance cannot be acquired for the model with the lowest AIC, then the 

model with the lowest AIC and valid variance is used. 

A prairie falcon detection does not necessarily imply that occupancy has been established. 

Designation of site occupancy requires multiple site visits within a year and evidence of 

territorial behavior. The power to detect a 50% decline in occupancy after 10 consecutive survey 

years is provided in Table 1. Because the core sites must be censused each year to monitor highuse areas, the power to detect trends in non-core sites was examined separately (Table 2). 

Comparisons of Tables 1 and 2 indicate that the high power to detect trends across the set of 

territories is largely due to the census of core sites. If occupancy is substantially different 

between the two subpopulations, then inference on prairie falcon occupancy at PINN may be 

misleading unless at least between 9 and 12 non-core territories are surveyed each year. 

However, the two years of pilot data did not indicate a significant difference in occupancy 

between core and non-core sites (p=0.1413). 

Tables 1 and 2 also present revisit designs that specify the schedule of sites visitations by year 

for the duration of the monitoring period. Notations presented by MacDonald (2003) employ a 

string of numbers, dashes, and commas to identify the revisit design among panels. The numbers 

in odd-numbered positions will designate the number of consecutive visits to a panel before it 

rotates out of the revisit schedule. Digits in the even-numbered positions indicate the number of 

sampling occasions that the panel will not be revisited. Therefore, a revisit schedule of [1-0] 

indicates that the panel will be revisited each year and never rotated out of the schedule. This 

revisit schedule is equivalent to taking a random sample of sites and visiting them every year. 

The [1-n] revisit design represents a sampling design in which independent random samples are 

taken every year. A [2-2] revisit design consists of one panel visited for two consecutive years 

and then rested for two consecutive years before beginning the cycle again. Differing revisit 
 11 

schedules among panels are indicated by separating the digits in parentheses by commas within 

the brackets. For example, a revisit schedule of [(1-0), (1-3)] indicates that the revisit design 

includes an annual panel that is revisited every year and four panels that are visited for one year 

then not visited for the following three years before being rotated back into the design. The [1-0] 

revisit design is known to have the highest power to detect trend (Urquhart and Kincaid 1999). 

Table 1. Power to detect trend for different sample sizes and revisit designs. 

Annual 

sample size of 

territories 

Number of 

core sites 

surveyed 

annually 

Number of noncore sites 

surveyed annually 

Revisit 

design* 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

18 18 0 [1-0] 0.953 

21 18 3 [1-0] 0.973 

24 18 6 [(1-0),(1-4)] 1.000 

30 18 12 [(1-0),(1-1)] 1.000 

36 18 18 [1-0] 1.000 

Table 2. Power to detect trend for non-core sites only. 

Number of noncore sites 

surveyed annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

3 [1-0] 0.559 

6 [1-0] 0.754 

9 [1-0] 0.773 

12 [1-0] 0.852 

2.3.2 Fecundity Sample Design 

Fecundity is monitored with counts of hatchlings or fledglings observed at each nest. These 

outcomes are also modeled with the zero-inflated binomial model. Let ij y be the number of 

hatchlings or fledglings detected in occupied site i and year j. Let n be the number of occupied 

sites and T be the number of years monitored for fecundity. The probability mass function of Y

is: 

( ) ( ) ( )( ) 11 0 ij ij M y y

ij ij ij ij ij ij

ij

M

PY y p p I y

y

π π

⎛ ⎞ −

= = − +− = ⎜ ⎟ ⎝ ⎠

, 

where πij is the probability that an extra 0 is not observed at site i in year j, pij is the probability 

of a hatchling/fledgling at site i in year j, M is the maximum number of hatchlings/fledglings 

seen in any nest, and I(yij = 0) is 1 when yij = 0 and is 0 otherwise. Maximum likelihood 

estimation is used to obtain estimates of the regression coefficients in each model, and the 

invariance property of maximum likelihood estimates is used to obtain estimates of π ij and ij p . 

To test for trend in fecundity, the year covariate is included as a predictor in the binomial 

probability model and then tested for significance with a likelihood ratio test. In contrast to the 

occupancy analysis, fecundity inference is made on the change in pij over time. 
 12 

Fecundity will be monitored by examining trends in the binomial probability of hatchlings and 

fledglings. The results of the power analysis indicate that annual surveys of at least 10 pairoccupied sites provides power of at least 0.8 to detect a 50% decline over 10 years in hatchling 

or fledgling rates (Tables 3 and 4; Starcevich and Steinhorst 2010). 

Table 3. Power to detect a 50% decline in the number of hatchlings. 

 

Annual 

sample size 

of occupied 

nests 

Power to detect a 50% decrease 

after 10 years 

(based on 2002-2009 pilot data) 

5 0.609 

10 0.910 

15 1.000 

20 1.000 

25 1.000 

Table 4. Power to detect a 50% decline in the number of known fledglings. 

 

Annual 

sample size 

of occupied 

nests 

Power to detect a 50% decrease 

after 10 years 

(based on 2002-2009 pilot data) 

5 0.645 

10 0.922 

15 0.977 

20 1.000 

25 1.000 

Core sites are visited annually so that hiking and climbing pressure can be assessed throughout 

the breeding season. Given that the set of core sites is censused annually, power is examined for 

samples that contain all core sites and a range of non-core sites each year. For counts of both 

hatchlings (Table 5) and fledglings (Table 6), the power to detect trend in the binomial 

probability for each outcome is one. This level of power is attained even when no non-core sites 

are surveyed. However, if inference to non-core sites is of interest, then this subpopulation 

should be sampled with sufficient effort. Power to detect a 50% decline over 10 consecutive 

years exceeds 0.8 for as few as three sites each year for hatchlings (Table 7) and fledglings 

(Table 8). 

Table 5. Power to detect trend in the binomial probability for hatchlings for a census of core sites and 

a sample of non-core sites. 

 

Annual 

sample size 

of territories 

Number of 

core sites 

surveyed 

annually 

Number of noncore sites 

surveyed 

annually 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

18 18 0 1.000 

21 18 3 1.000 

24 18 6 1.000 

30 18 12 1.000 

36 18 18 1.000 
 13 

Table 6. Power to detect trend in the binomial probability for fledglings for a census of core sites and a 

sample of non-core sites. 

 

Annual 

sample size 

of territories 

Number of 

core sites 

surveyed 

annually 

Number of noncore sites 

surveyed 

annually 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

18 18 0 1.000 

21 18 3 1.000 

24 18 6 1.000 

30 18 12 1.000 

36 18 18 1.000 

Table 7. Power to detect trend in the binomial probability for hatchlings for non-core sites only 

Number of noncore sites 

surveyed 

annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

3 [1-0] 0.875 

6 [1-0] 0.957 

9 [1-0] 1.000 

12 [1-0] 1.000 

Table 8. Power to detect trend in the binomial probability for fledglings for non-core sites only 

Number of noncore sites 

surveyed 

annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

3 [1-0] 0.816 

6 [1-0] 0.969 

9 [1-0] 0.988 

12 [1-0] 0.996 

2.4 Sample Design Summary 

The power analysis indicates that a census of the 18 core sites should provide power greater than 

0.80 for trend detection. Because inference to the subpopulation of non-core sites is of interest, 

then at least 12 of the non-core sites will be surveyed each year for occupancy and 10 for 

fecundity (Table 9). To select the 12 non-core territories, we will randomly order the non-core 

sites and use that random order to allot territories to the panels. Stratified random sampling 

within the non-core subpopulation will allow inference to that subpopulation of territories. 

MacKenzie et al. (2006) recommend the [1-0] revisit design so that the additional unexplained 

variation due to rotating different territories into the survey in different years will not affect 

variance estimates and therefore the power to detect trends.
 14 

Table 9. Sample design [(1-0),(1-2)] for Pinnacles National Monument.

 Year 

Panel Core/ 

non-core 

1 2 3 4 5 6 

1 Core 18 18 18 18 18 18 

2 Non-core 6 6 6 6 6 6 

3 Non-core 6 6 

4 Non-core 6 6 

5 Non-core 6 6 

ANNUAL 

TOTAL 30 30 30 30 30 30 

* At least 10 territories will be randomly drawn from each year’s occupied territories to track fecundity 

(hatching and fledgling success).
 15 

3 - Field Methods 

The Prairie Falcon breeding season runs from late January until July. During that time, surveys 

are made of historical territories to determine if territories are occupied by single or paired birds. 

Once nests are located, repeated surveys are made to document any hatching and fledgling 

activities. To reduce disturbance to nesting falcons, the park establishes advisories around 

historical territories in the core areas at the beginning of the field season. Advisories are lifted 

around territories that are not occupied by the middle of the breeding season. All advisories are 

lifted by the end of the season. 

3.1 Field Season Preparations 

The field season begins with hiring a Raptor Technician by January of each year. For the first 

month, the technician becomes familiar with the protocol, SOPs, the project database, 

equipment, and data sheets. Training in field methods, including field note taking and locating 

territories, will be provided by the Wildlife Biologist at PINN. If data management training is 

needed, this will be provided by the SFAN Data Manager. If first-aid or other safety training is 

necessary, this will be provided by the park. 

At the beginning of the field season, the technician gathers, checks, and becomes familiar with 

all of the required field equipment including binoculars, telescope, camera, data sheets, 

rangefinder, GPS, and the Kestrel weather meter with thermometer. Faulty equipment is repaired 

or replaced. A complete list of equipment is provided in SOP 1: Field Methods. 

Climbing/hiking advisories are established around all core area territories occupied by prairie 

falcon, peregrine falcon, and golden eagle pairs within any of the last 3 years. While advisories 

are not closures, any visitors found within advisory areas can be cited and fined by park law 

enforcement staff. Advisories are established by posting signs at trailheads, posting updates to 

bulletin boards, notifying appropriate staff, “Friends Groups”, and sending press releases to 

various media outlets. Locations for posting signs are presented in SOP 1: Field Methods. 

3.2 Field Season Surveys 

Surveys begin by late January or early February when prairie falcons begin establishing 

territories. All historical territories in the core areas and 12 selected territories in the non-core 

areas are selected for monitoring occupancy and fecundity annually. Although only 12 non-core 

territories are selected for monitoring to determine both occupancy and fecundity trends, the park 

has consistently contributed funding to supplement the monitoring budget over the last seven 

years so that all non-core territories can be tracked each year to meet management requirements. 

Territories are observed from watch spots. Directions and maps are provided in SOP 2: Watch 

Spot Locations. It will be helpful for the technician to plan survey routes to maximize the 

number of territories visited without retracing steps. For example, when visiting North 

Balconies, raptor activity may also be seen en route at Guard Rock (at the foot of Pig Canyon), 

along Old Pinnacles Trail, at the North Wilderness Trail junction, and at Eagle Rock. While 

observing North Balconies, raptor activity may also be seen at Crowley, Machete, the Balconies 

Cave Trail junction, etc. Routing suggestions are provided in both SOP 1: Field Methods and 

SOP 2: Watch Spot Locations. 
 16 

Unpredictable weather (e.g., rain, fog) necessitates maintaining some flexibility in scheduling 

site visits throughout the season. While light rain or mist would not cancel surveys, moderate or 

heavy rain will terminate surveys if visibility is impaired or if trails become too slippery and 

unsafe. Lightning always results in a cancellation of surveys for safety considerations. 

Initially, most fieldwork is conducted from mid-afternoon to evening when territorial raptors at 

PINN are most active in the park and visible. During daytime, falcons are typically outside of the 

park to feed. The birds typically return to the breeding areas in the park toward dusk. Most 

observations will take place just before raptors fly into cliff crevices to roost for the night. 

Each territory will be visited at least three times to determine occupancy, with each visit in a 

different month. The technician spends up to four hours observing at each site before concluding 

that a territory is unoccupied. Observations are typically made through binoculars or telescope 

depending on distance from watch spot to the territory. Data are collected on a standard 

datasheets each time that a territory is visited. The same territory may be seen from multiple 

watch spots. Additional data may be collected following the Grinnellian method in a running 

diary style for birds sighted and associated behaviors observed. Data noted on the datasheet 

includes name of the territory, date, weather conditions (including air speed and thermometer 

collected with a weather meter), number of birds sighted, territorial behavior (e.g. territorial 

displays, food exchanges between males and females, copulations, food exchanges between 

males and females, use of perches or night roosts), and nesting behavior (e.g. incubation, food 

carries, nest switches). The datasheet also includes space to draw maps of nest or perching 

locations. If territorial behaviors that define occupancy are observed in less than four hours, the 

technician may move on to a watch spot in another territory. 

As soon as all prairie falcon pairs have chosen nest sites, usually in late March to mid-April, 

advisories are lifted for unoccupied territories. Staff is notified, bulletin boards are updated, and 

notifications are sent out to climbing shops. During the nesting period, prairie falcons may be 

found during any time of the day. The number of eggs in a nest should be documented if visible 

from the watch spot. Observations of nests are made frequently enough to document the number 

and timing of hatching. The length of observations will depend largely on how cooperative the 

birds are to allow the documentation of eggs or hatchlings. 

After raptors hatch, the technician estimates ages of young on at least 3 monitoring visits to 

accurately estimate fledging dates. Two weeks after fledging has finished, usually in June or 

July, climbing / hiking advisories at the park are lifted. 

On a daily basis data sheets and notebooks need to be reviewed for completeness. Throughout 

the field season, data are transcribed from data sheets and notebook into the database (described 

below and SOP 3: Data Management). Data are verified by the data manager at the end of the 

season. 

3.3 Ancillary Data 

In addition to tracking prairie falcons, collection of presence/absence and productivity 

information of all other breeding raptor species at PINN is important to park managers. The 
 17 

technician will conduct surveys in non cliff habitats to determine species diversity, nesting, 

productivity, and phenology of other raptors in the park as time allows. Most of these surveys are 

made on the way to and from watch spots. It is recognized that because these data are not 

collected with systematic, standardized sample efforts, population trends cannot be determined 

with statistical estimates of certainty. 

Given the recent breeding records of peregrine falcons at PINN in 2005–2007 and the similarity 

of nesting habits between peregrine and prairie falcons (Hickey 1942, 1969; Bond 1946; Olsen 

and Olsen 1978, 1980; Porter et al. 1987; Cade et al. 1996; Steenhof 1998; White et al. 2002), 

breeding peregrine falcons will be documented and monitored according to the measurable 

objectives and sampling designs outlined for prairie falcons. As the amount of data for peregrine 

falcons mounts, it may be possible to conduct statistical trend analyses in the future. 

Beginning in December 2003, PINN and the Ventana Wildlife Society (VWS) began a California 

Condor Recovery Program at PINN. Breeding pairs of California condors were thought to occur 

at PINN through the 1930's, and solitary birds were seen into the 1980's. After years of releases, 

the first condors have nested in PINN in 2010. The PINN condors are closely monitored and 

actively managed by the PINN and VWS biologists. Once a population is reestablished, a formal 

long-term monitoring program will be developed. 

3.4 End of the Field Season 

At the end of the field season, all remaining advisories are taken down. Staff, “Friends Groups”, 

and media are informed of the lifted advisories. Equipment including scope, tripod, binoculars, 

rangefinders, etc. is cleaned and stored at the Resource Management Office. Any remaining data 

are entered, verified, and archived as described below. An annual report is written and circulated 

as described below and in SOP 5: Data Analysis and Reporting. Photos and project highlights are 

submitted to the network program manager for the network’s annual report. End of season 

procedures are further detailed in SOP 1: Field Methods. 

 19 

4 - Data Management 

4.1 Management of Statistical Data 

All data used to meet the monitoring objectives are maintained in the Raptor Monitoring 

Database (S:\RRM\RRM_Data\RM Workgroups\PROJECTS\Breeding 

Raptors\database\important database files). The database, includes data for prairie falcon 

productivity for each year (from 1987 onward), including nesting pairs, successful nests, young 

of year (YOY) hatched, YOY fledged, and fledglings/nest throughout the park (core and noncore areas together), and specifically in core areas. These data are listed in SOP 5: Data Analysis 

and Reporting. The numeric data are the culmination of field monitoring observations and data 

entry into the PINN raptor monitoring databases described below. The numeric data stored in 

this database are used by biometricians working with park staff to determine prairie falcon 

productivity and population trends over 3-5-year intervals, as described further in the Data 

Summaries and Analysis section below. 

4.2 Overview of Database Design 

The San Francisco Bay Area Network Inventory and Monitoring Program (SFAN) staff has 

developed a relational Microsoft (MS) Access XP database for the raptor monitoring program at 

PINN compliant with the Natural Resource Database Template (NRDT) Version 3.1, an 

application developed by the NPS Natural Resource Inventory and Monitoring Program (I&M). 

The data are organized around survey events from fixed observation points, which are described 

and geographically defined in a locations table. Besides actual raptor observations, copulation, 

and feeding events, the database is used to maintain records of end-of-season summaries of 

occupancy and breeding phenology, data history, field personnel, photo records, and media 

contact addresses. The raptor database has a separate front-end (user interface) and back-end 

(data tables). The user interface for the database is modeled after the NRDT Front-end 

Application Builder (FAB) Version 1.0, an MS Access user-interface template designed by the 

NPS Natural Resource GIS Program. The database is further described in SOP 3: Data 

Management. 

 

4.3 Data Entry and Verification 

Data entry is best performed by a person who is familiar with the data and ideally takes place as 

soon as data collection is complete. The single goal of data entry is the transcription of the data 

from paper records into the computer with 100% accuracy. However, because transcription 

errors are virtually unavoidable during data entry, they will have to be corrected during the data 

verification process. Observation of certain data entry guidelines, however, will minimize 

verification work. The SFAN Data Manager, in conjunction with the PINN Wildlife Biologist, 

should provide training in the use of the database to all data entry technicians and any other 

users. The Wildlife Biologist will ensure that data entry technicians understand how to enter data 

and that they follow the protocol. Specific procedures for entering data into the project database 

are detailed in SOP 3: Data Management. 

The most robust quality assurance and quality control (QA/QC) measures for data entry are built 

into the raptor database design. Several additional QA/QC procedures must be followed by the 

database user: 
 20 

• Have a familiarity with the database software, database structure, and any standard codes 

for data entry that have been developed. 

• Enter data in a timely manner. All data should be entered into the project database as 

soon as possible, preferably no less than once a week. 

• Enter the data, one logical "set" at a time. Record in a notebook any known errors or any 

questions that arise about the data content; these will be useful during data verification. 

Initial and date each paper form as it is completed to avoid confusion about what has 

been entered and what has not with a different color then the data. 

• Interrupt data entry only at logical stopping points. When reaching stopping points, make 

a working backup copy of the data for safety's sake if the software does not do so 

automatically. 

Manual effort is generally required to get data into electronic format. Any typographical errors 

made will accumulate in the permanent database unless the data are verified and the errors 

detected. By implementing data verification procedures, these errors can be reduced, if not 

eliminated. Data verification immediately follows data entry and involves checking the accuracy 

of computerized records against the original source, usually hard copy field records, and 

identifying and correcting any errors. When the computerized data are verified as accurately 

reflecting the original field data, the paper forms can be archived and most data manipulation can 

be done on the computer. 

The Raptor Monitoring Program utilizes visual and audio review after data entry for data 

verification. Visual review after data entry requires two people. One person sits with the field 

datasheets in hand while the other sits at the computer with the database open. As data sheets are 

read aloud, the database record is followed and analyzed for data entry errors. Errors are 

corrected as they are discovered. Verified data sheets should be dated and initialed. 

4.4 Metadata and Data Archival Procedures 

The NPS GIS Committee recently required all NPS GIS data layers be described with the NPS 

Metadata Profile, which combines the Federal Geographic Data Committee (FGDC) standard, 

elements of the ESRI metadata profile, the Biological Data Profile, and NPS-specific elements. 

Although no standard has been applied to natural resource databases, the SFAN will complete 

the NPS Metadata Profile to the greatest extent possible to document databases developed for the 

SFAN I&M program. 

A complete metadata record for the raptor monitoring MS Access database will be generated in 

compliance with current NPS standards by the SFAN data manager. When completed, the 

metadata record, but not the data themselves, will be posted to the NPS Data Store for public 

discovery and consumption. Contact information within the metadata record will direct interested 

parties to the Network Data Manager for further inquiries. The metadata record posted to the 

NPS Data Store will be updated annually after the annual data has been entered and errorchecked or following database revision to a new version whole number (i.e., v1_3 to v2_0, but 

not v2_0 to v2_1). 

Each season, once data entries have been entered and proofed, the physical documents, including 

datasheets, field notebooks, printouts of end-of-season reports, nest data forms for CDFG and 
 21 

SCPBRG, are all kept in the Research and Resource Management (RRM) office, in a fire-proof 

cabinet in folders, each marked by subject and year. The archived photo negatives and photo 

prints are also kept in the RRM office in another fire-proof cabinet as detailed in the photo 

management section above. 

All digital documents and data related to the raptor monitoring program, including scanned and 

digital photos, are stored on the “SharedData” drive on the PINN file server Inppinn001. 

At the SFAN offices at Golden Gate National Recreation Area (GOGA), copies of all final 

documents and digital data relating to the raptor monitoring program are archived on the Marin 

Headlands server at: 

Inpgogamahe1\Divisons\Network I&M\IM_Archive\VS_Indicators\Raptors 

Data and documents are stored in separate folders. The raptor database will be copied and 

archived here annually after that year’s field data has been entered and error-checked. All final 

documents relating to the raptor monitoring program, including the protocol and all annual 

reports, will also be stored in the raptor archive directory. All files in the archive directory are 

stored in read-only format. 

 23 

5 - Analysis and Reporting 

Reporting and analysis are essential components of the raptor monitoring process. The 

effectiveness of the PINN raptor monitoring study over the past 20 years lies not only in the field 

work and data management, but also in how the information gathered is shared throughout the 

season and how it is summarized at the end of the season. Reporting within-season allows for the 

information gathered to be transferred to Law Enforcement and Interpretation Rangers for the 

most effective internal support of the program. It also allows visitors to understand what 

Resource Management does in a NPS unit and how resources are protected while providing for 

visitor enjoyment of those resources. Data analysis at the end of the season allows for consistent 

scientific rigor to be applied to the raptor monitoring program, and allows resource managers to 

identify prairie falcon population and productivity trends and triggers for management actions 

and resource protections. 

5.1 Reporting Schedule and Formats 

Reporting results is a critical component of long-term vital signs monitoring in order to ensure 

that information generated through the program is available to all levels of park management 

including planning, interpretation, maintenance, and law enforcement. A summary of reports that 

will be developed is provided in Table 10 and details are provided in SOP 5: Data Analysis and 

Reporting. 

5.1.1 Bi-Weekly Update 

One of the most critical components of updating park staff about active nesting territories and 

climbing advisories is the Bi-Weekly Update. The Updates include the following information: 

the number of territorial and nesting raptor pairs, which territories are occupied and the status of 

each territory (e.g., defense, incubation, nestlings including number and age). Any unique or 

noteworthy observations (new territories or nests, unusual raptor sightings, etc.) are also 

included. The Updates are distributed to all employees via e-mail. Because updates are also 

distributed to organizations and individuals not associated with PINN, specific nest locations are 

not disclosed. 

5.1.2 Annual Report 

The post-season or annual report provides a concise summary of the field season, enables readers 

to determine if the goals of the project are being met and provides an administrative and 

scientific record of monitoring activities. Format follows the national guidelines established for 

Natural Resource Reports in the Natural Resource Publications Management guidance (NRPM; 

http://nature.nps.gov/publications/NRPM). The annual report content includes an abstract, 

introduction, study area and methods, results, discussion and conclusion. The results section of 

the report summarizes the following: 

1. Effort: hours of observations made during the season. 

2. Occupancy Status: identifies the total number of territories occupied by singles and pairs; 

lists occupancy status of each territory monitored. 
 24 

3. Fecundity Status: identifies the total number of nests, number of successful nests (at least 

one egg hatched), and number of hatchlings and fledglings. The reproductive outcome of 

each nest monitored is provided along with causes of nest failure, if known. 

4. Phenology: list dates of first arrivals, hatch dates, and fledging dates. 

5. Human Interactions: indicates nesting activity that may have been affected by human 

interaction. Notes include which territories may have been affected and if any actions 

were taken by staff. 

Table 10. Summary of reporting and communication products. 

Communication 

Product Schedule Summary 

Annual Report Annually • Archive data and document monitoring activities 

• Describe current condition of the resources 

• Document changes in the monitoring protocol 

Analysis and 

Synthesis Report 

3–5 years • Determine patterns and trends 

• Discover correlations among resources being monitored 

• Analyze data to determine the level of change that can be 

detected using the existing sampling scheme 

• Provide context, interpret data for the park within a multi-park, 

regional, or national context 

• Recommend changes to management practices 

Program and 

Protocol Reviews 

5 years • Periodic formal reviews of operations and results 

• Review of protocol design and product to determine if changes 

are needed 

• Part of the quality assurance – peer review process 

Executive 

Briefing 

Annually 

(upon 

completion 

of annual 

report) 

• Two-page summary that lists monitoring objectives and 

questions, discusses annual results, and provides a regional 

context. 

Bi-weekly 

Update 

Bi-Weekly 

during the 

field season. 

• Brief update listing species and names of active territories. 

Highlights areas where advisories are in effect. 

Quarterly IM 

Update 

Quarterly • This one-page monthly e-mail provides park staff with a short 

update on vital signs projects. Text should be no more than one 

paragraph. 

The discussion section of the annual report provides a context for each year’s result in 

comparison to previous years. Recommendations including management, research, and changes 

in the protocol (changing monitoring intervals and timing, moving/adding sites, etc.) will also be 

included in the discussion. 
 25 

These annual reports will be distributed to the SFAN parks, and can be used for park 

management reporting. Portions may be included in the network’s Annual Administrative Report 

and Work Plan (AARWP). 

5.1.3 Analysis and Synthesis Report 

A comprehensive data analysis and synthesis will be written every 3–5 years to summarize 

general trends in prairie falcon occupancy and fecundity. Having more than three years of 

monitoring data are available, linear trends in the logged odds of occupancy and fecundity may 

be estimated and tested for significance. Having this extra time allows for more thorough data 

analysis and review of protocols and may give greater opportunity for adaptive management. 

Format follows the national guidelines established for Natural Resource Reports in the Natural 

Resource Publications Management guidance (NRPM; 

http://nature.nps.gov/publications/NRPM). The report content includes an abstract, introduction, 

study area and methods, results, discussion and conclusion. 

The main focus of this report will be to determine patterns and trends of prairie falcon population 

change and nesting success. This will allow the staff to assess whether the data from the last 

several years fall within the historical “natural range of variability”. 

The key analyses presented in the report will be: 

Occupancy: Occupancy estimates will be obtained annually and will provide measures of status. 

When more than three years of monitoring data are available, linear trends in the logged odds of 

occupancy may be estimated and tested for significance. The number of detections is modeled as 

a zero-inflated binomial random variable (MacKenzie et al. 2006). To test for trend in 

occupancy, the year covariate is included as a predictor in the occupancy model and then tested 

for significance with a likelihood ratio test. 

Fecundity: Fecundity estimates will be obtained annually and will provide measures of status. 

When more than three years of monitoring data are available, linear trends in the logged odds of 

the binomial probability of fecundity may be estimated and tested for significance. The number 

of detections is modeled as a zero-inflated binomial random variable (MacKenzie et al. 2006). 

To test for trend in fecundity, the year covariate is included as a predictor in the binomial 

probability model and then tested for significance with a likelihood ratio test. 

Methodology for trend analysis of occupancy and fecundity rates is provided in SOP 5 Appendix 

A, and pilot data are provided in SOP 5 Appendix B. Instructions are given for the VGAM 

package of the R Project for Statistical Computing. The VGAM package is used for obtaining 

maximum likelihood estimates from zero-inflated mixture distributions. 

In addition to reporting trends in occupancy and fecundity, the analysis and synthesis report will: 

• Provide context and interpret data for the park within a multi-park, regional, or national 

context. 

• Demonstrate if management triggers have been met. 
 26 

• Discover correlations among resources being monitored. 

• Recommend any changes to monitoring and/or management practices. 

• List research questions. 
 27 

6 - Operational Requirements 

6.1 Roles and Responsibilities 

The network Raptor Technician (GS-7) is responsible for conducting field work, data entry, and 

communicating results to some degree. The position is seasonal (Dec – July) and will have a 

flexible schedule (“maxi-flex”) due to the need for travel time and long hours in the field. The 

duty station will be at the PINN headquarters. The park provides office space and administrative 

support. The technician is directly supervised by the PINN wildlife biologist (GS-9/11). The 

supervisor provides training and consistency in implementing the protocol. 

The data management aspect of the monitoring effort is the shared responsibility of the program 

manager and raptor technician. Typically, the raptor technician is responsible for data collection, 

data entry, data verification and validation, as well as data summary, analysis and reporting. The 

wildlife biologist is ultimately responsible for adequate quality assurance/quality control 

(QA/QC) procedures built into the database management system and appropriate data handling 

procedures followed by raptor technician. 

6.1.1 Tasks for the SFAN Raptor Technician: 

• Be well-versed in all aspects of the SFAN Raptor Monitoring Protocol and conduct 

protocol revisions. 

• Coordinate logistics for field work. 

• Coordinate field assistance for protocol implementation and provide training to field 

assistants such as volunteers. 

• Maintain equipment in good working order and keep maintenance records. 

• Collect field data and implement field QA/QC measures. 

• Coordinate data entry, verification, and validation and consult with network data 

managers. 

• Perform basic statistical analyses on data; present and interpret results in annual reports. 

• Coordinate with PINN Wildlife Biologist regarding staff and training needs, data analysis 

and data interpretation. 

• Coordinate with PINN Wildlife Biologist regarding budget, vehicle, and equipment 

needs. 

• Complete Bi-Weekly Updates, annual report and other communication products. 

6.1.2 Broad tasks for PINN Wildlife Biologist 

• Provide technical assistance and supervision for the SFAN Raptor Technician. 

• Develop and conduct performance review (to be reviewed by network coordinator). 

• Manage Raptor Monitoring Program budget. 

• Provide or coordinate training for the SFAN Raptor Technician. 

• Conduct annual QA/QC field checks. 

• Present issues with Network Ecologist for consultation with the Technical Advisory 

Committee. 

• Reviews and provides comments on annual report. 

• Initiates and completes analysis and synthesis report. 
 28 

6.1.3 Tasks for Network Data Manager 

• Provide assistance to the Network Raptor Technician regarding data management, 

archiving, reporting 

• Assist with GIS needs 

• Assist with compilation of metadata for past and current monitoring programs 

6.1.4 Broad tasks for Network Program Manager 

• Coordinate guidance on data management, data analysis and reporting 

• Provide information related to I&M program requirements including reporting 

requirements and deadlines 

• Review technical reports and provide programmatic oversight 

• Plan program budget in coordination with Wildlife Biologist 

• Coordinate peer review of analysis and synthesis reports 

Broad tasks for the SFAN I&M Network Program Manager are not included in the project 

budget below (Table 11). 

6.2 Budget 

Personnel expenses for field work are based on using one raptor technician (GS-07 Raptor 

Technician) for 15 pay periods. All of the territories are accessed via hiking either from the 

Eastside Headquarters or Chalone Maintenance Yards, thus minimal travel costs are expected. 

Equipment including binoculars, scope, tripod, GPS, digital camera are long-lasting but may 

need to be replaced from time to time. 

Additional periodic costs may be incurred if new equipment (e.g., spotting scopes and 

binoculars) are needed beyond those allowable by the annual budget. Periodic costs may also 

include expense for technical assistance through cooperative agreements (e.g., through a NPS 

cooperative ecosystem studies unit) or contracts to assist with long-term data analysis if these 

needs can not be met by park or SFAN staff. 

We estimate that approximately 30% of the budget is allocated to data management and 

reporting. Most of the data entry is done by the raptor technician throughout the field season. The 

final two pay periods is dedicated to data quality control, data analysis, report writing. The 

network’s data manager also assists with data management and reporting throughout the season. 

The majority of the long-term funding is provided through the SFAN I&M Program. Table 11 

also indicates that Pinnacles provides significant funding. Funding from the park is allocated to 

allow the raptor technician to conduct a full census of prairie falcon territories in both the core 

and non-core areas and to track breeding activities of other raptor species in the park. While all 

of the raptor data are tracked through the program’s database, long-term trends are only for 

prairie falcons. If budget shortfalls occur, the decision to monitor species other than prairie 

falcons may be revisited. Similarly, the desire to make inference to the non-core areas may be 

revisited and the number of territories monitored could be reduced. 
 29 

Table 11. Annual budget for FY09. 

Description Category Amount 

Funding Source Park Contribution (PINN) $10,000 

 SFAN I&M Contribution $27,000 

 

 Sub-Total $37,000 

Expense Type Raptor Technician GS-7/4, 15 pp $26,000 

 Data Manager GS-11, 1–2 pp $4,000 

 Wildlife Biologist, PINN GS-11, 1–2 pp $4,000 

 

 Vehicle $2,000 

 Equipment and Supplies $500 

 Travel $500 

 

 Sub-Total $37,000* 

 

 Balance $0 

* Approximately 30% of budget is allocated to data management by raptor technician and data manager. 

6.3 Qualifications and Training 

The raptor technician must be able to visually and/or aurally identify birds, and be proficient at 

interpreting breeding behavior. Training requirements are discussed in SOP 1: Field Methods. 

 

6.4 Annual Workload and Field Schedule 

Raptor monitoring will begin in early January and will continue through fledging, usually from 

mid-June to mid-July (Table 12). The first month of fieldwork is dedicated to training and 

orientation. Monitoring efforts will require, at a minimum, 1 full time raptor technician who 

works 5 days a week. An additional person available 1–2 days per week would greatly enhance 

the data collected. Each territory should be visited once every 7–28 days. Only one territory can 

be visited in a day during territory establishment and in general for the entire season. On 

occasion two territories can be visited during incubation and nestling rearing – but only if travel 

time between the territories is short. Inclement weather (primarily rain or low fog) will preclude 

the scheduling of sampling events to specific annual dates. At the end of each field season, 1–2 

payperiods are needed to write the annual report, complete data entry and perform data QA/QC, 

which should be completed by the end of July. 

6.5 Facility and Equipment Needs 

The nature of bird survey work does not require special facilities beyond normal office space and 

equipment storage needs. A table of field equipment needs for one raptor technician is listed in 

SOP 1: Field Methods. If two or more raptor technicians work simultaneously, equipment 

requirements will increase accordingly. Computer equipment for data entry and report writing 

will be supplied by PINN. 
 30 

Table 12. Summary of annual work schedule (Jan–Aug). 

 Jan Feb Mar Apr May Jun Jul Aug 

Prepare for field 

season 

Organize 

equipment 

 

Field surveys Training Locate 

territories 

/nests 

Locate 

territories 

/nests 

Locate 

territories 

/nests/ 

young 

Young/ 

fledglings 

Fledglings Survey 

end 

Maintain climbing/ 

hiking advisories 

Establish 

advisories 

 Updated 

advisories 

Lift advisories 

Press releases to 

media 

 

Bi-weekly highlights 

sent to park staff 

 

Write annual report 

6.6 Permit Procedures 

All data collection consists of field observations collected along or near existing trails. The 

procedures are considered non-invasive and are no different that activities that might be 

conducted by any park visitor. Research permits are, therefore, not needed by the park. 

6.7 Procedures for Revising the Protocol 

Over time, revisions to both the Protocol Narrative and to specific SOPs are to be expected. 

Careful documentation of changes to the protocol, and a library of previous protocol versions are 

essential for maintaining consistency in data collection and for appropriate treatment of the data 

during data summary and analysis. The MS Word documents for each monitoring component 

contain a ‘Revision History Log’ that identifies which version of the protocol was being used 

when the data were collected. The steps for changing the protocol (either the Protocol Narrative 

or the SOPs) are outlined in SOP 4: Revising the Protocol. Each SOP contains a Revision 

History Log that should be filled out each time a SOP is revised to explain why the change was 

made, and to assign a new Version Number to the revised SOP. The new version of the SOP 

and/or Protocol Narrative should then be archived in the network protocol library under the 

appropriate folder (SFAN Network/ Individual Vital Signs). SOP 4 also includes peer review 

comments on previous version of the protocol along with the authors’ responses to document 

rationale for changes to the document. 
 31 

7 - Glossary 

Adult: a raptor characterized by adult plumage and coloration. 

Advisory: management action or technique used at PINN to protect cliff-nesting raptors from 

potential human disturbance, through enactment of voluntary climbing and off-trail hiking 

closures to all core areas in the park used by nesting raptors in a given season. Advisories are in 

effect each breeding season from early January until 2 weeks after the last fledging of cliffnesting raptors in a core area affected by the advisories, usually by mid-July. Advisories are not 

closures because they are based on visitors voluntarily refraining from climbing and hiking offtrail in core areas affected by the advisories. Also called “climbing / hiking advisories” because 

the advisories directly impact these visitor activities. 

Advisory areas: locations within core areas that are affected by advisories, as detailed in press 

releases, park bulletin boards, bi-weekly raptor updates distributed to park staff through email, 

and informational brochures distributed by the PINN interpretative division. 

Apteria: bare featherless area between pterylae or feather tracts. 

Auriculars: feathers covering the ear opening and the area immediately around it. 

Base jumping: the activity or sport of parachuting from a high structure (as a building, tower, or 

bridge) or cliff. 

Bill-swipe: a technique used by prairie and peregrine falcons in courtship and nest preparation, 

in which an adult falcon swipes and shovels at the soil of a prospective nest site to shape it for a 

scrape. 

Breeding season: period from the start of nest-building (refurbishment) or courtship to 

independence of fledged young. 

Brood size at fledging: the number of young produced by successful pairs. 

Clutch size: the number of eggs laid in a nest. 

Core area: encompasses historic prairie falcon territories where climbing impacts could occur 

based on the proximity to climbing routes. The territory names for these locations are as follows: 

Crowley Towers, South Balconies, North Balconies, Scout Peak, Goat Rock, Resurrection Wall, 

Tunnel, Teapot Dome, Egg, Hawkins Peak, Pipsqueak Pinnacles, Frog / Hand, Little Pinnacles. 

See also Figure 2. 

Coronal region: the area on top of the head. 

Coverts: small feathers that overlie or cover the bases of the large flight feathers of the wings 

and tail. 
 32 

Eyass: a young or nestling raptor still in the nest and not yet fledged. Also spelled “eyas.” 

Eechip: a call made by prairie and peregrine falcons in courtship, greetings, nest switches, food 

exchanges, and occasionally during territorial defense. The term “eechip” is derived from the 

sound produced by the call. 

Eyrie: the nest of a bird of prey. Also spelled “aerie.” 

Fecundity: used synonymously with productivity. 

Fledged young: young raptors that have fledged or flown from the nest where they originally 

hatched. Can refer to raptor young that were not observed flying from a particular nest, but are 

recently fledged as evidenced by feather down still visible or confirmed food exchanges with 

adults. Interchangeable with “post-fledglings.” 

Fledging: a fully-feathered young raptor voluntarily leaving the nest for the first time. 

Fledgling: young raptor that has just (within 1–5 days) fledged or become capable of flying from 

the nest where they hatched, confirmed through direct observation. 

Hard incubation: an adult raptor or raptors consistently on eggs at a nest in incubation posture 

with periods of less than 20 consecutive minutes not on eggs. This can include nest switches 

involving both adults as long as the duration of the switch from one adult incubating eggs to 

another is less than 20 minutes. 

Hatchling: a young raptor in the nest just hatched from an egg, 1–5 days old, not yet capable of 

thermoregulation. 

Historical nest: see “nest site.” 

Immature: a fledged raptor – including juveniles, second-year birds, and sub-adults – that has 

not reached its adult form, maturity, or size, as distinguished by plumage, coloration, and body 

size. 

Incubation period: the time between the start of incubation and the hatching of an egg, during 

which the egg is kept at or near body temperature by the parent. 

Juvenal feathers: the sequence of flight feathers which replace the natal down. 

Juvenile: a fledged raptor that still has not yet reached its second-year, sub-adult, or adult forms, 

maturity or size, as distinguished by plumage, coloration, and body size. 

Natal down: first feathers to form on young birds, replaced by the development of juvenile 

feathers. 
 33 

Nest: the structure made or the place used by birds for laying their eggs and sheltering their 

young. 

Nest failure: Failure of a raptor nest to produce any fledglings, due to failure of eggs to hatch, or 

failure of nestlings to fledge. Egg failure can be due to abandonment by the adult nesting pair, 

eggs beings crushed in the nest, eggs falling out of nests, or eggs being predated. Nestling failure 

can be due to abandonment by the adult nesting pair, starvation, temperature extremes leading to 

death, nestlings falling out of the nest, or predation. 

Nest selection: a site chosen by a raptor female and/or pair for nesting, as confirmed by 

observed incubation or nestling presence at the site. Prior to incubation, nest selection behavior 

includes regular visits to the site, clearing a scrape with feet or beak, adding nesting material to a 

site, laying down in and getting back up from the site, calling from the site with the mate 

following to perch at the site. However, these activities alone are not sufficient to determining 

nest selection; incubation and/or nestling presence at the site must be observed to confirm nest 

selection. 

Nest site: a raptor nest, confirmed currently or historically by presence of raptor eggs or 

nestlings. Interchangeable with “eyrie,” “historical nest,” or simply “nest.” 

Nesting attempt: a raptor nest used in a given season by an adult raptor pair, confirmed by: 1) 

eggs or nestlings in the nest, 2) eggshell fragments in the new nesting material of raptors that 

build nest constructs (i.e., buteos, accipiters, eagles, kites), 3) an adult soft- or hard-incubating 

eggs on a nest or scrape. Includes successful nests and nest failures. 

Nesting pair: a pair of raptors that has been confirmed nesting in a specific territory based on at 

least one of the following: 1) incubation of eggs at a nest, 2) incubation of hatchlings at a nest, 3) 

feeding of hatchlings / nestlings at a nest, 4) feeding of fledglings at / near a nest. 

Nestling: young raptor in nest, older than a hatchling and not yet fledged. 

Non-core areas: encompass all historical prairie falcon territories not included within core areas, 

i.e., where there is no possibility of climbing impacts and there are no historical climbing routes. 

Observation Point: see Watch Spot 

Occiput: posterior portion of the crown. Also known as the “hind head.” 

Occupancy: any territory containing at least one adult prairie falcon engaged in nest defense. 

Presence of a territorial prairie falcon on any of the three surveys confirms occupancy, and lack 

of presence after three surveys confirms lack of occupancy. Evidence of a pair with a nest 

indicates Territorial Occupancy. 

Primaries: the outermost and longest flight feathers on a bird’s wing. Members of the Order 

Falconiformes have ten primaries. 
 34 

Productivity: used synonymously with fecundity. The number of raptor young that reach the 

minimum acceptable age for assessing success. We report both the number chicks that hatch per 

nest and the number of raptors that fledge to allow comparison with other studies. 

Post-fledglings: see “fledged young.” 

Rectrices: tail feathers. 

Remiges: large feathers of the wing, the primaries, and secondaries. 

Roost: a perch or nest where raptors return to rest or sleep. Also see “sleep-roost.” 

Scapulars: a group of feathers on the shoulder, along the side of the back. 

Scrape: a type of nest, common to prairie and peregrine falcons, shaped as a bowl-like shallow 

depression in soil or rocky material; site where falcons, owls, and New World vultures (species 

that do not construct nests) lay eggs; the depression in substrate (rotting wood chips, old pellets, 

dust, sand, or gravel) where eggs are deposited. 

Second-year: a raptor in its second year, characterized by second-year feather development, 

plumage, coloration. Also called “yearling” and “sub-adult.” 

Secondaries: large flight feathers located in a series along the rear edge of the wing, 

immediately inward from the primaries. 

Sleep-roost: a roost in an occupied territory that a raptor will return to near dusk to perch and 

sleep at during the night. 

Soft incubation: an adult raptor consistently on eggs at a nest in incubation posture with periods 

of 20 or more consecutive minutes not on eggs between each period incubating eggs. 

Stoop: a raptor descending quickly in flight by folding its wings forward, bringing its talons 

forward, and diving steeply. Used as a hunting technique – especially by peregrine falcons – and 

as a defensive behavior by a territorial raptor against threatening animals, often other raptors but 

also including people and potential predators. 

Sub-adult: a raptor characterized by sub-adult feather development, plumage, coloration. Refers 

to all raptors not termed juvenile or adult birds. For most raptors, this refers to second-year birds, 

but can include third-year birds for golden and bald eagles. 

Successful nest: nest that produces at least 1 confirmed fledgling. 

Supercilliary region: area below the boundary of the forehead and the crown; the area above the 

eye. 
 35 

Territorial occupancy: any territory containing one or more adults engaged in courtship or 

reproductive activity, nest defense, nest affinity, or containing eggs, young, or any field sign 

indicating that eggs were laid, or young were present. Evidence for territorial occupancy can 

include: 1) eggs or nestlings in the nest, 2) eggshell fragments in the new nesting material of 

raptors that build nest constructs, 3) an adult on the nest in incubation posture or an adult in a 

scrape for at least 1 hour (this can be a combination of both adults’ time as long as the scrape has 

been under constant surveillance), 4) fledged young in the immediate area of the nest, or 5) field 

sign (fresh whitewash, decorated nests, etc.). 

Territorial pair: a pair of raptors that has been confirmed occupying a territory based on at least 

1 of the following: 1) an adult raptor pair actively defending and sleep-roosting in a territory, 2) 

adults engaged in courtship or reproductive activity, nest defense, or nest affinity, 3) incubation 

of hatchlings at a nest, 3) feeding of hatchlings / nestlings at a nest, 4) feeding of fledglings at / 

near a nest. Territorial pairs of raptors may occupy a territory throughout a season without 

nesting, may have a nest attempt that fails, or may abandon a territory. 

Territory: within this protocol, territory refers to breeding territory. This is an area that contains, 

or historically contained, one or more nests (or scrapes) within the home range of a mated raptor 

pair; a confined locality where nests are found, usually in successive years, and where no more 

than one pair of a given species is known to have bred at one time. The definition does not 

encompass feeding areas much of which is outside of the park and is not synonymous with nest 

site. 

Thermoregulation: the ability of an organism to keep its body temperature within certain 

boundaries, even when the surrounding temperature is very different. This process is also known 

as homeostasis. 

Tiercel: a male raptor. 

Wailing: a call made by prairie and peregrine falcons during copulations, in courtship, greetings, 

nest switches, food exchanges, and during territorial defense. Also described as “kak-kakking.” 

Scold and alarm calls are characterized by more rapid, insistent, and harsh wailing; nestling 

begging calls are often ragged, rattling, and insistent wails. 

Watch Spot: a location chosen by a raptor raptor technician / park biologist to observe raptor 

presence, behavior, and nests from. Also called an “observation point.” 

 37 

8 - Literature Cited 

Becker, D. M., and I. J. Ball. 1981. Impacts of surface mining on prairie falcons: 

recommendations for monitoring and mitigation. Montana Cooperative Wildlife Research 

Unit, Montana State University, Missoula, MT. 

 

Bednarz, J. C. 1984. Effect of mining and blasting on breeding prairie falcon (Falco mexicanus) 

occupancy in the Caballo Mountains. New Mexico Raptor Research 18:16–19. 

Bennetts, R. E., J. E. Gross, K. Cahill, C. McIntyre, B. Bingham, A. Hubbard, L. Cameron, and 

S. L. Carter. Linking monitoring to management and planning: assessment points as a 

generalized approach. George Wright Forum 24 (2): 59-77. 

Bildstein, K. L., and K. Meyer. 2000. Sharp-shinned Hawk (Accipiter striatus). No. 482 in A. 

Poole and F. Gill, editors. The Birds of North America. The Birds of North America, Inc., 

Philadelphia, PA. 

Bloom, P. H. 1994. The biology and current status of the long-eared owl in coastal southern 

California. Bulletin Southern California Academy of Science 93:1–12. 

Bond, R. M. 1946. The peregrine populations of western North America. Condor 48:101–116. 

Boyce, D. A., Jr. 1982. Prairie falcon fledgling productivity in the Mojave Desert, California. 

Thesis. Humboldt State University, Arcata, CA. 

Brown, D. E. 1982. Madrean evergreen woodland. Pages 59–65 in Brown, D. E. editor. Biotic 

communities of the American southwest-United States and Mexico. Desert Plants 4:1–

342. 

Brown J. S., B. P. Kotler, R. J. Smith and W. O. Wirtz II. 1988. The effects of owl predation on 

the foraging behavior of heteromyid rodents. Oecologia 76:408–415. 

Buranek, S. 2006. Pinnacles prairie falcon home range and habitat analysis. Thesis. California 

State Univeristy, Sacramento, CA. 

Buhl-Mortensen, L. 1996. Type II statistical errors in environmental science and the 

precautionary principle. Marine Pollution Bulletin 32:528-531. 

Cade, T. J., J. H. Enderson, and J. Linthicum. 1996. Guide to management of peregrine falcons at 

the eyrie. The Peregrine Fund, Inc., Boise, ID. 

California Department of Fish and Game. 2008. Special animals (865 taxa). California 

Department of Fish and Game, Biogeographic Data Branch, California Natural Diversity 

Database, Sacramento, CA. Available online: 

http://www.dfg.ca.gov/biogeodata/cnddb/pdfs/SPAnimals.pdf (accessed 1 September 
 38 

2008). 

Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences (2nd ed.), New York: 

Academic Press. 

 

Croll, D. A., J. L. Maron, J. A. Estes, E. M. Danner, and G. V. Byrd. 2005. Introduced predators 

transform subarctic islands from grassland to tundra. Science 307:1959–1961.

Delannoy, C. A., and A. Cruz. 1988. Breeding biology of the Puerto Rican sharp-shinned hawk 

(Accipiter striatus venator). Auk 105:649–662. 

Dunk, J. R. 1995. White-tailed kite (Elanus leucurus). No. 178 in A. Poole and F. Gill, editors. 

The Birds of North America. The Academy of Natural Sciences, Philadelphia, and The 

American Ornithologists’ Union, Washington, D.C. 

Ellis, D. H. 1973. Behavior of the golden eagle: an ontogenic study. Dissertation. University of 

Montana, Missoula, MT. 

Emmons, G. 2006. Raptor breeding season report for Pinnacles National Monument – 2006. 

Unpublished report. National Park Service, San Francisco Bay Area Network Inventory 

and Monitoring Program, San Francisco, CA. 

Fesnock, A. 1993. Seasonal duties of the raptor technician. Unpublished. Pinnacles National 

Monument, Paicines, CA. 

Fraser, J. D., L. D. Frenzel, J. E. Mathisen, F. Martin, and M. E. Shough. 1983. Scheduling bald 

eagle reproduction surveys. Wildlife Society Bulletin 11:13–16. 

Fuller, M. R., and J. A. Mosher. 1981. Methods of detecting and counting raptors: a review. 

Studies in Avian Biology 6:235–246. 

Fyfe, R. W., and R. R. Olendorff. 1976. Minimizing the dangers of nesting studies to raptors and 

other sensitive species. Canadian Wildlife Serv. Occas. Pap. 23:1–17. 

Gibbs, J. P., S. Droege, and P. Eagle. 1998. Monitoring populations of plants and animals. 

BioScience 48:935–940. 

Haemig, P. D. 2001. Symbiotic nesting of birds with formidable animals: a review with 

applications to biodiversity conservation. Biodiversity Conservation 10:527–540. 

Harmata, A. R., J. E. Durr, and H. Geduldig. 1978. Home range, activity patterns and habitat use 

of prairie falcons nesting in the Mojave Desert. Unpublished report to the U.S. 

Department of the Interior, Bureau of Land Management, Riverside, CA. 

Hickey, J. J. 1942. Eastern population of the duck hawk. Auk 59:176–204. 
 39 

Hickey, J. J. 1969. Peregrine falcon populations: their biology and decline. University of 

Wisconsin Press, Madison, WI. 

Holthuijzen, A. M. A., W. G. Eastland, A. R. Ansell, M. N. Kochert, R. D. Williams, and L. S. 

Young. 1990. Effects of blasting on behavior and productivity of nesting prairie falcons. 

Wildlife Society Bulletin 18:270–281. 

Jaksic, F. M., P. Feinsinger, and J. E. Jimenez. 1996. Ecological redundancy and long-term 

dynamics of vertebrate predators in semiarid Chile. Conservation Biology 10:252–262. 

Kochert, M. N., K. Steenhof, L. B. Carpenter, and J. M. Marzluff. 1999. Effects of fire on golden 

eagle territory occupancy and reproductive success. Journal of Wildlife Management 

63:773–780. 

Kochert, M. N., K. Steenhof, C. L. McIntyre, and E. H. Craig. 2002. Golden eagle (Aquila 

chrysaetos). No. 684 in A. Poole and F. Gill, editors. The Birds of North America. The 

Birds of North America, Inc., Philadelphia, PA. 

Lundberg, J. and F. Moberg. 2003. Mobile link organisms and ecosystem functioning: 

implications for ecosystem resilience and management. Ecosystems 6:87–98. 

MacDonald, T. L. 2003. Review of Environmental Monitoring Methods: Survey Designs. 

Environmental Monitoring and Assessment 85:277-292. 

MacKenzie, D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, J. E. Hines. 

2006. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of 

Species Occurrence. Elsevier Academic Press. Burlington. 

Mapstone, B. D. 1995. Scalable decision rules for environmental impact studies: effect size, 

Type I, and Type II errors. Ecological Applications 5:401–410. 

Marks, J. S. 1986. Nest-site characteristics and reproductive success of long-eared owls in 

southwestern Idaho. Wilson Bulletin 98:547–560. 

Marshall Jr., J. T. 1957. Birds of the pine-oak woodland in southern Arizona and adjacent 

Mexico. Pacific Coast Avifauna 32:1–125. 

Marti, C. D. and J. S. Marks. 1989. Medium-sized owls. Pages 124–133 in B. G. Pendleton, 

editor. Proceedings of the Western Raptor Management Symposium and Workshop. 

National Wildlife Federation, Science and Technical Series No. 12. 

Marzluff, J. M., M. S. Vekasy, and C. Coody. 1994. Comparative accuracy of aerial and ground 

telemetry locations of foraging raptors. Condor 96:447–454. 

Marzluff, J. M., B. A. Kimsey, L. S. Schueck, M. E. McFadzen, M. S. Vekasy, and J. C. 

Bednarz. 1997. The influence of habitat, prey abundance, sex, and breeding success on 

the ranging behavior of prairie falcons. Condor 99:567–584. 
 40 

Mazur, K. M. and P. C. James. 2000. Barred owl (Strix varia). No. 508 in A. Poole and F. Gill, 

editors. The Birds of North America. The Birds of North America, Inc., Philadelphia, PA. 

McFadzen, M.E., and J. M. Marzluff. 1996. Mortality of prairie falcons during the fledging 

dependence period. Condor 98:791–800. 

Meyer, K. D. 1995. Swallow-tailed kite (Elanoides forficatus). No. 138 in A. Poole and F. Gill, 

editors. The Birds of North America. The Academy of Natural Sciences, Philadelphia, 

and The American Ornithologists’ Union, Washington, D.C. 

Millenium Ecosystem Assessment. 2005. Ecosystems and human well-being: synthesis. Island 

Press, Washington, D.C. 

Mitani, J. C., W. Sanders, J. Lwanga, and T. Windfelder. 2001. Predatory behavior of crownedhawk eagles in the in Kibale National Park, Uganda. Behavioral Ecology and 

Sociobiology 49:187–195. 

Newton, I. 1979. Population ecology of raptors. Buteo Books, Shipman, VA. 

Newton, I. 1990. Birds of prey. Facts on File, Inc., New York, NY. 

Ogden, V. T., and M. G. Hornocker. 1977. Nesting density and success of prairie falcons in 

southwestern Idaho. Journal of Wildlife Management 41:1–11. 

Olsen, P., and J. Olsen. 1978. Alleviating the impact of human disturbance on the breeding 

peregrine falcon I. Corella 2(1):1–7. 

Olsen, J., and P. Olsen. 1980. Alleviating the impact of human disturbance on the breeding 

Peregrine Falcon II. Public and recreational lands. Corella 4(3):54–57. 

Parrish, J. K., M. Marvier, and R. T. Paine. 2001 Direct and indirect effects: interactions between 

bald eagles and common murres. Ecological Applications 11:1858–1869. 

Platt, S. W. 1974. Breeding status and distribution of the prairie falcon in northern New Mexico. 

Thesis. Oklahoma State University, Stillwate, OK. 

Porter, R. D., M. A. Jenkins, and A. L. Ganski. 1987. Working bibliography of the peregrine 

falcon. National Wildlife Federation, Science and Technical Series, No. 9. 

Preisser, E. L., D. I. Bolnick, and M. F. Benard. 2005. Scared to death? Behavioral effects 

dominate predator–prey interactions. Ecology 86:501–509. 

Press, D. T. 2005. Data management plan for the San Francisco Bay Area Network Inventory 

and Monitoring Program. U. S. Department of the Interior, National Park Service, San 

Francisco, CA. 
 41 

Ralph, C. J., G. R. Geupel, P. Pyle, T. E. Martin, and D. F. DeSante. 1993. Handbook of field 

methods for monitoring landbirds. General Technical Report PSW-GTR-144. U. S. 

Forest Service, Pacific Southwest Research Station, Albany, CA. 

Rechtin, J. A. 1992. Raptor nesting at Pinnacles National Monument: 1984–1992. Pinnacles 

National Monument, Paicines, CA. 

Rechtin, J., and T. Leatherman. 1992. Directions for taking raptor observations. Unpublished 

report. Pinnacles National Monument, Paicines, CA. 

Roemer, G. W., C. J. Donlan, and F. Courchamp. 2002. Golden eagles, feral pigs and insular 

carnivores: how exotic species turn native predators into prey. Proceedings of the 

National Academy of Sciences 99(2):791–796. 

Rubine, D. 1995. Climber’s guide to Pinnacles National Monument, 2nd edition. Chockstone 

Press, Inc. Evergreen, CO. 

Scott, T. A. 1985. Human impacts on the golden eagle population of San Diego County. Thesis. 

San Diego State University, San Diego, CA. 

Sekercioglu, C. H. 2006a. Ecological significance of bird populations. Handbook of the Birds of 

the World. 11:3–39. 

Sekercioglu, C. H. 2006b. Increasing awareness of avian ecological function. Trends in Ecology 

and Evolution. 21(8):464–471. 

Sitter, G. 1983. Feeding activity and behavior of prairie falcons in the Snake River Birds of Prey 

Natural Area in southwestern Idaho. Thesis. University of Idaho, Moscow, ID. 

Smith, J. P., and A. Hutchins. 2006. Northeast Nevada nest survey 2005. Hawkwatch 

International, Inc., Salt Lake City, Utah. 

Sodhi, N. S., A. Didiuk, and L. W. Oliphant. 1990. Differences in bird abundance in relation to 

proximity of merlin nests. Canadian Journal of Zoology 68:852–854. 

Squires, J. R. and R. T. Reynolds. 1997. Northern goshawk (Accipiter gentilis). No. 298 in A. 

Poole and F. Gill, editors. The Birds of North America. The Academy of Natural 

Sciences, Philadelphia, PA, and The American Ornithologists’ Union, Washington, D.C. 

Starcevich, L. A. and K. Steinhorst. 2010. Analysis of Power to Detect Trends in Occupancy and 

Fecundity in Prairie Falcon and Spotted Owl Populations. Unpublished report to San 

Francisco Bay Area Network of the Inventory and Monitoring Program, National Park 

Service, Golden Gate National Recreation Area, Sausalito, CA.
 42 

Steenhof, K. 1987. Assessing raptor reproductive success and productivity. Pages 157–170 in B. 

A. Giron Pendleton, B. A. Milsap, K. W. Cline, and D. M. Bird, editors. Raptor 

Management Techniques Manual. National Wildlife Federation, Washington, D.C. 

Steenhof, K. 1998. Prairie falcon (Falco mexicanus). No. 346 in A. Poole and F. Gill, editors. 

The Birds of North America. The Birds of North America, Inc., Philadelphia, PA. 

Steenhof, K., M. N. Kochert, and T. L. McDonald. 1997. Interactive effects of prey and weather 

on golden eagle reproduction. Journal of Animal Ecology 66:350–362. 

Steenhof, K., M. N. Kochert, L. B. Carpenter, and R. N. Lehman. 1999. Long-term prairie falcon 

population changes in relation to prey abundance, weather, land uses, and habitat 

conditions. Condor 101:28–41. 

Steidl, R. J., K. D. Kozie, G. J. Dodge, T. Pehovski, and E. R. Hogan. 1993. Effects of human 

activity on breeding behavior of golden eagles in Wrangell-St. Elias National Park and 

Preserve: a preliminary assessment. WRST Research and Resource Management Report 

no. 93-3. National Park Service, Wrangell-St. Elias National Park and Preserve, Copper 

Center, AK. 

Stiles, F. G. 1985. On the role of birds in the dynamics of neotropical forests. Pages 49–59 in A. 

W. Diamond and T. E. Lovejoy, editors. Conservation of Tropical Forest Birds. 

International Council for Bird Preservation, Cambridge, UK. 

Suter, G. W., and J. L. Joness. 1981. Criteria for golden eagle, ferruginous hawk, and prairie 

falcon nest site protection. Raptor Research 15:12–18. 

U.S. Department of the Interior. 1979. Snake River birds of prey special research report to the 

Secretary of the Interior. U.S. Bureau of Land Management, Boise, ID. 

U.S. Fish and Wildlife Service. 1984. American peregrine falcon rocky mountain/southwest 

population recovery plan (Rocky Mountain/Southwest population). U.S. Fish and 

Wildlife Service, Denver, CO. 

Urquhart, N. S., and T. M. Kincaid. 1999. Trend detection in repeated surveys of ecological 

responses. Journal of Agricultural, Biological, and Environmental Statistics 4:404-414. 

Watson, J. 1997. The golden eagle. 1st edition. T and A. D. Poyser Ltd, London, U.K. 

White, C. M., N. J. Clum, T. J. Cade, and W. G. Hunt. 2002. Peregrine falcon (Falco 

peregrinus). No. 660 in A. Poole, editor. The Birds of North America Online. Cornell 

Laboratory of Ornithology, Ithaca, NY. Retrieved from The Birds of North American 

Online database: http://bna.birds.cornell.edu/BNA/account/Peregrine_Falcon/ (accessed 

on 1 September 2008). 
 43 

Appendix A. Analysis of Power to Detect Trends 

in Occupancy and Fecundity in Prairie Falcon 

and Spotted Owl Populations 

San Francisco Bay Area Network 

of the 

National Park Service 

Authors: 

Leigh Ann Harrod Starcevich 

Kirk Steinhorst 

March 2010

In Partial Fulfillment of 

Contract Number J8W07060004 

Contractor: Kirk Steinhorst, University of Idaho

Subcontractor: Leigh Ann Harrod Starcevich 

PO Box 1032 

Corvallis, OR 97339 

lah@peak.org
 44 

TABLE OF CONTENTS 

1. INTRODUCTION ................................................................................................................... 45

2. GENERAL ANALYSIS APPROACH ..................................................................................... 47 

2.1 Occupancy and trend modeling..................................................................................... 47 

2.2 Fecundity and trend modeling ...................................................................................... 48 

2.3 Power approach ............................................................................................................. 49 

2.4 Revisit designs .............................................................................................................. 49

3. PRAIRIE FALCON POWER ANALYSES ............................................................................ 51 

3.1 Survey design ................................................................................................................ 51 

3.2 Occupancy analysis ....................................................................................................... 51 

3.2.1 Evaluation of detection probabilities ................................................................. 52 

3.2.2 Power analysis .................................................................................................... 54 

3.3 Fecundity power analysis .............................................................................................. 56 

3.4 Conclusions ................................................................................................................... 60

4. SPOTTED OWL POWER ANALYSES ................................................................................. 61 

4.1 Sampling design ............................................................................................................ 61 

4.2 Occupancy analysis ....................................................................................................... 63 

4.3 Fecundıty analysis ......................................................................................................... 69 

4.4 Conclusions ................................................................................................................... 73

5. CONCLUSIONS....................................................................................................................... 74

6. REFERENCES ........................................................................................................................ 75
 45 

APPENDIX A: Power to detect trends in spotted owl 

occupancy status categories by revisit design ............................................................................... 76 

ACKNOWLEDGMENTS 

The authors would like to thank Marcus Koenen, Dave Press, Bill Merkle, and 

Gavin Emmons of the San Francisco Area Network for providing helpful 

background for this analysis and Kathryn M. Irvine for her help with 

occupancy modeling. 

 47 

1. INTRODUCTION 

The San Francisco Bay Area Network (SFAN) of the National Park Service (NPS) has identified 

prairie falcon and spotted owl populations as two of the network's Vital Signs for long-term 

monitoring. Specifically, trends in occupancy and fecundity will be monitored so that 

detrimental changes in the population can be identified to inform timely management decisions. 

The analysis approaches for measures of occupancy and fecundity are first discussed, and then 

power to detect trends in occupancy and fecundity is computed for all metrics of interest for the 

two species. 

2. GENERAL ANALYSIS APPROACH 

The models for both occupancy and fecundity incorporate zero inflation. For occupancy 

analysis, extra zeroes may result from imperfect detection. When measuring fecundity, counts of 

hatchlings or fledglings might be subject to zeroes from nest failures due to non-nesting pairs, 

predation, or environmental factors. Analysis methods for zero-inflation apply mixture models 

that combine one distribution for the extra zeroes and another distribution for the remaining 

zeroes and non-zero outcomes so that measures of occupancy and fecundity can be accurately 

estimated (MacKenzie et al. 2006). The number of detections for occupancy analysis and the 

number of hatchlings or fledglings for fecundity analysis are modeled as zero-inflated binomial 

random variables. 

2.1 Occupancy and trend modeling 

Let yijk be the outcome for site i, year j, and visit k, and let yijk take a value of 1 if a prairie falcon 

is detected and 0 otherwise. Let ijk y be the number of detections made at site i and year j during 

the k

th visit. The zero-inflated binomial distribution is expressed as: 

( ) ()() ( ) 1

11 0 ijk ijk y y PY y p p I y ijk ij ij ij ij ijk π π −= = − +− = , 

where πj is the occupancy rate in year j and ij p is the detection rate for site i and year j. Assume 

that there are S sites, T years, and K visits to a site each year. Define the indicator function as 

( ) 0 ij I y = as 1 when 0 ij y = and 0 otherwise. This model assumes an equal number of visits to a 

site within a year, but K can vary among sites or among years. 

Occupancy (π) is modeled with logistic regression as a function of related covariates: 

0 1 log , 1

ij

ij

ij

x

π

γ γ

π

⎛ ⎞ ⎜ ⎟ = + ⎜ ⎟ − ⎝ ⎠

where ij x represents a covariate for the i

th site and the jth year. Multiple site-level or year-level 

covariates may be incorporated. To test for linear trend in the logged odds ratio of occupancy, 
 48 

the year covariate should be included as a predictor in the occupancy model. The probability of 

zero inflation (p) is simultaneously estimated by logistic regression: 

0 1 log 1

ij

ijk

ij

p

x

p

β β

⎛ ⎞

= + ′ ⎜ ⎟ ⎜ ⎟ − ⎝ ⎠

, 

where ijk x represents a covariate collected at the i

th site and kth visit during the jth year. 

The implicit dynamics occupancy model is assumed for this analysis. In contrast to the explicit 

dynamics model, estimates of colonization and local extinction are not explicitly measured in the 

implicit dynamics model (MacKenzie et al. 2006). The net effect of extinction and colonization 

rates on occupancy is monitored rather than estimating the parameters separately since 

monitoring net change over time is the primary goal. 

Maximum likelihood is used to estimate the regression coefficients from the models for 

occupancy and detection rates. Occupancy can be modeled at the site-by-year level so predictors 

should be collected at the site level, year level, or site-by-year level. Detection probabilities are 

allowed to vary at each visit for each site and year, so covariates are often environmental 

covariates that vary from visit to visit (MacKenzie et al. 2006). However, if detection rates are 

also changing over time, a model that includes a year covariate may be needed for accurate 

occupancy estimation. Model selection is conducted using the Akaike Information Criterion 

(AIC). Model output is examined to be certain that valid variance estimates are obtained. When 

valid estimates of the variance cannot be acquired for the model with the lowest AIC, then the 

model with the lowest AIC and valid variance is used. 

2.2 Fecundity and trend modeling 

Fecundity is monitored with counts of hatchlings or fledglings observed at each nest. These 

outcomes are also modeled with the zero-inflated binomial model. Let ij y be the number of 

hatchlings or fledglings detected in occupied site i and year j. Let n be the number of occupied 

sites and T be the number of years monitored for fecundity. The probability mass function of Y

is: 

( ) ( ) ( )( ) 11 0 ij ij M y y

ij ij ij ij ij ij

ij

M

PY y p p I y

y

π π

⎛ ⎞ −

= = − +− = ⎜ ⎟ ⎝ ⎠

, 

where πij is the probability that an extra 0 is not observed at site i in year j, pij is the probability 

of a hatchling/fledgling at site i in year j, M is the maximum number of hatchlings/fledglings 

seen in any nest, and I(yij = 0) is 1 when yij = 0 and is 0 otherwise. Maximum likelihood 

estimation is used to obtain estimates of the regression coefficients in each model, and the 

invariance property of maximum likelihood estimates is used to obtain estimates of π ij and ij p . 

To test for trend in fecundity, the year covariate is included as a predictor in the binomial 

probability model and then tested for significance with a likelihood ratio test. In contrast to the 

occupancy analysis, fecundity inference is made on the change in pij over time. 
 49 

2.3 Power approach 

Power is often calculated assuming large-sample properties for hypothesis tests of significance. 

However, the assumptions necessary for assuming asymptotic normality are extensive and often 

difficult to verify (Sumathi and Aruna Rao, 2009). Ridout et al. (2001) observed that the normal 

approximation to the score test underestimates the true test size for testing the significance of the 

dispersion parameter when comparing the zero-inflated negative binomial and zero-inflated 

Poisson distributions. Jung et al. (2005) corrected this problem with a parametric bootstrapping 

approach to significance testing, which provided uniformly higher power. For both prairie 

falcon and spotted owl monitoring, trend is tested with the likelihood ratio test, which 

demonstrates power equal to or higher than that provided by the Wald test (Lyles et al. 2006). 

Model selection for occupancy and fecundity is conducted using the pilot data for prairie falcons 

and spotted owls. In each case, the model with the lowest AIC score with a valid variancecovariance matrix is used in the power analysis. In all four cases, power is computed via a 

parametric bootstrap. For each bootstrap sample, the likelihood ratio test of trend is conducted 

by applying the selected model with and without the term for trend. Power is calculated as the 

proportion of times that the null hypothesis is rejected for the one-sided alternative hypothesis of 

decreasing trend, i.e. 

: 0 Ho β = vs. : 0 Ha β < . 

Power must be approximated assuming that tests of trend are conducted at a specific Type I error 

rate. The Type I error rate, designated as α, is the probability of rejecting a true null hypothesis. 

For two-sided tests of trend, a Type I error would mean that the population was found to be 

changing when it was not. A Type II error occurs by failing to reject a false null hypothesis and 

thus concluding no change has occurred. For long-term monitoring, the cost of a Type I error 

may be far less than the cost of a Type II error. Mistakenly rejecting a true null hypothesis (Type 

I error) may trigger a management conservation action that is not actually needed. However, 

failing to detect a significant trend may have deleterious effects that cannot be reversed by the 

time the trend is actually detected. A conservative approach is to use a larger α value for higher 

power and reduced probability of a Type II error (Buhl-Mortensen 1996; Gibbs, et al. 1998; 

Mapstone 1995). For this power analysis, an α value of 0.20 is used. Power does not fall below 

the α value assumed for the trend test. 

2.4 Revisit designs 

Revisit designs specify the schedule of sites visitations by year for the duration of the monitoring 

period. Revisit design allow balance over space and time of available survey effort. The 

notation of McDonald (2003) is used to describe the revisit designs. 

The notation employs a string of numbers, dashes, and commas to identify the revisit design 

among panels. The numbers in odd-numbered positions will designate the number of 
 50 

consecutive visits to a panel before it rotates out of the revisit schedule. Digits in the evennumbered positions indicate the number of sampling occasions that the panel will not be 

revisited. Therefore, a revisit schedule of [1-0] indicates that the panel will be revisited each 

year and never rotated out of the schedule. This revisit schedule is equivalent to taking a random 

sample of sites and visiting them every year. The [1-n] revisit design represents a sampling 

design in which independent random samples are taken every year. A [2-2] revisit design 

consists of one panel visited for two consecutive years and then rested for two consecutive years 

before beginning the cycle again. Differing revisit schedules among panels are indicated by 

separating the digits in parentheses by commas within the brackets. For example, a revisit 

schedule of [(1-0), (1-3)] indicates that the revisit design includes an annual panel that is 

revisited every year and four panels that are visited for one year then not visited for the following 

three years before being rotated back into the design. 

A random site effect may be incorporated in the occupancy or detection model to account for 

variation among sites. However, implementing the appropriate correlation structure in a mixed 

model approach may be problematic with maximum likelihood estimation (MacKenzie et al. 

2006). Incorporating numerous fixed site effects may exhaust available degrees of freedom 

making variance estimates unreliable and trend testing inaccurate. Without fixed or random site 

effects, the data are treated as if collected from a [1-n] revisit design. This revisit design has the 

lowest power for trend detection (Urquhart and Kincaid, 1999), so power results are 

conservative. Since site effects are difficult to incorporate, MacKenzie et al. (2006) suggest 

using a [1-0] design so that unexplained variation among sites is minimized for more accurate 

tests of trend. Incorporating relevant site-level covariates into the occupancy model will also 

help explain differences in occupancy from site-to-site. 
 51 

3. PRAIRIE FALCON POWER ANALYSES 

SFAN, in cooperation with Pinnacles National Monument (PINN), has monitored the occupancy 

rate and fecundity measures of the prairie falcon population in PINN since 1984. SFAN 

personnel are interested in monitoring trends in occupancy and fecundity measures. This section 

will address the power to detect changes in prairie falcon occupancy and fecundity 

measurements over time. 

3.1 Survey design 

The target population includes 36 territories historically used by prairie falcon in PINN. Of the 

36 territories in the target population, 18 of these territories fall into the "core" area. The core 

area consists of territories that are more accessible to climbers and hikers. These areas have been 

surveyed more consistently and frequently over the last 22 years to assess hiking/climbing 

pressure during the breeding season. Eighteen non-core areas were identified and added to the 

sample over time. Due to the added monitoring importance of the core areas, the core and noncore areas will be treated as separate strata with core territories censused annually. Depending 

on survey resources and the results of the power analysis, the non-core territories will be either 

censused or allocated to a set of panels that are visited on an alternating cycle. 

Sites are visited at least 3 times each year with visits lasting 1 to 4 hours. At least 3 visits to a 

site are needed to determine that a site is unoccupied, and visits are spaced 21 to 28 days apart. 

In sites where prairie falcon detections are made, territorial occupancy is assessed by examining 

the behavior of each detected prairie falcon. Territorial behavior is verified by observing 

courtship or reproductive behavior or by evidence of offspring. 

Pilot data collected since 1984 are available with a more consistent effort made since 2002. 

Detections from Ball Pinnacle and Central High Peaks were excluded because these territories 

are near other historic territories and are not considered independent territories. Some survey 

records for territories in which no detections were made were omitted from the database prior to 

the 2008 survey, potentially causing invalid inference on occupancy and detection rates from the 

incomplete data set. For this reason, only the 2008 and 2009 data are used for the power 

analysis. This database problem does not affect the fecundity data set because the subset of pairoccupied sites is the target population for fecundity monitoring. Fecundity monitoring data were 

collected from 2002 to 2009. While fecundity data are available since 1984, the data collected 

since 2002 represent the current methodology and are thought to be more consistent. 

SFAN is interested in approximating the power to detect trends in occupancy and fecundity 

measures. For a Type I error rate of 0.2, the power to detect a 50% decline in each indicator of 

interest over a 10-year period in a one-sided alternative hypothesis is examined for several 

sample sizes and revisit designs. 

3.2 Occupancy analysis 

The definition of occupancy requires a verification of territorial behavior. A prairie falcon 

detection does not necessarily imply that occupancy has been established. Designation of site 
 52 

occupancy requires multiple site visits within a year and evidence of territorial behavior. To 

estimate occupancy according to the SFAN definition of occupancy, only detections verifying 

territorial behavior should be used. Detection probabilities from this analysis would reflect the 

detection of an individual or pair displaying territorial behavior. However, the current data set 

reflects all detections regardless of behavior. Therefore, occupancy may be overestimated in this 

analysis. Data analysis may be improved by recording an indicator that the detection provides 

proof of territorial occupancy. This field could be recorded as a 0/1 binary field rather than a 

descriptive field for easier data analysis. 

The pilot data are examined to determine if the estimated detection rates for prairie falcon are 

substantially less than one, indicating that occupancy estimates should be calculated assuming 

imperfect detection. In the next section, the occupancy analysis approach and concurrent 

estimation of trend over time are discussed. All occupancy estimates relate to pair occupancy 

since no single prairie falcons were observed in 2008 or 2009. 

3.2.1 Evaluation of detection probabilities 

Maximum likelihood estimation was used to obtain estimates of regression coefficients for 

models of occupancy and detection rates. Occupancy models were based on a site-level 

covariate (an indicator for core area) and the survey year. Detection models were based on 

covariates collected within each site and year such as month, high temperature, low temperature, 

average wind speed, cloud cover, and level of precipitation. The occupancy model selected from 

the modeling exercise contained covariates for the year and core area inclusion. The three 

detection models with the lowest AIC are given in Table 3.1. The lowest detection probabilities 

from the first model were obtained for the relatively-few PRFA surveys conducted when 

precipitation was present. Because the AIC for the month-only detection model is only slightly 

larger, this more parsimonious model is used for model stability. 
 53 

Table 3.1: Best detection models from 2008-2009 pilot data 

Detection model variables AIC 

Range of 

estimated 

detection 

rates 

2008 

estimated 

occupancy 

rate (SE) 

2009 

estimated 

occupancy 

rate (SE) 

Hi_TempF, CCPct, Precip_Code 518.31 0.3011 – 

0.6965 

0.9271 

(0.1020) 

1.0000 

(0.0031) 

Month, Precip_Code 519.84 0.3281 – 

0.6511 

0.8788 

(0.0957) 

1.0000 

(0.0016) 

Month 520.27 0.3305 – 

0.6513 

0.8787 

(0.0956) 

1.0000 

(0.0010) 

Plotting the estimated detection probabilities against the month predictor (Figure 3.1), one may 

observe that detection probabilities for PRFA are highest later in the season. A t-test of the twosided hypothesis that the mean detection rate is equal to 1 is highly significant (p-value < 

0.0001). There is no evidence that PRFA detection probabilities are so high as to warrant 

conducting a trend analysis that assumes perfect detectability. Therefore, the zero-inflated 

Bernoulli distribution is assumed. 

Figure 3.1: Estimated detection probabilities by month 
 54 

3.2.2 Power analysis 

The power analysis is conducted using the parametric bootstrapping approach. The models for 

occupancy and detection obtained from the pilot data are used. The month covariate is randomly 

selected from the pilot data so that months in which prairie falcon surveys are more common will 

occur more frequently in the bootstrap samples. 

Power is examined for a range of sample sizes that are visited using several revisit designs. 

Figures 3.2 through 3.5 provide illustrations of several possible revisit designs for a range of 

annual sample sizes. For each revisit design, the set of core territories is visited annually and all 

territories are visited at least once over the course of the revisit cycle. Notice that, for the [(1-0), 

(1-4)] revisit design, 10 years are required to obtain complete replication at all territories. For 

the [(1-0), (1-1)] design, all sites are visited at least twice in four survey years. When the [1-0] 

revisit design is used, all sites are visited annually and a complete replicate is obtained after the 

second survey year. 

An issue that arose in the occupancy modeling exercise is the difficulty in obtaining stable 

maximum likelihood estimates from occupancy and detection models that incorporate an effect 

for the territory. Fixed effects for each site require considerable degrees of freedom that can 

result in erroneous variance estimation. Incorporating a random effect into the detection model 

for a heterogeneous detection probability model generated occupancy estimates very close to 1 in 

every case. Furthermore, occupancy and detection models with any complexity generally 

produced Hessian matrices that were not positive-definite, so standard errors for trend estimation 

could be wrong. Since neither a fixed nor a random effect for territory can be incorporated into 

the models, the sample is treated as if were collected in a [1-n] design. The revisit designs 

cannot be distinguished from another because the benefits of sampling the same sites annually 

cannot be integrated into the model. However, the [1-n] revisit design has lowest power for 

trend detection, so the power results provided here are conservative. 

MacKenzie et al. (2006) suggest that the heterogeneous detection probability model, while 

difficult to implement in a maximum likelihood setting, may be conducted with a Bayesian 

approach. When incorporating a fixed or random site effect is not possible, including site-level 

covariates that are related to occupancy will help explain site-to-site differences in occupancy 

estimates. Unexplained site-to-site variation is included in the residual error term which 

increases the standard error of the estimate of the trend coefficient and decreases power to detect 

trend. 
 55 

 Year 

Panel Core/ 

non-core 

1 2 

1 Core 18 18 

2 Non-core 3 3 

ANNUAL 

TOTAL 21 21 

Figure 3.2: [1-0] revisit design for annual census of core sites and sample of 3 non-core sites 

Year 

Panel Core/ 

non-core 

1 2 3 4 5 6 7 8 9 10 

1 Core 18 18 18 18 18 18 18 18 18 18 

2 Non-core 3 3 3 3 3 3 3 3 3 3 

3 Non-core 3 3 

4 Non-core 3 3 

5 Non-core 3 3 

6 Non-core 3 3 

7 Non-core 3 3 

ANNUAL 

TOTAL 24 24 24 24 24 24 24 24 24 24 

Figure 3.3: [(1-0),(1-4)] revisit design 

 Year 

Panel Core/ 

non-core 

1 2 3 4 

1 Core 18 18 18 18 

2 Non-core 6 6 6 6 

3 Non-core 6 6 

4 Non-core 6 6 

ANNUAL 

TOTAL 30 30 30 30 

Figure 3.4: [(1-0),(1-1)] revisit design 

 Year 

Panel Core/ 

non-core 

1 2 

1 Core 18 18 

2 Non-core 18 18 

ANNUAL 

TOTAL 36 36 

Figure 3.5: [1-0] revisit design for annual census of all sites 
 56 

The power to detect a 50% decline in occupancy after 10 consecutive survey years is provided in 

Table 3.2 for the revisit designs described in Figures 3.2 through 3.5. The [1-0] revisit design is 

known to have the highest power to detect trend (Urquhart and Kincaid, 1999). As discussed 

previously, the default revisit design is assumed to be a [1-n] design since the effect of territory 

cannot be explicitly modeled. Therefore, the difference among the power calculations presented 

in Table 3.2 is a function of annual sample size rather than an artifact of the revisit design. 

Table 3.2: Power to detect occupancy trends for different sample sizes and revisit designs 

Annual 

sample size 

of 

territories 

Number of 

core sites 

surveyed 

annually 

Number of 

non-core sites 

surveyed 

annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years

18 18 0 [1-0] 0.953 

21 18 3 [1-0] 0.973 

24 18 6 [(1-0),(1-4)] 1.000 

30 18 12 [(1-0),(1-1)] 1.000 

36 18 18 [1-0] 1.000 

Because the core sites must be censused each year to monitor high-use areas, the power to detect 

trends in non-core sites is examined separately (Table 3.3). The samples will be stratified by 

core and non-core areas, so trends may be estimated separately for each subpopulation. 

Comparisons of Tables 3.2 and 3.3 indicate that the high power to detect trends across the set of 

territories is largely due to the census of core sites. The pilot data did not indicate a significant 

difference in occupancy between core and non-core sites (p=0.1413). However, if future prairie 

falcon trends occur at different rates between core and non-core areas or if inference to the 

subpopulation of non-core sites is of interest, then results for trends in prairie falcon occupancy 

at PINN may be misleading unless at least between 9 and 12 non-core territories are surveyed 

each year. If historic data are corrected so that non-detections are included in the data set, the 

pilot data may be helpful in determining if sample sizes for non-core areas should be considered 

independently from the annual census of core sites. 

Table 3.3: Power to detect occupancy trends for non-core sites only 

Number of 

non-core sites 

surveyed 

annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years

3 [1-0] 0.559 

6 [1-0] 0.754 

9 [1-0] 0.773 

12 [1-0] 0.852 

3.3 Fecundity power analysis 

Fecundity will be monitored by examining trends in the binomial probability of hatchlings and 

fledglings. Histograms of prairie falcon hatchlings and fledglings per nest from data pooled over 

the years 2002 to 2009 indicate a large number of zeroes in each outcome (Figures 3.6 and 3.7, 
 57 

respectively). The additional zeroes may be a result of mechanisms such as imperfect detection 

rates, predation, or environmental factors and are accounted for using a zero-inflated binomial 

model. The maximum number of eggs observed in the pilot data is 5, so this is assumed to be the 

maximum number of hatchlings or fledglings per nest in PINN. 

Assuming that the numbers of hatchlings and fledglings follow zero-inflated binomial 

distributions and assuming that a random sample of occupied sites are visited each year, the 

power to detect a 50% decline in fecundity after 10 consecutive survey years is provided for 

hatchlings (Table 3.4) and fledgling (Table 3.5). The results of the power analysis indicate that 

annual surveys of at least 10 pair-occupied sites provides power of at least 0.8 to detect a 50% 

decline over 10 years in hatchling or fledgling rates. 

Figure 3.6: Histogram of hatchlings across years, 2002 - 2009 
 58 

Figure 3.7: Histogram of fledglings across years, 2002 - 2009 

Table 3.4: Power to detect a 50% decline in the probability of hatchlings 

Annual 

sample size 

of occupied 

nests 

Power to detect a 50% decrease 

after 10 years 

(based on 2002-2009 pilot data) 

5 0.609 

10 0.910 

15 1.000 

20 1.000 

25 1.000 

Table 3.5: Power to detect a 50% decline in the probability of known fledglings 

Annual 

sample size 

of occupied 

nests 

Power to detect a 50% decrease 

after 10 years 

(based on 2002-2009 pilot data) 

5 0.645 

10 0.922 

15 0.977 

20 1.000 

25 1.000 
 59 

Core sites are visited annually so that hiking and climbing pressure can be assessed throughout 

the breeding season. Given that the set of core sites is censused annually, power is examined for 

samples that contain all core sites and a range of non-core sites each year. For counts of both 

hatchlings (Table 3.6) and fledglings (Table 3.7), the power to detect trend in the binomial 

probability for each outcome is one. This level of power is attained even when no non-core sites 

are surveyed. However, if inference to non-core sites is of interest, then this subpopulation 

should be sampled with sufficient effort. Power to detect a 50% decline over 10 consecutive 

years exceeds 0.8 for as few as three sites each year for hatchlings (Table 3.8) and fledglings 

(Table 3.9). 

Table 3.6: Power to detect trend in the binomial probability for hatchlings for a census of core 

sites and a sample of non-core sites 

Annual 

sample size 

of 

territories 

Number of 

core sites 

surveyed 

annually 

Number of 

non-core sites 

surveyed 

annually 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

18 18 0 1.000 

21 18 3 1.000 

24 18 6 1.000 

30 18 12 1.000 

36 18 18 1.000 

Table 3.7: Power to detect trend in the binomial probability for fledglings for a census of core 

sites and a sample of non-core sites 

Annual 

sample size 

of 

territories 

Number of 

core sites 

surveyed 

annually 

Number of 

non-core sites 

surveyed 

annually 

Power to detect a 50% 

decrease after 10 

consecutive survey years 

18 18 0 1.000 

21 18 3 1.000 

24 18 6 1.000 

30 18 12 1.000 

36 18 18 1.000 
 60 

Table 3.8: Power to detect trend in the binomial probability for hatchlings for non-core sites 

only 

Number of 

non-core sites 

surveyed 

annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years

3 [1-0] 0.875 

6 [1-0] 0.957 

9 [1-0] 1.000 

12 [1-0] 1.000 

Table 3.9: Power to detect trend in the binomial probability for fledglings for non-core sites only 

Number of 

non-core sites 

surveyed 

annually 

Revisit 

design 

Power to detect a 50% 

decrease after 10 

consecutive survey years

3 [1-0] 0.816 

6 [1-0] 0.969 

9 [1-0] 0.988 

12 [1-0] 0.996 

3.4 Conclusions 

Annual surveys of 27 to 30 territories consisting of a census of the 18 core sites and 9 to 12 of 

the non-core sites for occupancy surveys and at least 10 occupied territories for fecundity 

surveys should provide power greater than 0.80 for trend detection. Stratified random sampling 

within the non-core subpopulation will allow inference to that subpopulation of territories. 

MacKenzie et al. (2006) recommend the [1-0] revisit design so that the additional variation from 

alternating territories in and out of the survey will not affect variance estimates and therefore the 

power to detect trends. However, management goals may necessitate the use of seriallyalternating revisit designs so that all territories may be visited intermittently. 

Gavin Emmons stated that some territorial pairs arrive in the spring rather than in the late winter, 

so territories designated as "occupied" for that year may actually be unoccupied early in the 

survey season. This would cause underestimation of detection rates. The pilot data indicate that 

detection probabilities are lowest in January and gradually increase over the course of the survey 

season (Figure 3.1). Most of these late-winter visits occur in core territories which are visited 

throughout the hiking and climbing season. The occupancy model used in this application 

applies the assumption that occupancy is consistent throughout the monitoring period. Defining 

the monitoring window so that this assumption is true will allow accurate and precise estimation 

of occupancy and detection rates. Determining the occupancy sampling time frame a priori will 

not bias the results but will provide more accurate estimation of detection and occupancy rates 

from a more balanced data set. 
 61 

4. SPOTTED OWL POWER ANALYSES 

The monitoring goals of the spotted owl protocol are to detect substantial long-term trends in 

spotted owl occupancy and fecundity rates at activity sites within NPS boundaries in Marin 

County, California. The methods described in Section 2 are used to calculate the power to detect 

trend in spotted owl metrics. Some background on the sampling design is followed by 

discussions of the power to detect trend in spotted owl occupancy and fecundity rates. For a 

Type I error rate of 0.2, the power to detect declines of 4%, 10%, 12%, and 15% in each 

indicator of interest over 5- and 10-year periods in a one-sided alternative hypothesis is 

examined for several sample sizes of sites and revisit designs. Results from an initial analysis 

provide the basis for further power analysis to inform survey design choices. 

4.1 Sampling design 

The spotted owl study area consists of all federal lands within Marin County and includes 

Golden Gate National Recreation Area, Muir Woods National Monument, Point Reyes National 

Seashore, and Samuel P. Taylor State Park, as well as a 400m buffer around these parks. The 

sampling frame consists of all sites within the study area where a spotted owl has been observed 

in any survey conducted between 1997 and 2006. A total of 66 sites were initially identified 

from this list. Eight sites were removed due to inaccessibility (on private land or unsafe to 

access) or close proximity to another site. Two sites were added to the frame that fell just 

outside of the GIS buffer. The final 60 sites serve as the sampling frame for occupancy 

monitoring. Occupancy surveys are conducted between March 1 and August 31. 

Between March 1 and May 31, nesting status is assessed. From May 1 to August 31, nests are 

monitored and fecundity measurements are collected. Fecundity surveys are only conducted at 

sites containing territorial females, including resident single females and both nesting and nonnesting pairs. Sites that do not meet these criteria of a territorial female are omitted from 

fecundity estimation based on a "non-target" assessment. Inclusion probabilities may be 

adjusted to account for changes in the sampling frame so that unbiased estimation is achieved. 

The number of locations monitored for fecundity is given for the pilot data collected from 1999 

to 2008 (Table 4.1). For fecundity monitoring, the subset of 48 sites containing a nesting pair 

during at least one year is used as the sampling frame. 

Since fecundity measurements are conditional on territorial female at a monitored territory, the 

target population can change from year to year. Furthermore, locations that had not previously 

hosted a territorial female could later be colonized, thus meeting the definition of the target 

population for fecundity monitoring. Unfortunately, some nests cannot be evaluated for 

occupancy by territorial females until later in the survey season, so selecting a sample from the 

set of sites meeting the criteria for fecundity sampling is not possible. Therefore, a random 

sample from the sampling frame of 48 sites will be used for monitoring. The fecundity sample 

should be larger than necessary given that roughly 70% of the sites are eligible for fecundity 

surveys (David Press, personal communication). 
 62 

Table 4.1: Number of locations sampled for SPOW fecundity monitoring by year 

Year Number of locations Number of fecundity locations 

1999 34 19 

2000 34 25 

2001 34 25 

2002 41 27 

2003 34 28 

2004 24 24 

2005 21 21 

2006 NA NA 

2007 25 12 

2008 25 12 

2009 30 26 

One of six possible occupancy status categories is assigned for each location each year (Table 

4.2). These occupancy categories are mutually exclusive, with all locations falling into only one 

category each year. Ultimately, the most appropriate occupancy model would estimate 

occupancy trends for all status categories simultaneously so that the sum of occupancy estimates 

across categories is always one. However, the available literature has not incorporated 

techniques for estimating occupancy for more than two categories with methods for trend 

estimation. Given the instability of occupancy and detection models in the univariate case 

(discussed in the next section), the multivariate case was not explored. 

Table 4.2: Occupancy categories 

Occupancy code Description 

PR Resident pair detected 

PU Pair detected but pair occupancy not confirmed 

RS Resident single 

SU Resident single detected but occupancy not confirmed 

UK Occupancy status unknown 

UO Unoccupied 

To estimate occupancy according to the SFAN definition of occupancy, only detections verifying 

territorial behavior are used. The proportion of detections falling within each status category is 

provided in Table 4.3. The majority of detections occurred within locations with a resident pair. 

Resident single occupancy is rare in comparison. Status categories for which territoriality is 

unknown (PU, SU, and UK) also occur less frequently. Without adjusting for imperfect 

detection, observed detection rates imply high occupancy rates for resident pairs. However, 

some of the categories are quite rare and estimates of the binomial probabilities may be unstable 

because they are close to 0 (Olkin, et al., 1981). 
 63 

Table 4.3: Observed status category frequency among all detections by year 

Year PR PU RS SU UK UO 

1999 0.82 0.03 0.03 0.03 0.02 0.06 

2000 0.81 0.00 0.05 0.04 0.01 0.09 

2001 0.85 0.00 0.02 0.00 0.01 0.13 

2002 0.89 0.00 0.04 0.02 0.00 0.05 

2003 0.96 0.01 0.00 0.00 0.01 0.02 

2004 0.92 0.00 0.05 0.00 0.02 0.01 

2005 0.88 0.00 0.04 0.00 0.04 0.03 

2006 0.67 0.00 0.16 0.11 0.05 0.02 

2007 0.79 0.07 0.06 0.04 0.00 0.04 

2008 0.79 0.02 0.04 0.07 0.01 0.08 

4.2 Occupancy analysis 

The pilot data for occupancy modeling are taken from the set of sites chosen for monitoring. 

Sites outside the SFAN monitoring area were excluded because they do not represent the target 

population for which trends will be estimated. Covariate information available for occupancy 

and detection modeling is described in Table 4.4. These variables represent all modeling 

variables included by SFAN personnel in the pilot data set. 

Table 4.4: Covariate information available for occupancy and detection modeling 

Covariate code Description 

Year Year of the survey 

Month Month of the survey 

Barred Indicator of barred owl detection at a site for a given year 

Daytime Indicator that survey occurred during the day 

Call Indicator that a call method was used 

ObsNum Number of observers present during the survey 

The process of model selection was problematic for the spotted owl pilot data. Because 

occupancy analysis is based on a nonlinear model, obtaining a positive-definite Hessian matrix 

was not possible for some models. The Hessian matrix affects the estimates of variance for 

occupancy and detection regression parameter estimates which affects trend testing. Model 

selection was conducted using AIC as the model selection criterion (Bayes Information Criterion 

was also examined but selected similar models). However, models chosen with AIC often did 

not provide positive-definite Hessian matrices. The model with the lowest AIC and a positivedefinite Hessian matrix was used in the power analysis for trend testing. The final occupancy 

and detection models selected with this approach are provided for each status category in Table 

4.5. The final models were often a reduced version of the model with the lowest AIC indicating 

that simpler models are more stable for occupancy estimation. 
 64 

The occupancy estimates by category are provided in Table 4.6. For each status category, 

occupancy estimates are provided either for the model including the indicator of barred owl 

presence (estimates of occupancy with and without barred owl presence are provided in the 

second and third columns) or the model without a factor for barred owl presence (occupancy 

estimates given in the fourth column). Columns for the unused model contain a "-" to indicate 

that this model was not used. Due to the complexities of estimating trend with a multi-category 

occupancy classification, a univariate approach was used and occupancy estimates do not sum to 

1. Notice that the presence of barred owls in a site and year decreases pair occupancy but 

increases the single-unknown and unoccupied status categories. This result suggests that the 

effect of barred owl presence is to reduce pair occupancy and inhibit territorial behavior as 

evidenced by the positive effects observed in the SU and UK status categories. 

Table 4.5: Final occupancy and detection models for each spotted owl occupancy category 

Status category Occupancy model 

log 1

ij

ij

π

π

⎛ ⎞ ⎜ ⎟ ⎜ ⎟ − ⎝ ⎠

Detection model 

log 1

ij

ij

p

p

⎛ ⎞ ⎜ ⎟ ⎜ ⎟ − ⎝ ⎠

PR 0 1Barredij γ + γ β 01 2 3 + βββ Day Call ObsNum ij ij ij + +

PU 0 γ 01 2 Day Call β + β β ij ij +

RS 0 γ 01 2 Day Call β + β β ij ij +

SU 0 1Barredij γ + γ 01 2 Day Call β + β β ij ij +

UK 0 γ 01 2 Month Call β + β β ij ij +

UO 0 1Barredij γ + γ 0 1Call β + β ij
 65 

Table 4.6: Occupancy estimates (and standard errors) from the final model for each spotted owl 

occupancy category 

Status 

category 

Est. occupancy rate 

for sites 

with barred owls 

(SE) 

Est. occupancy rate 

for sites 

without barred owls 

(SE) 

Est. occupancy rate 

(SE) 

PR 0.9197 (0.0505) ~ 1.000 (0.00001) - 

PU - - 0.0301 (0.0146) 

RS - - 0.1614 (0.0428) 

SU 0.2562 (0.0960) 0.1127 (0.0384) - 

UK - - 0.0208 (0.0058) 

UO 0.1172 (0.0371) 0.0601 (0.0124) - 

Fixed and random effects for location were examined in the modeling effort for occupancy and 

detection rates. Adding a fixed effect for location prohibitively reduced the number of degrees 

of freedom available for error estimation. Modeling the location effect as a random effect in the 

detection model produces the heterogeneous detection probability model which is often difficult 

to implement in a maximum likelihood approach (MacKenzie et al. 2006). In practice, this 

approach resulted in site-by-year level occupancy estimates very near 1 and with nearly zero 

variation and corresponding detection probabilities very near zero. Incorporating the random 

effect into the occupancy model produced similar problems. A Bayesian approach may be more 

appropriate for a random effects modeling approach (MacKenzie et al. 2006). 

The benefit of incorporating a fixed or random effect for location is that repeat visits to the same 

locations over time can reduce variance and provide more accurate trend estimation. Without a 

location effect, the data are treated like random samples taken independently each year (a [1-n] 

revisit design). MacKenzie et al. (2006) argue that the [1-0] revisit design is best for trend 

estimation because the models cannot distinguish between variation among different locations 

and that observed over time. However, the implicit occupancy model used in this analysis does 

not require that the same sites are visited annually because the local extinction and colonization 

parameters are not explicitly estimated. MacKenzie et al. (2006) recommend that the [1-0] 

revisit design be used unless destructive sampling occurs. However, the needs of the monitoring 

program may dictate that all sites be visited periodically, making a revisit design that 

accommodates those requirements more desirable. 

The revisit designs initially under consideration were the [1-0], [2-2], [(1-0)5

,(2-2)], and [(1-

0)10,(2-2)] designs (Figures 4.1 – 4.4, respectively, are provided for a set annual sample of 40 

sites). The difference in the latter two designs is that an annual panel of 5 sites is used in the [(1-

0)5

,(2-2)] design while an annual panel of 10 sites is used in the [(1-0)10,(2-2)] design. As 

discussed, the trend analysis treats the data as if a [1-n] design has been used. The [1-n] revisit 

design has the lowest power for trend detection and therefore provides conservative power 

results. The replication within a location cannot be used to induce a correlation within a location 

over time to reduce the variance of the trend estimate. The results of the power analysis for the 

six spotted owl occupancy status categories, monitoring periods of 5 and 10 years, and all revisit 

designs are provided in Appendix A. Power is given for a one-sided hypothesis test of no change 
 66 

versus a decreasing trend over time. The results of the initial power analysis demonstrate that 

the revisit designs are indistinguishable when neither the occupancy nor the detection model 

includes an effect for location. 

The detection model exhibited considerable influence on the final estimates of occupancy. 

Estimated occupancy could vary widely depending on what covariates were included in the 

detection model. The problems described above indicate that detection models might perform 

best when they are simple and covariates uncorrelated with predictors used in both the 

occupancy and detection models are used. 

 Year 

Panel 1 2 3 4 5 6 7 8 

1 40 40 40 40 40 40 40 40 

ANNUAL TOTAL 40 40 40 40 40 40 40 40 

Figure 4.1: [1-0] revisit design for an annual sample of 40 sites 

 Year 

Panel 1 2 3 4 5 6 7 8 

1 40 40 40 40 

ANNUAL TOTAL 40 40 0 0 40 40 0 0 

Figure 4.2: [(2-2)] revisit design for an annual sample of 40 sites 

 Year 

Panel 1 2 3 4 5 6 7 8 

1 5 5 5 5 5 5 5 5 

2 35 35 35 35 

ANNUAL TOTAL 40 40 5 5 40 40 5 5 

Figure 4.3: [(1-0)5, (2-2)] revisit design for an annual sample of 40 sites 

 Year 

Panel 1 2 3 4 5 6 7 8 

1 10 10 10 10 10 10 10 10 

2 30 30 30 30 

ANNUAL TOTAL 40 40 10 10 40 40 10 10 

Figure 4.4: [(1-0)10, (2-2)] revisit design for an annual sample of 40 sites 

Power to detect trends in pair occupancy over time (Figures A.1 and A.2) is nearly 1 for trends 

over either 5 or 10 years. For pairs whose occupancy status is unknown (Figures A.3 and A.4), 

power is considerably lower, only reaching 0.8 for annual declines of over 10% and censuses 

over 10 consecutive years. Power to detect declines in resident single occupancy within 5 years 

is below 0.8 unless all of the sites in the population are visited and declines are at least 15% 

annually (Figure A.5). However, the power to detect trend in resident single status is at or above 

0.8 for annual samples of as few as 20 locations per year when the annual decline is 10% or 

greater over 10 years (Figure A.6). Power for trend detection within 5 years is consistently 

below 0.8 for the single unknown, unknown, and unoccupied categories, but power to detect 
 67 

trends within 10 years are near 0.8 for the single unknown and unoccupied categories when 

declines are at least 10% annually. 

Overall, the power to detect trend in pair occupancy is excellent at the α = 0.2 level. However, 

higher levels of effort or change are required for trend detection in other status categories 

because of their relative rarity. Maximum likelihood estimates of binomial probabilities that are 

close to 0 are generally unstable (Olkin et al., 1981). This may explain the decrease in power for 

an increasing number of sites in the power plot of unoccupied status (Figure 4.16). 

Based on the results of the initial power analysis, the spotted owl workgroup determined that 

monitoring the occupancy of spotted owl pairs is of greatest importance. The relative rarity of 

the other status categories causes trend estimation to be more difficult unless changes are 

extreme. Data collection will continue for the other status categories so that baseline information 

is available for monitoring any future changes, but trends will only be calculated for spotted owl 

pairs. 

In addition to focusing inference on the occupancy of spotted owl pairs, the results of the initial 

occupancy power analysis indicated that power to detect trend was adequate for a 5-year 

monitoring period. The final sampling design for occupancy and fecundity sampling needed to 

meet some additional criteria, including visiting all occupancy and fecundity sites at least once 

within one revisit cycle, obtaining an adequate fecundity sample size given that about 70% of 

occupancy sites are eligible for fecundity surveys, and possible restrictions on annual funding. 

Two possible funding scenarios were proposed: annual funding and funding for two consecutive 

year of high survey intensity followed by two years with low survey intensity. Based on these 

two funding scenarios and two proposed sample sizes from the fecundity analysis, four revisit 

designs were examined (Figures 4.5 to 4.8). 

The four proposed revisit designs provide options for sampling designs given different levels and 

cycles of funding and for annual samples of 35 and 43 locations so that 25 and 30 fecundity sites 

may be visited in fully-funded years. The revisit design notation (1-3)1/2 indicates that only half 

of the [1-3] panels are visited, so there are two [1-3] panels visited and no sites from this revisit 

design are surveyed in the other two years. 

 
 68 

 

 Year 

Panel Revisit design 1 2 3 4 5 6 7 8 

1 [1-0] 15 15 15 15 15 15 15 15 

2 [1-3] 20 20 

3 [1-3] 20 20 

ANNUAL TOTAL 35 35 15 15 35 35 15 15 

Figure 4.5: [(1-0)10, (1-3)1/2] revisit design for an annual sample of 35 sites 

 

 Year 

Panel Revisit design 1 2 3 4 5 6 7 8 

1 [1-0] 26 26 26 26 26 26 26 26 

2 [1-3] 17 17 

3 [1-3] 17 17 

ANNUAL TOTAL 43 43 26 26 43 43 26 26 

Figure 4.6: [(1-0)26, (1-3)1/2] revisit design for an annual sample of 43 sites 

 

 Year 

Panel Revisit design 1 2 3 4 5 6 7 8 

1 [1-0] 28 28 28 28 28 28 28 28 

2 [1-3] 8 8 

3 [1-3] 8 8 

4 [1-3] 8 8 

5 [1-3] 8 8 

ANNUAL TOTAL 36 36 36 36 36 36 36 36 

Figure 4.7: [(1-0)28, (1-3)] revisit design for an annual sample of 36 sites 

 

 Year 

Panel Revisit design 1 2 3 4 5 6 7 8 

1 [1-0] 40 40 40 40 40 40 40 40 

2 [1-3] 5 5 

3 [1-3] 5 5 

4 [1-3] 5 5 

5 [1-3] 5 5 

ANNUAL TOTAL 45 45 45 45 45 45 45 45 

Figure 4.8: [(1-0)40, (1-3)] revisit design for an annual sample of 45 sites

If location effects could be incorporated into the model, we would expect power to be lowest for 

the revisit design represented in Figure 1 and the highest for Figure 4 based on the increasing 

size of the annual revisit panel. The [1-0] annual panel sites will be selected from the list of sites 

that fall in both the occupancy and fecundity sampling frames. Then the remaining sites could 
 69 

be ordered randomly and allotted to the other panels. If needed, the sites included in both the 

occupancy and fecundity frames could be balanced among years in the remaining panels, thus 

also balancing the sites that historically have not contained successful nests. 

The power for each revisit design is provided in Table 4.7. The power to detect trend is very 

high because the estimated occupancy rate is near 1, making declines easy to detect. Power was 

estimated to be near or equal to one for all revisit scenarios and both levels of change given a 

Type II error of 0.20, so power was also examined at the α = 0.10 level. At this level, power is at 

or above 0.98 for all revisit designs. Because site-level effects are not incorporated in this 

occupancy analysis, power is conservative for trend detection. Furthermore, power calculated 

from this model is a function of the total sample size rather than of panel sample sizes. 

Table 4.7: Power to detect trends in spotted owl pair occupancy within five consecutive survey 

years for four revisit designs 

Revisit design Power to detect a 4% annual 

decrease in occupancy 

Power to detect a 10% annual 

decrease in occupancy 

α = 0.10 α = 0.20 α = 0.10 α = 0.20 

[(1-0)15, (1-3)1/2] 0.992 1.000 1.000 1.000 

[(1-0)26, (1-3)1/2] 0.992 1.000 1.000 1.000 

[(1-0)28, (1-3)] 0.992 1.000 1.000 1.000 

[(1-0)40, (1-3)] 0.996 1.000 1.000 1.000 

4.3 Fecundity analysis 

Fecundity of spotted owls is measured by the number of fledglings per territorial female. 

Nesting spotted owls can produce 0 to 3 fledglings, though a maximum of two fledglings was 

observed from the pilot data. The 1998-2008 pilot data indicate that zero inflation may be an 

issue (Figure 4.5). Zero inflation may result from a high number of non-nesting pairs or nest 

failures related to predation or environmental factors. 
 70 

Figure 4.5: Histogram of detected spotted owl fledglings across sites and years from the pilot 

data 

Fecundity is measured as the binomial probability for fledglings. These estimates are provided 

in Table 4.8 by year. While the estimates indicate an increasing trend (Figure 4.6), the increase 

is not significant (LRT test statistic: 0.0043, p-value: 0.9479). The probability of not detecting 

an extra zero is estimated as 0.6395 (SE 0.0466). 

Table 4.8: Estimated binomial probabilities for fledgling 

Year Estimated binomial probability for fledglings (SE) 

1999 0.4715 (0.0686) 

2000 0.4724 (0.0574) 

2001 0.4732 (0.0473) 

2002 0.4741 (0.0393) 

2003 0.4750 (0.0349) 

2004 0.4758 (0.0353) 

2005 0.4767 (0.0405) 

2006 0.4776 (0.0490) 

2007 0.4784 (0.0593) 

2008 0.4793 (0.0707) 

 
 71 

Figure 4.6: Estimated binomial probabilities for spotted owl fledglings by year 

The power to detect trends in fledgling rates is calculated as described in section 2.2. The 

intercept-only model for zero-inflation is constant across all locations and years. The fledgling 

rate model is allowed to change linearly across years for trend testing. The power to detect 

decreasing trends in spotted owl fecundity is based on the likelihood ratio test for monitoring 

periods of 5 years (Figure 4.7) and 10 years (Figure 4.8). Twenty to 25 locations with resident 

females must be surveyed annually to detect decreasing trends of at least 10% within 5 years. 

Power to detect a 4% decline within 5 years never exceeds 0.5. For a 10-year monitoring period, 

power to detect decreasing trends of at least 10% is at least 0.8 for samples as low as 10 occupied 

locations per year. Around 30 nests must be visited annually to detect a 4% decline with power 

of 0.8 within 10 years. Out of 25 sites selected for fecundity monitoring, the 2007 and 2008 

surveys resulted in 12 sites with territorial females for fecundity analysis. At this level of effort, 

longer monitoring time frames are required to achieve adequate power for trend detection. 
 72 

Figure 4.7: Power to detect trend in the 

binomial probability of spotted owl 

hatchling success in 5 consecutive survey 

years for four rates of change 

Figure 4.8: Power to detect trend in the 

binomial probability of spotted owl 

hatchling success in 10 consecutive 

survey years for four rates of change 
 73 

4.4 Conclusions 

The power to detect decreasing trends in spotted owl occupancy is nearly 1 for the pair 

occupancy status. Power to detect declines in sites with resident singles, sites with single spotted 

owls with unknown status, and in unoccupied sites exceeds 0.8 when annual declines are at least 

10% for monitoring of at least 10 years. Power to detect trends in the pair unknown and 

unknown categories is uniformly low based on the rarity of these status categories. 

The power to detect trends in spotted owl fecundity within 5 years is at least 0.8 when the annual 

decline is at least 10% and at least 25 to 30 sites with known reproductive outcomes are visited 

annually. Given that roughly 70% of the sites visited qualify for fecundity monitoring, an initial 

sample of 35 to 43 sites is needed to obtain the desired sample size. For a 10-year monitoring 

window, power is around 0.9 even for annual fecundity samples as small as 10 territories when 

annual declines are at least 10%. When the annual decline is 4% over 10 years, at least 30 

territories must be surveyed annually to achieve power of 0.8 or more. 
 74 

 5. CONCLUSIONS 

Overall, power for trend detection is high for prairie falcon and spotted owl monitoring. For 

prairie falcon monitoring, 27 to 30 (18 core and 9 to 12 non-core) territories should be visited 

annually to achieve power of 0.8 to detect a net decline in pair occupancy of 50% in 10 years. 

Power to detect a similar trend in fecundity measures may be obtained by monitoring fecundity 

in at least 10 pair-occupied sites. 

A preliminary power analysis indicated that power to detect trends in spotted owl pairs exceeds 

0.9 at the α = 0.2 level. Because other status categories were relatively rare and power for trend 

detection was low, these categories will be tracked over time but not monitored for trend. Four 

possible revisit designs at various levels of effort in a five-year period are proposed, each 

provided power of about 1.00 for trend detection in pair occupancy at the α = 0.2 level. 

The power analysis was ultimately based on a [1-n] design because a fixed or random site effect 

could not be incorporated into the zero-inflated binomial models. Therefore, the benefits of 

sampling the same sites through time could not be used to reduce the estimate of the trend 

variance for higher power. However, this restriction means that the power analysis is 

conservative. MacKenzie et al. (2006) suggest that a Bayesian analysis might resolve this 

problem encountered in maximum likelihood estimation. If maximum estimation is to be used to 

trend estimation and testing, then MacKenzie et al. (2006) recommend using a [1-0] revisit 

design so that additional sites do not rotate into the sample from year to year and increase site-tosite variation. This additional variation cannot be explained by the model, and the additional 

unexplained error reduces power to detect trend. Site-level covariates related to occupancy could 

be incorporated into the occupancy model to account for some site-to-site differences in 

occupancy. 

From panel design theory, it is known that revisiting the same sites each year provides the 

highest power for trend detection and visiting an independent random sample each year gives the 

lowest power to detect trend (Urquhart and Kincaid, 1999). Skipping survey years increases the 

amount of time required to detect trends, but adding a panel of sites visited annually can increase 

the power to nearly that of the always-revisit design. Without specific information to compare 

revisit designs, SFAN personnel may need to rely on management needs to determine what 

revisit design is best. If all prairie falcon territories or spotted owl locations need to be visited in 

a cycle, then incorporating a revisit design that visits a majority of the same sites annually will 

help reduce site-to-site variation. Visiting the remaining sites with a serially-alternating revisit 

schedule will ensure that all sites are evaluated regularly. 
 75 

6. REFERENCES 

Buhl-Mortensen, L. 1996. Type II statistical errors in environmental science and the 

precautionary principle. Marine Pollution Bulletin 32:528-531. 

Gibbs, J. P., S. Droege, and P. Eagle. 1998. Monitoring populations of plants and animals. 

BioScience 48:935–940. 

Jung, B.C., Jhun, M., and Lee, J.W. (2005). Bootstrap tests for overdispersion in a Zero-Inflated 

Poisson Regression Model, Biometrics 61, 626-8. 

Lyles, Robert H., Hung-Mo Lin, and John M. Williamson (2007). A practical approach to 

computing power for generalized linear models with nominal, count, or ordinal 

responses. Statistics in Medicine 26, 1632-48. 

MacDonald, T.L. (2003). Review of environmental monitoring methods: survey designs. 

Environmental Monitoring and Assessment 85, 277-292. 

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey, and J.E. Hines (2006). 

Occupancy Estimation and Modeling. Academic Press: San Francisco. 

Mapstone, B. D. 1995. Scalable decision rules for environmental impact studies: effect size, 

Type I, and Type II errors. Ecological Applications 5:401–410. 

Olkin, L. A.J. Petkau, and J.V. Zidek (1981). A comparison of n estimators for the binomial 

distribution. Journal of the American Statistical Association 76, 637-642. 

Ridout, M., J. Hinde, and C.G.B. Demetrio (2001). A score test for testing a zero-inflated 

Poisson regression model against a zero-inflated negative binomial alternatives. 

Biometrics 57, 219-233. 

Sumathi K. and Aruna Rao K. (2009) On Estimation and Tests for Zero Inflated Regression 

Models, InterStat (on-line), August 

http://interstat.statjournals.net/YEAR/2009/abstracts/0908004.php

Urquhart, N.S. and T.M. Kincaid (1999). Designs for detecting trend from repeated surveys of 

ecological resources. Journal of Agricultural, Biological, and Environmental Statistics

4(4), 404-414. 
 76 

APPENDIX A: Power to detect trends in spotted owl occupancy status categories by revisit 

design 

Figure A.1: Power to detect population declines in PR occupancy in tests of trend over 5 

consecutive survey years for the α=0.20 significance level 
 77 

Figure A.2: Power to detect population declines in PR occupancy in tests of trend over 10 

consecutive survey years for the α=0.20 significance level 

 
 78 

Figure A.3: Power to detect population declines in PU occupancy in tests of trend over 5 

consecutive survey years for the α=0.20 significance level 
 79 

Figure A.4: Power to detect population declines in PU occupancy in tests of trend over 10 

consecutive survey years for the α=0.20 significance level 
 80 

Figure A.5: Power to detect population declines in RS occupancy in tests of trend over 5 

consecutive survey years for the α=0.20 significance level 
 81 

Figure A.6: Power to detect population declines in RS occupancy in tests of trend over 10 

consecutive survey years for the α=0.20 significance level 

 
 82 

Figure A.7: Power to detect population declines in SU occupancy in tests of trend over 5 

consecutive survey years for the α=0.20 significance level 
 83 

Figure A.8: Power to detect population declines in SU occupancy in tests of trend over 10 

consecutive survey years for the α=0.20 significance level 
 84 

Figure A.9: Power to detect population declines in UK occupancy in tests of trend over 5 

consecutive survey years for the α=0.20 significance level 
 85 

Figure A.10: Power to detect population declines in UK occupancy in tests of trend over 10 

consecutive survey years for the α=0.20 significance level 
 86 

Figure A.11: Power to detect population declines in UO occupancy in tests of trend over 5 

consecutive survey years for the α=0.20 significance level 
 87 

Figure A.12: Power to detect population declines in UO occupancy in tests of trend over 10 

consecutive survey years for the α=0.20 significance level 
 88 

Appendix B. Raptor Monitoring Data Sheet 

Date: 

Observer: 

Observation Time Start: 

Observation Time End: 

Easting: 

Northing: 

Nest Code (if nesting): 

Territory: 

Species (and # seen): 

Observation Limits: 

Precipitation: 

High Temp. (F): 

Low Temp. (F): 

0.1"Average Windspeed (mph) and Direction: 

Cloud Cover: 

Courtship (fill in all that apply) 

 Territorial Defense (stooping, etc.): ____________________________________________________ 

 Display (wailing, courting flights, etc.): 

_________________________________________________ 

 Food Exchange (prey species, times, etc.): 

_______________________________________________ 

 Copulation (# times, start times, duration): 

_______________________________________________ 

 Nest Site Inspections (specify sites, behavior, etc.): 

________________________________________ 

 Adult Feeds Self (prey species, times, etc.): 

______________________________________________ 

 Sleep Roost: 

_______________________________________________________________________ 

Incubation (fill in all that apply and provide details) 

 Incubation (sex of adult, duration, etc.):_________________________________________________ 

 Nest switch (# times, etc.):___________________________________________________________ 

 Food Drop (specify prey, male to female, times, etc.):______________________________________ 

___________________________________________________________________________________ 

 Adult Feeds Self (prey species, times, etc.): 

______________________________________________ 

 Eggs Seen (yes/no, #, etc.): 

___________________________________________________________ 

Nestlings / Fledglings (fill in all that apply and provide details) 

 Nestlings (#, age, explain aging): ______________________________________________________ 

 Fledglings (#, days out of nest, explain): ________________________________________________ 

 Food Drop (prey species, times, to adult, nestlings, or fledglings): ____________________________ 

___________________________________________________________________________________ 

 Adult Feeds Self (prey species, times, etc.): 

______________________________________________ 

 Adult Feeds Young (prey species, times, # nestlings / fledglings 

fed)___________________________ 

___________________________________________________________________________________ 

 Nestlings / Fledglings Feed Selves (prey species, times, # young feeding): 

______________________ 

___________________________________________________________________________________

______________________________________________________________________ 
 89 

Photo Code __________ OR Drawing (label & detail 

activity): 

Version 1.1 (August 2007) 

Extra Comments: 

 91 

Appendix C. Raptor Monitoring Data Sheet: Example 

This is an example of a filled out data sheet used during the data sheet testing phase. 
 92 
The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and 

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and 

affiliated Island Communities. 

NPS 114/104176, June 2010 
National Park Service 

U.S. Department of the Interior 

Natural Resource Program Center 

Natural Resource Program Center 

1201 Oakridge Drive, Suite 150 

Fort Collins, CO 80525 

www.nature.nps.gov 

EXPERIENCE YOUR AMERICA TM