U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2007-1381

In cooperation with Northern Arizona University

The Ecology of the Southwestern Willow Flycatcher in 

Central Arizona—a 10-year Synthesis Report

By Eben H. Paxton1, Mark K. Sogge1, Scott L. Durst1, Tad C. Theimer2, and James R. Hatten3

1 U.S. Geological Survey, Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, 

Ariz.; 2 Department of Biological Sciences, Northern Arizona University, Flagstaff, Ariz.; 3 U.S. Geological

Survey, Western Fisheries Research Center, Columbia River Research Laboratory, Cook, Wash.
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The Ecology of the Southwestern Willow 

Flycatcher in Central Arizona—a 10-year 

Synthesis Report

By Eben H. Paxton, Mark K. Sogge, Scott L. Durst, Tad C. Theimer, and James R. 

Hatten

In cooperation with Northern Arizona University

Open-File Report 2007–1381

2007

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark Myers, Director

U.S. Geological Survey, Reston, Virginia: 2007

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Suggested citation:

Paxton, E.H., Sogge, M.K., Durst, S.L., Theimer, T.C., and Hatten, J.R., 2007, The ecology of the southwestern willow 

flycatcher in central Arizona—a 10-year synthesis report: U.S. Geological Survey Open-File Report 2007-1381, 143 p. 
iii

Contents

Executive Summary.......................................................................................................................................1

Chapter 1—Introduction...............................................................................................................................5

Introduction............................................................................................................................................5

Report Structure....................................................................................................................................5

Project Objectives.................................................................................................................................5

Southwestern Willow Flycatcher ......................................................................................................7

History and Recent Operations of Roosevelt Lake..........................................................................9

Description of Roosevelt Lake and San Pedro/Gila Study Sites ................................................11

Acknowledgements............................................................................................................................14

References...........................................................................................................................................15

Chapter 2—Survivorship ............................................................................................................................19

Introduction..........................................................................................................................................19

Methods................................................................................................................................................19

Results. ..................................................................................................................................................26

Discussion............................................................................................................................................36

Management Considerations ...........................................................................................................39

References...........................................................................................................................................39

Chapter 3—Productivity .............................................................................................................................43

Introduction..........................................................................................................................................43

Methods................................................................................................................................................43

Banding and Resighting.....................................................................................................................45

Nest Monitoring ..................................................................................................................................45

Statistical Analysis .............................................................................................................................46

Results. ..................................................................................................................................................47

Discussion............................................................................................................................................53

Management and Research Considerations ................................................................................54

References...........................................................................................................................................55

Chapter 4—Movement................................................................................................................................59

Introduction..........................................................................................................................................59

Methods ...............................................................................................................................................59

Banding and Resighting.....................................................................................................................62

Statistical Analysis .............................................................................................................................63

Results. ..................................................................................................................................................64

Discussion............................................................................................................................................74

Management and Research Considerations .................................................................................77

References...........................................................................................................................................78
iv

Chapter 5—Demographic Modeling ........................................................................................................81

Introduction..........................................................................................................................................81

Methods ...............................................................................................................................................81

Vital Rates ............................................................................................................................................83

Demographic Models.........................................................................................................................84

Results. ..................................................................................................................................................84

Discussion............................................................................................................................................89

Management and Research Considerations ................................................................................93

References...........................................................................................................................................94

Chapter 6—Spatial Modeling ....................................................................................................................97

Introduction..........................................................................................................................................97

Methods................................................................................................................................................98

Surveys and Monitoring.....................................................................................................................98

Spatially Explicit Modeling .............................................................................................................102

Demographic Analysis.....................................................................................................................105

Results. ................................................................................................................................................105

San Pedro/Gila Models....................................................................................................................107

Flycatcher Habitat: 1985–2005 .......................................................................................................113

Demographic Relationships to Spatial Characteristics .............................................................118

Discussion..........................................................................................................................................118

Management and Research Considerations ...............................................................................121

References.........................................................................................................................................122

Chapter 7—Non-territorial Floaters .......................................................................................................125

Introduction........................................................................................................................................125

Methods..............................................................................................................................................126

Results. ................................................................................................................................................128

Discussion..........................................................................................................................................129

References.........................................................................................................................................133

Chapter 8—Conclusions...........................................................................................................................137
Background

From 1996 to 2005, the U.S. Geological Survey (USGS) 

conducted a demographic study of the Southwestern Willow 

Flycatcher (Empidonax traillii extimus) in Arizona in collaboration with the Arizona Game and Fish Department (AGFD). 

The study was begun the year following the listing of the 

Southwestern Willow Flycatcher as an endangered species. At 

the time of the listing, very little was known about the biology 

and threats to the flycatcher, and one of the main objectives of 

the study was to gather detailed long-term information on the 

biology of the flycatcher. 

This report is organized into eight chapters. Following the introductory chapter, we deal with specific aspects 

of flycatcher ecology and habitat use in each of six separate 

chapters. We end with a concluding chapter that synthesizes 

information into broad topical themes that address key management issues. Each of the core chapters (chapters 2 through 

7) conclude with a list of management considerations derived 

from the findings of the respective chapter.

Chapter 1—Introduction

The Willow Flycatcher is a small Neotropical migrant 

that breeds across much of the United States and southern 

portions of Canada, and winters from central Mexico south to 

northern South America. There are four commonly recognized 

subspecies, with the southwestern subspecies breeding in Arizona, western New Mexico, and southern portions of California, Nevada, Utah, and Colorado. The southwestern subspecies 

is dependent on generally dense riparian habitat, a habitat that 

has been greatly reduced over the last 100–150 years. In 1995, 

the Southwestern Willow Flycatcher was declared a federally endangered species because of sharp declines in numbers 

across its range.

The same year the Southwestern Willow Flycatcher 

was listed as endangered (1995), the Bureau of Reclamation 

completed modifications to Roosevelt Lake dam in central 

Arizona. The purpose was to increase water storage capacity, 

which would lead to inundation of riparian vegetation adjacent 

to the reservoir. Because breeding flycatchers occupied much 

of the habitat that would be inundated, Reclamation (in consultation with the U.S. Fish and Wildlife Service) proposed to 

mitigate for the potential habitat destruction, in part, by funding a 10-year research project on flycatchers at Roosevelt 

Lake, and at the San Pedro/Gila River confluence area. Both 

study sites (Roosevelt Lake and San Pedro/Gila) are typical 

of Sonoran Desert Riparian Woodland, and contain multiple 

patches of varying size and stand age. Additionally, both study 

sites hosted some of the largest breeding populations of flycatchers across the range throughout the study period. Research 

was conducted jointly by USGS and AGFD, with AGFD taking 

lead on surveys and nest monitoring, and USGS taking the lead 

on banding and tracking of individual flycatchers.

Chapter 2—Survivorship

We used Maximum Likelihood methods to estimate 

flycatcher survivorship probabilities, based on the banding and 

tracking of 1,080 adults and 498 nestlings, from 1996 to 2005. 

Overall, average survivorship was 64% for adults and 34% for 

juveniles, with considerable yearly variation. For adults, yearly 

variation was the most important influence on survivorship, 

with no difference between sites or sex. Mean life expectancy 

was 1.9 years, but some individuals lived to at least 9 years of 

age; for those few that lived beyond the average lifespan, there 

was an increase in survivorship compared to younger adults. 

For juveniles, the most important predictor of survivorship 

was fledge date, with nestlings fledging later in the breeding 

season having lower survivorship than those fledged early in 

the breeding season. We could not test for site and sex differences in juvenile survivorship.

In addition, we evaluated several other factors that might 

influence survivorship. Habitat type (native, exotic, or mixed) 

in which adults bred did not appear to influence survivorship. However, the breeding status of an individual did, with 

successful breeders having higher survivorship than non-successful breeders, unpaired individuals, and those of unknown 

status. To understand where mortality occurs (i.e., breeding 

grounds, wintering grounds, or migration), we combined 

our breeding ground study with that of a study of wintering 

flycatchers; the combination of the two studies suggests that 

most mortality occurs off the breeding grounds, primarily 

Executive Summary
2 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

during migration. Finally, we evaluated the potential impacts 

of permanent emigration on our survivorship estimates. To do 

so, we calculated survivorship at different spatial scales, from 

individual patches up to the entire study area. We concluded 

that survivorship would be underestimated if we use data from 

only small spatial scales, but because we conducted this study 

at a larger spatial scale we are probably detecting most birds 

and have robust survivorship estimates.

Chapter 3—Productivity

Using the extensive information derived from the tracking 

of banded birds over multiple years, we calculated seasonal 

nest success (percent of nests that successfully fledged at least 

one young per breeding season), seasonal fecundity (total 

number of young fledged per breeding season), age-based seasonal fecundity, and Minimum Lifetime Productivity (the total 

number of young fledged per individual over their estimated 

lifetime). Seasonal measures of reproductive success varied by 

site and year, although overall the San Pedro/Gila had higher 

reproductive success. Average seasonal fecundity for females 

was 1.6 at Roosevelt Lake and 2.0 at the San Pedro/Gila. Male 

seasonal fecundity was 0.4 higher than females at Roosevelt 

Lake and 0.5 higher at San Pedro/Gila. Older females had 

higher seasonal productivity than second-year females, and 

there was no difference in productivity by habitat type (native, 

saltcedar, and mixed habitats). Average Minimum Lifetime 

Productivity was 3.3 for females and 3.8 for males. Over a 

third of individuals did not fledge any young that we detected, 

and over 50% of the young fledged were contributed by just 

16% of the breeding adults.

Chapter 4—Movement

Movement of banded birds was observed both within 

and between years and at multiple scales (between-drainage, 

between-patches, and within-patch). Natal dispersal was on 

average 11 km farther than adult movements, and the longest 

observed movements were from natal dispersal (up to 444 km). 

Fewer than 1% of banded nestlings returned to their natal patch. 

We documented within-season and between-season 

movement by adults. Within season movements occurred from 

small scales (within-patch) to large scale (between-drainage), 

and represented strategies from territory switching to postbreeding non-territorial movements. Between-year movements 

were far more common, and ranged from 0.1km to 214 km. 

There was no difference in mean movement distance between 

males and females, but flycatchers at the San Pedro/Gila 

study site moved significantly farther within-drainage than 

those at Roosevelt Lake. This difference primarily reflected 

differences in the spatial arrangement of breeding patches. 

The proportion of flycatchers that returned to their previous 

year territory, moved to another territory in the same patch, or 

moved to another patch also varied by year and study site. One 

important factor in whether a flycatcher returned to their previous year territory was their previous year nest success, with 

the odds of showing territory fidelity increasing 1.5 for every 

young fledged. Additionally, the decision to move or show territory fidelity, which is based in part on reproductive performance, has implication on the subsequent year’s performance. 

Successful individuals that returned to their territory tended to 

do well, while those that did not do well in the previous year 

and moved tended to do better than in the previous year.

While reproductive success may strongly influence 

whether an individual returns to the same territory, other factors also appear to influence such decision making. One cause 

may be habitat selection. Young riparian habitat patches can 

be quickly colonized by flycatchers, and immigration into the 

young habitat is the dominant movement pattern. 

As the habitat matures, immigration declines while 

emigration from the patch increases. While territory fidelity 

also increases as the patch ages, this is by a dwindling number 

of individuals. Such changes in movement patterns may be a 

key reason for the often ephemeral pattern of breeding patch 

occupation observed across the range of the flycatcher.

Chapter 5—Demographic Modeling

Survivorship, productivity, and movement can all influence whether a population increases, decreases, or remains 

constant in size. However, how each of these vital rates influences a population can only be understood via the simultaneous consideration of all the vital rates synergistically combined. We constructed demographic models that evaluated how 

these vital rates, considered simultaneously, contributed to the 

growth potential of the two study site populations. Further, to 

assess the strength of the model’s ability to accurately mimic 

flycatcher population dynamics, we applied the model retrospectively to the study sites, comparing predicted numbers to 

those actually observed. In general, the demographic models 

underestimated the growth potential of the breeding populations when compared to the observed population changes from 

1996 to 2005. This was surprising given that the models were 

based on the robustly-measured vital rates from these populations. The reason for the discrepancy between observed and 

modeled growth rates is unknown, though it could be because 

of underestimations of productivity, survivorship, or immigration rates. We suggest development of models that will 

accurately reflect flycatcher population dynamics.

Chapter 6—Spatial Modeling

The decline of the Southwestern Willow Flycatcher is 

believed to be tightly linked to the decline in the riparian habitat they depend on for breeding, and the ability to accurately 

describe and predict suitable flycatcher breeding habitat is an 

important step in the ultimate recovery of the species. We used 

spatially explicit models to describe the habitat of flycatchers, 

taking an important next step in modeling from earlier efforts. 

Specifically, we expanded upon earlier efforts of modeling 
 3

flycatcher habitat by developing models that added additional 

environmental parameters, modeled specifically for a reservoir 

and riverine systems, and incorporated a temporal component. 

The models constructed were then tested against 10 years of 

territory location data from both study areas to evaluate the 

temporal robustness of the models. We found that modeling reservoir and riverine systems separately, in contrast to 

earlier efforts that averaged across the two, provided much 

better accuracy, especially for the reservoir system. However, 

the greatest improvement in the models came when temporal 

change in habitat was incorporated into the models. This highlights the dynamic nature of riparian habitat, and the importance of incorporating such dynamics into habitat models. 

Applying the best models to environmental information 

collected from 20 years at both study sites, the amount of 

predicted habitat changed substantial over the years at the two 

systems. Roosevelt Lake varied from a low of 41 ha in 2005 

to a high of 483 ha in 2004. The San Pedro/Gila had a low 

of 406 ha in 2002 and high of 1,392 ha in 1987. Changes in 

the amount of predicted habitat were driven largely by water. 

At Roosevelt Lake, the fluctuations in the reservoir level had 

profound effects on the amounts of predicted habitat, while at 

the San Pedro/Gila scouring floods could reduce the amount 

of habitat, as could severe droughts. Matching flycatcher 

dynamics to the habitat was difficult. At Roosevelt Lake, a 

general increase in the amount of predicted habitat from 1996 

to 2004 coincided with an increase in the breeding population 

size there. However, at the San Pedro/Gila study site, increasing population sizes did not match changes in the amount of 

predicted habitat; this may indicate that habitat was not limiting on the San Pedro/Gila during the modeled period. Further, 

while these models performed very well in predicting where 

a flycatcher territory may occur, the elements of the spatial 

models (e.g., NDVI, distance from river) were only weakly 

related to measure of reproductive success, suggesting that the 

factors that flycatchers use to locate territories may be different from factors that determine ultimate nesting success.

Chapter 7—Floaters

The prevalence of floaters (non-territorial individuals) 

in a population is difficult to detect and thus is poorly understood. Based on field work in the first half of this study, we 

suspected that there was a population of floaters, and that they 

may constitute a not-insignificant proportion of the population. 

From 2001 to 2005 we enacted a passive netting operation to 

detect floaters at Roosevelt Lake. Overall, we detected 134 

individuals during this period that were present at the breeding 

site but which we believed were non-territorial for an entire 

season. In most years the number of floaters was relatively 

small (less than 10% of population), but it was high in the one 

year of severe drought (2002; 25% of population). Most floaters were male (85%) and most were young adults (88% were 

second-year adults).

To understand why a flycatcher may become non-territorial, and whether they contribute to a breeding population, we 

evaluated the breeding performance of individuals in pre- and 

post-floater years. Productivity did not differ pre- or postfloating, suggesting no benefit to floating for a year. Lifetime 

productivity of birds that floated was lower than individuals 

that were territorial in every year detected, suggesting a cost to 

forgoing a year of breeding. There was a negative relationship 

between the average productivity of a site (all breeding individuals considered) and the prevalence of floaters, suggesting 

that years with poor productivity had higher numbers of floaters (such as in the year of the severe drought, 2002). Thus, 

while floaters do form a part of the population at Roosevelt 

Lake, contributing substantially to numbers in some years, in 

most years they constitute a small portion of the population.

Chapter 8—Conclusions

Taking the insights into flycatcher ecology and biology derived from the previous chapters, we synthesize 

findings from across chapters to address a range of management concerns.

Role of Reservoirs

Although reservoirs and their operation can lead to 

alteration and degradation of riparian systems, in certain situations they can support large tracts of apparently high-quality 

riparian habitat in their drawdown zones. Roosevelt Lake 

is one example, and Southwestern Willow Flycatchers bred 

extensively in the ephemeral riparian habitat that developed 

within the lakebed during low water years. Given that reservoirs fluctuate over time, they can sometimes create conditions 

for habitat creation and destruction cycles that can lead to 

development of a mosaic of different aged riparian woodland 

stands. Even if the habitat patches are periodically destroyed, 

such mosaics can benefit Southwestern Willow Flycatchers 

and other riparian obligate species.

Response to Reservoir Rise 

While the drawdown of Roosevelt Lake created hundreds 

of hectares of riparian vegetation between 1996 and 2004, the 

high lake levels in 2005 wholly or partially inundated most 

of the previously occupied flycatcher habitat. Flycatchers 

responded by using partially inundated habitat or by shifting to 

new patches above the lake level. We did not observe largescale dispersal to other breeding locations in the Southwest. 

Thus, the existence of “refuge” habitat near current breeding 

populations may be important for the long-term sustainability 

of flycatchers in an area. This may be particular important for 

reservoirs, where high water levels and associated habitat loss 

can be expected to regularly reoccur. 
4 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Scale of Management

Flycatchers showed a high degree of movement from 

1996 to 2005, with movements common among sites that were 

30–40 km apart and within the same drainage. Therefore, our 

perception of what constituted a biologically meaningful site 

has shifted from considering every patch as a distinct site, to 

considering a network of patches within the same drainage as 

a site. At a larger geographic scale, infrequent movements that 

connect different drainages allow for metapopulation-scale 

processes to occur. Thus, management of the flycatcher will 

be most effective if conducted at drainage and regional scales, 

with a consideration of patch connectivity.

Dynamic Species and Dynamic Habitats 

Flycatchers showed high variability in most of their 

life history traits, which may reflect their adaptation for the 

dynamic riparian habitats in which they evolved and upon 

which they depend. Riparian habitat can establish rapidly, 

and be colonized by flycatchers when the structure becomes 

suitable. Therefore potential breeding habitat can be developed 

quickly, under the right conditions. Flycatchers appear to leave 

habitat as it matures, at least if there is younger alternative 

habitat nearby. Riparian habitat can disappear rapidly through 

scouring floods, inundation, fire, or other disturbances. When 

riparian habitat is lost, flycatchers appear to be able to shift to 

new breeding locations, if suitable habitat is present. Recognizing the dynamic nature of riparian habitat is one key to 

successful long-term management of the flycatcher.

Habitat Use

Southwestern Willow Flycatchers depend on typically 

dense riparian habitat, but the exact structure and species 

composition can be variable within and between sites. This use 

of diverse plant species suggests that vegetation structure, not 

species composition, is the most important feature of flycatcher habitat. Flycatchers appear to prefer young habitat, and 

bred in some riparian patches that were only 3 years of age. 

However, they will occupy older habitat, perhaps as refugia 

until younger habitat becomes available. At Roosevelt Lake 

and the San Pedro/Gila, flycatchers bred in exotic saltcedar, 

with no apparent negative effects. 

Riparian Restoration

Despite the many challenges to successful riparian 

restoration activities, it is possible to create riparian habitat 

relatively quickly and for it to be rapidly colonized by Southwestern Willow Flycatchers. Newly created habitat can be 

colonized by flycatchers within as little as 3 years, especially 

if within 30–40 km of extant breeding locations. GIS models 

developed as part of this study can be used as tools for planning and assessing riparian habitat restoration.

Monitoring Implications

Local flycatcher population sizes can change rapidly, 

and flycatchers can quickly colonize new habitat. Therefore, 

periodic monitoring is needed in order to have high confidence 

about the status of local Southwestern Willow Flycatcher populations. Similarly, riparian habitat can also develop, change 

structure, or be lost quickly. Accordingly, periodic habitat 

monitoring is important to be certain of the current distribution 

of potential flycatcher breeding habitat.

Drought Impacts

Drought has negative effects on breeding flycatchers 

and their habitat. The extreme drought of 2002 caused near 

complete reproductive failure of flycatchers at Roosevelt Lake, 

and caused a dramatic rise in the prevalence of non-breeding 

floaters. More frequent or more sustained droughts—as predicted by many climate change models—could have long-term 

negative effects on the sustainability of flycatchers. Thus, 

long-term management of the flycatcher will be more effective if it takes into account, and investigates possible ways to 

ameliorate, the negative effects of future droughts.

Future Research Needs and Directions

While we have learned much about the flycatcher over 

this 10-year study, there are still many important areas for 

future research. These include: whether small populations have 

different population dynamics than the large populations we 

studied; development of improved or new demographic models to help guide complex management decisions; development of spatial models that link key determinants of flycatcher 

habitat (e.g., groundwater, stream flow) to the characteristics 

of suitable habitat (as identified by our models); development 

of decision tools to conduct scenario planning and address 

management issues; the role and impact that emerging disease 

threats may have on flycatcher populations; and a better understanding of the migration ecology of flycatchers.
 5

Introduction

This project was funded by the Bureau of Reclamation, 

as a result of a Section 7 Biological Opinion on the effects 

of raising Roosevelt Dam, located in central Arizona. An 

increase in the height of the dam structure provided additional 

water conservation space (as well as flood control and dam 

safety improvements), but also increased the amount of Southwestern Willow Flycatcher habitat that would be periodically 

inundated. The Biological Opinion issued by the U.S. Fish and 

Wildlife Service (USFWS 1996) identified a Reasonable and 

Prudent Alternative (RPA) that would avoid “jeopardy” to the 

species. As part of the RPA, the Opinion required that Reclamation fund a comprehensive Southwestern Willow Flycatcher 

research program. As outlined in RPA 1.e.2, Reclamation 

was required to collect flycatcher demographic data though 

color-banding and monitoring studies at Roosevelt Lake and at 

select locations along the lower San Pedro/Gila River confluence area (hereafter referred to as the San Pedro/Gila site; fig. 

1). Roosevelt Lake was the focal area because of potential 

dam-related impacts to the flycatchers breeding there. However, when the Opinion was written it was anticipated that the 

lake might rise to the new capacity level before the 10-year 

research period ended. Therefore, areas along the San Pedro 

and Gila Rivers were included as research locations because 

they were not subject to reservoir inundation, and birds dispersing as a result of Roosevelt Dam operations might move to 

or from these areas.

Reclamation contracted with both the U.S. Geological 

Survey (USGS) Southwest Biological Science Center and the 

Arizona Game and Fish Department (AGFD) to collect data on 

various aspects of Southwestern Willow Flycatcher demography. A series of USGS annual reports summarized the results 

of our efforts each year (Paxton and Sogge 1996, Paxton et al. 

1997, Netter et al. 1998, English et al. 1999, Luff et al. 2000, 

Kenwood and Paxton 2001, Koronkiewicz et al. 2002, Newell 

et al. 2003, Newell et al. 2005, Causey et al. 2005); AGFD 

produced a similar series detailing their project components. 

Summary data compilation, database integration, and analysis 

occurred in 2006 and 2007. This synthesis report is intended 

to (1) summarize the 10 years of research activity; (2) analyze 

and model the data to provide insight into the flycatcher 

population and habitat dynamics; and (3) provide interpretations of the data that can inform management and conservation 

of the Southwestern Willow Flycatcher at Roosevelt Lake, and 

potentially elsewhere in its range. 

Report Structure

This final report consists of an Introduction, six standalone chapters, and a Conclusion chapter. The Introduction 

(chapter 1) provides the background necessary to understand 

the overall research project: a general description of the origin 

and scope of the project, an overview of the natural history of 

the Southwestern Willow Flycatcher, a brief summary of the 

history and operations of Roosevelt Lake, and descriptions of 

the Roosevelt Lake and San Pedro/Gila study sites. The next 

three chapters cover the topics of Survivorship (chapter 2), 

Productivity (chapter 3), and Movement (chapter 4). These are 

key drivers of how a population functions and changes over 

time and space; it is crucial to understand how and why they 

vary to understand why a population grows, remains steady, 

or declines. The next two chapters, Demographic Modeling 

(chapter 5) and Spatial Modeling (chapter 6), bring together 

the previous population parameters to explore how they interact with one another in a spatial and temporal context. Chapter 

6 also evaluates refined models of predicted flycatcher habitat 

and changes in predicted habitat over time. Floaters (chapter 

7) describes the nature and extent of non-territorial flycatchers at Roosevelt Lake, to determine what factors influence 

territoriality and non-territoriality, and whether floaters are an 

important component of the population. Chapters 2 through 7 

each include introductory material, methods, results, conclusions, and management considerations specific to each topic. 

The Conclusion (chapter 8) summarizes key data and management considerations contained in previous chapters. 

Project Objectives

At a broad scale, our goal was to understand important 

demographic parameters and their potential drivers, and 

to develop models that can provide useful information to 

resource managers who are responsible for long-term perChapter 1—Introduction
6 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 1. Location of the Roosevelt Lake and San Pedro/Gila study areas in central Arizona. 
 7

sistence of Southwestern Willow Flycatchers in Arizona. In 

particular, this information was anticipated to be of use in the 

recovery of the flycatcher. The interim data produced during this 

study were widely used in the development of the Southwestern 

Willow Flycatcher recovery plan (USFWS 2002), critical habitat designation (USFWS 2005), and other recovery planning.

 Each year, we color-banded and resighted as many 

Southwestern Willow Flycatchers as possible within our 

study sites in order to gather detailed demographic information about the populations. Over the course of this study 

(1996–2005), USGS captured and banded 1,080 adult and 498 

nestling/fledgling willow flycatchers. The presence of banded 

birds at each site also contributed to on-going flycatcher studies by AGFD, providing a more accurate assessment of the 

number of breeding birds and the ability to document breeding 

activities (e.g., pairing, nesting attempts, reproductive success) 

of individuals within and between years. Specific objectives of 

the USGS-based demography study were to collect data on:

1. between-year survivorship and mortality of adults and 

young; 

2. immigration, emigration, site and patch fidelity, and 

movement between patches and sites; and

3. sex and age structure of flycatcher population.

In addition to directly addressing the research objectives of the Roosevelt Dam Biological Opinion, our activities at Roosevelt Lake provided an opportunity to conduct 

supplemental studies on topics such as morphology (Paxton 

et al. 2005), home range and habitat use (Cardinal et al. 2006, 

Sogge et al. 2006), physiology (Owen et al. 2005), genetic 

variation and structuring (Sogge et al. 1998, Busch et al. 

2000, Paxton 2000), nestling sex ratio (Paxton et al. 2002b), 

and food habits (Drost et al. 2003, Durst 2004). These studies 

provided valuable context in which to better understand the 

ecology of the Southwestern Willow Flycatcher at Roosevelt 

Lake, and elsewhere in the bird’s range. Furthermore, during 

this project we developed or refined several new field techniques, including targeted mist-net capture (Sogge et al. 2001), 

a floating mist-net technique for flooded habitats (Pollock and 

Paxton 2006), low-impact aluminum color bands (Koronkiewicz et al. 2005), radio telemetry (Paxton et al. 2002a), and 

a nestling aging guide (Paxton and Owen 2002). Each of 

these new techniques proved valuable in meeting our project 

objectives, and many have been adopted by other flycatcher 

research projects.

Coordination with Arizona Game and Fish 

Department

Throughout this research effort, project leaders and field 

staff of the USGS and AGFD worked cooperatively to collect 

the data upon which this report and other related science products are based. AGFD and USGS had concurrent field crews 

working within the same study sites at Roosevelt Lake and 

the San Pedro/Gila from 1996 to 2000. Generally, AGFD had 

lead responsibility for surveying and locating flycatcher territories, and monitoring nests; USGS conducted flycatcher capture and banding efforts, and focused on resighting of banded 

individuals. However, both groups collected information 

on flycatcher locations, nests, and band identifications, and 

shared all field data accordingly. Beginning in 2001, USGS 

focused field work totally on the flycatchers at Roosevelt 

Lake, and AGFD took over responsibility for all field efforts 

on the San Pedro/Gila while also remaining at Roosevelt 

Lake. This collaborative approach allowed the collection of 

more and better field data than would have otherwise been 

possible. A separate 10-year synthesis report by AGFD (Ellis 

et al. 2008) details their components of the overall project, 

including an evaluation of how the flycatchers responded to 

rising reservoir levels and habitat inundation at Roosevelt 

Lake during the 2005 season.

Southwestern Willow Flycatcher 

The following is a brief summary of the natural history, 

distribution and abundance, and conservation status of the 

Southwestern Willow Flycatcher; it does not include the new 

information from our 10-year study that is reported in later 

chapters. We focus here on the breeding season, as that is the 

time during which we conducted our flycatcher research at 

Roosevelt Lake. For more detailed overviews of flycatcher 

biology and habitat use (including images of breeding habitats 

throughout the Southwest), and migration and winter ecology, 

see Sogge et al. (1997), Sogge (2000), Finch and Stoleson 

(2000), Koronkiewicz (2002), USFWS (2002), Sogge et al. 

(2003a), USFWS (2005), and Koronkiewicz et al. (2006). 

Breeding Ecology

The Willow Flycatcher (Empidonax traillii) is a widespread species, breeding across much of the conterminous 

United States. There are four widely recognized subspecies 

(Unitt 1987), all of which are Neotropical migrants that winter 

in Central and South America, coming to North America for 

only 4–5 months each year to breed (Sedgwick 2000). The 

Southwestern Willow Flycatcher (E.t. extimus) is a distinct 

subspecies. It is generally paler than other Willow Flycatcher 

subspecies, although this difference is subtle and requires considerable experience and taxonomic training to detect, and the 

use of known subspecies study skins as comparative reference 

material (Unitt 1987). Despite the subtle morphological differences, the taxonomic validity of E.t. extimus has been critically 

reviewed and confirmed multiple times (Hubbard 1987, Unitt 

1987, Browning 1993, Paxton 2000). The known breeding range of the southwestern subspecies includes southern 

California, Arizona, New Mexico, southwestern Colorado, and 

extreme southern portions of Nevada and Utah (USFWS 2002).
8 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Willow Flycatchers generally depart their wintering 

grounds in April and May (USGS unpub. data). They will 

frequently sing during migration, perhaps to establish temporary territories for short-term defense of food resources. 

Southwestern Willow Flycatchers generally arrive at their 

breeding sites from late April through May, earlier than is 

true for the more northern subspecies. Therefore, from May 

through mid-June, northbound migrant Willow Flycatchers of 

other subspecies are passing through the Southwest, which can 

make it challenging to determine local breeders from migrants 

(Sogge et al. 1997). In the same manner, southbound migrants 

can appear in late July and August, when some Southwestern 

Willow Flycatchers are still tending nests or young. USFWS 

(2002) presents a generalize migration and breeding chronology for Southwestern Willow Flycatchers.

Breeding Southwestern Willow Flycatchers are riparian 

obligates; they typically establish territories and nest in relatively dense riparian vegetation where surface water is present 

or soil moisture is high enough to maintain the appropriate 

vegetation characteristics (Sogge and Marshall 2000). However, because hydrological conditions in the Southwest can 

be highly variable within a season and between years, water 

availability at a site may change dramatically (ranging from 

flooded to dry) over the course of a breeding season or from 

year to year. Vegetation characteristics of Southwestern Willow Flycatcher breeding habitat generally include high canopy 

or midstory cover, dense twig structure, and high levels of live 

green foliage (Allison et al. 2003); dense midstory vegetation 

in the 2–5-m range is common. Beyond these generalities, 

the flycatcher shows adaptability in habitat selection, as the 

dominant plant species (both native and exotic), size and shape 

of breeding patch, and canopy height and structure can be 

variable (USFWS 2002). Unlike breeders, migrating Willow 

Flycatchers are regularly found in riparian habitats that would 

not be suitable for breeding, as well as in some non-riparian 

habitats (Sogge et al. 1997).

Overall, male Southwestern Willow Flycatchers arrive 

on the breeding grounds before females, with older males 

generally arriving before younger ones. Although females 

usually arrive a few weeks after males, some older females are 

present on territories before late-arriving males. Flycatchers 

will sometimes travel extensively through large areas of the 

Roosevelt Lake study site before and after breeding, possibly 

as a way to evaluate potential breeding habitat (Cardinal et 

al. 2006). Males establish territories and exclude other males 

through singing and aggressive interactions. Male song rate 

is very high early in the season, then declines after pairing 

(Yard and Brown 2003). Territory size tends to be larger when 

a male first arrives, then gets smaller after a female pairs with 

the male. At Roosevelt Lake, average territory size (after 

pairing) was approximately 1 ha; however, estimated territory 

sizes can vary widely and range from 0.06 ha to 2.3 ha (Sogge 

1995, Sogge et al. 1995, Whitfield and Enos 1996). Although 

the Willow Flycatcher as a species is considered predominantly monogamous during the breeding season (Sedgwick 

2000), some Southwestern Willow Flycatcher populations 

have a relatively high degree of polygyny whereby one male’s 

territory can include more than one breeding female (generally 

two, but up to four have been recorded; Davidson and Allison 

2003). 

Females generally begin nest construction within a week 

or two of pair formation; males play little or no role in nest 

building. The nests are built as open cups, typically woven in 

place on the branch of a tree or shrub where several smalldiameter stems can provide support. Nest height is highly 

variable and depends on the available plant structure within 

the territory; nests have been found from 0.6 m to 18 m above 

ground. Southwestern Willow Flycatchers will commonly 

place their nests in native plants such as willows (Salix spp.) 

and box elder (Acer negundo; in the Gila-Cliff Valley, NM 

only), and in exotics such as saltcedar (Tamarix spp.) and Russian olive (Elaeagnus angustifolia; Sogge and Marshall 2000, 

Stoleson and Finch 2003, Durst et al. 2006). In Arizona, most 

nests are in tamarisk or willows (Paradzick and Woodward 

2003, McLeod et al. 2007). Flycatcher nest location appears to 

be driven more by plant structure than by species composition. 

Egg laying generally begins in early June, but a few early 

clutches occur by mid-May (Sogge et al. 1997). Clutch size 

averages 3–4 eggs for first nests, with one egg laid per day. 

Only the female develops a brood patch and incubates the 

eggs. The incubation period is typically 12–13 days from the 

date the last egg is laid, and all eggs generally hatch within 

24–28 hrs of each other. 

Flycatcher chicks are altricial and weigh only about 1–2 g 

at hatching, but grow rapidly and are ready to leave the nest at 

12–15 days of age (Sedgwick 2000, Paxton and Owen 2002). 

The female initially provides most care for the young, but the 

male will help with feeding, especially as the chicks grow and 

their food needs increase. Both adults will defend the nest 

and nestlings from intruders and predators, using distraction 

behavior (trying to draw attention away from the nest) and 

physical aggression. After young fledge, they generally stay 

together and close to the nest for 3–5 days, then travel increasingly farther distances from the nest and, eventually, their natal 

territory. The adults continue feeding the fledged young for 

approximately 2 weeks.

Southwestern Willow Flycatchers readily re-nest, 

especially after a nest is unsuccessful; they will also sometimes nest again after a successful nesting attempt. Clutch 

size generally decreases with each successive nesting attempt 

(Whitfield and Strong 1995). Second nests are often built 

very close to the first (sometimes in the same tree), but can be 

placed anywhere within the same territory. Reuse of old nests 

is uncommon, but does occur (Yard and Brown 1999, D. Ahlers, personal commun.). Pairs may even attempt a third nest if 

the second one fails. 

Based on studies of banded birds, most Southwestern 

Willow Flycatchers that survive from one year to the next will 

return generally to the same river drainage, often in proximity 

to the same breeding site, though some long-distance movements within and between drainages have been observed 

(USFWS 2002, Causey et al. 2006, McLeod et al. 2007). 
 9

Between-year survivorship of adults appears to be similar to 

that of most small passerine birds studied, generally ranging 

from approximately 40% to 60% (Stoleson et al. 2000, McLeod 

et al. 2007). Survivorship of young has been studied at only a 

few sites, but appears generally to be lower than adult survivorship (Stoleson et al. 2000, McLeod et al. 2007). However, 

first-year birds (i.e., returning nestlings) may be more prone to 

dispersal to new sites, which would artificially increase estimates of first-year mortality. Overall, the flycatcher population 

in the Southwest appears to persist as one or more widely dispersed metapopulations. There is movement of individuals, and 

thus genetic exchange, across the landscape, but the amount of 

interchange is smaller among more distant sites.

During the breeding season, Southwestern Willow Flycatchers are exclusively insectivorous, and consume a wide 

range of prey taxa (Drost et al. 2003, Durst 2004) ranging in 

size from small leafhoppers (Homoptera) to large dragonflies 

(Odonata). Major prey taxa include bugs (Hemiptera), bees 

and wasps (Hymenoptera), flies (Diptera), and leafhoppers; 

however, diet can vary widely between years and among different habitat types. Owen et al. (2005) investigated the effects 

of habitat-based diet differences on Southwestern Willow 

Flycatchers breeding in native-dominated and saltcedar-dominated habitats, and found no negative physiological or immunological impacts from breeding in saltcedar. 

Conservation Status

Conservation attention was first focused on the Southwestern Willow Flycatcher at a rangewide scale by Unitt 

(1987), who described declines in flycatcher abundance and 

distribution throughout the Southwest. E.t. extimus populations 

have declined during the twentieth-century, primarily because 

of habitat loss and modification from activities such as dam 

construction and operation, groundwater pumping, water 

diversions, and flood control. In 1991 the U.S. Fish and Wildlife Service designated the Southwestern Willow Flycatcher as 

a candidate category 1 species (USFWS 1991). In July 1993, 

the USFWS proposed to list E.t. extimus as an endangered 

species and to designate critical habitat (USFWS 1993). A 

final ruling listing E.t. extimus as endangered was published in 

February 1995(USFWS 1995); critical habitat was designated 

in 1997 (USFWS 1997) and re-designated in 2005 (USFWS 

2005). Both the Roosevelt Lake and San Pedro/Gila study sites 

are included within the designated critical habitat. The Southwestern Willow Flycatcher is also listed as an endangered 

species or species of concern in Arizona (AGFD 1998), New 

Mexico (New Mexico Department of Game and Fish 1996), 

California (California Department of Fish and Game 1991), 

and Utah (Utah Division of Wildlife Resources 1997). Riparian habitat loss and alteration continues to occur, with potential negative effects to the flycatcher in some areas (Marshall 

and Stoleson 2000, USFWS 2002).

When proposed for listing in 1993, there were only 30 

known Southwestern Willow Flycatcher breeding sites and 

111 estimated territories rangewide (Sogge et al. 2003b). As 

a result of extensive survey efforts since 1995, following its 

listing as endangered, Southwestern Willow Flycatchers have 

now been recorded breeding at 275 sites, with an estimated 

total of 1,214 territories in 2005 (Durst et al. 2006). Arizona, 

New Mexico, and California account for approximately 90% 

of territories. Within Arizona, Roosevelt Lake and the San 

Pedro/Gila sites supported the majority of known flycatcher 

territories during our study period (Paradzick and Woodward 

2003; Durst et al. 2006). 

History and Recent Operations of 

Roosevelt Lake

Roosevelt Dam was constructed at the confluence of the 

Salt River and Tonto Creek, approximately 75 km (50 miles) 

northeast of Phoenix. Standing 85 m (280 ft) high, the first 

stone was laid in 1906, and the dam completed in 1911 (http://

www.srpnet.com/water/dams/ roosevelt.aspx). Named after 

then-President Theodore Roosevelt, it was the largest masonry 

dam in the world and created a reservoir capacity of 526,875 

acre-feet. An 8-year, $430 million project to increase the dam 

height was completed in 1996 (fig. 2). Raising the dam by 

approximately 25 m (77 ft), to a height of roughly 115 m (357 

ft), provided for a total 2.9 million acre-feet of water storage at 

full capacity. 

Although the dam modification project was completed 

in 1996, lake levels did not approach capacity for almost a 

decade. Low rainfall in the mid- and late-1990s led to very low 

water levels from 1998 through 2004 (fig. 3), during which 

time substantial amounts of new riparian habitat developed 

in the exposed reservoir bottom. In 2005, much of the riparian habitat was inundated (fig. 4) when the new conservation 

space at Roosevelt Lake filled to 95 percent capacity for the 

first time. Roosevelt Lake exceeded 2,136 ft from February to 

September 2005 and filled to elevation 2,148 ft in April and 

May 2005. 

Southwestern Willow Flycatchers were first detected at 

Roosevelt Lake in 1993 (Sferra et al. 1995). As lake levels 

subsequently dropped and riparian vegetation expanded, the 

flycatcher population expanded from 48 territories in 1998 to 

140 territories in 2001 (Smith et al. 2002), moving into newly 

created habitat within the exposed lakebed. The number of 

flycatchers continued to increase through 2004, with a high of 

209 territories (Munzer et al. 2005). As riparian habitat was 

lost or altered through inundation, the Southwestern Willow 

Flycatcher population declined to 153 territories in 2005 and 

111 territories in 2006 (English et al. 2006, Graber et al. 2007).

The Bureau of Reclamation owns Roosevelt Dam, but 

it is operated and maintained by the Salt River Project (SRP) 

pursuant to a 1917 agreement between the two agencies, in 

conjunction with the downstream dams on the Salt River. Lake 

levels are generally lowest in the winter and early spring, rise 

in late spring due to the capture of winter runoff, and then 
10 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 3. Lake level fluctuations at Roosevelt Lake reservoir from 1985 to 2005, with 1995-2004 

regression line of general trend toward lower lake levels. Each solid circle is a mean monthly value 

of lake level elevation. Data are from the Bureau of Reclamation. 

Year

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 

Roosevelt Lake elevation (feet)

2020

2040

2060

2080

2100

2120

2140

2160

Figure 2. The modified 

Roosevelt Dam, now 115 m 

(357 ft) high and providing a 

storage capacity of 2.9 million 

acre-feet for Roosevelt Lake 

(in background). 

Figure 3. Lake level fluctuations at Roosevelt Lake reservoir from 1985 to 2005, with 1995–2004 regression line of general trend toward 

lower lake levels. Each solid circle is a mean monthly value of lake level elevation. Data are from the Bureau of Reclamation.
 11

decline over the summer and fall as releases are made from 

the dam. The Bureau’s Section 7 Biological Opinion (USFWS 

1996) addressed only the Federal action of raising the dam, 

and the corresponding new conservation and flood control 

space above 2,136 ft (689-m) elevation—not the operation 

of Roosevelt Dam. SRP received authorization to “take” 

Southwestern Willow Flycatchers below 2,136-ft level, by 

developing a Habitat Conservation Plan (HCP) as authorized 

in Section 10 of the Endangered Species Act (ESA). The 

resulting HCP (SRP 2002) includes all impacts associated 

with SRP’s storage of water, both in the new and existing 

conservation space. The Bureau of Reclamation continues 

to be responsible for implementation of its 1996 Biological 

Opinion requirements.

Description of Roosevelt Lake and San 

Pedro/Gila Study Sites 

Our two focal study sites (fig. 1) were breeding populations at Roosevelt Lake (33o39’N, 110o58’W) and the San 

Pedro and Gila Rivers confluence (hereafter San Pedro/Gila; 

32o59’N, 110o46’W). Roosevelt Lake was the area subject to 

potential reservoir inundation impacts because of the operation 

of Roosevelt Dam; the San Pedro/Gila site provided a comparison area that was not subject to possible reservoir effects. 

We conducted our demographic research in cooperation with 

AGFD from 1996 to 2005 (Causey et al. 2005, English et al. 

2006). We defined sites as a collection of riparian woodland 

patches, occupied by breeding flycatchers, which are collectively closer to one another than to other breeding patches, and 

are found within the same drainage (or along the same lake). 

Exact definitions of a site are difficult, and the definition of a 

site differs regionally (Durst et al. 2006). Likewise, we defined 

a breeding patch as a distinct patch of riparian vegetation 

which breeding flycatchers occupied, that is separated from 

other riparian vegetation by non-riparian habitat (e.g., open 

ground, scrub, river). We delineated breeding habitat patches 

by projecting territory locations for all years onto rectified 

aerial photographs, and used natural breaks or distribution of 

territories to estimate their approximate boundaries manually 

in ArcView 3.0 (ESRI, Inc). 

Our two study sites were among the largest known Southwestern Willow Flycatcher breeding populations throughout 

the period of this study, with mean population sizes of 201 and 

239 individuals at Roosevelt Lake and San Pedro/Gila, respectively. At each site, extensive survey efforts were conducted at 

least three times in the breeding season to detect all territorial 

flycatchers within the immediate and surrounding areas. In 

addition, other breeding sites in Arizona and throughout the 

Southwest (49–444 km away) were periodically visited, which 

of the loss of flycatcher breeding habitat that occurred when lake levels rose dramatically during 

the winter/spring of 2005. 

A. 

B. 

 4

Figure 4. Panoramic views of the Orange-peel Campground site at Roosevelt Lake, in April 2004 (A) and April 2005 (B). The extensive 

Southwestern Willow Flycatcher breeding habitat in mid-photograph (2004) was completely inundated during the 2005 breeding season. 

This is one example of the loss of flycatcher breeding habitat that occurred when lake levels rose dramatically during the winter/

spring of 2005.
12 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

along with cooperative efforts of other researchers, allowed 

detection of dispersing or moving flycatchers. 

The breeding habitat at the Roosevelt Lake and San 

Pedro/Gila study sites consisted of a heterogeneous mosaic 

of discrete riparian forest patches of varying ages and vegetation composition, ranging from 0.2 ha to 43 ha in size. At 

both study sites, we characterized habitat at both the territory 

and patch level based on vegetation composition as: (1) native 

(≥90% native vegetation), (2) mixed native (50%–89% native 

vegetation), (3) mixed exotic (50%–89% exotic vegetation), 

and (4) exotic (≥90% exotic vegetation). Native habitat was 

characterized by Goodding’s willow (Salix gooddingii) and 

Fremont cottonwood (Populus fremontii). Exotic habitat was 

dominated by saltcedar. The understory vegetation consisted 

of a variety of grasses, forbs, and shrubs (mesquite [Prosopis

spp.], coyote willow [S. exigua], tamarisk, Baccharis spp., 

and cocklebur [Xanthium strumarium]). Adjacent and surrounding non-riparian vegetation was primarily composed of 

Sonoran Desert Uplands (Brown 1994), and in some cases 

agricultural lands. 

Figure 5. Location and 

names of habitat patches 

occupied in at least one year 

by Southwestern Willow 

Flycatchers at the Tonto 

Creek inflow at Roosevelt 

Lake, 1996–2005. Because of 

changes in lake levels and 

riparian habitat, not all sites 

were present or occupied by 

flycatchers in all years.
 13

Figure 6. Location and names of habitat patches occupied in at least one year by Southwestern Willow Flycatchers at the Salt River 

inflow at Roosevelt Lake, 1996–2005. Because of changes in lake levels and riparian habitat, not all sites were present or occupied by 

flycatchers in all years.

Roosevelt Lake Study Site

The Roosevelt Lake study site consisted of two sub-sites 

approximately 30 km apart at the inflows of Tonto Creek (fig. 5) 

and the Salt River (fig. 6). Breeding was documented as far as 

12 km upstream on Tonto Creek and 9 km on the Salt River. The 

number of riparian patches in which breeding was documented 

varied over time as a result of colonization or desertion of patches, 

in many cases driven by fluctuating lake levels. Over the 10-year 

period, we documented breeding occurring in a maximum of 23 

patches encompassing approximately 242 ha of riparian habitat. 

San Pedro/Gila Study Site

The San Pedro/Gila study site encompassed 101 km of 

the San Pedro River and Gila River systems, centered at the 

confluence. On the free-flowing San Pedro River, flycatcher 

breeding patches extended from just south of Aravaipa Creek 

downstream to CB Crossing (fig. 7). Along the regulated Gila 

River (fig. 8), occupied patches were located between the San 

Pedro River confluence downstream to the Kelvin Bridge. As 

with Roosevelt Lake, the number of patches changed over the 

10 years, with as many as 29 habitat patches documented as 
14 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 7. Location and names 

of habitat patches occupied in 

at least 1 year by Southwestern 

Willow Flycatchers within 

the San Pedro River portion 

of the San Pedro/Gila study 

area, 1996–2005. Because of 

changes in river flows and 

riparian habitat, not all sites 

were present or occupied by 

flycatchers in all years.

supporting breeding efforts, comprising approximately 222 ha 

of riparian forest along this stretch. 

Acknowledgements

This project was made possible by the support and 

cooperation of many persons, agencies, and private organizations and companies. Funding was provided by the Bureau of 

Reclamation (Reclamation) Phoenix Area Office, the USGS 

Southwest Biological Science Center, and Northern Arizona 

University. The coordination, sharing of information, and/or 

land access permission from the AGFD, EcoPlan Associates, 

Inc., the U.S. Forest Service (USFS) Tonto Ranger District, 

The Salt River Project (SRP), SWCA Environmental Consultants, Inc., and Westland Resources, Inc. was of particular 

importance. We truly appreciate the AGFD’s outstanding 

contributions to this project, through logistical assistance, 

planning and coordination, resighting and information sharing, 

collegiality, and good humor; our thanks especially to Rebecca 

Davidson, Lisa Ellis, Heather English, Tracy McCarthey, 

Olivia Munzer, Chuck Paradzick, Jay Rourke, Alex Smith, 

Mike Summner, and April Woodward. Special thanks also to 
 15

Susan Sferra and Henry Messing (Reclamation), and Gary 

Smith, Craig Woods, and Heidi Plank (USFS) for their essential help in this project. The success of our project is ultimately due to the efforts of the field personnel on the USGS 

banding crew: Amanda Bakian, Nathan Banfield, Suzanne 

Cardinal, Michelle Davis, Noel Dodge, Robert Emerson, 

Lisa Fitzgerald, Mark Gillilan, Phil Heavin, Patty Hodgets, 

Joanna Hubbard, Katie Kauffman, Kerry Kenwood, Jessie 

Knowlton, Tom Koronkiewicz, Suzanne Langridge, Therese 

Littlefeather, Yen Luc, Jen Luff, Andy McIntyre, Michael 

Moore, Tawna Morgan, Jessica Murray, Renee Netter, Jen 

Owen, Amy Porter, Casey Richart, Justin Schoefer, J.D. 

Semones, Valerie Steen, Valerie Stein, and Jocelyn Tutak. 

We are grateful to the peer reviewers who provided excellent 

feedback that greatly improved this report: Terry Arundel, 

Greg Beatty, Lisa Ellis, Barbara Kus, Jeff Lovich, Tom 

Koronkiewicz, Scott Stoleson, Charles van Riper, and Mary

Whitfield. Editorial and other assistance from Linda Sogge,

Kristina Paxton, Lara Schmit also contributed greatly to this 

report. Our thanks also to Susan Sferra and Henry Messing for 

their thorough and insightful reviews of each chapter.

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in Avian Biology 26. Cooper Ornithological Society, Camarillo, California. 
18 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

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B.E. Kus. 2003b. Distribution and characteristics of Southwestern Willow Flycatcher breeding sites and territories: 

1993–2001. Studies in Avian Biology 26:5–11. 

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breeding Southwestern Willow Flycatchers on the Gila 

River in New Mexico. Studies in Avian Biology 26:91–95. 

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83–94 in Status, Ecology, and Conservation of the Southwestern Willow Flycatcher. Finch, D.M. and S.H. Stoleson 

(eds). USDA Forest Service Rocky Mountain Research Station General Technical Report RMRS-GTR-60. 131 pp

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subspecies. Western Birds 18:137–162.

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candidate review for listing as endangered or threatened 

species. Federal Register 56:58804-58836 (November 21, 

1991).

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Southwestern Willow Flycatcher as an endangered species 

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the operation of the modified Roosevelt Dam. Arizona Ecological Services Office, Phoenix, Arizona.

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Flycatcher (Empidonax traillii extimus) final recovery plan. 

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habitat for the Southwestern Willow Flycatcher (Empidonax traillii extimus), Final Rule. Federal Register 

70:60886–61009 (October 19, 2005).

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Resources, Salt Lake City, Utah. 28 pp.

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Sacramento, California. 14 pp. 

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Cowbird control program and monitoring for the Southwestern Willow Flycatcher, South Fork Kern River, California. California Department of Fish and Game, Bird and 

Mammal Conservation Program Report 95-4, Sacramento, 

California. 17 pp. 

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resuse in Arizona. Journal of Field Ornithology 70:211–

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Avian Biology 26:125–130. 
 19

Introduction

Survival of individuals between successive time periods determines a population’s survivorship rate, which both 

influences and results from fitness, life history characteristics, 

and population dynamics (Williams et al. 2002). Survivorship 

is a key parameter in demographic studies (Lebreton et al. 

1993, Beissinger et al. 2000), and is a crucial determinant of 

population trends (Lebreton et al. 1993). For threatened and 

endangered species, understanding survivorship rates is necessary for making decisions regarding competing management 

alternatives and for effective recovery planning (Lebreton et al. 

1993, Forsman et al. 1996). Within species, survivorship may 

vary between years (Brownie et al. 1986, Lebreton et al. 1992, 

Lebreton et al. 1993), within years (Sillett and Holmes 2002), 

among different locations (Lebreton et al. 1992, Lebreton et 

al. 1993), and in response to demographic factors such as sex 

and age (Lebreton et al. 1992). Thus, understanding how and 

where survivorship varies within and among populations is a 

crucial step in understanding population trajectories.

Although accurate population survivorship estimates are 

essential for many purposes, they can be difficult to obtain 

because they often are limited by field effort, spatial scale, and 

duration of a study. Robust estimates of survivorship require 

an “adequate” (typically large) number of marked individuals 

that are intensively tracked over time to ensure a high detection probability (White and Burnham 1999). Because of the 

inherent difficulty of detecting all living individuals over time, 

maximum likelihood methods of estimating survivorship (Cormack 1964, Jolly 1965, Seber 1965) were developed and are 

used to take into account the uncertainty of individual birds 

being detected within a given period. Even with maximum 

likelihood methods, higher detection rates lead to better survivorship estimates; therefore, survivorship studies need to be 

conducted with intensive efforts to detect all marked individuals. However, because maximum likelihood estimates cannot 

distinguish between true mortality and permanent emigration 

from a study area, survivorship studies should also be conducted over as large a spatial area as possible to maximize the 

probability of detecting individuals that move from core study 

areas. Finally, stochastic events can have large impacts on 

yearly survivorship, such that long-term studies are needed to 

more fully capture the range of variation in yearly survivorship 

(Pollock et al. 1990, Lebreton et al. 1992, Martin et al. 1995). 

The Southwestern Willow Flycatcher (Empidonax traillii 

extimus) is a small, migratory passerine that breeds exclusively 

in riparian habitats scattered throughout portions of the southwestern U.S. (Unitt 1987, Marshall 2000 ), and winters from 

central Mexico south to northern South America (Sedgwick 

2000). Flycatcher numbers have declined precipitously as riparian habitats on the breeding grounds have been lost or modified 

(USFWS 1993, Marshall and Stoleson 2000), and E. t. extimus

was listed as a federally endangered species in 1995 (USFWS 

1995). At the time of listing, most aspects of the flycatcher’s 

biology, and threats to the species’ persistence, were poorly 

understood (Marshall and Stoleson 2000, Stoleson et al. 2000). 

From 1996 to 2005, we collaborated with the Arizona 

Game and Fish Department (AGFD) to conduct a large-scale 

demographic study of flycatchers at two large breeding sites 

in central Arizona. Additionally, collaborative banding and 

monitoring efforts at many other breeding sites across the 

Southwest allowed for the detection of dispersing individuals. 

This field effort allowed us to document long-term variation in 

both adult and juvenile flycatcher survivorship and to evaluate 

factors that might affect survivorship, as well as to determine 

mean life expectancy. Factors that we believed may influence 

juvenile and/or adult flycatcher survivorship, and therefore 

evaluated, were breeding site, habitat type, sex, age, breeding 

status, and fledge date. Because estimates of annual survivorship in the flycatchers are the product of survivorship across 

the 12-month calendar year, we estimated flycatcher survivorship for each portion of their annual cycle (i.e., breeding, 

wintering, migration) to evaluate whether survivorship was 

constant across these periods. We accomplished this by linking 

data from our breeding ground study sites in Arizona to data 

collected on the wintering grounds in Costa Rica (Koronkiewicz et al. 2006). Lastly, we examined survival estimates at 

multiple spatial scales to determine the appropriate spatial 

scale necessary for robust survival estimates and to evaluate 

how reliable our survivorship estimates were to the confounding effects of permanent emigration.

Methods

Study Site 

Our two focal study sites (fig. 1) were breeding populations at Roosevelt Lake (33o39’N, 110o58’W) and the San 

Pedro/Gila River confluence (hereafter San Pedro/Gila; 

32o59’N, 110o46’W) from 1996 to 2005, where we conducted 

Chapter 2—Survivorship
20 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 1. Location of the Roosevelt Lake and San Pedro/Gila study areas in central Arizona. 
 21

demographic research in cooperation with AGFD (English 

et al. 2006). These two sites supported among the largest 

known Southwestern Willow Flycatcher breeding populations 

throughout the period of this study (Durst et al. 2005), with 

mean population sizes of 201 and 239 individuals at Roosevelt 

Lake and the San Pedro/Gila, respectively. At each site, 

extensive survey efforts were conducted at least three times 

in the breeding season to detect territorial flycatchers within 

the immediate and surrounding areas. In addition, multiple 

other breeding sites in Arizona and throughout the Southwest 

(49–444 km away) were periodically visited, which along with 

cooperative efforts of other researchers allowed detection of 

dispersing or moving flycatchers (see chapter 4). 

The breeding habitat at Roosevelt Lake and the San 

Pedro/Gila consisted of a heterogeneous mosaic of discrete 

riparian forest patches of varying ages and vegetation composition, ranging from 0.2 ha to 43 ha in size. At both study sites, 

we characterized habitat at both the territory and patch level 

based on vegetation composition as: (1) native (≥90% native 

vegetation), (2) mixed native (50%–89% native vegetation), 

(3) mixed exotic (50%–89% exotic vegetation), and (4) exotic 

(≥90% exotic vegetation). Native habitat was characterized by 

Goodding’s willow (Salix gooddingii) and Fremont cottonwood (Populus fremontii). Exotic habitat was dominated by 

tamarisk (saltcedar; Tamarix spp.). The understory vegetation 

consisted of a variety of grasses, forbs, and shrubs (mesquite 

[Prosopis spp.], coyote willow [S. exigua], tamarisk, Baccharis spp., and cocklebur [Xanthium strumarium]). Adjacent and 

surrounding non-riparian habitats were primarily composed 

of Sonoran Desert Uplands (Brown 1994), and in some cases 

agricultural lands. 

Roosevelt Lake Study Site 

The Roosevelt Lake study site consisted of two sub-sites 

each concentrated at the confluence of the reservoir and the 

inflows of the Salt River and Tonto Creek, with breeding documented as far as 12 km upstream on Tonto Creek. The number 

of riparian patches in which breeding was documented varied 

over time as a result of colonization or desertion of patches, in 

many cases driven by fluctuating lake levels. We defined habitat patches as contiguous riparian patches distinct from other 

patches, with their boundaries estimated manually in ArcView 

3.0 (ESRI, Inc) using rectified aerial photographs. Over 

the 10-year period, we documented breeding occurring in a 

maximum of 23 patches encompassing approximately 242 ha 

of riparian habitat. The Tonto Creek inflow sub-site contained 

as many as seven habitat patches covering a maximum of 49 

ha of riparian habitat in which breeding was documented. The 

Salt River inflow sub-site supported as many as 16 breeding 

patches covering a maximum of 193 ha. Long-term persistent 

drought conditions between 1996 and 2005 in the Southwest 

(McCabe et al. 2004) resulted in reservoir levels dropping 

to a low of 10% capacity in 2002; the exposed lakebed was 

colonized by riparian vegetation, which was subsequently 

colonized by breeding flycatchers (see chapter 6). In 2005, 

following unusually high winter precipitation, Roosevelt Lake 

filled to near capacity, inundating much of the breeding habitat 

that was occupied in 2004 (see chapter 6). 

San Pedro/Gila Study Site

The San Pedro/Gila study site encompassed 101 km of 

river system, centered at the confluence of the free-flowing 

San Pedro River and the regulated Gila River, and extending upstream of the San Pedro to San Manuel Crossing and 

downstream of the Gila River to Kelvin Bridge. As with 

Roosevelt Lake, the number of patches changed over the 

10 years, with as many as 29 habitat patches documented 

as supporting breeding efforts, comprising approximately 

222 ha of riparian forest along this stretch. The San Pedro 

River sub-site included approximately 77 km of the river, containing 13 habitat patches covering a maximum of 173 ha of 

riparian forest. The Gila River sub-site supported 16 breeding 

patches containing as much as 49 ha of riparian forest along a 

24-km stretch of the river. 

We shared data with researchers at other study sites, 

effectively creating a broader study area. Other sites included 

Greer and Alpine in the White Mountains, AZ; Camp Verde, 

AZ; Gila River near Safford, AZ; Horseshoe Reservoir, AZ; 

Browns crossing near Alamo Lake, AZ; and several sites along 

the Colorado River including the Grand Canyon, Lake Mead, 

and Topock Marsh, AZ; and Mesquite and Mormon Mesa, NV 

(see Durst et al. 2005).

Banding and Resighting

To provide robust survivorship estimates, our objective 

was to band as many adults and nestlings as possible over 

the period of this study. Nestlings and adults were banded 

at both Roosevelt Lake and the San Pedro/Gila from 1996 

to 2005, with the exception that nestling banding at the San 

Pedro/Gila ceased after 2000. Each nestling was banded at 

7–10 days of age (as determined via Paxton and Owen 2002) 

with a color-anodized, numbered federal bird band; banded 

nestlings detected and recaptured as adults in subsequent years 

were given an additional color band to produce a unique color 

combination to assist in resight tracking (Koronkiewicz et al. 

2005). A drop of blood was taken at the time of banding for 

genetic gender determination (Paxton et al. 2002). Each adult 

captured was banded with a color-anodized, numbered federal 

bird band and a second color band (Koronkiewicz et al. 2005) 

to create a unique color combination for each individual. 

Adults were primarily target-netted (Sogge et al. 2001, Pollock 

and Paxton 2006) to allow for efficient, focused capture effort. 

From 2001 to 2005, we also conducted extensive passive 

netting operations (see Ralph et al. 1993) aimed at capturing 

non-territorial flycatchers that were not readily detectable with 

conventional survey techniques (i.e., tape-playback; Sogge 

et al. 1997). Overall, the combination of targeted and passive 
22 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

netting resulted in an average of 74% (range: 68%–88%) of 

detected flycatchers being banded in a given year. To determine gender of adult flycatchers, we used a combination of 

physical characteristics (presence of a cloacal protuberance 

for males or brood patch for females), behavioral cues, and/or 

genetic sexing methods (Paxton et al. 2002). Adult flycatchers were aged as “known,” if the individual was first banded 

as either a nestling or a second-year (SY: based on retained 

rectrices; Pyle 1998), or “unknown” (2 or more calendar years 

of age). 

We tracked banded flycatchers over the 10 years primarily through resighting, supplemented by occasional recaptures. Resighting consisted of using binoculars to determine 

the unique color band combination on a flycatcher’s legs. 

This is minimally intrusive method of “recapture,” and the 

most reliable method for establishing the particular territory 

a flycatcher belongs to because playback capture techniques 

can lure adults in from neighboring territories. To ensure high 

accuracy in the identification of resighted individuals, only 

multiple high-confidence resights from at least two different 

observers on different days were used to confirm the identity and territory of individual banded flycatchers. Computer 

databases were updated and queried daily to prioritize resighting efforts, allowing us to track as many as 393 uniquely color 

banded individuals in a given year. Field crew sizes of up to 35 

personnel (USGS and AGFD combined) allowed for intensive detection and resighting of banded individuals, resulting in nearly all territorial flycatchers detected at the study 

sites being positively identified multiple times in each year. 

Recaptures, especially via passive netting, provided additional 

detection resolution, especially for non-territorial birds. 

Statistical Analysis

Estimating Survivorship and Detection 

Probability 

We used Program MARK (White and Burnham 1999) 

to estimate survivorship (Φ) and detection (p) probabilities. 

Program MARK uses encounter history files (detections or 

non-detections per individual per year) to produce maximum 

likelihood estimates of survivorship. Survivorship is the probability of an individual being alive from one period to the next, 

and detection probability is the probability of an individual 

being detected, conditional on being alive and present at the 

study area. Because some individuals are not detected in a 

given year, yet are detected in subsequent years, a simple 

return rate (i.e., the percentage of individuals in year A that 

are detected in year A+1) underestimates true survival. Thus, 

survivorship estimates use both the return rate and the detection probability. Permanent emigration cannot be distinguished 

from mortality, thus reported estimates are often referred to 

as apparent survivorship (i.e., minimum survivorship). When 

reporting annual estimates of survivorship, we fixed the detection probability (p) for the first and last encounter periods 

(study years) at the overall mean detection probability, because 

the binomial model underlying the survivorship estimates cannot separate survivorship and detection probability for the first 

and last periods (White and Burnham 1999); however, we did 

not fix parameters for the purposes of model selection, and all 

other detection probabilities were not fixed.

For each question, we evaluated multiple models in 

Program MARK, with each model consisting of different 

combinations of explanatory variables (e.g., age, sex, site). To 

choose between models based on their relative strengths, we 

used the Akaike Information Criteria (AIC) model selection 

framework to rank multiple models (Burnham and Anderson 

2002), with the best AIC model typically used to conduct our 

interpretation of the data and to select the parameter estimates 

for survivorship and detection probability. The AIC model 

selection framework is rooted in the Principle of Parsimony, 

where the best model is that which explains as much variation as possible with the least amount of information (the least 

number of parameters). Thus, AIC weights the contribution 

of each parameter (i.e., how much variation it can explain) 

against the cost of adding it. While AIC is a commonly used 

model selection tool, it cannot differentiate between statistical 

and biological significance; therefore all of our models were 

chosen a priori to reduce Type I statistical error and model 

selection results were accepted or rejected based on their 

apparent biological significance. Because AIC might reject 

a parameter that is nonetheless biologically important, we 

reported parameter estimates from weakly supported models 

in cases where we were interested in exploring a particular 

facet of flycatcher biology. 

Several values listed in the AIC tables help interpret the 

weight of evidence for selecting one, or multiple, competing models. The AIC (QAIC, AICc) value is used to rank the 

models, with the lowest value ranked first. The AIC number 

is the product of the amount of variation unexplained by the 

variables included (deviance) weighted by the number of 

parameters (variables; K). Thus, a model that explains much 

of the variation in the data (low deviance) with just a few 

variables will have a lower AIC value than an alternative 

model that explains an equal amount of variation but with 

more variables (K). Likewise, a model that explains more 

variation (lower deviance) but with more variables (higher 

K) compared to another model may have a higher AIC score, 

and thus be ranked lower. Typically, global (fully parameterized) models explain the most variation but are rarely ranked 

as the best models because they have the largest number of 

parameters and therefore higher AIC values. The delta AIC is 

the difference in that AIC score of a particular model compared to the best AIC model; thus, the delta AIC value for the 

top model is always zero. Models with delta AIC values ≤2.0 

are considered to have strong support, given the data and the 

models considered, while those with delta AIC values >10.0 

are considered to have essentially no support, given the data 

and the models considered (Burnham and Anderson 2002). 

The AIC weight is another approach to comparing the relative 
 23

strength of multiple models considered. It can be interpreted 

as the weight of evidence that a specific model is the best of 

all those considered. AIC values are relative to one another for 

the particular analysis, but are not comparable to AIC values 

from other analyses. When appropriate, we corrected for small 

sample sizes in our AIC model selection (AICc; Burnham and 

Anderson 2002). 

Goodness-of-fit Testing

Goodness-of-fit tests are used to ensure that the survivorship data conforms to the underlying binomial model 

distribution, tested on the most parameterized (global) models 

for each distinct dataset. Failure of goodness-of-fit could be 

caused by violation of assumptions of the Cormack-JollySeber (CJS) model or from the failure of the model to accurately describe the data (Cormack 1964, Jolly 1965, Seber 

1965). These assumptions are: (1) survivorship and detection 

probability are equal for all individuals, within a given group; 

(2) no bands are lost from the flycatchers; and (3) the time 

intervals considered are the same for all individuals. In general, evidence of a poor fit is believed to be derived from a violation of the first assumption, which is usually caused by not 

identifying all the important groups. For example, if males and 

females in some population had very different survivorship 

and detection probabilities, but an analysis of survivorship 

did not group individuals by sex, then the assumptions would 

be violated and the data may fit the underlying model poorly. 

When this occurs in a binomial model, the data are said to 

be overdispersed. Overdispersion does not affect the maximum likelihood estimates, just the estimate of error, which 

can be corrected. Depending on the goodness-of-fit measures 

available for different model structures, we used Program 

RELEASE (Test 2 + Test 3) (Burham et al. 1987), bootstrap 

goodness-of-fit, or median ĉ values (where ĉ = 1 equates to 

perfect model fit). In cases of deviation from goodness-of-fit, 

we attempted to identify the source of deviation or adjusted 

median ĉ values (which measure overdispersion) to obtain 

adjusted QAIC values for model selection and estimates. 

Survivorship Models

We ran separate analyses in Program MARK for different 

datasets, questions, and/or modeling strategies. The specifics 

of each of these analyses, including factors in the models, 

along with results of goodness-of-fit testing, are detailed 

below. Each dataset has a global (fully parameterized) model, 

and a number of nested models which present different subsets 

of the variables in the global model. To describe the combination of variables in different models succinctly, we used the 

format proposed by Lebreton et al. (1992). This model description convention includes: “*” to indicate interactions among 

two variables, “+” to separate variables and treat them as main 

effects, and “.” to indicate the value for the parameter (e.g., Φ, p) is 

the average value (i.e., not considering any factors).

Juvenile Survivorship

Juvenile survivorship is the probability that a banded 

nestling will survive from the time it fledges to the following 

breeding season in its second calendar year of life. Although 

approximately 40% of returning juveniles are not detected in 

their second calendar year of life as SY adults, but rather as 

TY or older adults, it is only this first year that is calculated 

as juvenile survivorship. To evaluate sources of variation in 

juvenile survivorship, we assessed the contribution of the following factors: 

•	 YEAR: annual variation of between-year juvenile survivorship, 1996–2005; 

•	 HABITAT: plant composition of the natal territory 

(native, exotic, mixed native, or mixed exotic) derived 

from AGFD nest measurements of habitat; and 

•	 FDATE: The exact fledge date was not always known, 

so we used banding date as a surrogate to fledge date, 

which is 2–5 days before fledging is estimated to 

occur. Nestlings from nests that failed prior to fledging were excluded. The variable FDATE was used as a 

continuous covariate in Program MARK.

Our global (fully parameterized) model for juvenile survivorship and detection probability was Φ(habitat*year+Fdate) 

p(habitat*year+Fdate). We did not evaluate study site differences, as no nestlings were banded at San Pedro/Gila after 

2000, and few were banded before, and thus we could not 

separate site differences from year differences. The model had 

a median ĉ = 2.13, indicating moderate overdispersion, and we 

used a QAIC with the adjusted ĉ value for model selection and 

variance estimates. 

Adult Survivorship

Adult survivorship is the probability of annual survivorship, breeding season to breeding season, beginning with flycatchers in their first year as an adult (i.e., second-year adults 

or after-hatch-year adults). As with juveniles, we predicted 

that yearly differences would contribute to survivorship rates 

over the 10 years of this study. We also believed that the following factors could contribute to variation in adult survivorship:

•	 STUDY SITE: differences between the two study sites, 

Roosevelt Lake and San Pedro/Gila;

•	 SEX: differences between males and females.

Thus the global model for adult survivorship 

and detection probability was Φ(sex*year+site*year) 

p(sex*year+site*year); other factors, such as habitat, are more 

complex for adults and are evaluated separately, below. The 

goodness-of-fit tests for the global model of adult survivorship and detection probability revealed mixed results. Program

RELEASE indicated lack of fit with the global model (χ2 = 

149.4, df = 86, p < 0.01), but we found no systematic bias in 

the Program RELEASE subtests (Program RELEASE tests: 

Test3.SR, Test3.Sm, or Test2C), indicating that the CJS model 
24 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

was adequate but the lack of fit was due to overdispersion of 

the data. However, median ĉ = 1.14, suggesting negligible 

overdispersion, and on the weight of ĉ evidence we accepted 

the model as adequate, did not adjust the ĉ value, and used a 

non-corrected AIC for model selection. 

Breeding Habitat and Adult Survivorship

Adults may occupy multiple riparian habitat types on the 

breeding grounds over the course of their lifetimes. To evaluate potential effects of breeding habitat on adult survivorship, 

we needed to account for this adult movement into different 

habitats over multiple years tracked. To do this, we used a 

multistate model in Program MARK to model adult flycatcher 

survivorship and detection probabilities among native, mixed 

native, mixed exotic, and exotic habitats, as characterized at 

the patch level. The multistate models not only provide estimates of survivorship (Φ) and detection (p) probabilities based 

on the habitat occupied, but also account for the probability 

of moving from one habitat to another, or transitions (Ψ). We 

used a multinomial logit (Mlogit) link to constrain the model 

to ensure that the transition probabilities among the four different habitats all summed to 1. In other words, since there are 

only four habitat types in which we detected banded flycatchers at our study sites (native, mixed native, mixed exotic, 

exotic), the sum of probabilities of returning to the same habitat as occupied in the previous year (e.g., exotic to exotic), or 

moving to another habitat (e.g., native to mixed exotic) must 

equal 100%. The survivorship estimate is the probability that 

an adult detected in a particular habitat in year “t” survives to 

year “t+1” regardless of which habitat it returns to, and the 

detection probability is the probability that it is detected in 

year “t” at the habitat type specified.

Breeding Status and Adult Survivorship

We also used a multistate model to estimate the survivorship and detection probabilities among individuals classified 

into four breeding statuses per season: (1) successful breeders, 

consisting of individuals that fledged at least one young; (2) 

unsuccessful breeders, consisting of individuals that attempted 

to breed, but did not fledge any young; (3) territorial but nonnesting, consisting of individuals that were territorial but did 

not attempt to breed; and (4) non-territorial and non-nesting, 

consisting of individuals that were thought to be floaters (see 

chapter 7). Flycatchers shift between these breeding statuses 

over their lives, and the multistate model accounts for the 

probability of transitions (Ψ) among these different breeding 

statuses while providing an estimate for adult survivorship 

(Φ) and detection probability (p) for each group. We used the 

Mlogit link to constrain the sum of transition probabilities for 

all breeding status over a single time period to one. 

Age-class and Adult Survivorship

To evaluate changes in survivorship by age, we used a 

subset of our data to look at known-age adults only (n = 266; 

25% of banded adults). These birds were either initially 

banded as nestlings or captured as SY adults. Because of small 

sample sizes, we pooled estimates of age-specific survivorship 

for sixth-year and older birds. While bootstrap results suggested a good fit of the data to the model, a ĉ = 1.4 indicated 

slight overdispersion and we used the median ĉ value to rank 

models by QAIC. 

Adult Survivorship Throughout the Annual Cycle

The annual cycle of migratory birds includes breeding, 

wintering, and two migration periods (spring and fall), and 

each may have a different rate of survivorship. True partitioning of survivorship across these annual periods would require 

tracking the same individuals continuously throughout the 

year, something nearly impossible to do. Instead, we partitioned survivorship among the four periods using the methods 

of Sillett and Holmes (2002) by combining our breedingground survivorship estimates (this study) with similar data 

from a wintering study in Costa Rica (Koronkiewicz et al. 

2006), with the understanding that the strength of the comparison is dependent on the strength of the connectivity between 

the two populations. We believe there is at least moderate connectivity between these populations, because (1) Southwestern Willow Flycatchers appear to winter primarily, perhaps 

exclusively, in Mexico and Central America (Unitt 1997); and 

(2) two individuals detected at the Costa Rica study areas were 

initially banded in the Southwest, one of them at Roosevelt 

Lake and the other at Ash Meadows NWR, Nevada (Koronkiewicz and Sogge 2001). While we believe that more than one 

subspecies wintered at the Costa Rica sites (Koronkiewicz et 

al. 2006), the evidence for regional connectivity is sufficient to 

combine the two studies. 

We calculated within-season survivorship (i.e., estimates 

of survivorship during the breeding and wintering stationary 

periods) separately for the two studies. We estimated withinseason survivorship on the breeding grounds by grouping 

all resight and recapture encounters of banded flycatchers 

throughout the breeding season into two equal periods: May 1 

to June 15 and June 16 to early-August. The average interval 

between these periods, 1 month, was used to estimate the average survivorship in a 1-month period. We estimated monthly 

winter survivorship from the 3-month interval between the 

mid-winter (December/January) and late-winter (March/April) 

research periods. The within-season monthly survival estimates obtained from Program MARK were used to obtain seasonal estimates for the 3-month average breeding period and 

the 6-month average wintering period by raising the monthly 

estimates to the third and sixth power, respectively. 

Between-season estimates for the breeding and wintering populations are needed to estimate survivorship during 

the migratory period (Sillett and Holmes 2002). We define 

between-season survivorship for a population at any one 

period as the product of the survivorship during the other two 

periods. For example, between-season survivorship for the 
 25

breeding population is the product of survivorship during the 

migration and wintering periods. Average between-season 

survivorship estimates for each period are derived by dividing 

their average annual survivorship by their respective average 

within-season survivorship; e.g., for breeding grounds:

.

season -within (breeding)

(breeding) annual

breeding)-(nonseason -between Φ

Φ

Φ =

To estimate survivorship during the migratory periods, 

we combined the mean survivorship estimates from both the 

breeding and wintering studies (Sillett and Holmes 2002). 

Separately, between-season survivorship estimates from both 

stationary periods were divided by the within-season survivorship of the opposite stationary period to estimate survivorship 

during the migratory period; e.g., for breeding grounds:

.

season -within )(wintering

breeding)-(nonseason -between

migration lspring/fal survivorship Φ

Φ

Φ =

The estimates for survivorship during migration are for 

both spring and fall migration combined, which are estimated to be 3-months long on average. To estimate survivorship separately for the spring and fall migratory periods we 

assumed equal survivorship and took the square-root of the 

migratory period estimate, and the cubed-root of the migratory 

period survivorship estimate to obtain monthly migration survivorship. We used a reduced portion of the breeding season 

data, corresponding to the time period of the Costa Rica study 

(1999–2002) to estimate survival for this procedure. Percentages of mortality were calculated by applying the different 

survival rates to a hypothetical group of individuals and calculating the proportion that died in each period of interest. 

Adult Survivorship by Spatial Scale

We evaluated detection data at multiple spatial scales to 

better understand how the scale of a study influences survivorship estimates through its effect on the ability to distinguish 

between mortality and undetected permanent emigration. The 

spatial scales, described more fully in the Study Site section, 

were: 

•	 PATCH: a distinct area of riparian vegetation, spatially 

separated from other nearby riparian patches by nonriparian vegetation; 

•	 SUB-SITE: comprised of all patches found within each 

distinct drainage (i.e., Tonto Creek inflow, Salt River 

inflow, Gila River, and San Pedro River); 

•	 SITE: a combination of the two sub-sites (i.e., both of 

Roosevelt Lake sub-sites and the entire San Pedro/Gila 

Rivers confluence area); and 

•	 STUDY AREA: comprising the Roosevelt Lake and 

San Pedro/Gila study sites, plus breeding sites throughout the Southwest from where we or collaborative 

researchers collected resight and recapture information 

from. 

We ran separate analyses for each spatial scale to determine average survivorship. At the patch scale, individual 

flycatchers were coded as alive and returning as long as they 

returned to the patch in which they were originally banded. If 

the flycatcher was not subsequently detected within the same 

patch, the model considered it a non-survivor (either permanently emigrated or dead). In a similar manner, survivorship 

estimates were calculated at increasingly larger spatial scales 

for the sub-site, site, and study area scales. Average apparent 

survivorship estimates for each of these spatial scales were 

plotted to evaluate changes by scale. We predicted that if the 

survivorship estimates increased and quickly converged as we 

moved from the smallest to the larger scales, then the survivorship estimates based on a geographically large study area, such 

as ours, would not be substantially biased by undetected emigration even though there is certain to be some level of missed 

emigration. If the estimates did not converge with increasing 

scale, but continued to increase with each larger spatial scale 

considered, we would conclude that permanent and undetected 

emigration might have a major influence on our survivorship 

estimates, biasing them downward. At the patch, sub-site, and 

site level, resight effort was similar. Beyond these scales, the 

likelihood of detecting an emigrating flycatcher decreases 

as they could disperse to an unknown or unstudied site, or 

to a site in which resight efforts are not as thorough; either 

scenario would have the same effect of biasing downward the 

true survival estimate due to undetected emigrants. In each 

case, we averaged across years as we were only interested in 

the overall effect of different spatial scales on survivorship and 

detection probabilities. 

Influence of Global Climate Patterns on Survivorship 

Recent studies have indicated that global climate patterns may impact demographic characteristics of migratory 

songbirds (Sillett et al. 2000, Nott et al. 2002). We suspected 

that changes in the Southern Oscillation/El Niño weather 

pattern (as measured by the Southern Oscillation Index, SOI) 

might impact Southwestern Willow Flycatcher survivorship 

because of the strong climatic effects of El Niño at both the 

breeding grounds in the southwestern United States and on 

wintering grounds in Central America (Kiladis and Diaz 1989, 

Ropelewski and Halbert 1987, Ropelewski and Halbert 1989, 

Ropelewski and Halbert 1996). We calculated a single SOI 

value per year as the average monthly SOI value between 

arrival on the breeding grounds (May) in the previous year 
26 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

to the end of spring migration in the current year (April). We 

used a Pearson’s correlation of both mean juvenile and adult 

survivorship to these yearly mean SOI values to examine 

relationships between survivorship and this measure of 

global climate. 

Flycatcher Mean Life Expectancy

Mean life expectancy (MLE) uses both average adult 

and juvenile survivorship values to obtain an average life 

expectancy estimate (Brownie et al. 1986), where Φ a and Φ j

refer to adult and juvenile survivorship, respectively:

{ aj ja Φ−Φ+Φ−Φ+Φ−= j }.)Ln((/)Ln((/)Ln(/1MLE

In addition, we calculated the observed distribution of the 

maximum ages of banded individuals, based on the frequency 

of the ages of known-age and minimum-age birds when last 

detected.

Results

Juvenile Survivorship

From 1996 to 2004, we banded 498 nestlings from 223 

nests at the Roosevelt Lake and San Pedro/Gila study sites. 

Of these, we recaptured 122 from 1997 to 2005, resulting in 

a juvenile return rate of 24% (table 1). Most (69%) were first 

detected as second-year adults (SY: the year following their 

natal year), 26% as third-year adults (TY: in the second year 

following their natal year), and the remaining 5% at 3–4 years 

after they were banded as nestlings. While 59% of returning nestlings exhibited territorial behavior in the first year 

they were detected, 41% did not do so and were primarily 

detected via passive netting operations that were conducted at 

Roosevelt Lake from 2001 to 2005. 

Overall, juvenile survivorship estimates averaged 34% 

(range = 13%–57%; table 1). We evaluated the effects of natal 

habitat, fledge date, and year on juvenile survivorship, and 

found the strongest support for the model with fledge date 

only, and weak support for the model that also included effects 

of year (table 2). Survivorship estimates were virtually identical (26%–27%) for native, mixed native, and exotic habitats, 

but higher (42%) for mixed exotic habitat; however, widely 

overlapping confidence intervals and poor support from model 

selection suggests that any habitat-based differences in the 

estimated values are not important for understanding juvenile 

survival. Fledge date, however, had a strong effect on survivorship. For each day progressively further into the breeding 

season (mid-June to mid-August) that a nestling was banded, 

and thus fledged, its odds of survivorship decreased by 2% 

(beta = -0.01998). Thus, the odds that a nestling fledged 

around July 15 would survive and be detected the following 

year were only 55% that of a nestling fledging around June 15. 

Overall, first-year survivorship declined from an early season 

high of 44% to a low of 19% by the end of the season (fig. 2). 

This relationship was linear, indicating the effect was constant 

throughout the season. 

Adult Survivorship

We tracked 1,080 adult Southwestern Willow Flycatchers that were either newly captured as adults, or recaptured as 

returning nestlings and subsequently tracked as adults. From 

1996 to 2005, the overall return rate for adult flycatchers was 

55% (range = 43%–61%; table 3). Evaluating the influence 

of year, sex, and site on survivorship suggested that only year 

affected survivorship and detection probabilities in the most 

parsimonious model (table 4), producing an annual survivorship estimate of 64% (range = 53%–73%; table 3). Consistent with model selection results, average point estimates of 

survivorship between the sites and sexes were identical to one 

another and to the overall mean of 64%. 

Influence of Global Climate Patterns on 

Survivorship

We suspected that global climate patterns, especially the 

El Niño Southern Oscillation, could explain some of the variation in adult annual survivorship. Over the period of our study, 

the Southern Oscillation Index varied substantially (fig. 3); 

however, we found no correlation between yearly mean SOI 

values and juvenile or adult survivorship (juveniles: r = 0.3, df 

= 1, p = 0.43; adult: r = 0.2, df = 1, p = 0.61). 

Breeding Habitat and Adult Survivorship

Because adults may occupy different habitats in different 

years over the course of their lives, we used a multistate model 

to test for a relationship between the habitat an individual 

occupies during the breeding season and their subsequent 

survival probability. The best AIC model indicated that habitat 

(interacting with year) did appear to influence survivorship, 

detection, and transition probabilities (table 5). However, models including the effects of habitat but excluding a year interaction fared poorly (table 5), suggesting weak effects of habitat 

alone. Furthermore, the differences in survivorship by habitat 

were slight, with the largest differences found in detection and 

transition probabilities (table 6). 

Breeding Status and Adult Survivorship

A multistate model was used to examine the relationship 

between a flycatcher’s breeding status and the probability of 

its survival and detection the following year. The best model 

suggested that breeding status (interacting with year) influ-
 27

Year n

Return 

rate (%)

Apparent 

survivorship (%)

95% confidence 

interval (%)

Detection 

probability (%)

95% confidence 

interval (%)

1996–97 4 25 38 4–89 51* N/A*

1997–98 21 10 13 3–43 35 3–90

1998–99 24 21 21 8–42 100 0–100

1999–2000 62 16 26 14–44 35 13–65

2000–01 71 27 31 20–45 85 55–96

2001–02 107 20 41 28–54 41 24–60

2002–03 2 50 57 7–96 100 100–100

2003–04 121 28 41 27–56 37 21–56

2004–05 86 16 32 19–50 51* N/A*

Average 498 24 34 27–40 51 40–62

* Detection probability fixed at 10-year average (0.510) to allow for estimation of first and last year survivorship

Table 1. Apparent annual survivorship of juvenile Southwestern Willow Flycatchers in Arizona, 1996–2005. N is the number of nestlings 

banded in the first year, and return rate is the percentage returning in a subsequent year. Survivorship estimates were calculated using 

maximum likelihood estimates in Program MARK. Survivorship probabilities are from the model Φ(year+Fdate)p(year+Fdate). 

Model QAICc Delta 

QAICc QAICc weights K QDeviance

Φ(Fdate)p(Fdate) 954.16 0.00 0.84011 4 946.12

Φ(.)p(.) 958.78 4.63 0.08311 4 950.75

Φ(year+Fdate)p(year+Fdate) 959.91 5.75 0.04741 18 923.37

Φ(year)p(year) 960.86 6.71 0.02937 18 924.33

Φ(Fdate)p(.) 1051.27 97.11 0.0000 3 1045.25

Φ(habitat*year+Fdate) p(habitat*year+Fdate) 1148.83 194.67 0.0000 108 912.89

Table 2. Program MARK QAIC model selection results for Southwestern Willow Flycatcher juvenile survivorship (Φ) and detection 

probability (p), considering year, natal habitat type, and nestling banding date as a proxy for nestling fledging date (Fdate). The top five 

models are presented along with the global model. QAIC model selection was used to account for overdispersion (ĉ = 2.13). 
28 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

declined as fledging date progressed through the breeding season. The X-axis date is banding 

date, a proxy for fledge date (generally 2 to 5 days prior to fledging). The average date nestlings 

were banded was July 7, and mean survival probability was 34% (dashed line). The number of 

nestlings banded (y-axis, vertical bars) by date are shown for all nestlings, 1996-2004. 

Date Banded as Nestling

Expected Juvenile Survival Probability

0.0

0.1

0.2

0.3

0.4

0.5

Number of Nestlings Banded

0

5

10

15

20

25

30

15 June 1 July 15 July 1 Aug

Figure 2. Estimated juvenile 

survivorship (solid line) 

of Southwestern Willow 

Flycatchers declined as 

fledging date progressed 

through the breeding season. 

The X-axis date is banding 

date, a proxy for fledge date 

(generally 2 to 5 days prior to 

fledging). The average date 

nestlings were banded was 

July 7, and mean survival 

probability was 34% (dashed 

line). The number of nestlings 

banded (y-axis, vertical bars) 

by date are shown for all 

nestlings, 1996–2004. 

Table 3. Apparent annual survivorship of adult Southwestern Willow Flycatchers in Arizona, 1996–2005. N is the number of adults 

banded in a given year year, and return rate is the percentage returning in a subsequent year. Survivorship estimates and detection 

probabilities were calculated using maximum likelihood estimates in Program MARK. The survivorship estimates are based on the best 

AIC model for adult survivorship, Φ(year)p(year). Note, sample sizes from each year include individuals detected in multiple years, and 

thus do not sum to the overall sample size of the study.

Year n

Return rate 

(%) 

Survivorship 

estimate (%)

95% Confidence 

interval (%)

Detection 

probability (%)

95% Confidence

 interval (%)

1996–97 87 51 53 41–65 78* N/A*

1997–98 120 52 53 45–62 90 78–96

1998–99 137 56 63 54–71 84 72–91

1999–2000 197 55 56 50–63 93 86–97

2000–01 241 61 66 59–72 84 76–89

2001–02 264 59 73 66–80 62 54–69

2002–03 282 57 67 61–73 77 70–83

2003–04 301 59 68 61–74 78 70–84

2004–05 393 43 57 51–63 78* N/A*

Average 1080 55 64 62–66 78 75–80

* Detection probability fixed at 10-year average (0.775) to allow for first- and last-year estimations of survivorship
 29

Figure 3. Relationship between adult and juvenile Southwestern Willow Flycatcher 

survivorship (vertical bars) and the yearly mean Southern Oscillation Index (SOI) values (solid 

line), 1996-2005. SOI values overlaid for a particular year refer to the SOI values averaged for 

the previous 12 months prior to the return date. Error bars represent the upper 95% confidence 

interval for each bar. 

Year-Year Survivorship

1996-19971997-19981998-19991999-20002000-20012001-20022002-20032003-20042004-2005

Survivorship (%)

0

20

40

60

80

100

Yearly Mean SOI

-25

-20

-15

-10

-5

0

5

10

15

Adult Survivorship 

Juvenile Survivorship 

Yearly Mean SOI (year prior to return) 

Figure 3. Relationship between adult and juvenile Southwestern Willow Flycatcher survivorship (vertical bars) and the yearly mean 

Southern Oscillation Index (SOI) values (solid line), 1996–2005. SOI values overlaid for a particular year refer to the SOI values averaged 

for the previous 12 months prior to the return date. Error bars represent the upper 95% confidence interval for each bar.

Model AIC Delta AIC AIC weight K Deviance

Φ(year)p(year) 7083.56 0.00 1.00000 17 741.09

Φ(site*year)p(year) 7118.99 39.08 0.00000 28 754.07

Φ(sex*year+site*year)p(sex*year+site*year) 7122.21 42.30 0.00000 68 675.32

Φ(sex+site)p(year) 7124.10 44.19 0.00000 13 789.50

Φ(sex*year)p(year) 7125.84 45.92 0.00000 28 760.92

Table 4. AIC model selection results for adult Southwestern Willow Flycatcher survivorship by sex, study site, and year, 1996–2005, 

calculated in Program MARK. The top five models that we analyzed are presented including the global model. 
30 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

ences survivorship and detection probabilities, but only breeding status (no interactions with year) influences the transition 

probabilities (table 7). Averaging across years, successful 

breeders had higher survivorship (73%) than birds in the 

other three status groups (59%–60%; fig. 4). While the average detection probability for all adults combined was 78% 

(table 3), the detection probabilities for the three territorial 

groups were nearly 100%, while the probability of detection 

for birds in the non-territorial group was only 13% (fig. 4). 

This suggests that within a given year, a territorial flycatcher 

at our main study areas was nearly always detected. The transition probabilities among the groups indicate that flycatchers frequently move between these different states (fig. 4). 

For example, 45% of successful breeders bred successfully 

again in the following year, but 25% became non-territorial. 

Similarly, 44% of non-territorial flycatchers remained so in 

the following year, but 35% of those returning became successful breeders.

Mean Life Expectancy and Differential Adult 

Survivorship

We calculated the mean life expectancy of flycatchers 

by combining the average survivorship estimates for juveniles 

(34%) and adults (64%) to yield a mean life expectancy of 

1.9 years following fledging. The distribution of observed 

maximum ages (the age of a flycatcher in the last year it was 

detected) of birds in our populations was consistent with this 

estimate (table 8, fig. 5), as most individuals were detected for 

only 1–2 adult years, but some individuals lived much longer. 

The pattern for minimum-age adults was similar to that of 

known age birds (fig. 5). There was no significant difference in 

the frequency of maximum ages for known-age and minimumage flycatchers (χ2

 = 3.1, df = 7, p = 0.88), suggesting that a 

large component of unknown age birds assigned to the AHY 

(after-hatch-year) category were in fact SY individuals. 

Model AICc Delta AICc AICc weight K Deviance

Φ(habitat*year)p(habitat*year)Ψ(habitat*year) 5434.81 0.00 1.00000 122 1348.74

Φ(.)p(habitat)Ψ(habitat) 5523.18 88.37 0.00000 17 1662.73

Φ(habitat)p(habitat)Ψ(habitat) 5528.90 94.10 0.00000 20 1662.34

Φ(.)p(.)Ψ(habitat) 5535.47 100.66 0.00000 14 1681.11

Φ(habitat)p(.)Ψ(habitat) 5539.84 105.04 0.00000 17 1679.39

Table 5. AIC model selection results for the multistate model of Southwestern Willow Flycatcher breeding habitat characterized at 

the patch-level. Habitat was characterized at the patch-level as native, mixed native, mixed exotic, and exotic. The top five models are 

presented. 

Table 6. Survival, detection, and transition probabilities among different types of breeding habitats for adult Southwestern Willow 

Flycatchers, 1996–2005. Fidelity represents flycatchers that returned to the same habitat type between years, while emigration and 

immigration represent between-year movements among different habitat types. Percent fidelity and emigration sum to 1.0. Values 

shown are averaged over all years, although yearly variation was influential on these probabilities.

Habitat type Survival

(%)

Survivorship 

95% confidence 

interval (%)

Detection (%) Fidelity (%) Emigrate (%) Immigrate (%)

Native 67 58–75 60 88 12 11

Mixed native 64 58–71 60 52 48 15

Mixed exotic 64 60–67 83 74 26 48

Exotic 64 60–68 83 73 27 39
 31

Model AIC Delta AIC AIC Weight K Deviance

Φ(status*year)p(status*year)Ψ(status) 5850.74 0.00 0.99975 69 1663.02

Φ(status*year)p(status*year)Ψ(status*year) 5867.35 16.61 0.00025 135 1533.62

Φ(status*year)p(status)Ψ(status) 6036.21 185.47 0.00000 47 1895.06

Φ(status)p(status)Ψ(status) 6045.91 195.17 0.00000 20 1960.57

Φ(status)p(.)Ψ(status) 6059.82 209.08 0.00000 17 1980.59

Φ(.)p(status)Ψ(status) 6064.25 213.51 0.00000 17 1985.01

Figure 4. Adult Southwestern Willow Flycatcher survivorship, detection probability, and 

transition rates as a function of breeding status, 1996-2005. Breeding status categories were 

successful breeders, unsuccessful breeders, territorial but non-nesting, and non-territorial. Each 

group is graphically represented by an appropriately labeled box, with its associated survival and 

detection probabilities. Directional lines between octagons refer to transition probabilities 

among the four breeding statuses; line width is proportional to the transition probability. Curved 

arrows (returning to each octagon) refer to probability of remaining in that breeding status 

between years. 

Figure 4. Adult Southwestern Willow Flycatcher survivorship, detection probability, and transition rates as a function of breeding 

status, 1996–2005. Breeding status categories were successful breeders, unsuccessful breeders, territorial but non-nesting, and 

non-territorial. Each group is graphically represented by an appropriately labeled box, with its associated survival and detection 

probabilities. Directional lines between octagons refer to transition probabilities among the four breeding statuses; line width is 

proportional to the transition probability. Curved arrows (returning to each octagon) refer to probability of remaining in that breeding 

status between years. 

Table 7. AIC model selection results for the multistate model of adult Southwestern Willow Flycatcher breeding status survivorship 

and transitions. Breeding status groups are successful breeder, unsuccessful breeder, territorial but non-nesting, and non-territorial. 

The top five models are presented along with the global model. 
32 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Known age n Proportion Minimum age n Proportion

SY 165 0.52 AHY 463 0.50

TY 79 0.25 ASY 199 0.21

4Y 28 0.09 ATY 119 0.13

5Y 22 0.07 A4Y 61 0.07

6Y 13 0.04 A5Y 43 0.05

7Y 8 0.03 A6Y 22 0.02

8Y 1 <0.01 A7Y 13 0.01

9Y 1 <0.01 A8Y 12 0.01

Figure 5. Distributions of the age when adult Southwestern Willow Flycatchers were last 

detected (maximum age) over the course of the study, and are presumed to be the age that they 

died. Ages (in calendar years) are SY=second year, TY=third year, 4Y=fourth year, 5Y=fifth 

year, 6Y=sixth year, 7Y=seventh year, 8Y=eighth year, 9Y=ninth year, AHY second year, 

ASY third year, ATY fourth year, A4Y fifth year, A5Y sixth year, A6Y seventh year, 

A7Y eight year, and A8Y ninth year. 

Age Classes

AHY-SY

ASY-TY

ATY-4Y

A4Y-5Y

A5Y-6Y

A6Y-7Y

A7Y-8Y

A8Y-9Y

Number of maximum age individuals

0

100

200

300

400

500

Minimum Age

Known Age

Figure 5. Distributions of the age when adult Southwestern Willow Flycatchers were last detected (maximum age) over the course of 

the study, and are presumed to be the age that they died. Ages (in calendar years) are SY=second year, TY=third year, 4Y=fourth year, 

5Y=fifth year, 6Y=sixth year, 7Y=seventh year, 8Y=eighth year, 9Y=ninth year, AHY≥ second year, ASY≥ third year, ATY≥ fourth year, A4Y≥

fifth year, A5Y≥ sixth year, A6Y≥ seventh year, A7Y≥ eight year, and A8Y≥ ninth year.

Table 8. Numbers and proportions of the age when adult Southwestern Willow Flycatchers were last detected over the course of 

the study, and are presumed to be the age that they died. Ages (in calendar years) are SY=second year, TY=third year, 4Y=fourth year, 

5Y=fifth year, 6Y=sixth year, 7Y=seventh year, 8Y=eighth year, 9Y=ninth year, AHY≥ second year, ASY≥ third year, ATY≥ fourth year, A4Y≥

fifth year, A5Y≥ sixth year, A6Y≥ seventh year, A7Y≥ eight year, and A8Y≥ ninth year.
 33

 

Age Interval

HY-SY SY-TY TY-4Y 4Y-5Y 5Y-6Y >6Y

Survivorship (%)

0

20

40

60

80

100

Figure 6. Apparent survival probability of Southwestern Willow Flycatchers as a function of age. The X-axis represents the age 

interval from one year to the next, with some individuals tracked over multiple age intervals as they survived successive years. The 

last age interval (>6Y) represents all the age intervals of known age birds older than 6Y (6Y-9Y). The Y-axis represents the percent 

survivorship estimate for each age class. Error bars represent the upper 95% C.I. Estimates for HY-SY survivorship are based on 

Φ(age+Fdate)p(age+Fdate) from table 3 and survivorship estimates for all older age classes are from the Φ(age)p(.) model from table 9. 

Model QAIC Delta QAIC QAIC Weight K QDeviance

Φ(year)p(year) 555.56 0.00 0.66696 14 62.66

Φ(age)p(.) 557.22 1.67 0.28938 6 81.13

Φ(age)p(age) 560.96 5.40 0.04466 10 76.54

Φ(age+year)p(age+year) 625.09 69.53 0.00000 49 50.49

Φ(age*year)p(age*year) 626.72 71.17 0.00000 50 49.58

Table 9. QAIC model selection results for effects of adult age on Southwestern Willow Flycatcher survivorship probabilities, using 

Program MARK. We used QAIC to correct for slight overdispersion (ĉ = 1.401). The table displays all the models we analyzed for 

survivorship by age. 
34 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

However, survivorship of adults was not constant; it 

increased in older individuals (fig. 6). Based on known age birds 

(n = 266), we found moderate support for an effect of age on 

adult survivorship (table 9). Thus, while most flycatchers live 

for just 1–2 years as adults, those that live beyond the mean 

life expectancy experience increasingly lower annual mortality 

probabilities, though occurring in decreasing numbers (fig. 6).

Adult Survivorship Throughout the Annual Cycle 

Breeding Season Survivorship

Within-breeding season survivorship was estimated at 

100% for most years, with an average breeding season (May to 

August) survivorship of 97% (95% C.I.: 88%–99%; table 10). 

Therefore, given the mean annual adult survivorship estimate 

of 64%, most (92%) adult flycatcher mortality must occur 

outside of the breeding grounds.

Non-breeding Season Survivorship

Within-season winter survivorship averaged 88% (95% 

C.I.: 77%–94%) during the 6-month wintering period from 

1999 to 2002 (table 10). While the average monthly estimate 

for winter season survival was 98%, approximately the same 

as the monthly survival on the breeding grounds (99%), the 

longer time period spent on the wintering grounds (twice the 

average time period) resulted in an overall 10% lower seasonal 

survivorship estimate on the wintering grounds. The average 

annual survivorship of the Costa Rican wintering flycatchers 

was 65% (95% C.I.: 59%–70%; Koronkiewicz et al. 2006), 

nearly identical to our breeding ground annual survivorship 

estimate of 64% (95% C.I.: 62%–66%). Thus, much of the 

flycatcher’s annual mortality did not appear to occur during 

the stationary periods (winter and breeding grounds), and thus 

probably occurred during migration. 

We estimated survival over the migratory period using 

the within-season survival estimates from the breeding and 

wintering grounds, and the opposite season’s annual survival 

Source Time period

Monthly estimates Seasonal estimates

Survivorship 

estimate (%)

95% 

Confidence 

interval

Survivorship 

estimate (%)

95% 

Confidence 

interval

Detection 

probability 

(%)

95% 

Confidence 

interval

Breeding Grounds

1997 100 100–100 100 100–100 76 69–81

1998 97 89–99 91 70–97 92 86–96

1999 100 96–100 100 88–100 86 80–90

2000 99 85–100 97 61–100 89 85–92

2001 97 91–99 91 75–97 84 79–88

2002 100 100–100 100 100–100 60 56–65

2003 100 100–100 100 100–100 79 76–82

2004 100 100–100 100 100–100 74 71–78

2005 100 100–100 100 100–100 78* N/A*

Winter

1999/2000 96 93–98 81 65–90 100 100–100

2000/2001 100 98–100 100 94–100 82 68–97

2001/2002 98 93–100 89 63–97 91 78–96

Breeding Grounds Average 99 96–100 97 89–99 78 77–80

Wintering Grounds Average 98 95–99 88 77–94 97 92–99

Table 10. Willow Flycatcher within-season monthly and seasonal estimates for survivorship and detection probabilities for breeding 

grounds (central Arizona, 1997–2005), and wintering grounds (Costa Rica, 1999–2003). Seasonal estimates were calculated based on an 

average of 3 months on the breeding grounds and 6 months on the wintering grounds. (* Detection probability fixed at 10–year average 

(0.775) to allow for estimation of survivorship in last year.)
 35

different than the mean values for the entire 10-year study. Migration survivorship rates were 

assumed to be equal for the spring and fall periods. 

JUN

JUL

AU

AP

G

R

MAY

Spring Migration

87% survivorship

Breeding Grounds

 97% survivorship

Wintering Grounds

 88% survivorship

Fall Migration

87% survivorship

 Annual

65% survivorship

Figure 7. Estimated survival probabilities for Willow Flycatchers, partitioned over the major periods of the annual cycle based on 

breeding season and wintering season data from 1999 to 2003. Shown are the months (represented by a three letter month code), and 

approximate time periods for each period are represented by the curved arrows with their associated seasonal survival values. The 

estimates for the breeding season cover the period of 1999–2003, rather than the entire 1996–2005 Roosevelt Lake/San Pedro dataset, 

in order to match the Costa Rica winter season dataset (Koronkiewicz et al. 2006); thus, estimate for the breeding ground survivorship 

is different than the mean values for the entire 10-year study. Migration survivorship rates were assumed to be equal for the spring and 

fall periods.

Spatial scale

Estimated 

survivorship 

(%)

95% Confidence 

interval (%)

Estimated 

detection (%)

95% Confidence 

interval (%)

Number of 

patches

Amount of 

occupied 

habitat (ha)

Patch 41 38–44 71 64–76 1 0.2–43

Sub-site 60 57–62 74 70–77 7-16 49–193

Site 63 61–65 78 74–81 23-29 222–242

All sites 64 62–66 78 75–80 52* 444*

* Additional sites where emigrating flycatchers were detected are not included in this estimate. 

Table 11. Survivorship and detection estimates for Southwestern Willow Flycatchers in Arizona increase as the spatial scale of study 

is increased, from a single patch to the entire study of over 52 patches. See Methods section for description of scale categories. 
36 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

estimate. Survival during each migratory period (spring 

and fall migration, equal time periods assumed), calculated 

separately from breeding and wintering ground data, was 

estimated by both datasets at 87% (95% C.I.: 83%–96% 

and 84%–97%, wintering and breeding data, respectively; 

fig. 7). Monthly survivorship rate during the migratory 

period (approximately 3 months of the year, spring and fall 

combined) was estimated at 91%, substantially lower than 

the monthly estimates of the breeding (99%) and wintering 

(98%) grounds. Thus, although the migratory period comprises only one quarter of a flycatcher’s year, it contributes 

64% of the estimated annual mortality that occurs. 

Adult Survivorship by Spatial Scale

At the smallest scale (patch), annual survivorship estimates were only 41%. At increasingly larger spatial scales, 

survivorship estimates increased to 60% (sub-site), 63% (site), 

and ultimately to 64% for the overall study area (table 11, fig. 

8). Detection probability also increased, though less rapidly, 

from 71% to 78% with increasing spatial scale.

Discussion

Juvenile Survivorship

Estimating juvenile survivorship can be difficult (Newton 

1998), as the detection probability of surviving second-year 

adults is often less than that for adults (Gryzybowski 2005). 

In addition, juveniles in most species disperse away from their 

natal area, often outside of a researcher’s study area (Marshall 

et al. 2004). Such is the case for Willow Flycatchers. Mean 

detection probability of second-year adults (51%) was lower 

than for older adults (78%), with 31% of returning birds 

banded as nestlings first detected in their third to fifth years 

of life. Furthermore, 41% of SY adults detected (via passive 

netting efforts) were never observed exhibiting territorial 

behavior in that year, and would have been undetected using 

standard survey methods. This may be a major reason why our 

average juvenile survivorship estimates increased from 19% 

prior to passive netting (1997–2000) to 37% in the years with 

passive netting (2001–05). Other reasons for this increase may 

include yearly differences in the number of nestlings banded, 

with higher sample sizes in later years; unfortunately, the 

changing sample sizes cannot be isolated from the effects of 

year. Nonetheless, the higher juvenile survivorship in the latter 

half of the study may more accurately reflect true juvenile 

survivorship, and is closer to the 46% maximum likelihood 

survivorship estimate from the Kern River Preserve (M. Whitfield, personal commun.) Juvenile dispersal also influenced 

detection rates; almost no SY adults returned to their natal 

patches, mean juvenile dispersal distance was greater than 

adult between-year movement, and the longest distance movements detected were all made by juveniles (see chapter 4). 

However, the large spatial scale and intensive field efforts of 

this study allowed us to better understand and document how 

these difficulties influenced our data and interpretations. 

Despite the inherent difficulties, we believe our juvenile 

survivorship estimates are robust, with the mean survival rate 

for juveniles (34%) averaging nearly one-half that of adults 

(64%). Although annual survivorship ranged from 13% to 

patch to the entire study area (x-axis), with the corresponding survivorship estimate (filled circle) 

and 95% C.I. error bars. Refer to the Methods section for detailed description of the different 

spatial scales. 

Spatial Scale

Patch Sub-Site Site Study Area

Survivorship Estimate (95% C.I.)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Figure 8. Average 

survivorship probability 

estimates for Southwestern 

Willow Flycatchers as a 

function of the spatial scale of 

capture/recapture. As spatial 

scales increased, survivorship 

estimates quickly converged 

on the largest spatial scale 

estimate. Spatial scales 

ranged from patch to the entire 

study area (x-axis), with the 

corresponding survivorship 

estimate (filled circle) and 95% 

C.I. error bars. Refer to the 

Methods section for detailed 

description of the different 

spatial scales.
 37

57%, year received weak support (delta QAICc = 6.7) as an 

important explanatory factor. This lack of importance may 

be the result of highly variable annual sample sizes, which 

resulted from changing population sizes, productivity, and 

shifts in breeding patches (with some patches more difficult 

to access nests than others). Nonetheless, the wide range of 

annual survivorship rates suggest, as with adults, that survivorship is not constant through time. 

Survivorship estimates for Willow Flycatchers are sparse, 

and all published information is in terms of return rates, not 

maximum likelihood estimates (Stoleson et al. 2000). The 

lack of maximum likelihood estimates makes comparisons 

between studies difficult, as return rates are very sensitive to 

detection efforts, a difference that likelihood estimates adjusts 

for. Nonetheless, return rates may be roughly comparable for 

studies of equivalent field effort, as our average return rate of 

24% is lower than the 34% juvenile return rate observed at the 

Kern River Preserve, just as our survivorship estimate (34%) 

is lower than at the Kern River (46%; M. Whitfield, personal 

commun.). Comparatively, our return rate is higher than the 

1% reported from Michigan (Walkinshaw 1966) and the 13% 

from Oregon (Sedgwick and Klus 1997). Estimates of juvenile 

return rates for other species range from 15% in Common 

House-martins (Delichon urbica; Stokke et al. 2005), 25% 

in Swainson’s Thrushes (Catharus ustulatus; Gardali et al. 

2003), and 26% in Western Bluebirds (Sialia mexicana; 

Keyser et al. 2004). 

The most important factor that influenced juvenile survivorship in our study was the negative relationship between 

survivorship and fledge date. For every increase in fledge 

date by one day, the odds of surviving decreased by 2%, 

such that flycatchers fledging in mid-June had a 44% probability of surviving versus 19% for flycatchers fledging in 

mid-August. Flycatchers frequently renest following a failed 

nesting attempt, which can help to increase their chances of 

fledging young. However, there is a hidden cost to renesting 

as each successive nesting attempt has a lower probability 

of producing young that survive to adulthood. Thus, early 

flycatcher nests are likely to be the most successful in having 

young survive and return in subsequent years. This pattern of 

decreasing survivorship with fledge date has been documented 

in other species (Great Tit [Parus major], Monros et al. 2002 

and Smith et al. 1989; European Starlings [Sturnus vulgaris], 

Kremenz et al. 1989), as well as Willow Flycatchers. Studies 

at both the Kern River (M. Whitfield, personal commun.) and 

Malheur NWR (Sedgwick and Iko 1999) also found similar 

relationships with return rates and fledge dates. Explanations 

for higher apparent survivorship by earlier fledging flycatchers vary, and are not mutually exclusive. There may be greater 

arthropod food resources earlier in the breeding season, 

although Durst (2004) found no seasonal difference in total 

arthropod biomass at Roosevelt Lake. Birds that fledge earlier 

may reach a further developmental maturity prior to their first 

fall migration (Westmoreland and Best 1987, Krementz et al. 

1989). Earlier fledging birds may in turn arrive on the wintering grounds earlier and be better able to attain higher quality 

wintering habitats (Nilsson 1989, Smith 1994). Finally, it is 

important to remember that estimated mortality is true mortality plus permanent emigration, and an alternative explanation 

to the decreasing survivorship linked with later fledging is that 

late fledglings are more likely to disperse away from our study 

sites and not be detected again. Additional research exploring 

the reasons for increased survivorship of early-fledged young 

will yield useful management and scientific insights. 

Our model suggests that juvenile survival is not influenced by the habitat in which a nestling is hatched and raised; 

the 95% confidence intervals of survivorship estimates for 

different habitats overlap considerably, even though the point 

estimate for survival in mixed exotic habitat was much greater 

than those for other habitat types (42% versus 26%–27%). An 

important consideration in this comparison is that we could 

reach and band relatively few nestlings in native habitats 

(because of high nest placement), which led to unbalanced 

sample sizes (e.g., only 13 nestlings from native habitats compared to 89 from mixed native habitats). Therefore, although 

we believe that any influence of natal habitat type on juvenile 

survivorship is weak, additional research with larger and more 

balanced sample sizes may be useful in verifying these results. 

Adult Survivorship

Apparent adult survivorship averaged across the 10 years 

was 64% (range = 53%–73%), with yearly variation being the 

most important explanatory factor. This (along with juvenile 

survivorship estimates) yielded a mean life expectancy of 1.9 

years, meaning most flycatchers do not live for more than 2 

years as adults. As adults age, their survivorship probability 

increases, suggesting that they may learn optimal strategies for 

foraging, predator avoidance, and migrating, and have presumably found high-quality wintering grounds. Thus the relatively 

small percentage of flycatchers that survive through their first 

few years of life have high probabilities of returning each 

successive year, although annual variation is still an important 

effect. Our longevity record of a flycatcher of at least 9 years 

is exceeded by one of ≥10 years in Oregon (Sedgwick 2000) 

and another in California (M. Whitfield, personal commun.). 

Although a few individuals will realize lifespans exceeding the 

period of time of this study, this 10-year study is 5-times the 

expected lifespan of the average flycatcher and thus captures 

many generations of flycatchers.

Our estimated average adult survival probability of 64% 

exceeds that of other passerine studies, likely because of the 

duration and geographic extent of our study. Our survivorship 

estimate is higher than the estimate of 58% for the Kern River 

site (M. Whitfield, personal commun.), which is the only other 

maximum likelihood estimates of survivorship available for 

the Willow Flycatcher. Our average adult flycatcher return rate 

(55%) is higher than reported in Oregon (~45%; Sedgwick and 

Klus 1997) and in Michigan (41% male, 23% female; Walkinshaw 1966). Additionally, our maximum likelihood survivorship estimates are higher than those from other passerine 
38 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

species, such as 51% and 40% for male and female Blackthroated Blue Warblers (Dendroica caerulescens), respectively 

(Sillett and Holmes 2002), 52% in Male Florida Grasshopper 

Sparrows (Ammodramus savannarum; Perkins and Vickery 

2001), and 56% in Swainson’s Thrush (Gardali et al. 2003). 

More studies are needed of species similar to the flycatcher 

before we can put the survivorship estimates from this study 

fully in context.

The most apparent difference between other survivorship studies and ours is the large spatial extent of this study. 

Because birds can easily move long distances, and permanent 

emigration from a study site cannot be distinguished from 

mortality, studies conducted over small areas may underestimate survivorship. We examined how survivorship estimates 

derived from different spatial scales affected our estimates, 

and attempted to better understand the contribution of permanent emigration. As expected, as the spatial extent increased, 

survivorship estimates also increased from a low of 41% for 

the patch scale and quickly converged asymptotically to our 

overall value of 64% as the spatial scale approached the full 

extent of the entire study area. An important consideration 

in this analysis is whether tracking efforts are similar at all 

spatial scales; for our study, tracking effort was equivalent for 

all scales through the site level, but this is certainly not true 

for the last category, study area (all sites). There are breeding sites both known and unknown where there are minimal 

or no resighting activities and where banded flycatchers 

could go undetected, reducing our estimate of true survivorship. However, because the estimates converged so rapidly 

on the most inclusive number, we believe that the unknown 

numbers of undetected dispersing flycatchers were not 

large enough to significantly influence our survivorship 

estimates. Thus, we believe further increases in the spatial 

extent of our study would likely have had a minimal effect 

on survivorship estimates. 

High survivorship within the breeding season (97%) and 

an annual survivorship rate of 64% (36% mortality) suggests that only 8% of yearly mortality occurs on the breeding 

grounds; therefore most mortality occurs elsewhere. By linking our study with the Costa Rica wintering study (Koronkiewicz et al. 2006), we estimated that approximately 28% of 

the flycatcher’s annual mortality is occurring on the wintering grounds, while 64% occurred on migration. Sillett and 

Holmes (2002) found a similar pattern for Black-throated Blue 

Warblers - lowest mortality on the breeding grounds, slightly 

higher monthly mortality on the wintering grounds, and greatest monthly and season mortality risk during migration. If 

migration is a limiting period for flycatchers, as our data suggest, then increasing pressures on their stop-over sites could 

continue to suppress the population, delaying or hindering 

recovery efforts. In addition, a shortage of suitable migratory 

stop-over habitat may increase the time spent on migration, 

thus possibly increasing mortality during this already perilous 

time period. More research is needed to understand threats to 

flycatchers on migration, and whether different wintering and 

migration stop-over sites have different levels of mortality.

Adult survivorship and detection probabilities were best 

explained by annual variation. In light of the evidence that 

little mortality occurs at the breeding grounds, seasonal variation in migration and/or wintering ground mortality may be 

the drivers of the overall observed annual variation in survivorship. As this study was conducted on the breeding grounds, we 

were not able to measure the many factors that might contribute to non-breeding season mortality. Some studies have found 

relationships between yearly survivorship and global climate 

patterns, such as El Niño and La Niña events as measured by 

the Southern Oscillation Index (Sillett et al. 2000, Nott et al. 

2002). El Niño events, indicated by low negative SOI values, 

are characterized by heavy winter precipitation in the Southwestern states, leading to high spring and summer run-off, 

and hot and dry conditions on the wintering grounds. La Niña 

events are indicated by high positive values and have nearly 

opposite climatic effects to El Niño. However, while these 

global patterns clearly have dramatic affects on the landscape 

(e.g., drought, flooding), we did not detect any relationship 

with flycatcher survivorship. 

We found no support for a sex-bias in adult survivorship, 

with both females and males having identical survivorship 

(64%), although detection probability for females (82%) was 

greater than males (72%). These findings are similar to those 

from the wintering study (Koronkiewicz et al. 2006) where 

survivorship differences between the sexes were weak, though 

these authors found a slight bias in favor of males. Females 

may have a higher detection probability on the breeding 

grounds because if they are present they are more likely to 

engage in breeding activities and thus be detected, while males 

may be more likely to be present in a non-territorial (i.e., 

floater) state and more difficult to detect (see chapter 7). 

We found weak support for differences in survivorship 

by habitat type; habitat differences could only be understood 

in context of yearly variations. Given that the size, length of 

occupation by breeders, and habitat composition of the 52 

breeding patches varied greatly, some of this habitat-based 

variation could simply be an artifact of the type of suitable 

habitat that was available to flycatchers at each site, each 

year, over the course of this study. Factors such as vegetation 

density, structure and age of patches could also have influenced habitat suitability and associated survivorship, masking 

patterns evaluated only at the coarse habitat-type level. Mean 

survivorship values by habitat indicated no effects of breeding 

habitat on survivorship.

A number of studies have found a relationship between 

productivity and survivorship, with higher survivorship in 

those birds that were successful breeders (Koivula et al. 1996, 

Garamszegi et al. 2004). These results are often interpreted as 

evidence that superior breeders have superior survival skills. 

We found that Willow Flycatchers that were successful breeders had higher annual survivorship (73%) compared to birds 

that were unsuccessful breeders (59%), non-nesting (60%), 

or non-territorial (60%), indicating a relationship between 

productivity and survivorship in flycatchers as well. One pos-
 39

sible explanation for this pattern is that adults that are more 

successful at surviving, possibly through high-quality wintering grounds and better migration strategies, are in better condition upon arrival on the breeding grounds and thus more likely 

to be successful at reproducing. Alternatively, this relationship may be an artifact of the tendency of successful breeders 

to return to their previous year’s site, and thus be detected, 

whereas unsuccessful breeders are more likely to emigrate 

outside of the study area (Sherry 2005). While there is support 

for the idea that non-productive flycatchers are more likely to 

move than successful flycatchers (see chapter 4), our ability to 

detect moving flycatchers over a large area makes this explanation less likely. Moreover, survivorship estimates of flycatchers 

exhibiting territory-fidelity, patch-fidelity, or between-patch 

movement were nearly identical (see chapter 4). Age may also 

be a factor, as second-year adults have lower nest success than 

older adults (see chapter 3), as well as lower survivorship. 

Despite the apparently adequate spatial extent of our 

study, and intensive field efforts to detect all flycatchers at our 

two sites, our average adult detection probability was 78%. A 

major factor explaining why our detection probability was not 

closer to 100%, despite intensive field effort, was the presence 

of non-territorial birds, which had a detection probability of 

only 13% compared to 100% for territorial birds. While we 

do not believe the actual detection rate for territorial birds was 

100% (binomial models have difficulty in estimating numbers 

close to 1.0, so such an estimate is only approximately 100%), 

it appears that flycatchers were very likely to be detected if 

alive, present at our sites, and territorial. It is largely through 

the use of passive netting efforts started in 2001 that we were 

able to detect many of the non-territorial flycatchers; 75% of 

all birds classified as unknown breeding status occurred during 

the 5-year period after passive netting began. This illustrates 

the importance of passive netting as a technique to account for 

birds that would otherwise be undetected. Because flycatchers 

transition between territorial and non-territorial states during 

the course of their lives, it is important to track this sometimes 

“invisible” component of the population (see chapter 7).

Management Considerations

Juvenile and Adult Survivorship

Our survivorship estimates for adults and juveniles are at 

the high end of estimates for other passerines, and combined 

with increasing local population numbers, suggest that the 

flycatchers breeding at Roosevelt Lake and the San Pedro/Gila 

were not experiencing depressed survivorship during the course 

of our study. However, survivorship of flycatchers at different breeding sites may not have similar rates, so extrapolating 

the results of this study to other areas across the range may 

not be valid. In particular, small populations may be subject 

to lower or more variable survivorship, especially in response 

to stochastic events, which may influence their long-term persistence compared to a large and growing populations such as 

at our study sites. For example, smaller sites may concentrate 

predators whereas a larger site would be more likely to disperse 

them, and larger sites may have more food availability.

Because juvenile survivorship is often a major factor in 

determining a population’s growth potential, it can theoretically be manipulated to help increase the growth potential 

of a population of concern (Beissinger et al. 2000). In our 

study, the most important factor influencing juvenile survivorship was fledge date, with decreasing survival probabilities 

as fledge date increased. Therefore, if there were a goal to 

increase the growth rate of specific flycatcher populations 

(e.g., sites with declining numbers), managing nesting conditions to reduce early-season nest failures and allow earlier 

fledging of young would seem to hold promise. Mechanisms 

to do this are not yet known, but possibilities that could be 

studied include presence of surface water within territories, 

timing and manipulation of water levels below nests, density 

of understory vegetation within a patch, influence of adjacent 

habitat types, timing of cowbird management activities, and 

nest predator barriers or management. More study is needed 

on these and other management methods to determine their 

feasibility and effectiveness.

Understanding Migration and Wintering 

Survivorship 

Our data suggest that most adult, and presumably juvenile, mortality occurs away from the breeding grounds, and 

that migration may be the period of greatest mortality. However, we still lack much basic information on habitat needs 

and threats to migrating flycatchers (Whitfield et al. 2003). 

Similarly, although our understanding of winter distribution 

of flycatchers has increased dramatically over the last 6 years, 

only one study has evaluated survivorship on the wintering 

grounds (Koronkiewicz et al. 2006). Additional studies of 

migrating and wintering flycatchers can help identify factors 

that may be amenable to conservation and management activities aimed at increasing flycatcher survivorship.

Research Design and Field Effort Considerations

Because survivorship estimates are often used as an index 

of a population’s “health”, and to compare among different 

populations, habitat types, or management alternatives, it is 

important that the estimates be robust. Based on our results, 

banding and resight efforts at the scale of a single patch will 

result in survivorship estimates that are biased low. Small, 

isolated populations may be appropriate for small scale studies, but movement to and from many isolated sites has been 

documented (see chapter 4). Increasing the scale of a study to 

include all known breeding patches within at least the contiguous drainage will provide more accurate parameter estimates. 

Another way to increase detection rates is through the use of 
40 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

intensive field efforts (e.g., large crews to conduct banding and 

resighting), and techniques such as passive netting that help 

detect easily-overlooked segments of a population. Finally, 

the temporal scale of a survivorship study is an important 

consideration because many flycatchers are not detected in all 

years; multiyear studies allow researchers to account for birds 

that may be absent in some years and to achieve more accurate 

survivorship estimates, as well as document yearly variation. 

However, given the intensive banding and resighting effort 

required, a multifaceted, long-term study covering a large 

geographic area entails substantial effort and cost. Management and regulatory agencies must ultimately balance the 

scale, intensity, and duration of study with the cost, timing and 

importance of the information need. 

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 43

Introduction

The viability and growth of a population is often most 

directly influenced by the productivity of breeding birds (Perrins et al. 1991, Anders and Marshall 2005, Grzybowski and 

Pease 2005), and population productivity is often of particular 

concern for many threatened and endangered species. Multiple 

factors can have both short- and long-term influences on productivity, including predation, parasitism, and food resources 

(Rodenhouse and Holmes 1992, Robinson et al. 1995, Arcese 

et al. 1996, Donovan et al. 1997, Woodworth 1999, Weidinger 

2002, Smith and Moore 2003). In arid environments, rainfall 

can be an important environmental factor, with lack of precipitation negatively affecting productivity (Boag and Grant 

1984; Rotenberry and Wiens 1989, 1991; Li and Brown 1999). 

Additionally, factors may interact to affect productivity; for 

instance climate can affect nest predation rates, which in turn 

influences reproductive success (Woolfenden and Fitzpatrick 

1984, Grant and Grant 1987). Variations in annual productivity 

can reveal important sources of environmental influences on 

the ability of a bird to fledge young, while variation in lifetime 

productivity highlights individual differences (Clutton-Brock 

1988). Ultimately, an understanding of the factors that influence productivity may have critical management implications 

that could enhance prospects for the viability and sustainability of endangered species. 

To adequately characterize the productivity of a population, it is important to measure the reproductive success 

of individuals over the entire breeding season (Clobert and 

Lebreton 1991). Most bird species can have multiple nesting events within a breeding season, by renesting following a 

failed nesting attempt or “double brooding” after a successful nest. Further, to estimate lifetime reproductive success, 

the seasonal reproductive success of individuals needs to be 

tracked for their entire lifespan. Measurement of both seasonal 

and lifetime productivity is therefore challenging, and requires 

carefully following uniquely marked individuals over multiple 

seasons and years (Beissinger and McCullough 2002). 

The Southwestern Willow Flycatcher (Empidonax traillii 

extimus) is a small, migratory passerine that breeds exclusively in riparian habitats scattered throughout portions of the 

southwestern United States (Unitt 1987, Marshall 2000 ), and 

winters from central Mexico south to northern South America 

(Sedgwick 2000). Flycatcher numbers have declined precipitously as riparian habitats on the breeding grounds have been 

lost or modified (USFWS 1993, Marshall and Stoleson 2000), 

and E.t. extimus was listed as a federally endangered species in 

1995 (USFWS 1995). At the time of listing, most aspects of the 

flycatcher’s biology, including variability in productivity and 

the driving forces responsible for that variability, were poorly 

understood (Marshall and Stoleson 2000, Stoleson et al. 2000). 

From 1996 to 2005, we collaborated with the Arizona 

Game and Fish Department (AGFD) to conduct a large-scale 

demographic study of flycatchers at two large breeding sites in 

central Arizona. A major goal of this project was to document 

long-term variation in flycatcher productivity by tracking the 

reproductive success of individual nests and color-banded individuals over the successive breeding seasons and across years. 

In this chapter, we focus on three aspects of productivity 

that required the tracking of banded birds. The first is seasonal 

nest success, which measures how many nests were successful 

out of all nesting attempts per individual. A second measure, 

seasonal fecundity, is the sum of all fledglings produced from 

all nesting attempts by a given individual in a given season. 

Lastly, lifetime productivity is the sum of seasonal fecundity 

over the lifetime of an individual. For each measure, we evaluated several factors which explain variation in the documented 

estimates. However, most aspects of flycatcher productivity at 

Roosevelt Lake and the San Pedro River will be analyzed and 

presented by AGFD in their final project report.

Methods

Study Site

Our two study sites (fig. 1) were breeding populations 

at Roosevelt Lake (33o39’N, 110o58’W) and the San Pedro/

Gila River confluence (hereafter San Pedro/Gila; 32o59’N, 

110o46’W), where we conducted demographic research in 

cooperation with AGFD from 1996 to 2005 (Causey et al. 

2006, English et al. 2006). We defined sites as a collection of 

riparian woodland patches, occupied by breeding flycatchers, which are collectively closer to one another than to other 

breeding patches, and are found within the same drainage (or 

along the same lake). Exact definitions of a site are difficult, 

and the definition of a site differs regionally (Durst et al. 

2006). However, based on documented movement patterns, 

each site has a high degree of movement-based connectivity 

among patches suggesting a distinct breeding population.

Our two study sites supported some of the largest known 

Southwestern Willow Flycatcher breeding populations 

throughout the period of this study (Durst et al. 2006), with 

mean population sizes of 201 and 239 individuals at Roosevelt 

Chapter 3—Productivity
44 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 1. Location of the Roosevelt Lake and San Pedro/Gila study areas in central Arizona. 
 45

Lake and San Pedro/Gila, respectively. At each site, extensive 

surveys were conducted at least three times in the breeding 

season to detect all territorial flycatchers within the immediate and surrounding areas. In addition, multiple other breeding 

sites in Arizona and throughout the Southwest (49–444 km 

away) were periodically visited, which along with cooperative 

efforts of other researchers allowed detection of dispersing or 

moving flycatchers (see chapter 4). 

The breeding habitat at the Roosevelt Lake and San 

Pedro/Gila study sites consisted of a heterogeneous mosaic 

of discrete riparian forest patches of varying ages and vegetation composition, ranging from 0.2 to 43 ha in size. Native 

habitat was characterized by Goodding’s willow (Salix gooddingii) and Fremont cottonwood (Populus fremontii). Exotic 

habitat was dominated by tamarisk (saltcedar; Tamarix spp.). 

The understory vegetation consisted of a variety of grasses, 

forbs, and shrubs (mesquite [Prosopis spp.], coyote willow [S. 

exigua], tamarisk, Baccharis spp., and cocklebur [Xanthium 

strumarium]). Adjacent and surrounding non-riparian habitats 

were primarily composed of Sonoran Desert Uplands (Brown 

1994), and in some cases agricultural lands. 

Roosevelt Lake Study Site

The Roosevelt Lake study site consisted of two sub-sites, 

each concentrated at the confluence of the reservoir and the 

inflows of the Salt River and Tonto Creek, with breeding documented as far as 12 km upstream on Tonto Creek. The number 

of riparian patches in which breeding was documented varied 

over time as a result of colonization or desertion of patches, in 

many cases driven by fluctuating lake levels. Over the 10-year 

period, we documented breeding occurring in a maximum of 

23 patches encompassing approximately 242 ha of riparian 

habitat. Long-term persistent drought conditions between 1996 

and 2005 in the Southwest (McCabe et al. 2004) resulted in 

reservoir levels dropping to a low of 10% capacity in 2002; the 

exposed lakebed was colonized by riparian vegetation, which 

was subsequently colonized by breeding flycatchers (see chapter 5). In 2005, following unusually high winter precipitation, 

Roosevelt Lake filled to near capacity, inundating much of the 

breeding habitat that was occupied in 2004 (see chapter 5). 

San Pedro/Gila Study Site 

The San Pedro/Gila study site encompassed 101 km of 

river system, centered at the confluence of the free-flowing 

San Pedro River and the regulated Gila River, and extending 

upstream of the San Pedro to San Manuel Crossing and downstream of the Gila River to Kelvin Bridge. As with Roosevelt 

Lake, the number of patches changed over the 10 years, with 

as many as 29 habitat patches documented as supporting 

breeding efforts, comprising approximately 222 ha of riparian 

forest along this stretch. 

Banding and Resighting

To measure seasonal and lifetime productivity of individual flycatchers, we banded as many adult and nestling 

flycatchers as possible, and tracked them throughout the 

course of this study. Nestlings were banded at 7–10 days of 

age (Paxton and Owen 2002), from nests that were safely 

accessible, with a color-anodized, numbered federal bird band. 

A small drop of blood was taken for genetic gender determination (Paxton et al. 2002). Adults were banded with a coloranodized federal bird band and a second color band to create a 

unique color combination (Koronkiewicz et al. 2005). Adults 

were primarily target-netted (Sogge et al. 2001, Pollock and 

Paxton 2006) to allow for efficient, focused capture effort. 

From 2001 to 2005, we also conducted extensive passive netting at Roosevelt Lake (see Ralph et al. 1993) aimed at capturing non-territorial flycatchers that were not readily detectable 

with conventional survey techniques (i.e., territorial response 

to tape-playback). Overall, the combination of targeted and 

passive netting resulted in the banding of an average of 74% 

(range: 68%–88%) of detected adult flycatchers in a given 

year. To determine gender of adult flycatchers, we used a 

combination of physical characteristics (presence of a cloacal 

protuberance for males or brood patch for females), behavioral cues, and/or genetic sexing methods (Paxton et al. 2002). 

Flycatchers were aged as known if the individual was first 

banded as either a nestling or a second-year adults (SY; based 

on retained rectrices (Pyle 1998), or unknown (2 or more years 

of age). 

We tracked banded flycatchers over the 10 years primarily through resighting, supplemented by occasional recaptures. 

Resighting consisted of using binoculars to determine the 

unique color band combination on a flycatcher’s legs. Resighting is a minimally intrusive method of “recapture”, and the 

most reliable method for establishing the particular territory 

a flycatcher belongs to, because playback capture techniques 

can lure adults in from neighboring territories. To ensure the 

accuracy of resight data, only high-confidence resights from 

at least two different observers on different days were used 

to confirm the identity and territory of individual banded 

flycatchers. Computer databases were updated and queried 

daily to prioritize resighting efforts, allowing us to track as 

many as 393 uniquely color banded individuals in a given 

year. Field crew sizes of up to 35 personnel (USGS and AGFD 

combined) allowed intensive resighting of banded individuals, 

and resulted in high detection rates. Recaptures, especially 

via passive netting, provided additional detection resolution, 

especially for non-territorial birds. 

Nest Monitoring

Nest searching and monitoring were primarily coordinated and conducted by AGFD. Nests were discovered through 

systematic searches of flycatcher territories and monitored fol-
46 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

lowing the Southwestern Willow Flycatcher Nest Monitoring 

Protocol (Rourke et al. 1999). After incubation was confirmed, 

each monitored nest was visited every 2–4 days until it either 

successfully fledged at least one young or failed. Nests were 

considered successful if one of the following conditions were 

met (see English et al. 2005): (1) one or more young were 

visually confirmed fledging from the nest or located near the 

nest; (2) adults were seen feeding at least one fledgling; (3) 

parents behaved as if dependent young were nearby after the 

nest was empty; or (4) nestlings were observed in the nest 

within 2 days of the estimated fledge date (12 days). From 1996 

to 2000, all nests discovered at monitored breeding patches were 

monitored to determine reproductive success. Starting in 2001, 

the protocol was modified to track at least 35 banded females 

throughout each season at San Pedro/Gila, and 50 females at 

Roosevelt Lake. However, in most years there was sufficient 

time and resources for the majority of nests to be monitored. 

To determine seasonal productivity of individuals, we 

used a database that linked banded individuals associated with 

specific territories, and those territories to the reproductive 

outcomes of the nesting attempts made within the territory. 

Individuals that were associated with the territory, based on 

resights, were then assigned the reproductive outcome of the 

nest. Only birds for which we had high confidence of their 

association with specific territories were used. However, even 

this conservative approach cannot account for possible extrapair copulations, which could change the actual reproductive 

success of particular individuals (Pearson et al. 2006). Dates 

during which territories and nests were active were compared 

with the dates that an individual was detected to ensure that 

individuals were accounted for during each nesting attempt 

and throughout the breeding season. Those that could not be 

assigned to a territory, or that were missing for portions of the 

season, were excluded from the seasonal productivity analysis. 

Although not all nests were monitored each year, 

our resighting objectives were to track banded individuals 

throughout the breeding season, and to locate all nests and 

determine breeding status of individuals. Therefore, we have 

high confidence that most nesting attempts by banded individuals were documented, even if we did not have information on 

the outcome of each. As noted above, birds with uncertain nest 

outcomes were excluded from the productivity analyses. 

Statistical Analysis

We used analysis of variance (ANOVA) to test for differences in seasonal nest success and fecundity using the following factors: study site (Roosevelt Lake or San Pedro/Gila), 

year (1996–2005), sex (female or male), age (second-year to 

sixth-year), and habitat type (defined as native, mixed-native, 

mixed-exotic, or exotic vegetation). Additionally, we used 

regression to assess the relationship between average yearly 

nest predation (measured as the mean percentage of nests 

predated per individual per season) and precipitation (measured as the mean monthly precipitation in cm from January to 

May; data were from the weather station nearest to each study 

site). Age based productivity was compared via ANOVA, 

with known-aged females as the factor (up to age sixth-year, 

after which sample sizes were too small to use). Measures 

of productivity were sex-based when specified; otherwise, 

general patterns of seasonal nest success and fecundity were 

performed using information from females only.

Lifetime productivity requires knowledge of all productivity events of an individual over its entire lifespan. Because 

individuals can be alive before the start of a study and after its 

conclusion, the only birds certain to be tracked for their entire 

lifetimes are those that were first banded as nestlings or first 

captured as second-year adults (and therefore known not to 

have reproduced before the study), and then were not seen for 

at least the last 2 years of the study (and therefore likely dead). 

Applying these conservative selection criteria resulted in a set 

of 106 birds that had a mean lifetime fecundity of 1.2 young. 

Because this value is lower than the seasonal fecundity during 

some single years, we believed it to be a falsely low estimate, 

probably because the sample was too small to capture the 

full range of individual lifetime productivity. Therefore, we 

adopted a slightly less stringent approach that censored (i.e., 

removed) individuals that were only detected in the first 2 

years of the study, or only in the last 2 years. For example, 

individuals banded in either 1996 or 1997 and detected only 

in those years were excluded from the analysis, as were any 

individuals first banded in 2004 or 2005. Because the estimated life expectancy of flycatchers is just under 2 years 

(based on survivorship estimates; see chapter 2), most flycatchers live only one or 2 years as adults and therefore would 

have their entire lifetime productivity recorded over the course 

of this 10-year study. Although some individuals have lived 

as long as nine or more years, these are few, and their inclusion should change mean estimates of lifetime fecundity only 

slightly. While we cannot account for undetected permanent 

emigration from the study sites, the evidence suggests that 

such long-distance movements are fairly rare (see chapter 4), 

and thus should have minimal effects on the mean estimates. 

Nonetheless, we refer to this modified productivity estimator 

as Minimum Lifetime Productivity (MLP), in recognition that 

some amount of reproductive activity may have been missed 

for some long-lived individuals or emigrants.

Minimum lifetime productivity is the sum of an individual’s seasonal fecundity estimates over the lifetime, as tracked 

over the course of this study and meeting the criteria for inclusion as described above. Individuals that were not detected 

for an entire breeding season or were determined to be nonterritorial were included in the dataset, with zero productivity recorded for that year. However, individuals for which 

information was missing from one of their known breeding 

attempts in a season were excluded from the entire analysis. 

We conducted a t-test for differences in MLP by site and sex, 

and regressed MLP by longevity to understand the relationship 

between the two factors. To look at the cumulative contribution of individuals to the total number of young fledged in this 
 47

group, we ranked individuals from highest to lowest MLP, 

calculated their percent MLP to the total young fledged, and 

graphed the cumulative contribution. All statistical analysis 

was conducted in the software package JMP 6.0 (SAS, Inc.). 

Figures were plotted with SigmaPlot (SAS, Inc.).

Results

Seasonal Nest Success and Fecundity 

From 1996 to 2005, the seasonal reproductive success 

of 543 banded females and 636 banded males was tracked at 

Roosevelt Lake and the San Pedro/Gila. There was a significant interaction between year and site for both seasonal nest 

success (F9,1634 = 5.63, p<0.001) and seasonal fecundity (F9,1634 

= 4.85, p<0.001). Overall, seasonal nest success and fecundity 

was higher at San Pedro/Gila compared to Roosevelt Lake, 

but not in all years (table 1, fig. 2). Males had higher seasonal 

fecundity than females (F1,1634 = 24.09, p<0.001), but there was 

no difference in seasonal nest success (F1,1634 = 0.07, p = 0.79; 

table 1). On average, males had higher seasonal fecundity 

than females at both Roosevelt Lake (0.4 higher) and the San 

Pedro/Gila (0.5 higher).

Seasonal nest success and fecundity are largely influenced by factors affecting the fate of individual nesting 

attempts, and such factors will be covered in the final report by 

AGFD. However, two factors that have a strong influence on 

female seasonal measures of productivity, largely because they 

influence the amount of renesting, are nest predation rates and 

precipitation. Nest predation was the major cause of nest failure, accounting for 61% and 71% of failures at Roosevelt and 

San Pedro/Gila, respectively, and the overall seasonal nest success was strongly and negatively correlated with the average 

proportion of nests that failed due to nest predation (Roosevelt 

r2 = 0.69, San Pedro/Gila r2 = 0.60; fig. 3). Precipitation, 

measured as the average monthly amount of late winter and 

early spring rain/snow, was also a strong nest success predictor 

(Roosevelt: r2

 = 0.70, p = 0.027; San Pedro/Gila: r2 = 0.70, p = 

0.015); the relationship was non-linear (quadratic; fig. 4), with 

the strongest relationship at low precipitation conditions (from 

0.0 to 2.5 cm) but leveling off at higher precipitation levels. 

Average precipitation over the 10 years of this study was 2.8 

cm for Roosevelt and 1.7 cm for San Pedro/Gila, which is 

approximately at the inflection points of the regression line. 

Thus, the average precipitation level at each site approximates 

a threshold below which nest success is strongly reduced with 

decreasing precipitation; above this threshold, increasing precipitation does not have a strong increasingly positive effect. 

Finally, we detected no difference in seasonal nest success 

(F3,803 = 2.24, p = 0.08) or seasonal fecundity (F3,803 = 1.67, p = 

0.17) by habitat type (table 2).

Age-based Seasonal Productivity

There was a significant difference in seasonal fecundity 

of females, ages second-year to sixth-year (F4,131 = 2.60, p = 

0.04), but not seasonal nest success (F4,131 = 1.41, p = 0.23). 

Differences among the age groups was driven largely by lower 

productivity in second-year adults, with second-year seasonal 

fecundity averaging 1.0 versus 1.7–1.8 for the older females, 

and seasonal nest success averaging 45% for second-year 

adults versus 54%–75% for the older females (table 3).

Minimum Lifetime Productivity

From 1996 to 2005, we tracked the yearly breeding 

efforts of 802 individuals to determine their minimum lifetime 

productivity (MLP). Mean MLP averaged 3.5 offspring (range 

= 0–27). While males had a slightly higher mean number of 

lifetime fledges (3.8) than females (3.3), this difference was 

not significant (t764 = -1.6 p = 0.11); the median for both was 

3.0 young fledged per lifetime (fig. 5). In general, males had 

the highest number of MLP fledglings due to polygamy, but a 

higher proportion of males than females produced zero young, 

resulting in similar mean values. The number of lifetime 

fledges differed significantly between sites (t764 = -2.6, p = 

0.01), being higher at the San Pedro/Gila (mean = 4.0, CI = 

3.5–4.6, median = 3) than at Roosevelt Lake (mean = 3.2, CI = 

2.9–3.6, median = 2). 

The number of young fledged in a lifetime varied greatly 

among individuals (fig. 5). Over a third (37%, n = 299) of 

individuals tracked produced no offspring, but most of these 

individuals (76%) were detected for only one year; only 32% 

of individuals detected for a single year fledging at least one 

young. Some flycatchers that were detected for up to 5 years 

did not produce offspring, but all flycatchers detected for 6 or 

more years fledged at least one young (fig. 6). Overall, 50% of 

the total young fledged were attributed to just 16% of flycatchers tracked. For this dataset, the mean number of years alive 

was 2.5, and the mean number of years breeding was 1.8. As 

expected, the longer a flycatcher lived, the greater the number of lifetime fledges it produced (r2

 = 0.53, F1,777 = 890.6, 

p <0.001); however, there was considerable variation around 

this trend. We therefore arbitrarily classified flycatchers into 

three groups based on minimum lifetime productivity: 10 or 

more young, 1–9 young, and zero young fledged (table 4). The 

group producing 10 or more fledges in their lifetime comprised only 8% of the monitored population, but contributed 

31% of the total number of young fledged. Flycatchers in the 

1–9 young per lifetime group constituted 55% of the monitored population, and contributed the remaining 69% of young 

fledged. The remaining 37% of individuals were never detected 

fledging young. Characteristics of the more successful breeders included longer lifespan, more years of attempted breeding, 

higher percent nest success, more polygamous pairings, and 

higher mean number of young fledged per year (table 4).
48 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Female Male

Year Site n

Average % 

seasonal nest 

success 

(95% CI)

Average 

seasonal 

fecundity 

(95%CI)

n

Average % 

seasonal nest 

success 

(95% CI)

Average 

seasonal 

fecundity 

(95%CI)

1996

Roosevelt Lake 10 40 (7–72) 1.0 (0.2–1.8) 14 50 (22–78) 1.3 (0.6–2.0)

San Pedro/Gila 15 30 (7–53) 0.7 (0.2–1.3) 13 49 (24–74) 1.4 (0.7–2.1)

1997

Roosevelt Lake 18 51 (28–74) 1.7 (1.0–2.5) 24 60 (38–76) 2.2 (1.2–3.1)

San Pedro/Gila 18 53 (31–74) 1.6 (0.9–2.3) 25 63 (45–82) 1.6 (1.1–2.1)

1998

Roosevelt Lake 18 75 (54–96) 1.9 (1.3–2.6) 28 68 (53–83) 2.7 (2.0–3.5)

San Pedro/Gila 38 56 (41–70) 2.1 (1.5–2.7) 33 63 (50–76) 3.3 (2.4–4.2)

1999

Roosevelt Lake 36 62 (47–77) 2.1 (1.4–2.7) 43 61 (48–74) 2.6 (1.9–3.2)

San Pedro/Gila 39 49 (36–61) 1.8 (1.3–2.3) 40 44 (33–56) 2.6 (2.0–3.2)

2000

Roosevelt Lake 39 65 (52–79) 1.9 (1.6–2.3) 60 63 (52–73) 2.4 (1.9–2.8)

San Pedro/Gila 30 46 (31–61) 1.8 (1.2–2.3) 43 49 (37–61) 1.9 (1.3–2.4)

2001

Roosevelt Lake 70 69 (59–79) 2.2 (1.8–2.5) 66 65 (55–74) 2.7 (2.1–3.2)

San Pedro/Gila 28 61 (46–76) 2.7 (2.0–3.4) 32 55 (40–70) 3.0 (2.2–3.8)

2002

Roosevelt Lake 31 6 (0–14) 0.1 (0.0–0.3) 47 3 (0–7) 0.1 (0.0–0.2)

San Pedro/Gila 24 29 (10–49) 0.8 (0.3–1.4) 23 34 (15–53) 1.1 (0.5–1.7)

2003

Roosevelt Lake 90 72 (63–81) 2.0 (1.8–2.3) 77 71 (62–80) 2.6 (2.2–3.1)

San Pedro/Gila 51 69 (58–81) 2.3 (1.9 (2.7) 30 76 (61–90) 3.2 (2.4–3.9)

2004

Roosevelt Lake 72 40 (29–51) 1.1 (0.8–1.4) 74 46 (35–56) 1.6 (1.2–2.0)

San Pedro/Gila 55 49 (38–59) 1.9 (1.4–2.3) 41 61 (50–73) 2.8 (2.2–3.4)

2005

Roosevelt Lake 71 49 (38–60) 1.4 (1.1–1.7) 77 44 (35–54) 1.6 (1.3–1.9)

San Pedro/Gila 52 68 (57–80) 2.3 (1.9–2.8) 39 70 (57–83) 3.3 (2.5–4.1)

Mean

Roosevelt Lake 455 56 (52–60) 1.6 (1.5–1.8) 511 53 (49–57) 2.0 (1.8–2.2)

San Pedro/Gila 350 54 (50–59) 2.0 (1.8–2.1) 319 57 (53–61) 2.5 (2.3–2.8)

Table 1. Mean seasonal nest success (the percentage of successful nests per nesting attempts) and seasonal fecundity (total number 

of young fledged per individual per season) of Southwestern Willow Flycatchers at Roosevelt Lake and the San Pedro/Gila study sites. 

Sample size represents the number of banded individuals used for each estimate. 
 49

Figure 2. Average seasonal nest success and fecundity of Southwestern Willow 

Flycatcher females at the Roosevelt Lake and San Pedro/Gila study sites, 1996-2005.

Year

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

% seasonal nest success

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Roosevelt Lake

San Pedro/Gila

Average seasonal nest success

Year

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average number of young fledged/season

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Roosevelt Lake

San Pedro/Gila

Average seasonal fecundity

Figure 2. Average seasonal nest success and fecundity of Southwestern Willow Flycatcher females at the Roosevelt Lake and San 

Pedro/Gila study sites, 1996–2005. 
50 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Roosevelt Lake (black) and San Pedro (gray) for a given year. There was a negative 

correlation between nest success and predation at each site. 

Predation

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Nest Success

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Roosevelt Lake

San Pedro/Gila

San Pedro/Gila: 

r

2 = 0.60, p = 0.008

Roosevelt Lake:

r

2 = 0.69, p = 0.005

Figure 3. Yearly percent predation versus nest success, for Southwestern Willow Flycatchers at Roosevelt Lake (1996–2004) and the 

San Pedro/Gila (1996–2005). Each point represents the average female seasonal nest success and predation at the Roosevelt Lake 

(black) and San Pedro/Gila (gray) for a given year. There was a negative correlation between nest success and predation at each site.

Figure 4. Yearly precipitation from January to May and yearly mean seasonal nest 

success of female Southwestern Willow Flycatchers at Roosevelt Lake (1996-2004) and 

San Pedro/Gila (1996-2005). Each point represents the average nest success and January 

to May precipitation at the Roosevelt Lake (black) and San Pedro (gray) for a given year. 

Over the course of the study, mean January to May precipitation at Roosevelt Lake and 

San Pedro/Gila was 2.8 cm and 1.7 cm, respectively.

Precipitation (cm)

0 1 2 3 4 5 6

Nest Success

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Roosevelt Lake

San Pedro/Gila

San Pedro/Gila:

r

2

 = 0.70, p = 0.015

Roosevelt Lake:

r

2

 = 0.70, p = 0.027

Figure 4. Yearly precipitation as mean monthly values from January to May and yearly mean seasonal nest success of female 

Southwestern Willow Flycatchers at Roosevelt Lake (1996–2004) and San Pedro/Gila (1996–2005). Each point represents the average 

nest success and January to May precipitation at the Roosevelt Lake (black) and San Pedro/Gila (gray) for a given year. Over the course 

of the study, mean January to May precipitation at Roosevelt Lake and San Pedro/Gila was 2.8 cm and 1.7 cm, respectively. 
 51

Habitat n

Mean seasonal nest success 

(%) (95% CI)

Mean number of young fledged

(95% CI)

Native 92 54 (45–64) 1.8 (1.4–2.1)

Mixed Native 170 47 (40–54) 1.5 (1.3–1.8)

Mixed Exotic 221 58 (52–63) 1.9 (1.7–2.1)

Exotic 313 58 (53–63) 1.8 (1.6–2.0)

Figure 5. Frequency of the number of fledges produced (Minimum Lifetime 

Productivity; MLP) over the lifetime of Southwestern Willow Flycatcher females (top; 

mean MLP = 3.29) and males (bottom; mean MLP = 3.77). Data are for Roosevelt Lake 

and the San Pedro/Gila, combined. 

Number of Fledges by Males

0 5 10 15 20 25 30

Frequency

0

50

100

150

200

250

300

Number of Fledges by Females

0 5 10 15 20 25

Frequency

0

50

100

150

200

250

300

2.75 (2.48 - 3.01)

2.97 (2.64 - 3.30)

Table 2. Average seasonal nest success and fecundity of Southwestern Willow Flycatcher females by habitat type (sites and years 

combined). Habitat types are native broad leaf, exotic tamarisk, and mixtures of the two vegetation types. Large overlap of 95%

confidence intervals suggests no statistical difference in reproductive success by habitat type.

Figure 5. Frequency of the 

number of fledges produced 

(Minimum Lifetime Productivity; 

MLP) over the lifetime 

of Southwestern Willow 

Flycatcher females (top; mean 

MLP = 3.29) and males (bottom; 

mean MLP = 3.77). Data are for 

Roosevelt Lake and the San 

Pedro/Gila, combined.

 Table 3. Average seasonal nest success and fecundity of Southwestern Willow Flycatcher females as a function of their age. 

Differences by age were most apparent in comparisons of second-year adults with all older adults.

Seasonal nest success (%) Seasonal fecundity

Age n Mean 95% C.I. Mean 95% C.I.

Second-year adult 61 45 33–57 1.0 0.7–1.3

Third-year adult 32 65 48–82 1.8 1.3–2.4

Fourth-year adult 19 61 38–83 1.8 1.0–2.7

Fifth-year adult 14 54 27–80 1.7 0.9–2.5

Sixth-year adult 6 75 31–100 1.8 0.8–2.9
52 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

p, 

combined. 

Number of Years Detected

12345678

Number of Fledges Produced

0

5

10

15

20

25

30

Figure 6. Number of 

Southwestern Willow 

Flycatcher fledges produced 

as a function of the number 

of years detected, 1996–2005. 

Mean years detected was 

2.46, and the mean number of 

fledges produced was 3.49. 

Upper and lower bounds of 

box plot represents the 25th 

and 75th percentile, the line 

in the box represents the 

median, upper and lower 

whiskers show the 90th and 

10th percentile, respectively. 

Black dots represent data 

points outside the 10th and 

90th percentile. Data are for 

Roosevelt Lake and the San 

Pedro/Gila, combined.

Group of 

individuals N

Proportion of 

population (%)

Proportion of total 

fledglings (%)

Percent male (%)

Mean adult 

lifespan (%)

Years breeding

Mean lifetime 

nests 

Nesting 

success (%)

Polygamous 

nests (%)

Mean lifetime 

fledglings

Mean yearly 

fledglings

10 or more lifetime 

fledglings 62 8 31 73 5.39 91% 9.03 60 54 13.61 2.71

1 to 9 lifetime 

 fledglings 440 55 69 46 2.75 86% 3.37 53 37 4.26 1.60

0 lifetime fledglings 300* 37 0 58 1.40 48% 0.83 0 28 0.00 0.00

Table 4. Minimum lifetime productivity groups by number of lifetime fledglings produced (0 young, 1–9 young, and 10 or more 

young) and associated attributes. Both males and females are included. For individuals considered in this analysis only, proportion of 

population is the proportion of individuals belonging to each of the three groups; proportion of total fledglings is how many fledglings 

each group contribute to all recorded fledglings; percent male reflects the sex ratio of each group; mean adult lifespan is the average 

lifespan of individuals from each group; years breeding is the percentage of years alive that individuals from each group were recorded 

as breeding; mean lifetime nests is the average number of nests produced by individuals in each group over their lifetime; nesting 

success is the percentage of nests for each group that were successful; polygamous nests are the percentage of nests that resulted 

from a polygamous pairing; mean lifetime fledges is the average number of total lifetime fledglings recorded for individuals of each 

group; and, mean yearly fledglings is the average seasonal fecundity of individuals from each group. 

*Includes seven individuals of unknown sex
 53

Discussion

Seasonal Nest Success and Fecundity

The average seasonal fecundity of females in our study 

(1.6 and 2.0, Roosevelt Lake and San Pedro/Gila, respectively) 

was higher than reported for all southwestern breeding sites 

(summarized in Stoleson et al. 2000), with the exception of the 

San Luis Rey River, CA (2.5 per female). Seasonal fecundity

estimates for Willow Flycatchers outside of the range of the 

southwestern subspecies include 1.41 fledglings per pair in 

the Midwest (Holcomb 1972), 1.4 to 1.5 in northern California (Flett and Sanders 1987), and 1.81 in Oregon (Sedgwick 

and Iko 1999). Although the methodology used to estimate 

seasonal fecundity may vary among studies, and thus direct 

comparison of estimates may be imperfect, the range of values 

from other studies suggests that the level of productivity at 

our two study sites is at or above the normal range found 

elsewhere. The higher fecundity of males is presumably due to 

polygamy, a finding consistent with other studies (Lubjuhn et 

al. 2000, Davidson and Allison 2003).

The high variance in seasonal female productivity 

rates at our study sites, ranging from a low of 0.1 young per 

female (Roosevelt Lake in 2002) to a high of 2.7 (San Pedro/

Gila in 2001), suggests that many factors vary spatially and 

temporally to influence reproductive success. Identifying the 

factors associated with the variation can provide insight into 

threats, ecology, and natural variation in the environment, 

as well as provide information important for demographic 

modeling. Many of the factors will be explored by AGFD, 

but several were apparent from our analysis of seasonal 

reproductive success. The following are factors that we found 

contribute to the observed variability in flycatcher’s seasonal 

reproductive success.

Site and year interacted, explaining significant amounts 

of the variation observed in seasonal productivity, and functioned as “proxies” for a suite of unknown factors. Roosevelt 

Lake had higher measures of seasonal productivity in some 

years, and San Pedro/Gila in others, although the average (10 

year) seasonal productivity measures were generally greater 

at the San Pedro/Gila study site. Sites may differ as a result 

of a combination of vegetation characteristics, predator types 

and densities, hydrology, or historical occurrences unique to 

each site (i.e., how long since the last scouring flood), as well 

as random differences one would expect when comparing 

samples from two distinct locations. Yearly differences may 

result from fluctuations in precipitation (see below), arthropod 

abundance, changing vegetation structure (succession), fluctuating ground water levels, etc. Thus, site and year differences 

are primarily important to our interpretations because they 

highlight the inherently high temporal and spatial variation in 

the reproductive success of a species that breeds in dynamic 

and highly variable riparian landscapes.

Perhaps contributing to site and year differences were 

two strong predictors of seasonal productivity: predation and 

precipitation. Predation was the primary cause of nest failure 

for flycatchers in our study, as is the case for most opencup nesting Neotropical migrants as well (Martin 1992). In 

Arizona, where flycatcher nest predators were documented 

by AGFD video monitoring of nests, most predation was by 

hawks and snakes but other nest predators were noted as well 

(Tudor et al. 2004). 

Rainfall in arid environments like the Sonoran Desert 

can have important influences on breeding success (Grant 

and Grant 1987). In our study, the influence of late winter and 

spring precipitation levels on seasonal nest success was more 

pronounced in dry than in wet years. At Roosevelt Lake, three 

of four years with nest success >60% (long-term mean was 

52%) had higher than average late winter and spring rainfall. 

The case was similar at the San Pedro/Gila, where the years 

of highest nest success generally had the highest late winter 

and spring rainfall. However, drought conditions had a much 

more marked effect. At both study sites, the extreme drought 

year of 2002 clearly stood out as the worst year for seasonal 

productivity, with seasonal nest success at Roosevelt as low 

as 6% and seasonal fecundity as low as 0.1. The influence 

of rainfall on reproduction is likely the result of its effect on 

vegetation vigor and subsequent production of invertebrate 

food resources (Noy-Meir 1973, Cody 1981, Boag and Grant 

1984, Grant and Grant 1987). As evidence that prey availability may be a driving factor affecting reproductive success 

during drought years, a concurrent study at Roosevelt Lake 

(Durst 2004) documented a five-fold decrease in arthropod 

biomass in 2002 compared to 2003, a relatively normal year 

with regard to precipitation. Predictions of more frequent and 

more intense droughts in the southwestern U.S. in the face of 

global climate change (IPCC 2002), if realized, may negatively influence the productivity of flycatcher populations over 

long periods of time.

Breeding Age

The finding that older, presumably more experienced 

flycatchers were more productive breeders compared to 

first time, young breeders is consistent with other studies 

(McCleery and Perrins 1989, Hatch and Westneat 2007). The 

difference was most pronounced when comparing young 

adults to older adults, with the difference between older adults 

being slight. Presumably older adults gain valuable experience in foraging and nest site location, as well as survival and 

migration skills, which contribute to increased success. While 

the exact reasons for these age-based differences may not be 

apparent, they indicate that populations with a good representation of older adults may be more productive than populations 

comprised entirely or mostly of younger birds. The latter situation may occur at newly colonized sites, if the colonizers are 

primarily young birds. 
54 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Minimum Lifetime Productivity

Over this 10-year study, adult Southwestern Willow 

Flycatchers had a mean MLP of 3.5 fledglings per individual, 

with males having a higher MLP (3.8) than females (3.3). This 

is slightly lower than the 3.6 fledglings per adult reported for 

female flycatchers in Oregon by Sedgwick and Iko (1999), 

the only other researchers to date who have published values 

for Willow Flycatchers. Comparison with other bird species is 

of limited use, because differences in lifespan, clutch size, nest 

type, and many other factors can influence lifetime productivity.

Estimates of lifetime productivity are difficult to collect 

because they require long-term studies of sufficient length and 

scope to track many individuals throughout their lives. The 

mean life expectancy for flycatchers is just under 2 years (see 

chapter 2), and the mean number of years adults were detected 

(once having survived as juveniles) was 2.5. Although some 

banded individuals in this study lived for nine or more years, 

they were very few, and for the majority of individuals 3–5 

generations of breeders were encompassed in this study. 

Equally important is the ability to account for all breeding 

efforts made by individuals. The non-detection of breeding 

attempts and emigration away from the study sites could bias 

the estimates of lifetime productivity downward. While it is 

certain that we missed some reproductive efforts and some 

permanent emigration, we believe the number missed is low 

and that our mean estimates are close to the true values. First, 

although right and left censoring individuals reduces the likelihood of including most individuals that bred before or after 

the study period, some long-lived individuals certainly did, but 

probably in small numbers. Second, although not all nests at 

the study sites were monitored, efforts to track all birds were 

made in every year, such that confidence in knowing where 

individuals were in each year was high. Those individuals confirmed to a territory where we did not know their nest success 

were excluded. Finally, although emigration from the study 

site is known to occur, evidence is that it is at low levels (see 

chapter 4), and thus we believe not likely to significantly affect 

our productivity estimates.

As birds age and breed over more years, they have the 

opportunity to increase their lifetime productivity. The mean 

number of minimum lifetime fledglings produced by females 

(3.3) was approximately double the average seasonal fecundity (1.6–2.0, at Roosevelt Lake and the San Pedro/Gila, 

respectively). This suggests that the average flycatcher has 

two successful breeding seasons in their lifetime. There was 

not a single age class of flycatchers that produced the most 

offspring, but in general birds living to older age classes had 

greater lifetime productivity because they had more breeding 

opportunities. Longevity and reproductive success may also 

be interrelated if some birds consistently have higher quality 

territories that allow them not only to be more productive, but 

also to live longer (Hogstedt 1980, Smith 1981). 

There was substantial variation in minimum lifetime 

productivity; most flycatchers did not produce any offspring, 

while a few produced many. This closely matches a pattern 

seen in many bird species (Newton 1989). The 37% of the 

banded population that did not produce any fledglings were 

present as potentially breeding adults for only an average of 

1.3 years and only attempted to breed in approximately half 

(48%) of the years detected. For flycatchers in the categories of 

fledging 1–9 young and ≥10 young, there was a corresponding 

increase in the number of years they were present as potentially 

breeding adults (2.5 and 5.1 years, respectively), and in the 

proportion of years with attempted breeding (86% and 91%, 

respectively). Thus, an understanding of critical elements 

that determine lifetime productivity requires consideration of 

factors affecting survivorship (Newton 1989, Espie et al. 2000).

Management and Research 

Considerations 

Habitat Restoration and Management

We did not find a difference in seasonal reproductive 

success between flycatchers breeding in patches dominated by 

saltcedar compared with those in native-dominated patches. 

Therefore, within the Roosevelt Lake and San Pedro/Gila 

study sites, there does not appear to be a potential benefit to 

actively removing or controlling saltcedar to increase flycatcher productivity. In some patches, removal of the dense 

saltcedar midstory—if not replaced quickly by willow or other 

native trees of similar density and structure—could reduce the 

suitability of the patch by reducing the vegetation density and 

cover. The Southwestern Willow Flycatcher Recovery Plan 

(USFWS 2002) discusses these considerations, with regard 

to whether and how to proceed with saltcedar control within 

patches that are currently occupied by breeding flycatchers. 

Drought and Climate Change

Lower than average precipitation reduced flycatcher seasonal productivity at both Roosevelt Lake and the San Pedro/

Gila. If the predictions of some current climate change models 

are correct, the southwestern United States will experience 

increasingly more frequent and severe droughts (Frederick and 

Schwarz 1999). This could lead to more frequent incidents 

of extremely low reproductive success, such as occurred at 

Roosevelt Lake during the 2002 drought. Ultimately, successive years of low productivity could lead to unsustainable local 

populations. The difference in reproductive success between 

the two study sites in 2002 (with Roosevelt Lake more negatively affected) highlights the value of managing for large 

healthy populations at multiple sites where effects of drought 

and other calamities may impact sites differently. Further, it 

may be possible to develop management actions to reduce the 

negative effects of repeated or severe droughts on flycatcher 

breeding habitat. Possibilities at Roosevelt Lake include 

manipulation of water tables via management of reservoir 
 55

water levels in order to maintain a moist zone for riparian trees. 

For riverine landscapes such as the San Pedro River, idling of 

groundwater pumps adjacent to flycatcher breeding habitat in 

dry years, or provision of supplementary water, may slow the 

rate of water table decline and sustain healthy tree phenology. 

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58 The Ecology of the Southwestern Willow Flycatcher in Central Arizona
 59

Introduction

One of the most important life history traits of a species, 

and one of the least understood, is the movement of individuals over space and time (e.g., natal dispersal, immigration 

and emigration of adults, migration; Winkler et al. 2005). 

Movement and philopatry have often been associated with the 

acquisition and retention of higher-quality territories, respectively (Brooke 1979), and thus are adaptive to an individual’s 

lifetime reproductive success (Greenwood and Harvey 1982, 

Part and Gustafsson 1989). Movement is essential for genetic 

variation and inbreeding avoidance (Frankham 1995), and is 

the foundation of metapopulation dynamics (Hanski and Gilpin 

1997). Further, patterns of movement can indicate how a bird 

perceives its landscape and reacts to changing conditions, as 

well as inform our understanding of habitat choice (Cam et al. 

2004, Ward 2005, Breton et al. 2006). Yet, movement patterns 

for highly vagile animals such as migratory birds are difficult to 

document. Most bird studies are restricted to small geographic 

areas that are insufficient to detect medium to long-distance 

movements, and there is a growing consensus that movement 

patterns and dispersal distances are poorly documented in most 

studies (Greenwood and Harvey 1982, Winkler et al. 2004, 

2005). As a result, this crucial aspect of a species’ ecology is 

rarely well understood.

Every year migratory birds return to their breeding 

grounds and need to select suitable habitat in which to breed. 

For first time breeders, they must find suitable habitat and an 

unoccupied territory. Returning breeders must choose whether 

to return to their previous territory (if available), or move to 

a new location. Factors that influence these choices can have 

important and long-term impacts on the overall population 

numbers of their breeding site. Fidelity and immigration can 

sustain a site, while emigration can drain it (Pulliam 1988, 

Stacey and Taper 1997). Past reproductive success, predation 

risk, and changing landscapes can all influence the movement 

of birds. Thus, documenting the extent of movements and 

determining correlates that may elucidate the causal factors 

underlying choices of philopatry versus dispersal are critical to 

understanding population structure and dynamics.

The Southwestern Willow Flycatcher (Empidonax traillii 

extimus) is a small, migratory passerine that breeds exclusively in riparian habitats scattered throughout portions of the 

southwestern U.S. (Unitt 1987, Marshall 2000 ) and winters 

from central Mexico south to northern South America (Sedgwick 2000). Flycatcher numbers have declined precipitously 

as riparian habitats on the breeding grounds have been lost or

modified (USFWS 1993, Marshall and Stoleson 2000), and 

E.t. extimus was listed as a federally endangered species in 

1995 (USFWS 1995). At the time of listing, many aspects of 

the flycatcher’s biology were poorly understood (Marshall 

and Stoleson 2000, Stoleson et al. 2000). Few banding studies 

had been conducted and little was known about patterns of 

flycatcher movements. 

From 1996 to 2005, we collaborated with the Arizona 

Game and Fish Department (AGFD) to conduct a large demographic study of Southwestern Willow Flycatchers in central 

Arizona at two core study sites, as well as multiple auxiliary 

breeding sites. In this chapter, we describe natal and breeding 

dispersal, and explore factors influencing movement patterns 

and territory fidelity. By linking movement patterns with habitat dynamics and productivity, we investigated potential causal 

factors that drove movement over the 10-year period. With as 

many as 80% of detected flycatchers banded in a given year at 

the two primary study sites, and information from other sites 

across the Southwest, this study allows us to develop a robust 

understanding of flycatcher movement patterns and factors 

that influence them. 

Methods 

Study Site

Our two focal study sites (fig. 1) were breeding populations at Roosevelt Lake (33o39’N, 110o58’W) and the 

San Pedro/Gila River confluence (hereafter San Pedro/Gila; 

32o59’N, 110o46’W), where we conducted demographic 

research in cooperation with AGFD from 1996 to 2005 (Causey 

et al. 2006, English et al. 2006). We defined sites as a collection of riparian woodland patches, occupied by breeding 

flycatchers, which are collectively closer to one another than to 

other breeding patches, and are found within the same drainage 

(or along the same lake). Exact definitions of a site are difficult, 

and the definition of a site differs regionally (Durst et al. 2006). 

Likewise, we defined a breeding patch as a distinct patch of 

riparian vegetation which breeding flycatchers occupied, that is 

separated from other riparian vegetation by non-riparian habitat 

(e.g., open ground, scrub, river). We delineated breeding habitat 

patches by projecting territory locations for all years onto rectified aerial photographs, and used natural breaks or distribution 

of territories to estimate their approximate boundaries manually 

in ArcView 3.0 (ESRI, Inc). 

Chapter 4—Movement
60 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 1. Location of the Roosevelt Lake and San Pedro/Gila study areas in central Arizona. 
 61

Our two study sites supported among the largest known 

Southwestern Willow Flycatcher breeding populations 

throughout the period of this study (Durst et al. 2006), with 

mean population sizes of 201 and 239 individuals at Roosevelt 

Lake and San Pedro/Gila, respectively (see chapter 5). At each 

site, extensive surveys were conducted at least three times in 

the breeding season to detect all territorial flycatchers within 

the immediate and surrounding areas. In addition, multiple 

other breeding sites in Arizona and throughout the Southwest 

(49–444 km away) were periodically visited, which along with 

cooperative efforts of other researchers allowed detection of 

many dispersing or moving flycatchers (fig. 2). 

The breeding habitat at the Roosevelt Lake and San 

Pedro/Gila study sites consisted of a heterogeneous mosaic of 

discrete riparian forest patches of varying ages and vegetation 

composition, ranging from 0.2 to 43 ha in size. At both study 

sites, we characterized habitat at both the territory and patch 

level based on vegetation composition as: (1) native (≥90% 

native vegetation), (2) mixed native (50%–89% native vegetation), (3) mixed exotic (50%–89% exotic vegetation), and (4) 

exotic (≥90% exotic vegetation). Native habitat was characterized by Goodding’s willow (Salix gooddingii) and Fremont 

cottonwood (Populus fremontii). Exotic habitat was dominated by tamarisk (saltcedar; Tamarix spp.). The understory 

vegetation consisted of a variety of grasses, forbs, and shrubs 

(mesquite [Prosopis spp.], coyote willow [S. exigua], tamarisk, Baccharis spp., and cocklebur [Xanthium strumarium]). 

Adjacent and surrounding non-riparian habitats were primarily 

composed of Sonoran Desert Uplands (Brown 1994), and in 

some cases agricultural lands. 

Roosevelt Lake Study Site

The Roosevelt Lake study site consisted of two sub-sites 

each concentrated at the confluence of the reservoir and the 

inflows of the Salt River and Tonto Creek (approximately 30 

km apart), with breeding documented as far as 12 km upstream 

on Tonto Creek and 9 km on the Salt River. The number of 

riparian patches in which breeding was documented varied 

over time as a result of colonization or desertion of patches, in 

many cases driven by fluctuating lake levels. Over the 10-year 

period, we documented breeding occurring in a maximum of 

23 patches encompassing approximately 242 ha of riparian 

habitat. Long-term persistent drought conditions between 1996 

and 2005 in the Southwest (McCabe et al. 2004) resulted in 

reservoir levels dropping to a low of 10% capacity in 2002; the 

exposed lakebed was colonized by riparian vegetation, which 

was subsequently colonized by breeding flycatchers (see chapter 6). In 2005, following high winter precipitation, Roosevelt 

Lake filled to near capacity, inundating much of the breeding 

habitat that was occupied in 2004 (see chapter 6). 

Figure 2. Thirty between-drainage dispersal events by Southwestern Willow Flycatcher (natal, 

breeding) were documented from 1997-2005. Adult movement accounted for 21 cases (ranging 

from 49-214 km), while natal dispersal accounted for 9 cases (ranging from 52-444 km). Sites 

are coded as follows: KERN=Kern River Preserve; MEAD=the Lake Mead area; TOPO=Topock 

Marsh; ALAM=Alamosa Lake, Browns Crossing; CAVE=Camp Verde; VERD=Horseshoe 

Reservoir; ROS=Roosevelt Lake; SAPE=San Pedro/Gila River; GILA=Gila River near Safford; 

WHMT=White Mountains; ZUNI=Zuni Pueblo. 

Figure 2. Thirty betweendrainage dispersal events 

by Southwestern Willow 

Flycatchers (natal, breeding) 

were documented from 1997 

to 2005. Adult movement 

accounted for 21 cases 

(ranging from 49km to 214 

km), while natal dispersal 

accounted for 9 cases (ranging 

from 52km to 444 km). Sites are 

coded as follows: KERN=Kern 

River Preserve; MEAD=the 

Lake Mead area; TOPO=Topock 

Marsh; ALAM=Alamosa Lake, 

Browns Crossing; CAVE=Camp 

Verde; VERD=Horseshoe 

Reservoir; ROS=Roosevelt 

Lake; SAPE=San Pedro/

Gila River; GILA=Gila River 

near Safford; WHMT=White 

Mountains; ZUNI=Zuni Pueblo.
62 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

San Pedro/Gila Study Site

The San Pedro/Gila study site encompassed 101 km of 

river system, centered at the confluence of the free-flowing 

San Pedro River and the regulated Gila River, and extending 

upstream of the San Pedro to San Manuel Crossing and downstream of the Gila River to Kelvin Bridge. As with Roosevelt 

Lake, the number of patches changed over the 10-years, with 

as many as 29 habitat patches documented as supporting 

breeding efforts, comprising approximately 222 ha of riparian 

forest along this stretch. 

The Roosevelt Lake and San Pedro/Gila study sites, 

combined with other sites, formed our study area. Other sites 

included Greer and Alpine in the White Mountains, AZ; 

Camp Verde, AZ; Gila River near Safford, AZ; Pinal Creek, 

AZ; Horseshoe Reservoir, AZ; Brown’s Crossing near Alamo 

Lake, AZ; and several sites along the Colorado River including the Grand Canyon, Lake Mead, and Topock Marsh, AZ; 

and Mesquite and Mormon Mesa, NV (see Durst et al. 2006). 

Banding and Resighting

To provide robust estimates of movement, our objective 

was to band as many adults and nestlings as possible over 

the period of this study. Nestlings and adults were banded 

at both Roosevelt Lake and the San Pedro/Gila from 1996 

to 2005, with the exception that nestling banding at the San 

Pedro/Gila ceased after 2000. Each nestling was banded at 

7–10 days of age (as determined via Paxton and Owen 2002) 

with a color-anodized, numbered federal bird band; banded 

nestlings detected and recaptured as adults in subsequent years 

were given an additional color band to produce a unique color 

combination to assist in resight tracking (Koronkiewicz et al. 

2005). A drop of blood was taken at the time of banding for 

genetic gender determination (Paxton et al. 2002). Each adult 

captured was banded with a color-anodized, numbered federal 

bird band and a second color band (Koronkiewicz et al. 2005) 

to create a unique color combination for each individual. 

Adults were primarily target-netted (Sogge et al. 2001, Pollock 

and Paxton 2006) to allow for efficient, focused capture effort. 

From 2001 to 2005, we also conducted extensive passive 

netting operations at Roosevelt Lake (see Ralph et al. 1993) 

aimed at capturing non-territorial flycatchers that were not 

readily detectable with conventional survey techniques (i.e., 

tape-playback; Sogge et al. 1997). Overall, the combined netting efforts resulted in an average of 74% (range: 68%–88%) 

of detected flycatchers being banded in a given year at both 

study sites from 1996 to 2000 and at Roosevelt Lake from 

2001 to 2005 (exact percentages at San Pedro/Gila are not 

available for this latter period). To determine gender of adult 

flycatchers, we used a combination of physical characteristics 

(presence of a cloacal protuberance for males or brood patch 

for females), behavioral cues, and genetic sexing methods 

(Paxton et al. 2002). Adult flycatchers were aged as “known” 

if the individual was first banded as either a nestling or a 

second-year (SY: based on retained rectrices; Pyle 1998), or 

“unknown” (2 or more calendar years of age). 

We tracked banded flycatchers over the 10 years primarily through resighting, supplemented by occasional recaptures. 

This process f using binoculars to determine the unique color 

band combination on a flycatcher’s legs. Resighting is a minimally intrusive method of “recapture”, and the most reliable 

method for establishing the particular territory a flycatcher 

occupies because playback capture techniques can lure adults 

in from neighboring territories. To ensure high accuracy in the 

identification of resighted individuals, only multiple high-confidence resights from at least two different observers on different days were used to confirm the identity and territory of individual banded flycatchers. Computer databases were updated 

and queried daily to prioritize resighting efforts, allowing us 

to track as many as 393 uniquely color banded individuals in a 

given year. Field crew sizes of up to 35 personnel (USGS and 

AGFD combined) allowed for intensive detection and resighting of banded individuals, resulting in nearly all territorial flycatchers detected at the study sites being positively identified 

multiple times in each year. Recaptures, especially via passive 

netting, provided additional detection resolution, especially for 

non-territorial birds. 

The following are definitions of the multiple types of 

movement behaviors we observed:: 

•	 Between-year movement behavior—The term used to 

describe the choice an adult makes upon returning to 

the breeding grounds: territory fidelity, patch fidelity, 

or movement (either between-patch or between-site).

•	 Between-year movements—Adult movement from 

a breeding location (or last known location) in one 

breeding season to a different breeding location (or 

first known location) in a subsequent breeding season. 

In most cases, movements we observed were between 

consecutive years, but not always (unless noted otherwise; see chapter 7). Between-year movements could 

be between-sites, between-patches, or within-patch. To 

be considered a movement within the same patch (as 

opposed to Territory Fidelity), birds must have moved 

physically at least 50 m from their previous territory 

(twice the average territory radius at our study sites). 

For non-territorial individuals, movements must have 

been at least 50 m from the previous detection location.

•	 Breeding dispersal—Adult movement from one breeding site to a different breeding site, between years. This 

term is synonymous with between-year between-site 

movement (above).

•	 Emigration—The detected movement of birds away 

from a site or patch (dependent on context) to some 

other location.
 63

•	 Immigration—The detected movement of birds into a 

site or patch (dependent on context) from some other 

location.

•	 Natal dispersal—Movement from a flycatcher’s natal 

location to the first location it was detected at as an 

adult. When first detected as an adult, birds could be 

either territorial or non-territorial (see chapter 7). The 

first detection may not have occurred until several 

years after the natal year. Once detected as an adult, all 

subsequent movement is defined as adult movement.

•	 Patch fidelity—Between-year, within-patch movement, with movement distance greater than 50 m. 

Synonymous with between-year, within-patch movement (above). This term excludes those individuals that 

returned to their previous year’s territory, which could 

also be considered as a type of patch fidelity.

•	 Territory fidelity—An adult returning from the wintering grounds to its breeding territory from the previous 

year. We defined a return as territory fidelity if the nest 

location or territory center in “year t+1” was within 50 

m of the nest location or territory center of an individual in “year.” In this report, territory fidelity is synonymous with the term philopatry.

•	 Within-season movement—Adult movement from one 

location to another, detected within the same breeding season. Movement can be within the same patch, 

between patches, or between sites. To be considered 

a movement within the same patch (as opposed to 

Territory Fidelity), birds must have moved physically 

beyond neighboring territories or at least 50 m from 

their previous territory (twice the average territory 

radius at our study sites). For non-territorial individuals, movements must have been at least 50 m from the 

previous location. Within-season movement can be 

divided into the following four categories: 

1. pre-breeders—detected as non-territorial 

individuals that were then detected later in the 

season at another location as territory holders; 

2. post-breeders—flycatchers that were territory 

holders, but were detected in another location 

later in the season as non-territorial individuals; 

3. territory switchers—individuals that were territorial in one location, then left to become territorial in another area, with at least one territory 

separating the old and new location; and 

4. non-territorial movers—presumably floaters, 

that were detected at least twice at different 

locations, being territorial at none of these.

Statistical Analysis

Natal, Within-season, and Between-year 

Movement Distances

All measures of distance were calculated via straight-line 

distances between the two locations of interest. For example, 

the distance of a between-year movement is the distance from 

where the adult was last detected in “year t” to the first location it was detected in “year t+1”. If an adult is not detected 

for a year (see chapter 7), distance is from the locations it 

was known to be at before and after the year in which it was 

not detected. Thus, distances given are a minimum measure 

of movement between two points of detection (even though 

birds likely did not take a direct route between the two points). 

To determine distance, we used ArcView 3.3 (ESRI, Inc) to 

obtain straight line distances (in units of 0.01 km) via the 

Distance Matrix extension with an Albers Equal-area projection which ensures a spatially accurate measure of distance. 

We evaluated statistical differences in movement distances 

between various factors (e.g., site, sex, age) using an ANOVA 

in JMP 5.0 (SAS, Inc). In addition, to evaluate the frequency 

of movements by distance for the two study sites, we counted 

the number of movements for each 5 km block, and graphed 

the resulting frequencies in SigmaPlot 8.0 (SPSS, Inc). For 

San Pedro/Gila, we fitted a quadratic regression line to the 

frequency of movement by distance in JMP 5.0 (SAS, Inc).

Between-year Movement Behavior

We evaluated differences in the proportion of betweenyear movement behavior by sex, site, and year using a chisquared test (χ2

 ) in JMP 5.0 (SAS, Inc). To evaluate the frequency of between-year movement behaviors at the individual 

level, we used a multistate model in Program MARK (White 

and Burnham 1999). The multistate models provide estimates 

of survivorship (Φ) and detection (p) probabilities based on the 

between-year movement behavior, and account for the probability of transitions (Ψ), i.e., changing behaviors from one year 

to another. We coded an encounter history table (see chapter 

2) of individual adult flycatchers, 1996–2005 (excluding 

Roosevelt Lake in 2005 because of the confounding effects of 

the inundation-caused displacement), with one of three states: 

(1) territory fidelity, (2) patch fidelity, and (3) movement, with 

both between-patch and between-site movement combined; 

years undetected were coded as “0”. We used a multinomial 

logit (Mlogit) link to constrain the model to ensure that the 

transition probabilities among the three different behaviors 

all summed to 1 (see chapter 2). The survivorship estimate is 

the probability that an adult showing a particular behavior in 

“year t” survives to “year t+1”, regardless of which behavior 

it shows in the subsequent year, and the detection probability 
64 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

of a particular behavior is the probability that it is detected in 

“year t” for that behavior. 

Factors Influencing Territory Fidelity Versus 

Movement

To evaluate how different factors in one year influence 

between-year movement behavior in the next, we needed to 

consider only those individuals for which we had information in successive years. Of the initial 1,080 banded flycatcher 

adults at the two study sites, we detected 521 returning in at 

least one set of consecutive years. Of these, 276 (53%) were 

detected in 2 consecutive years, 122 (23%) were detected in 

3 consecutive years, 53 (10%) were detected in 4 consecutive years, and 70 (14%) were detected in 5–8 consecutive 

years. For this analysis, we were interested in territory fidelity 

versus all other between-years movement behaviors. We used 

a logistic regression (via proc genmod) in SAS 9.0 (SAS, Inc.) 

to model factors influencing territory fidelity decisions by 

adult flycatchers. Based on the literature and our experience 

with the flycatchers, we chose a priori the following predictive factors to model: study site, year, unique breeding patches, 

number of young fledged in the previous year, territory density 

(the number of territories within a 200 m radius of the territory 

being considered), and breeding status (monogamous, polygamous, unpaired). Consideration of interactions among factors 

was limited to 2-way interactions to simplify interpretations. 

To choose between the multiple models, we used the Akaike 

Information Criteria (AIC) model selection approach to 

choose the best model(s) given the data (Burnham and Anderson 2002; see chapter 2 for a description of AIC). 

Colonization and dispersal. To determine the minimum 

age of habitat when first colonized by breeding flycatchers, we 

concentrated on Roosevelt Lake where the receding lake level 

allowed the creation and growth of substantial riparian habitat 

within the period of this study. We overlaid patch boundary 

shapefiles over successive years of Normalized Difference 

Vegetation Indexes (NDVI) raster grids from LandSat TM 

images (1996–2005) in ArcGIS 9.0 (ESRI, Inc). The NDVI 

raster grids, which measure vegetation density, were masked 

for values below 0.33, a threshold value for riparian vegetation (see chapter 6). For a given patch, the first year that NDVI 

values >0.33 were detected was defined as year 1 of the riparian vegetation (i.e., the year that vegetation emerged from the 

ground) at that patch. 

At both the Roosevelt Lake and San Pedro/Gila study 

sites some patches were present at the start of the study and 

we could not determine their ages since germination, or when 

they were first colonized. Therefore, we used a relative age, 

with the year a patch was first detected as occupied by a 

breeding flycatcher denoted as year one. Although relative 

age estimates will be less accurate for patches that already had 

flycatchers when first detected, changes in the number of flycatcher territories by relative age were remarkably consistent

across patches, and relative age estimates were necessary to 

capture a full 10–11 year window of change. We chose patches 

with five or more territories in at least one monitored year and 

were active for 4 or more years, resulting in 24 patches from 

Roosevelt Lake and San Pedro/Gila (Arivaipa Inflow, North, 

South; CB Crossing; Cooks Lake Seep; Dudleyville Crossing; GN18; GS07; Indian Hills; Kearny; Lakeshore; Mudflats; 

Northshore 1 and 2; Orange Peel Campground and Flats; 

Old Salt; San Manuel Crossing; Schoolhouse North 1 and 2; 

Schoolhouse South 3; Shangri-la; Tonto; and Wheatfields; see 

Luff et al. 2000, Causey et al. 2006). Those sites that were 

known to have breeding flycatchers in 1995 were included, for 

a maximum of 11 years considered. We excluded information 

from Roosevelt Lake in 2005 because of the inundation event. 

To make different patches supporting differing numbers of 

breeding territories comparable, we normalized the number 

of territories per patch by calculating a relative percent of 

territories per patch per year (dividing the number of territories in a given year by the sum of territories ever detected 

at the patch). These were then plotted by relative age to look 

at changes in the proportion of territories over time, averaged 

across the 24 patches. 

We evaluated changes in productivity (total number of 

young fledged per territory) by running an ANOVA with productivity as the response (excluding 2002, an unusually low 

reproductive year; see chapter 3) and relative age as the predictor. To evaluate the relationship between immigration, emigration, and fidelity (territory and patch fidelity combined) and 

changes in the number of territories in a patch as a function 

of relative patch age, we correlated measures of between-year 

movement behaviors with changes in the number of territories. 

To evaluate the relative frequency of fidelity, immigration, 

and emigration by relative patch age (while controlling for 

different breeding population sizes at the different patches), 

we divided the number of individuals showing each of the 

three movement behaviors by the total number of banded birds 

present. We evaluated the percent change in territory numbers 

by dividing the number of territories in one year by the number detected in the previous year. We used a non-parametric 

correlation (Spearman’s rho) to quantify correlations among 

between-year movement behaviors and changing numbers of 

territories as a function of relative patch age. 

Statistical significance was accepted at p<0.05. We 

graphed data with SigmaPlot 8.0 (SPSS, Inc).

Results

Natal Dispersal

Of the 498 nestlings banded from 1996 to 2004, we 

detected 123 in subsequent years (1997–2005); a 25% return 

rate with an associated apparent survivorship rate of 34% 

(see chapter 2). All returning nestlings but two dispersed to a 

non-natal patch; the two exceptions were detected 0.3 and 0.8 
 65

km away from their natal nest locations but within the same 

patch, a distance equivalent to some between-patch movements. Average natal dispersal distance was 20.5 km (range 

= 0.03–444 km; table 1). Seven natal dispersers moved to 

different drainages (mean distance = 214.3 km; fig. 2), while 

the majority (n = 114) returned to the same drainage, but a 

different patch (mean distance moved = 9.0 km;). Considering 

only those natal dispersers that moved between patches within 

the same drainage, there was no significant difference in the 

distance moved (F1,110 = 0.20, p = 0.65) between males (9.5 km 

± 1.5 SE) and females (8.5 km ± 1.7 SE). 

Adult Movement

Within-season Movement

We documented 130 individuals making within-season 

movements, with a mean distance of 7.5 km ± 1.3 SE (range = 

0.1 to 117; table 2). Movements occurred within a patch (n = 

37, mean = 0.3 km ± 0.04 SE, range = 0.1 to 1.2 km), between 

patches (n = 91, mean = 8.8 km ± 1.2 SE, range = 0.3 to 58.2 

km), and between drainages (n = 2, mean = 84.7 km ± 32.3 

SE, range = 52 to 117 km). Females moved farther than males 

(F1,128 = 5.5, p = 0.02); mean distance for females was 12.0 km 

± 3.5 SE (range = 0.1–117 km) versus 5.5 km ± 1.0 SE (range 

= 0.1–32.2 km) for males.

We identified four categories of within-season movement 

(pre-breeders, post-breeders, territory switchers, and nonterritorial). Territory switchers generally moved the farthest, 

and pre-breeders the least (table 2); however, there was 

much variation within groups and the difference in distances 

moved was marginally non-significant (F3,126 = 2.53, p = 0.06). 

Approximately half the pre-breeders were detected in the 

same patch, while post-breeding movements were mostly to 

different patches (table 2). Territory switching was the least 

common movement detected, accounting for only 17% of the 

within-season movements. Non-territorial movement, which 

can be thought of as two points (i.e., detections) on a continuous movement track, was documented as being conducted 

primarily by males and between patches. 

Between-year Movement

From 1996 to 2005 we documented 712 adults making 

between-year movements, with distances ranging from 0.1 

to 214 km (table 3). The mean distance moved by adults (9.5 

km) was much less than the mean natal dispersal distance 

(20.5 km; F1,855 = 20.4, p<0.001). Adult between-year movements included movements within a patch (range = 0.1–1.6 

km), movements between patches (range = 0.2–55.4 km), 

and movement between drainages (range = 49–214 km; table 

3). With the exception of one adult that moved into New 

Type of dispersal n

Mean 

km (± SE)

Median 

km

Minimum 

km Maximum km

Patch fidelity 2 0.6 (± 0.3) 0.6 0.3 0.8

Between-patch movement 114 9.0 (± 1.1) 2.5 0.3 40

Between-site movement 7 214 (± 66) 108 52 444

Category of movement n % male % within

same patch

Mean km

(± SE) Median km Minimum km

Maximum 

km

Pre-breeding 47 74 49 3.6 (± 1.2) 0.6 0.1 32

Territory switchers 22 50 18 13.0 (± 5.4) 2.2 0.1 117

Non-territorial 23 83 13 6.4 (± 1.9) 2.2 0.1 29

Post-breeding 38 66 18 9.8 (± 2.4) 2.1 0.1 58

All 130 69 28 7.5 (± 1.3) 1.4 0.1 117

Table 2. Within-season movements of Southwestern Willow Flycatcher in central Arizona, 1996–2005. A within-season movement is 

defined as an adult detected at two locations greater than 50 m apart, within the same breeding season. Pre-breeders are non-territorial 

individuals encountered prior to becoming territorial in another location, territorial switchers are individuals switching from one territory 

to another, non-territorial adults are encountered in two different locations and non-territorial in both locations, and post-breeders are the 

non-territorial encounter of an individual after it has left its breeding territory of the season. The sexes of all 130 individuals were known.

Table 1. Natal dispersal distances of Southwestern Willow Flycatchers in central Arizona, 1997–2005. Distances and sample sizes are 

grouped by: patch fidelity, those that returned to their natal patch, between-year movement, movement to a non-natal patch within the 

natal drainage, and between-site movement, movement to another drainage outside of the natal area.
66 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Site Type of movement n

Mean 

km (± SE.)

Median 

km

Minimum 

km

Maximum 

km

Roosevelt Lake Patch Fidelity 151 0.3 (± 0.02) 0.2 0.1 1.4

Between-patch Movement 331 9.2 (± 0.6) 2.6 0.2 33.1

Between-site Movement 8 97 (± 11) 86 69 144

San Pedro/Gila Patch Fidelity 79 0.3 (± 0.04) 0.1 0.1 1.6

Between-patch Movement 134 13.4 (± 1.0) 10.9 0.4 55.4

Between-site Movement 9 120 (± 18) 98 49 214

Mexico near the Arizona border, all adult between-drainage 

movements we detected were among sites within the central 

Arizona region. There was no significant difference in the 

distance moved by male and female flycatchers (F1,710 = 0.19, 

p = 0.66), but San Pedro/Gila individuals moved significantly 

farther than Roosevelt Lake birds (F1,710 = 10.2, p = 0.001; 

fig. 3). Roosevelt Lake dispersal distances had a bimodal 

distribution, showing a high frequency of movement between 

patches within each inflow (left mode; fig. 4), and a more 

moderate amount of trans-lake movements (right mode; fig. 

4). Movements at the San Pedro/Gila declined more linearly 

with distance than at Roosevelt (fig. 4). Fitting the frequency 

of between-patch movements along the San Pedro/Gila by 

distance (in 5 km increments, within-drainage only) resulted 

in a strong quadratic relationship (r2 = 0.97, F2,9160.4, 

p<0.001), suggesting the frequency of movements declined 

by approximately 5% for every 5 km distance from the previous year’s breeding location. 

Between-year Movement Behavior

The proportion of the four between-year behaviors 

(territory fidelity, patch fidelity, between-patch and betweendrainage movements) did not differ between males and 

females (χ2 = 1.6, df = 3, p = 0.67), but they did vary significantly between study sites (χ2 = 41.2, df = 3, p<0.001) 

and among years (χ2 = 122.5, df = 18, p<0.001; table 4, fig. 

5). San Pedro/Gila adults were more likely to return to 

their territories, whereas Roosevelt Lake adults were more 

likely to move to different patches. However, the frequencies changed over time. Most noticeable was the decline in 

territory fidelity at Roosevelt Lake and San Pedro/Gila in 

2003, the year following a severe drought. There was also a 

large increase in movement at Roosevelt Lake in 2005 due to 

displacement of flycatchers through flooding-induced loss of 

most of their previous year’s breeding habitat.

To understand whether individuals differed in the frequency of the between-year behaviors, we evaluated whether 

certain individuals demonstrated consistent behavior (i.e., 

always moved or always returned to the same territory). 

Flycatchers with five or more consecutive movements exhibited both between-year movement and territory fidelity over 

those years, suggesting there were no individuals that either 

always moved or were always territory faithful. Results of the 

multistate model of the transition probabilities of betweenyear movement behaviors suggest that adults will frequently 

switch their between-year behaviors from year to year (fig. 6). 

Territory fidelity and between-patch movement were the most 

common behavior, with 57% of adults that were territory faithful in one year returning to the same territory in a subsequent 

year, and 46% of adults that moved in one year moving again 

in the subsequent year (fig. 6). Patch fidelity without returning to the same territory was the least observed behavior, with 

only approximately 20% of individuals doing so in a given 

year. Survivorship was nearly identical for all three groups, but 

detection probability differed: based on the multistate model, 

individuals that demonstrated territory and patch fidelity were 

highly likely to be detected, whereas those that moved had a 

67% detection rate (fig. 6). 

Influences and Consequences of Territory 

Fidelity Versus Movement

Evaluating causal factors that may influence betweenyear behavior (territory fidelity versus movement), we found 

the best AIC support for the model that included number of 

young fledged in the previous season, density of breeders in 

the previous year, patch, and an interaction of site by year 

(table 5). This model had 96% of the AIC weight, suggesting 

it was overwhelmingly the best model of those considered 

(table 5). The two factors that were in each of the top five 

models were the year by site interaction and the number of 

young fledged in the previous year. Site and year interaction 

captures a number of unmeasured and unknown environmental 

factors that vary by site and by year (fig. 5). The number of 

Table 3. Southwestern Willow Flycatcher between-year movement distances documented at our two study sites, 1996–2005. Patch 

fidelity is movement within the previous year’s patch but moving >50 m, between-patch movement represents birds returning to a 

different patch but within the same study site, and between-site movement represents dispersals to a different drainage.
 67

Sites

Roosevelt Lake 

Distance (km)

0

2

4

6

8

10

12

Males

Females

2

San Pedro/Gila

Figure 3. Average betweenyear movement distances 

of Southwestern Willow 

Flycatchers at the Roosevelt 

Lake and San Pedro/Gila 

study sites, 1996–2005. 

Movement distances were 

similar between sexes, but 

differed between sites. Error 

bars denote 95% confidence 

intervals.

Figure 4. Within-drainage distribution of Southwestern Willow Flycatcher between-year 

movement distances for each of the two study sites (Roosevelt Lake and San Pedro). Movement 

distance patterns reflect different spatial arrangements of breeding habitat patches. Roosevelt 

Lake (n=440) consisted of breeding patches clustered on either side of the reservoir (mean 

distance 30 km), resulting in a bimodal distribution of distance moved, while San Pedro (n=139) 

had a relatively continuous distribution of discrete breeding habitat patches.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Distance (km)

5 10 15 20 25 30 35 40 45 50 55 60

Frequency of movement 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

San Pedro/Gila

Distance (km)

Roosevelt Lake

Frequency of movement

5 10 15 20 25 30 35 40 45 50 55 60

Figure 4. Within-drainage 

distribution of Southwestern 

Willow Flycatcher betweenyear movement distances 

for each of the two study 

sites (Roosevelt Lake and 

San Pedro/Gila). Movement 

distance patterns reflect 

different spatial arrangements 

of breeding habitat patches. 

Roosevelt Lake (n=440) 

consisted of breeding patches 

clustered on either side of 

the reservoir (mean distance 

30 km), resulting in a bimodal 

distribution of distance moved, 

while San Pedro/Gila (n=139) 

had a relatively continuous 

distribution of discrete 

breeding habitat patches. 
68 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Year Site Territory fidelity Patch fidelity Between-patch 

movement

Between-site 

movement

Total 

individuals

1997 ROOS 4 10 5 0 19

SAPE 4 3 9 0 16

1998 ROOS 12 11 1 0 24

SAPE 13 7 14 2 36

1999 ROOS 9 11 7 1 28

SAPE 23 8 14 1 46

2000 ROOS 18 16 14 1 49

SAPE 34 8 16 0 58

2001 ROOS 42 17 27 1 87

SAPE 31 6 10 1 48

2002 ROOS 51 17 23 0 91

SAPE 17 5 9 1 32

2003 ROOS 34 19 59 3 115

SAPE 9 7 11 2 29

2004 ROOS 33 27 50 0 110

SAPE 21 16 15 1 53

2005 ROOS* 1 5 104 4 114

SAPE 34 12 10 1 57

Table 4. The number of observed Southwestern Willow Flycatcher between-year movement behaviors by year for each of our two 

study sites, Roosevelt Lake (ROOS) and San Pedro/Gila (SAPE), 1997–2005. Territory fidelity is a flycatcher returning to within 50 m of its 

previous year’s location, patch fidelity represents movement within a patch greater than 50 m, between-patch movement consists of 

movement to another patch within the same site, and between-site movement consists of movements to another site/drainage. 

young fledged was a strong predictor of territory fidelity; for 

every young fledged, the odds of territory fidelity in the following year increased by 1.5 (beta = 0.3739). Breeding density had 

a positive relationship with territory fidelity, with the probability of territory fidelity increasing as the density of territories 

increased. Effects of different patches on territory fidelity varied widely, and may be related in part to the age of the patch’s 

vegetation (see Colonization and Dispersal section below). 

Just as reproductive success influences between-season 

behavior (fig. 7), the choices that flycatchers make appear 

to affect their reproductive success in subsequent years (fig. 

8, table 6). For example, those flycatchers with higher than 

average reproductive success in a given “year t” tended to 

return to the same territory again in “year t+1”, and continued 

to do better than average (though not as well as in “year t”). 

Flycatchers that experienced far lower than average success 

in “year t” and then moved to another patch or site in “year 

t+1“ generally improved their success and fledged an average 

number of young. Comparatively, among those flycatchers that 

showed patch fidelity, average productivity remained nearly 

the same (table 6).

Colonization and Dispersal

Although the direct consequences of the previous year’s 

reproductive success had a strong influence on movement 

patterns, the importance of patch as a factor in the best AIC 

model suggests that other (unknown) influences related to 

patch were involved as well. Patch age may be one such consideration. During the course of this study, we observed riparian vegetation patches develop from immature stages through 

mature stages. Based on known-age patches at Roosevelt 

Lake, new riparian habitat was first colonized by breeding flycatchers when riparian vegetation was approximately 3 years 

old (95% C.I. = 2.5–3.5). Following colonization, the number 

of flycatcher territories in a patch increased for the first several 

years of a patch’s age (based on relative age, see methods), 

* Inundation of Roosevelt Lake in 2005 resulted in high movement rates due to the displacement of most flycatchers.
 69

is returning to the same territory; patch fidelity is returning to the same patch but > 50 m from 

the previous territory; and between-patch movement is a shift to a different patch or drainage. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Roosevelt Lake Territory fidelity

Patch fidelity

Between-patch move

Percentage

1997 1 998 1999 2000 2 99 2000 2001 2002 2003 2 02 2003 2004 2005

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Year

San Pedro/Gila Territory fidelity

Patch fidelity

Between-patch move

Percentage

1997 1 998 1999 2000 2 99 2000 2001 2002 2003 2 02 2003 2004 2005

Figure 5. Frequencies of Southwestern Willow Flycatcher territory fidelity, patch fidelity, and between-year movement behaviors by 

study area (Roosevelt Lake and San Pedro/Gila) and by year (1997–2005). High frequency of movements at Roosevelt Lake in 2005 was 

in response to habitat loss and subsequent displacement due to inundation of breeding habitat. Territory fidelity is returning to the same 

territory; patch fidelity is returning to the same patch but >50 m from the previous territory; and between-patch movement is a shift to a 

different patch or drainage.
70 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 6. Graphic representation of the multistate model 

results indicating the average 

probability of an adult Southwestern Willow Fly-catcher 

remaining in (curved arrows) or 

transitioning to (straight arrows) 

one of the three between-year 

movement behaviors: territory 

fidelity, patch fidelity, and 

between-patch movement. 

Line width of the transitional 

probabilities is proportional 

to each probability. Territory 

fidelity and movement over 

successive years was the most 

frequent movement decisions, 

with territory fidelity favored 

over movement. Survivorship 

and detection probability for 

each state are indicated within 

the triangles. Territory fidelity is 

returning to the same territory; 

patch fidelity is returning to 

the same patch but >50 m 

from the previous territory; 

and movement is a shift to a 

different patch or drainage. 

Model description AIC Delta 

AIC

AIC 

weight K

site+year+density+patch+NoFLD +year*site 1053.73 0 0.961 58

site+year+NoFLD+site*year 1060.87 7.14 0.027 19

site+year+sex+NoFLD+site*year 1062.61 8.88 0.011 20

site+year+density+patch+NoFLD+site*year +patch*year 1097.65 43.92 0.000 144

site+patch+year+sex+age+BDSTAT+density +NoFLD+site*year+patch*year+density*site 1101.67 47.94 0.000 164

Null 1267.58 213.85 0.000 1

Table 5. AIC model selection results of the logistic regression models evaluating territory fidelity versus between-year movement. 

With each set of explanatory variables (model description) are the associated AIC value, change in AIC value (delta AIC), the relative 

weight the model has compared to all others considered, and the number of parameters (K). AIC ranking suggests that previous 

year’s productivity (number of young fledged, NoFLD), territory density, individual patch characteristics, site, and year are the most 

parsimonious explanatory factors for territory fidelity in the Southwestern Willow Flycatcher. Only the top 5 and null (intercept only) 

models are shown. 
 71

Territory fidelity is returning to the same territory; patch fidelity is returning to the same patch 

but > 50 m from the previous territory; and movement is a shift to a different patch or drainage. 

Number of Young Fledged in Previous Breeding Season

0 fledged 1-3 fledged >3 fledged

Percent

Territory Fidelity Patch Fidelity Movement 

0.0

0.2

0.4

0.6

0.8

1.0

Proportion

6

Figure 7. Southwestern 

Willow Flycatcher’s betweenyear movement behavior as 

related to their reproductive 

success in the previous year. 

For 0, 1–3, or >3 young fledged, 

the proportion of adults that 

chose territory fidelity, patch 

fidelity, and between-patch 

movement changed. Territory 

fidelity is returning to the 

same territory; patch fidelity 

is returning to the same patch 

but >50 m from the previous 

territory; and movement is a 

shift to a different patch or 

drainage.

Figure 8. The relationship between Southwestern Willow Flycatchers productivity (number of 

young fledged per season) and movement type. Flycatchers that were territory faithful fledged 

more young than the overall average in the previous year, and continued to fledge more young 

than average in the subsequent year. Those flycatchers that moved territories within the same 

patch fledged slightly fewer than the average number of young and an approximately average 

number in the subsequent year. Flycatchers that moved between-patches tended to fledge far 

fewer young than average, but fledged an average number post-dispersal. Error bars are one 

standard error. Territory fidelity is returning to the same territory; patch fidelity is returning to 

the same patch but > 50 m from the previous territory; and movement is a shift to a different 

patch or drainage. 

Between-year Movement Type

Territory Fidelity Patch Fidelity Movement

Number of Young Fledged

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Pre-movement 

Post-movement

Mean number fledged

Between-year behavior 

“year t” 

“year t+1” 

Mean number fledged 

Figure 8. The relationship 

between Southwestern Willow 

Flycatchers productivity 

(number of young fledged per 

season) and movement type. 

Flycatchers that were territory 

faithful fledged more young 

than the overall average in the 

previous year, and continued 

to fledge more young than 

average in the subsequent year. 

Those flycatchers that moved 

territories within the same patch 

fledged slightly fewer than 

the average number of young 

and an approximately average 

number in the subsequent 

year. Flycatchers that moved 

between-patches tended to 

fledge far fewer young than 

average, but fledged an average 

number post-dispersal. Error 

bars are one standard error. 

Territory fidelity is returning to 

the same territory; patch fidelity 

is returning to the same patch 

but >50 m from the previous 

territory; and movement is a shift 

to a different patch or drainage.
72 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Decision n Year % nest success # young fledged

Mean 95% C.I. Mean 95% C.I.

Territory fidelity 341 t 60 56–64 2.6 2.4–2.8

t+1 50 45–54 2.2 2.0–2.4

Patch fidelity 164 t 52 46–59 1.8 1.6–2.1

t+1 47 42–54 1.9 1.7–2.2

Movement 237 t 29 24–34 0.9 0.7–1.1

t+1 56 51–62 1.9 1.7–2.1

Overall 742 49 47–52 1.9 1.8–2.0

Figure 9. The relative number of Southwestern Willow Flycatcher territories in breeding 

patches as a function of relative age (in years). The relative age of a patch is the number of years 

a patch was known to be occupied, with the year discovered/colonized denoted as year one. The 

percent territory is the proportion, for a given year, of the sum of territories discovered 

throughout a patch’s occupancy by flycatchers and/or the length of this study. Only patches with 

four or more years of occupancy and five or more territories in at least one year were included, 

resulting in 24 patches with a mean relative age of 7 years. Roosevelt Lake numbers from 2005 

were excluded due to the confounding effects of the inundation. Error bars are one standard 

error. Overall, the proportion of territories increased for approximately four years and then 

gradually decreased with the patch’s relative age. 

Relative patch age

1 2 3 4 5 6 7 8 9 10 11

Proportion

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Average proportion of territories 

8

Figure 9. The relative number of Southwestern Willow Flycatcher territories in breeding patches as a function of relative age (in 

years). The relative age of a patch is the number of years a patch was known to be occupied, with the year discovered/colonized 

denoted as year one. The percent territory is the proportion, for a given year, of the sum of territories discovered throughout a patch’s 

occupancy by flycatchers and/or the length of this study. Only patches with four or more years of occupancy and five or more territories 

in at least one year were included, resulting in 24 patches with a mean relative age of 7 years. Roosevelt Lake numbers from 2005 

were excluded due to the confounding effects of the inundation. Error bars are one standard error. Overall, the proportion of territories 

increased for approximately four years and then gradually decreased with the patch’s relative age. 

Table 6. The relationship between movement decisions and reproductive success of Southwestern Willow Flycatchers in central 

Arizona. Decisions on territory fidelity versus movement in Southwestern Willow Flycatchers are influenced by productivity, and in 

turn influence subsequent productivity. Flycatchers that reproductively did better than average tended to remain territory faithful in the 

following year, and continued to do better than average. Individuals that reproductively did far worse than average tended to move, and 

performed much better in the following year. The % nest success is the percent of nests per territory that fledged at least one young and 

the # young fledged is the sum of young fledged. Territory fidelity is the between-year return to an individual’s previous year’s location 

(within 50 m), patch fidelity is an individual returning to the same patch but greater than 50 m, and movement is the return to another 

patch or site from the previous year.
 73

increase by relative patch age. 

Relative patch age in years

12345678

Average number of young fledged per territory

0.0

0.5

1.0

1.5

2.0

2.5

9

Figure 11. Southwestern Willow Flycatcher average rates of immigration, emigration, and 

fidelity as a function of the relative age of a patch (in years). Between-year movement behavior 

percentages are the number of individuals, out of the total number of banded birds present at a 

patch in a given year, which made immigration, emigration, or fidelity choices. The number of 

adults that immigrate into a patch is highest when the patch is young, then declines as the patch 

ages, whereas the proportion that emigrate and show fidelity increases with increasing patch age. 

For each patch per year, the percentage of immigration, emigration, and fidelity sum to 1.0, but 

the average across the 24 patches does not necessarily sum to 1. Fidelity is returning to the same 

patch; immigration is movement into the patch from another location; and emigration is moving 

out of the patch to another location. 

Relative patch age

12345678

Percentage

0.0

0.1

0.2

0.3

0.4

0.5

0.6 % Immigration

% Fidelity 

% Emigration 

10

Figure 11. Southwestern Willow Flycatcher average rates of immigration, emigration, and fidelity as a function of the relative age of a 

patch (in years). Between-year movement behavior percentages are the number of individuals, out of the total number of banded birds 

present at a patch in a given year, which made immigration, emigration, or fidelity choices. The number of adults that immigrate into 

a patch is highest when the patch is young, then declines as the patch ages, whereas the proportion that emigrate and show fidelity 

increases with increasing patch age. For each patch per year, the percentage of immigration, emigration, and fidelity sum to 1.0, but the 

average across the 24 patches does not necessarily sum to 1. Fidelity is returning to the same patch; immigration is movement into the 

patch from another location; and emigration is moving out of the patch to another location.

Figure 10. Average 

reproductive success 

of Southwestern Willow 

Flycatchers as a function 

of patch relative age. 

Reproductive success is the 

total number of young fledged 

from a territory. Reproductive 

success varies by relative 

patch age, but there is no 

general pattern of decline or 

increase by relative patch age.
74 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

and then decreased until, in many cases, flycatchers no longer 

bred there (χ2 = 44.6, df = 10, p<0.001; fig. 9). This change in 

occupancy does not appear to be directly linked to productivity 

(fig. 10), for while there are changes in average productivity 

over time, the differences were marginally non-significant 

(F7,1181 = 1.9, p = 0.06) and did not match the observed patterns 

of occupancy (fig. 9). However, movement patterns do closely 

match the change in the population numbers of a patch. For 

the first several years of a patch’s relative age, immigration 

into the patch was the dominant movement pattern (fig. 11), 

and immigration was significantly related to the changes in 

population size (rho = 0.37, p<0.001). As the patch aged, 

immigration decreased steadily while emigration increased 

and was negatively correlated to the population size (rho = 

-0.51, p<0.001). Patch fidelity also increased with the relative 

age of a patch (rho = -0.24, p = 0.004; fig. 11), which may 

slow a patch-level population’s decline, but this fidelity was by 

an increasingly smaller number of individuals as the patch’s 

population size diminished. 

The probability of flycatchers colonizing new breeding 

habitat appears to depend on distance from occupied habitat. 

While we documented rapid colonization of breeding habitat 

adjacent to occupied habitat multiple times, colonization of 

more distant habitat was observed less frequently. On the San 

Pedro River, banded flycatchers from the core breeding area 

were documented moving to two new areas further upstream: 

Catalina Wash (mean distance moved = 37 km, n = 2) and 

San Manuel Crossing (mean distance moved = 35 km, n = 

5). Thus, some or perhaps all of the flycatchers colonizing 

these sites were from the San Pedro/Gila study site. Likewise, two of the closest sites near Roosevelt Lake, consisting 

of new breeding habitat, received Roosevelt Lake emigrants: 

Horseshoe Reservoir on the Verde River (mean distance = 52 

km, n = 3) and Pinal Creek (mean distance = 16 km, n = 4; 

with an additional flycatcher from San Pedro/Gila emigrating 

there, a distance of 72 km). The mean distance of flycatchers 

moving to colonize Pinal Creek, Catalina Wash, San Manuel 

Crossing, and Horseshoe Reservoir was 33 km, similar to the 

upper end (~ 35 km) of within-site movement observed at 

Roosevelt Lake and San Pedro/Gila (fig. 7). 

Discussion

Natal Dispersal

The pattern exhibited by flycatchers at our sites, where 

natal dispersal was more extensive than adult dispersal, is typical of most birds (Greenwood and Harvey 1982, Winkler et al. 

2005). Juvenile flycatchers in our study rarely returned to their 

natal patch, moved farther than adults on average, and were 

responsible for the longest distances of movement observed. 

Reasons for this may include intraspecific competition for territories by older, presumably more aggressive individuals that 

exclude younger adults, inbreeding avoidance, or the evolution 

of increased colonization potential (Payne 1991). Colonization of new habitat is an important adaptation to the dynamic 

nature of riparian habitat in which scouring floods and multisuccessional stage vegetation leads to frequent shifts in the 

spatial location of breeding habitat over time (Periman and 

Kelly 2000; see chapter 6). It is this high natal dispersal that 

makes quantifying juvenile survivorship so difficult, as juveniles can easily disperse outside the boundaries of relatively 

small studies (Baker et al. 1995).

The longest movements recorded over the course of 

this study were those of dispersing juveniles (fig. 2). Three 

Roosevelt Lake juveniles dispersed to the Lake Mead area and 

Topock Marsh, AZ (maximum distance = 444 km), while one 

juvenile from the Lake Mead area was detected at Roosevelt 

Lake. In addition, one nestling banded in the Lake Mead area 

was detected at the Kern River Preserve, CA (M. Whitfield, 

personal commun.), a movement of 318 km. Over the course 

of this study, only juvenile flycatchers provided population 

“connectivity” between central and western Arizona, and 

between western Arizona and central California. 

Only two other studies of Willow Flycatchers have 

detailed natal dispersal. The most similar comparison 

is from the Colorado River and tributaries in Arizona, 

California, and Nevada (Koronkiewicz et al. 2004, 2006; 

McLeod et al. 2005). Of 78 banded nestlings that were 

detected as adults, 38% moved to different study sites 

20–440 km away, distances comparable to our study. In 

southeast Oregon, Sedgwick (2004) found no juveniles 

returning to one study area while as many as 67% of 

returning and detected juveniles returned to their natal 

patch at two other study areas. Natal dispersal there ranged 

from 40 m to 7.8 km. However, the return rate of 7.8% 

of all banded nestlings was much lower than what we 

observed (25%), and presumably many of the juveniles 

dispersed outside his study area (Sedgwick 2004). It is 

important to consider that different studies are conducted 

at different spatial scales, such that what is classified as 

site fidelity in one study may be considered between-site 

movement in another. Furthermore, the geographic scale 

and distribution of suitable habitat within the local landscape will influence dispersal patterns. For example, the 

presence of adjacent suitable breeding habitat should facilitate movements, whereas isolated or fragmented breeding 

habitat may reduce natal dispersal potential (Martin et al. 

2006). Alternatively, isolated breeding patches separated 

by considerable distance from others could potentially 

increase dispersal distance (as there is nowhere else to 

settle in between). Given the high degree of natal dispersal 

we observed, and the rarity of fledglings returning to their 

natal patch, sites with multi-patch spatial configurations 

may provide optimal site characteristics for flycatchers.
 75

Adult Movement

Within-season Movement

We detected 130 within-season movements by flycatchers from 1996 to 2005. The majority (65%) of the movements 

were pre- and post-breeding, both of which may be a way for 

flycatchers to assess the breeding potential of adjacent habitat 

(Vega Rivera et al. 2003). Pre-breeding movements were the 

most frequent within-season movement type observed in our 

study, probably because the movements tended to be closer to 

the flycatcher’s eventual territories and thus adjacent to other 

breeding areas where we conducted the most intensive capture 

efforts. Nearly 75% of the detected pre-breeding movements were by males that may have been assessing habitat 

or neighboring territories, or determining arrival or availability of females. Telemetry work conducted at Roosevelt 

Lake (2003–05) indicated generally larger home ranges for 

males prior to arrival of females (Cardinal and Paxton 2004, 

2005), even if they had already established a territory. Females 

may be more discerning of breeding habitat quality than are 

males (Sedgwick and Knopf 1992), perhaps choosing territories more quickly than males, and thus are less likely to be 

detected making pre-breeding movements.

Adult post-breeding movements are generally interpreted 

as prospecting for future breeding sites (Bayne and Hobson 

2001), or as movement into a habitat that may not be suitable 

for breeding but is preferable or used for pre-migration staging 

(Vega Rivera et al. 2003). Most post-breeding movements we 

documented were via recaptures in other breeding locations, 

and thus the birds were in similar habitats for breeding and 

post-breeding. Radio tracking of post-breeding individuals at 

Roosevelt Lake documented long-distance movements within 

the study area, but all were confined to riparian habitat (Cardinal and Paxton 2004, 2005; Cardinal et al. 2006). Telemetered 

individuals moved to other breeding patches, visited areas 

with local insect outbreaks, and sometimes moved to relatively 

younger patches of riparian vegetation not yet occupied by 

breeding flycatchers. Given the changing nature of riparian 

vegetation, this prospecting behavior may allow flycatchers 

to gauge the suitability of future habitat, especially young 

developing habitat, and could be an important component of 

between-year movement choices. 

Territory switching within a single breeding season was 

observed infrequently among flycatchers at our study sites. 

Flycatchers arrive on their breeding grounds late relative to 

many other migrant species (Sedgwick 2000), with approximately 3 months to breed, and rarely nest more than twice in a 

season (see chapter 3). It may take longer to re-nest following 

territory switching than it does to re-nest within the same territory (Shields 1984), so it may be advantageous for flycatchers that are attempting a second nesting opportunity to do so 

within the same territory. An additional difficulty in switching 

territories within-season is finding another territory with an 

available mate (Jackson et al. 1989), and it may be that only a 

fraction of individuals searching for a second territory are successful in finding an unoccupied territory. 

Between-year Movement Behaviors

On average, 41% of adult flycatchers moved betweenyears to another patch. Most movements were confined to 

nearby patches within the same drainage, but occasionally 

were to different drainages. Distance moved was in part 

dependent upon distribution of available breeding habitat 

(Paradis et al. 2002). Along the San Pedro, flycatcher dispersal 

frequency declined 5% for every 5 km of distance, with most 

individuals moving less than 40 km. However, at Roosevelt 

Lake where two geographically distinct riparian areas were 

separated by 30 km of lake, the distribution of movement 

distances was bimodal (corresponding to nearby patches and 

cross-lake patches), with a higher frequency of movements in 

the 30 km range than observed at San Pedro/Gila. Mean breeding dispersal distance (41 km) seen during intensive flycatcher 

studies on the Lower Colorado River (Koronkiewicz et al. 

2004, 2006; McLeod et al. 2005) was less than our betweendrainages means (97 and 120 km at Roosevelt Lake and San 

Pedro/Gila, respectively), but greater than the mean betweenpatch distances at San Pedro/Gila (13 km) and Roosevelt Lake 

(9 km). The differences in dispersal distance among different 

study areas and regions reflects the varying spatial arrangement of breeding habitat, illustrating how flycatcher dispersal tendencies are influenced by the geographic distribution 

of habitat at the reach, drainage, and landscape scales. The 

relatively linear decline in the frequency of movements with 

increasing distance seen at San Pedro/Gila may be similar to 

what was historically the normal pattern of flycatcher dispersal 

along southwestern riverine systems, when riparian woodlands 

were more evenly distributed along rivers (Graf 2006).

Flycatchers travel thousands of kilometers each year for 

migration, and have the potential to disperse great distance 

across the landscape. However, only 1% of the movements we 

detected were to other drainages. The 1% estimate should be 

viewed as a minimum, as some unknown number of dispersal 

events will inevitably go undetected; however, the limited 

number of breeding sites available to flycatchers and the 

multiple resight efforts at many sites over many years strongly 

suggest that long-distance, regional-level dispersal occurs at 

low levels. Nonetheless, this infrequent between-drainage 

movement appears adequate to sustain genetic connectivity 

(Busch et al. 2000), and may be important for the periodic 

colonization of unoccupied drainages. However, such infrequent between-drainage movements detected are probably 

not sufficient to sustain declining populations in distant 

drainages through a rescue effect, such as in breeding sites 

that are reproductive “sinks.” Given that occupied breeding 

patches can experience fluctuating flycatcher populations and 

may be abandoned over time (see below), long-term sustainability of drainage-level populations may require a number 

of suitable breeding habitat patches, preferably of different 
76 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

successional stages, within the same drainage (USFWS 2002). 

Based on dispersal distances seen at our study sites, locations 

with breeding habitat that are within 30–40 km of each other 

will have higher meta-population connectivity, and there is 

a higher probability of colonization of new habitats that are 

within this distance. Being neotropical migrants, flycatchers 

clearly have high dispersal capacity, even greater than the 444 

km (natal) and 214 km (adult) distances that we documented. 

Nonetheless, as distance increases the frequency of movement decreases, and the probability of a site being colonized is 

probably related to its proximity to other breeding populations. 

For flycatchers, the previous year’s reproductive success 

strongly influenced the observed behavior in the subsequent 

year. The higher a flycatcher’s productivity in one year, the 

more likely it was to return to the same territory the following year. Those individuals that had higher than normal 

reproductive success and showed territory fidelity continued 

to reproduce above average, while those that did poorly and 

moved tended to do better than in the previous year. This is a 

common pattern in birds (Greenwood and Harvey 1982), and 

is believed to be an adaptive assessment of habitat quality and 

the probability of future breeding outcomes (Hoover 2003). 

In a study of Willow Flycatchers in Oregon, Sedgwick (2004) 

found similar patterns of increasing territory fidelity with 

increasing productivity. While we found this pattern for both 

males and females, Sedgwick (2004) found it only for females. 

He argued that males may be less likely to move, regardless of 

reproductive success, because it is more adaptive to return to 

and defend a familiar territory, as long as the habitat persists 

and females will settle into the territory. However, the dynamic 

nature of riparian systems in the Southwest may have selected 

for increased vagility in both male and female Southwestern 

Willow Flycatchers, as well as the evolution of reproductive 

cues associated with habitat (e.g., vegetation structure, arthropod abundance).

Colonization of Breeding Habitat

At both Roosevelt Lake and the San Pedro/Gila study 

sites, Southwestern Willow Flycatchers colonized breeding 

habitat that developed over the course of the study, suggesting they will quickly colonize new habitat when it becomes 

suitable for breeding (at least if it is adjacent to occupied 

habitat). As the level of Roosevelt Lake dropped and riparian vegetation grew on the exposed lakebed, the average age 

of the vegetation when first colonized was 3 years (range = 

2–4 years). Riparian vegetation can grow very quickly, and 

in just a few years can form dense stands several meters high. 

Thus, rapid colonization of new breeding habitat was possible 

because of the rapid growth rate of riparian vegetation and the 

flycatcher’s ability to find and quickly colonize young habitat. 

Based on observations of within-season movements and telemetry data (Cardinal and Paxton 2004, 2005; Cardinal et al. 

2006), flycatchers will move from their general breeding patch 

to visit younger non-breeding vegetation (both while territorial, and pre and post-breeding), possibly to prospect for future 

breeding habitat and to forage for food. It was not uncommon 

to hear flycatchers singing from younger vegetation a year before 

breeding was documented within the patch (E. Paxton, personal 

observation). Given the dynamic nature of riparian systems and 

the apparent preference flycatchers have for early successional 

stage habitat (see chapter 6), flycatchers may have evolved the 

capacity to assess the future breeding potential of vegetation in 

its early stages of development.

Not all colonization involved young breeding habitat; 

flycatchers moved into older habitat patches as well, and these 

older patches may serve as refugia (an area that has escaped 

ecological changes occurring elsewhere and so provides a 

suitable habitat for a species). Scouring floods can destroy 

large swaths of riparian forest, and fluctuating reservoir levels 

not only create habitat as they recede, but destroy habitat as 

they rise. These scouring/flooding events are most likely to 

impact the younger habitats that colonize the flood-prone 

zones of rivers and the drawdown zones of reservoirs, so 

these younger habitats will be lost more frequently than older 

habitats outside of these zones. We have seen examples of 

this phenomenon over the course of our study. When our 

research started in 1996, Roosevelt Lake was at full capacity 

and flycatchers were found only in mature habitat adjacent 

to the high water level. As the lake began to slowly recede 

over the following years, new riparian vegetation developed 

and the expanding flycatcher population at Roosevelt Lake 

generally moved into the newer, younger habitat (see chapter 

6). By 2004, flycatchers had abandoned the historical patches 

in which they bred exclusively from 1994 to 1998. In 2005, 

high winter and spring runoff caused the lake to reach near 

capacity, destroying much of the newer habitat. In response, 

flycatchers moved back into patches they had earlier vacated 

or had never occupied during our study. A similar pattern may 

have occurred at San Pedro/Gila, where scouring floods in 

1993 removed much of the vegetation adjacent to the rivers (see 

chapter 6). Flycatchers in 1996 were found in mature vegetation 

on higher ground away from the active channels on both the 

Gila and San Pedro Rivers. By 2005, much of the San Pedro/

Gila population had moved to younger habitat that developed 

within the primary flow zone. Thus, mature unoccupied habitats 

near younger occupied breeding patches may occasionally play 

an important role as refugia. In doing so, they may help dampen 

local population fluctuations that might otherwise occur if rapid 

loss of breeding habitat forced flycatchers to disperse widely in 

search of new breeding locations. 

Within a patch, flycatcher population sizes changed over 

time. The general pattern was for a rapid increase in the numbers of territorial flycatchers, followed by a gradual decline 

until, in some cases, abandonment of the patch as breeding 

habitat. Average productivity did not change as patches aged, 

and since very few natal dispersers return to their natal patch, 

changes in patch level population size over time appears to be 

driven primarily by between-year movement behavior and flycatcher lifespan. Behaviors such as immigration, emigration, 

and patch fidelity were significantly correlated with changes 

in patch population size, with immigration and emigration the 
 77

most strongly correlated. For the first several years of a patch 

being occupied, immigration was at its highest levels, and 

later declined to near zero. As immigration declined, emigration increased. Patch fidelity also increased with the age of 

the patch, which may have slowed the decline of a patch’s 

population size. Because mean flycatcher life expectancy is 

approximately 2 years (see chapter 2), patch fidelity of surviving adults can only sustain a breeding population temporarily. 

Thus, immigration (both adult and natal) into a breeding patch 

is essential for its establishment and early persistence, while 

emigration can hasten the decline (Stacey and Taper 1997, 

Ward 2005). This suggests that sites with multiple-patch metapopulation structures may be the most stable populations over 

time, albeit spatially dynamic.

If individuals make a choice between movement and 

fidelity each year in response to habitat conditions (e.g., prey 

abundance, predation risks, past breeding experience in a 

patch, vegetation characteristics), then the observed patterns of 

choices can shed light on what constitutes preferred habitat. Given that productivity was not related (or only weakly 

related) to the age of a patch, and assuming that predation risk 

and prey base are closely tied to productivity, vegetation characteristics may drive changes in population size. Flycatchers in 

the Southwest were historically widespread in young, earlysuccessional riparian habitat (Unitt 1987, Sedgwick 2000), and 

presumably have evolved a search image for the vegetation 

structure associated with young habitat. Thus, flycatchers may 

form spatially dynamic metapopulations that follow patches 

at different successional stages over time. Both Roosevelt 

Lake and San Pedro/Gila provided flycatchers with a choice 

of many potential breeding habitat patches of varying ages, 

and much of the flycatcher population growth occurred in the 

younger vegetation patches by the end of the study. However, 

many flycatcher breeding locations in the Southwest are more 

isolated, with apparently little nearby alternative breeding habitat. Based on our observations of flycatchers moving to and 

breeding in older habitats, some mature habitats are suitable 

for (though perhaps not preferred by) flycatchers, at least for a 

short period. Why flycatchers would abandon older vegetation 

patches when productivity appears to be constant is unknown, 

and deserves more research attention. 

Management and Research 

Considerations

Managing Meta-populations Rather than 

Individual Breeding Patches

Historically, riparian vegetation along stretches of rivers 

was a rich mosaic of different aged patches for flycatchers to 

colonize, occupy, and eventually abandon. All riparian habitat 

patches are ephemeral, persisting only for the period of time 

between their establishment and the next major flow and 

scouring event. The flycatcher appears to be well adapted to 

this dynamic system, and its current proclivity for movement 

and dispersal —especially to young habitat—is probably a 

result of that adaptation. 

Even in today’s less dynamic riparian landscapes (Graf 

2006), habitat patches are subject to loss from high flows, 

rising reservoirs, fire, and other causes, and flycatchers will 

sometimes abandon some older breeding sites that still exist. 

Therefore, it is not safe to assume that breeding flycatchers 

can be sustained in an area simply by protecting the sites in 

which they currently breed (USFWS 2002). Rather, management for the flycatcher will be most effective if it is based 

on the concept of maintaining a mosaic of sites of varying 

age and vegetation structure. If one or a few sites are lost or 

decline in suitability, there could be other patches to which 

flycatchers would move. Based on our study, most movements occur within drainages. Therefore, this suggests that 

individual drainages are an appropriate scale at which to 

manage a mosaic of riparian patches as potential breeding 

patches. Movements between major drainages are infrequent, 

so patches in different drainages are not as likely to facilitate 

meta-populations dynamics. On the other hand, one metapopulation can seed another, in a different drainage, and this 

is important for a regional framework. However, this low frequency of between-drainage movements is partially influenced 

by distance, and areas where drainages are close together may 

experience higher frequencies of movement. 

The Southwestern Willow Flycatcher Recovery Plan 

(USFWS 2002) recognized that the flycatcher persists as a 

complex of meta-populations, and included recovery goals 

for specific geographic areas termed Recovery Units and 

Management Units. The units were based on USGS Hydrologic Unit Codes, derived from varying scales of landscape 

drainages. Flycatcher populations within individual units were 

believed to be more closely linked than populations in differing units. Translating our findings to the Recovery Plan’s 

terminology, we found relatively frequent movements within 

the Management Unit scale, occasional movement at the 

Recovery Unit scale, and only infrequent dispersal from one 

Recovery Unit to another. Thus, results from our study of 

flycatcher movement behavior support the Recovery Plan’s 

spatially explicit approach to recovering the Southwestern 

Willow Flycatcher. 

Spatial Placement of New and Restored Habitats

The frequency of flycatcher dispersal generally decreases 

as the distance between patches increases. Flycatchers will 

readily colonize new habitat patches that are adjacent to 

current breeding sites, as rapidly as 3 years after vegetation 

establishment at Roosevelt Lake. More remote sites can also 

be colonized, but the frequency of flycatcher dispersal to more 

distant sites is lower. Therefore, the mere creation of suitable 

flycatcher habitat is no guarantee that the project will be suc-
78 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

cessful in attracting and supporting breeding flycatchers, at 

least in a 3 to 5-year timeframe.

Flycatcher habitat restoration and creation projects are 

likely to be most effective, in terms of rapid colonization by 

flycatchers, if they are located near existing breeding sites 

(within 30 to 40 km). Strategically placing riparian improvement projects near existing flycatcher breeding areas can also 

serve to strengthen the local meta-population. Considerations 

for riparian restoration projects are included in the Southwestern Willow Flycatcher Recovery Plan (USFWS 2002). It 

may be possible to establish new habitat in certain locations 

to serve as future stepping-stones for expanding the flycatcher 

population into other areas; to be effective, such sites should 

not be too remote from known breeding areas. Although an 

exact distance that would constitute “too remote” is difficult to 

determine, the mean adult between-site dispersal distances of 

97 and 120 km (Roosevelt Lake and San Pedro/Gila, respectively) could serve as an upper limit to the distance of planned 

projects from existing breeding sites.

Developing Techniques to Increase Reproductive 

Success

Flycatchers tended to move away from a patch if breeding success was poor. In large, multi-patch breeding areas, this 

may result simply in flycatchers moving from one local patch 

to another, with no net effect on the overall breeding population size (though overall lower productivity would depress the 

population as a whole). However, in small, isolated breeding 

sites, low productivity may hasten the departure of flycatchers, and the eventual extirpation of the site. Management 

actions that help improve reproductive success could theoretically slow or stop the loss of flycatchers from an area, at least 

until new habitat could be developed. Potential techniques to 

improve flycatcher breeding success have not been investigated, with the exception of cowbird trapping regimes which 

yield variable benefits (Rothstein et al. 2003). Additional 

research could be directed toward developing effective ways 

to increase local reproductive success of breeders, especially 

at small sites. Increasing surface flows, raising ground water 

levels, and preventing overgrazing is likely to improve habitat 

as well as reproductive success (USFWS 2002).

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 81

Introduction

Demography is the study of populations and the processes that influence their trajectories. Four primary processes, 

or vital rates drive demographic patterns: productivity, survivorship, immigration, and emigration. Whether a population 

is increasing, declining, or stable results directly from the 

synergism of these demographic vital rates, which collectively 

contribute to a population’s growth potential (Beissinger 

and Westphal 1998). Thus, the long-term sustainability of a 

population is typically measured by this population growth 

potential. The basic vital rates of survivorship, productivity, and immigration and emigration can all be modified by 

environmental factors, landscape characteristics, age, sex, etc. 

(Beissinger et al. 2006). For species of conservation concern, 

it is crucial to understand which of the multiple possible factors influence that population’s growth potential (Holmes et al. 

1996, Stoleson et al. 2000, Schrott et al. 2005).

While it is possible to look at individual vital rates to 

see if they are high (e.g., productivity) or low (e.g., mortality) 

compared to other populations or similar species, it is necessary to consider all the vital rates together in order to understand population processes and trajectories. Demographic 

modeling is a process to accomplish this by combining the 

vital rates to understand and predict a population’s potential 

trajectory. Reasons to construct demographic models include 

simplifying the relationships among the vital rates for better 

understanding, evaluating the impact that management actions 

might have on a species, and estimating future trajectories (i.e., 

Population Viability Analysis). The most common are deterministic models (Beissinger et al. 2006) that use fixed values for the 

vital rates and combine them in a predictable way. For example, 

a deterministic model may take a simple form such as “births + 

immigration – deaths – emigrations.” Another example would 

include matrix age structured models (Caswell 2001), which 

incorporate information on how vital rates may change at different age classes. Deterministic models are widely employed 

to understand population dynamics (Pulliam 1988), demography of wildlife species (Beissinger et al. 2006), and assess the 

health of sensitive species (Blakesley et al. 2001). 

The endangered Southwestern Willow Flycatcher (Empidonax traillii extimus) is a small, migratory passerine that 

breeds exclusively in riparian habitats scattered throughout 

portions of the southwestern U.S. (Unitt 1987, Marshall 2000 

) and winters from central Mexico south to northern South 

America (Sedgwick 2000). Flycatcher numbers have declined 

precipitously as riparian habitats on the breeding grounds have

been lost or modified (USFWS 1993, Marshall and Stoleson 

2000), and E.t. extimus was listed as a federally endangered 

species in 1995 (USFWS 1995). At the time of listing, many 

aspects of the flycatcher’s biology were poorly understood 

(Marshall and Stoleson 2000, Stoleson et al. 2000). In particular, little was known about the vital rates of most flycatcher 

populations, and early efforts to model the demographics of the 

flycatcher were hindered by a lack of robust vital rate estimates 

(Stoleson et al. 2000). Thus a major purpose for this study was 

to gather long-term data suitable for demographic modeling.

 From 1996 to 2005, we collaborated with the Arizona 

Game and Fish Department (AGFD) to conduct a large demographic study of Southwestern Willow Flycatchers in central 

Arizona at two core study sites, as well as multiple auxiliary 

breeding sites. One of our main objectives was to gather the 

demographic information necessary to evaluate population 

growth at the two study sites. Vital rates collected included survivorship, movement, and productivity, as well as information 

on census numbers. These vital rates, developed and described 

in earlier chapters of this report, are the essential building 

blocks of the demographic modeling presented in this chapter.

Methods 

Study Sites

Our two study sites (fig. 1) were breeding populations 

at Roosevelt Lake (33o39’N, 110o58’W) and the San Pedro/

Gila River confluence (hereafter San Pedro/Gila; 32o59’N, 

110o46’W), where we conducted demographic research in 

cooperation with AGFD from 1996 to 2005 (Causey et al. 

2006, English et al. 2006). We defined sites as a collection of 

riparian woodland patches, occupied by breeding flycatchers, which are found within the same river drainage and 

geographically isolated from other such breeding sites. Exact 

definitions of a site are difficult, and the definition of a site 

differs regionally (Durst et al. 2006). Based on movement data 

collected over the past 10 years, each site has a high degree of 

movement-based connectivity within drainages suggesting a 

distinct breeding population.

Our two study sites supported two of the largest known 

Southwestern Willow Flycatcher breeding populations 

throughout the period of this study (Durst et al. 2006), with 

mean population sizes of 201 and 239 individuals at Roosevelt 

Lake and San Pedro/Gila, respectively. At each site, extensive 

survey efforts were conducted at least three times in the 

Chapter 5—Demographic Modeling
82 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 1. Location of the Roosevelt Lake and San Pedro/Gila study areas in central Arizona. 
 83

breeding season to detect all territorial flycatchers within 

the immediate and surrounding areas. In addition, multiple other breeding sites in Arizona and throughout the 

Southwest (49–444 km away) were periodically visited, 

which along with cooperative efforts of other researchers 

conducting similar studies allowed detection of dispersing 

flycatchers (see chapter 4). 

The breeding habitat at the Roosevelt Lake and San 

Pedro/Gila study sites consisted of a heterogeneous mosaic 

of discrete riparian forest patches of varying ages and vegetation composition, ranging from 0.2 to 43 ha in size. Native 

habitat was characterized by Goodding’s willow (Salix gooddingii) and Fremont cottonwood (Populus fremontii). Exotic 

habitat was dominated by tamarisk (saltcedar; Tamarix spp.). 

The understory vegetation consisted of a variety of grasses, 

forbs, and shrubs (mesquite [Prosopis spp.], coyote willow [S. 

exigua], tamarisk, Baccharis spp., and cocklebur [Xanthium 

strumarium]). Adjacent and surrounding non-riparian habitats 

were primarily composed of Sonoran Desert Uplands (Brown 

1994), and in some cases agricultural lands. 

Roosevelt Lake Study Site

The Roosevelt Lake study site consisted of two river 

drainages, the Salt River and Tonto Creek, each converging 

at the confluence of a reservoir. Breeding was documented 

as far as 12 km upstream on Tonto Creek and 5 km on the 

Salt River. The number of riparian patches in which breeding occured varied over time as a result of colonization or 

desertion of patches, largely driven by fluctuating lake levels. 

Over the 10-year period, we documented breeding in a maximum of 23 patches encompassing approximately 242 ha of 

riparian habitat. Long-term persistent drought conditions 

between 1996 and 2005 (McCabe et al. 2004) resulted in 

reservoir levels dropping to a low of 10% capacity in 2002. 

The exposed lakebed was colonized by riparian vegetation, 

which was subsequently colonized by breeding flycatchers 

(see chapter 5). In 2005, following unusually high winter 

precipitation, Roosevelt Lake filled to near capacity, inundating much of the riparian breeding habitat occupied in 2004 

(see chapter 5). 

San Pedro/Gila Study Site

The San Pedro/Gila study site encompassed 101 km of 

river, centered at the confluence of the free-flowing San Pedro 

River and the regulated Gila River, and extending upstream 

on the San Pedro to San Manuel Crossing and downstream 

on the Gila River to Kelvin Bridge. As with Roosevelt Lake, 

the amount of riparian vegetation along the San Pedro/Gila 

changed over the 10-year study, with as many as 29 habitat 

patches supporting breeding flycatchers, comprising approximately 222 ha of riparian forest. 

Vital Rates

Robust estimates of vital rates are necessary for the 

development of useful models and estimates of a population’s 

demographic growth potential. The vital rate estimates that we 

used in our models are detailed in other chapters of this report 

(i.e., chapter 2, Survivorship; chapter 3, Productivity; chapter 

4, Movement), and are briefly outlined here.

Census Numbers

Formal tape-playback surveys (Sogge et al. 1997) were 

conducted by AGFD three times each breeding season (English et al. 2006), and were supplemented extensively by nearcontinuous searches of potential breeding habitat throughout 

the season by both AGFD and USGS. All areas of accessible, 

suitable habitat were searched within a large area, ensuring 

high confidence in census numbers and territory locations for 

each study site. By uniquely color-banding individuals and 

subsequently tracking them through resights and recaptures, 

we estimated population size in several ways, including the 

total number of territories, total number of breeding individuals, and the total number of individuals present at a given site 

in a given year (including non-territorial individuals; see chapter 7). The census numbers presented in this report are based 

on survey and resight information combined, and may vary 

slightly from past annual reports by AGFD and USGS.

Survivorship

Survivorship estimates were based on the return rates of 

1080 banded adults and 498 banded juveniles, using maximum likelihood estimates with program MARK (White and 

Burnham 1999), and AIC for model selection (Burnham and 

Anderson 2002; see chapter 2). For adults, the most parsimonious factor to explain variation in survivorship probabilities 

was the effect of year, with no differences in sex and site, and 

marginal differences among different aged adults. For juveniles, 

date fledged was the most important factor, with little support 

for the effects of yearly variation; we did not test for differences 

between study sites and sex among juveniles as very few juveniles were banded at San Pedro/Gila (see chapter 2).

Accurate estimates of survivorship probabilities are 

difficult to obtain. Survivorship probabilities often suffer the 

consequences arising from the practicalities of field studies, 

including small sample sizes, small proportions of the population banded, low detection rates, and unknown levels of 

permanent emigration. However, our survivorship estimates 

are robust because large numbers of flycatchers were banded 

annually, a majority of detected flycatchers at both sites were 

banded in any given year, and our annual detection rate was 

high (78% detection probability on average; see chapter 2). 

While banding and resighting efforts were reduced at San 
84 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Pedro/Gila from 2001–05, there was no apparent effect on 

survivorship estimates (see chapter 2). Although we detected 

adult and juvenile dispersal from our study sites (see chapter 4), emigration rates were relatively low and had minimal 

effects on our survivorship estimates, with most movements 

occurring within a study site.

Productivity

Measuring the productivity of banded birds allowed us 

to estimate seasonal and lifetime productivity (see chapter 3). 

Once territories were found, efforts were made to identify mating status (monogamous, polygamous, or unpaired) and locate 

nests. Nests were monitored regularly by AGFD (Rourke et al. 

1999) to determine the number of young fledged. Individuals 

were monitored throughout each breeding season and across 

years to determine seasonal fecundity (the total number of 

young fledged per individual per season) and an estimate of 

minimum lifetime productivity (the sum of an individual’s seasonal fecundity over their lifetime; see chapter 3). Productivity 

varied significantly by year, study site, and age (second-year 

adults versus older adults). 

Immigration and Emigration

Immigration and emigration, defined as juvenile and 

adult dispersal into or out of the study sites, respectively, were 

estimated by tracking the movement of banded individuals 

between our two study sites and at other breeding locations 

(see chapter 4). Over the 10-year study period, 98% of adult 

and 94% of juvenile movements we detected were within each 

of the two study sites, suggesting that between-site movements 

are rare and primarily by juvenile flycatchers. There was certainly some unknown number of individuals that dispersed out 

of the study sites undetected, and immigration of unbanded 

individuals is impossible to distinguish from local recruitment. 

However, modeling of movements within our study sites suggested there is a sharp decrease in the frequency of movements 

at increasing distances; movements over 30–40 km were infrequent. Because most between-site movements would require 

movements of more than 30–40 km, and usually much farther, 

and the evidence suggests that immigration/emigration was 

infrequent, we do not believe that immigration/emigration play 

a strong role in the population dynamics at the study site level. 

However, within the model (below), permanent emigration is 

accounted for as an undifferentiated portion of the survivorship estimates, because these estimates include both birds that 

die and birds that permanently leave the study area. Therefore, 

we also needed to provide the model an estimate of immigration as well. To do so, we used the average rate of between-site 

movement for both adults and juveniles.

Demographic Models

We utilized a deterministic model to explore the relationships among the vital rates, and estimated the population 

growth potential of the two study sites. The deterministic 

model we used is typically referred to as the “BIDE” model 

(Birth + Immigration - Death - Emigration). The model produces a value, lambda, which describes the discrete (yearly) 

per capita rate of growth of a population given the vital rates 

entered into the model. Values of lambda at 1.0 suggest a 

stable population, values above 1.0 suggest an increasing 

population, and values below 1.0 indicate a declining population. Several forms of the model exist, and we used the form 

(Pulliam 1988):

Lambda = adult survivorship + (juvenile survivorship x 

 (seasonal fecundity/2)) + immigration

As noted above, emigration is accounted for in the survivorship estimates, and so does not appear as a separate vital 

rate in this model equation. Seasonal fecundity is divided by 

two to generate the number of daughters produced per female 

(assuming a fledgling sex ratio of 50:50); this is a common 

demographic modeling approach that helps avoid issues of 

polygamy and extra-pair paternity. All graphs were created 

with SigmaPlot 8.0 (SPSS, Inc).

Results

Observed population trends

From 1996 to 2005, populations at both the Roosevelt 

Lake and San Pedro/Gila study sites increased in size overall 

and between most years (table 1, fig. 2). Year-to-year measures 

of change in the number of territories, the number of territorial adults, and the numbers of all detected adults (territorial 

and non-territorial combined) increased, with an overall10-

year average yearly increase of approximately 25% (table 

1). However, the early years of the study period had greater 

year-to-year changes than did the latter half, which may indicate a study-bias due to improved survey techniques and the 

addition of new breeding locations in later years (particularly 

at the San Pedro/Gila study site). Therefore, excluding the first 

several years may provide a more accurate estimate of average 

observed population growth. Doing so results in estimates of 

population change of 1.18 for an 8-year period (1998–2005) 

and 1.08 for a 5-year period (2001–05). While all estimates 

of average population change indicate growth, there were 

some years when population measures decreased compared 

to the previous year (table 1). At Roosevelt Lake there was a 
 85

# Territories Territorial individuals All detected

individuals

Year n change n change n change

Roosevelt Lake

1995 17 - 34 - 34 -

1996 35 2.06 62 1.82 64 1.88

1997 41 1.17 74 1.19 85 1.33

1998 50 1.22 93 1.26 99 1.16

1999 78 1.56 149 1.60 160 1.62

2000 111 1.42 212 1.42 233 1.46

2001 141 1.27 257 1.21 288 1.24

2002 142 1.01 177 0.69 296 1.03

2003 139 0.98 254 1.44 288 0.97

2004 215 1.55 326 1.28 413 1.43

2005 170 0.79 290 0.89 344 0.83

10-year mean - 1.26 - 1.24 - 1.26

8-year mean - 1.19 - 1.19 - 1.19

5-year mean - 1.09 - 1.06 - 1.08

San Pedro/Gila

1995 20 - 40 - 40 -

1996 35 1.75 70 1.75 73 1.83

1997 60 1.71 113 1.61 124 1.70

1998 87 1.45 167 1.48 176 1.42

1999 118 1.36 229 1.37 245 1.39

2000 125 1.06 230 1.00 247 1.01

2001 110 0.88 214 0.93 216 0.87

2002 158 1.44 271 1.27 278 1.29

2003 167 1.06 267 0.99 289 1.04

2004 166 0.99 311 1.16 321 1.11

2005 182 1.10 344 1.11 348 1.08

10-year mean - 1.25 - 1.24 - 1.24

8-year mean - 1.15 - 1.15 - 1.14

5-year mean - 1.08 - 1.08 - 1.07

Table 1. Census numbers of Southwestern Willow Flycatchers at the Roosevelt Lake and San Pedro/Gila study sites, Arizona, 1995–

2005. For each site and year are the census results for the number of territories detected and the change from the previous year (Nt+1/

Nt), the number of territorial adults detected and change from the previous year (Nt+1/Nt), and the total number of adults detected 

(territorial and non-territorial combined) and change from the previous year (Nt+1/Nt). Overall, these three change estimates were 

remarkable similar to each other and between the two study sites.
86 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

slight decline in 2003, following the 2002 drought year. There 

was also a sharper decline in 2005, when much of the 2004 

breeding habitat was inundated by rising reservoir levels at 

Roosevelt Lake. At the San Pedro/Gila, there was a decline 

in the number of territorial individuals in 2001; however, 

there was no declive after the 2002 drought. 

Flycatcher Vital Rates

Flycatcher vital rates varied among years and between the 

two study sites. Measures of both survivorship (table 2) and 

seasonal fecundity (table 3) indicate a large degree of variability from year to year. Adult survivorship averaged 64% (range 

= 53%–73%), while juvenile survivorship averaged 34% 

(range = 13%–57%; table 2, see chapter 2). Survivorship did 

not vary among sites for adults, and analysis of juvenile survivorship is for Roosevelt Lake only (see chapter 2). ProductivFigure 2. Observed changes in the number of territories and total number of Southwestern 

Willow Flycatchers detected at the Roosevelt Lake and San Pedro study sites, 1995 to 2005.

Average yearly observed change was 1.25, 1.17, and 1.08 for 10-year (1996-2005), 8-year 

(1998-2005), and 5-year (2001-2005), respectively. 

Year

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Count

0

50

100

150

200

250

300

350

400

450

# of territories

# of individuals

Year

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Count

0

50

100

150

200

250

300

350

400

450

# of territories

# of individuals

Roosevelt Lake

San Pedro

ity also varied significantly among years, and between the two 

study sites (table 3; see chapter 3). Average seasonal fecundity 

was 1.6 for Roosevelt Lake females and 2.0 for San Pedro/Gila 

females. Productivity was highest in the wettest years (1998, 

2001, 2003, [additionally 2005 for San Pedro/Gila]), and lowest in the driest years (1996, 2002; table 3). 

Rates of immigration and emigration based on movements of banded birds to and from each study site were low 

and ranged from 0%–2% in any given year (see chapter 4), 

and there was no evidence of unbalanced dispersal/movement, with immigration and emigration approximately equal. 

Though known emigration was infrequent, juveniles were 

more likely to disperse away from the study sites (6% of all 

detected juvenile movements) than were adults (2% of adult 

detected movements). To counteract the inclusion of emigration in the survivorship estimates, we used the average 

dispersal numbers of adults and juveniles to derive an average 

Figure 2. Observed changes 

in the number of territories and 

total number of Southwestern 

Willow Flycatchers detected 

at the Roosevelt Lake and San 

Pedro/Gila study sites, 1995 to 

2005. Average yearly observed 

change was 1.25, 1.17, and 

1.08 for 10-year (1996–2005), 

8-year (1998–2005), and 5-year 

(2001–05), respectively.
 87

Year Juvenile Adult

n Percent 95% C.I. n Percent 95% C.I.

1996–97 4 38 4–89 87 53 41–65

1997–98 21 13 3–43 120 53 45–62

1998–99 24 21 8–42 137 63 54–71

1999–2000 62 26 14–44 197 56 50–63

2000–01 71 31 20–45 241 66 59–72

2001–02 107 41 28–54 264 73 66–80

2002–03 2 57 7–96 282 67 61–73

2003–04 121 41 27–56 301 68 61–74

2004–05 86 32 19–50 393 57 51–63

10–year average 498 34 27–40 1080 64 62–66

Table 2. Maximum likelihood survivorship probabilities (percent) of adult and juvenile Southwestern Willow Flycatchers at the 

Roosevelt Lake and San Pedro/Gila study sites, Arizona, 1996–2005. Values given are the sample size (n), percent survivorship, and 95% 

confidence interval (C.I.). Values for juveniles were calculated only for Roosevelt Lake; rates for adults are combined for both sites, 

because we found no statistical difference between sites (see chapter 2).

Year Roosevelt Lake San Pedro/Gila

n fecundity 95% C.I. n fecundity 95% C.I.

1996 24 1.0 0.2–1.8 28 0.7 0.2–1.3

1997 42 1.7 1.0–2.5 43 1.6 0.9–2.3

1998 46 1.9 1.3–2.6 71 2.1 1.5–2.7

1999 79 2.1 1.4–2.7 79 1.8 1.3–2.3

2000 99 1.9 1.6–2.3 73 1.8 1.2–2.3

2001 136 2.2 1.8–2.5 60 2.7 2.0–3.4

2002 78 0.1 0.0–0.3 47 0. 8 0.3–1.4

2003 167 2.0 1.8–2.3 81 2. 3 1.9–2.7

2004 146 1.1 0.8–1.4 96 1.9 1.4–2.3

2005 148 1.4 1.1–1.7 91 2.3 1.9–2.8

Study period average 1.6 1.5–1.8 2.0 1.8–2.1

Table 3. Average seasonal fecundity of female Southwestern Willow Flycatchers at the Roosevelt Lake and San Pedro/Gila study sites, 

Arizona, 1996–2005. Seasonal fecundity is the total number of young fledged per female in a breeding season (see chapter 3). 
88 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

observed immigration rate of 3% which was incorporated into 

the demographic model.

Deterministic Projections of Population Growth

Using mean survivorship (adult = 0.64, juvenile = 0.34), 

study-period seasonal fecundity values (Roosevelt Lake = 

1.6; San Pedro/Gila = 2.0; tables 2 and 3), and an average 

immigration rate (0.03), the estimated lambda value is 0.94 for 

Roosevelt Lake and 1.01 for the San Pedro/Gila. This suggests that Roosevelt Lake has a population growth potential that 

should lead to a decline of 6% per year given the mean vital 

rates, while San Pedro/Gila is estimated to have the potential to 

increase slightly by 1% each year, on average. Yearly estimates 

of lambda at Roosevelt Lake ranged from 0.67 to 1.21, with a

geometric mean of 0.87. At the San Pedro/Gila, lambda ranged 

from 0.63 to 1.31, with a geometric mean of 0.91 (table 4). 

Our modeled population growth potential (lambda) was 

lower than the actual observed population growth at the study 

sites. Because the true means of the vital rates are unknown, 

and therefore could be higher or lower than our estimates, 

we evaluated how this uncertainty affected lambda estimates. 

Given that the discrepancy between calculated lambda and the 

observed population change suggests that the vital rates are 

underestimates, we only evaluated the upper ends of possible 

values. To do so, we repeated the modeling after increasing the 

mean values of all the parameters by one standard error (SE) 

and two standard errors. This resulted in higher lambda estimates of 1.06 and 1.19 for Roosevelt Lake and 1.14 and 1.28 

for San Pedro/Gila (one and two SE increases, respectively). 

Year Survivorship (%) Seasonal

Adult Juvenile fecundity Lambda

Roosevelt Lake

1996–97 53 38 1 0.75

1997–98 53 13 1.7 0.67

1998–99 63 21 1.9 0.86

1999–2000 56 26 2.1 0.86

2000–01 66 31 1.9 0.98

2001–02 73 41 2.2 1.21

2002–03 67 57 0.1 0.73

2003–04 68 41 2 1.12

2004–05 57 32 1.1 0.78

10-year average 64 34 1.6 0.94

San Pedro/Gila

1996–97 53 38 0.7 0.69

1997–98 53 13 1.6 0.66

1998–99 63 21 2.1 0.88

1999–2000 56 26 1.8 0.82

2000–01 66 31 1.8 0.97

2001–02 73 41 2.7 1.31

2002–03 67 57 0.8 0.93

2003–04 68 41 2.3 1.18

2004–05 57 32 1.9 0.90

10-year average 64 34 2.0 1.01

Table 4. Yearly vital rates and estimated lambda values for Southwestern Willow Flycatchers at the Roosevelt Lake and San Pedro/

Gila study sites, Arizona, 1996–2005. Survivorship values are the same for both sites, but seasonal fecundity is site specific. 
 89

The lambda values resulting from the two SE increases are 

derived from values of the vital rates that were approaching the 

95% upper confidence level of their respective mean, and therefore are statistically plausible values for the true mean of the 

vital rates. These increases in the vital rates brought the population growth potential closer to the observed population changes 

(fig. 3). This analysis assumes that these higher vital rates are 

realistically achievable by birds at our sites, an assumption supported by the fact that measured vital rates in some years had 

values that exceeded two SEs of the mean vital rates (table 4).

Estimates of lambda will vary as each of the vital rates 

changes, and it is instructive to explore what a specific vital 

rate would need to be to achieve a target lambda value. For 

example, at the mean adult survivorship rate of 64% and 

mean juvenile survivorship of 34%, the seasonal fecundity 

necessary for a lambda of 1.25, 1.18, and 1.08 (observed 

population growth at different temporal scales) would be 3.4, 

3.0, and 2.4, respectively (fig. 4). However, at a higher juvenile survivorship rate (45%), the female fecundity necessary 

for the same lambda of 1.25, 1.18, and 1.08 would be 2.6, 

2.3, and 1.8, respectively (fig. 4). Thus, higher juvenile survivorship (or adult survivorship) lowers the seasonal fecundity 

values needed to sustain a population. Conversely, higher 

seasonal fecundity values would mean that a population can 

be sustained with lower survivorship.

Discussion

The difference between our demographic model estimates 

and the observed population growth at the two study sites 

Figure 3. Projections of population growth at the Roosevelt Lake and San Pedro study sites, 

1996-2005. Projections include actual observed census numbers (closed circle), population 

change expected at average observed change for 10-year average (1996-2005; open circle), 8-

year average (1998-2005; closed triangle), and 5-year average (2001-2005; open triangle), 

predicted growth at observed lambda (closed square), predicted population growth with vital 

rates increased by 1 (open square) and 2 (closed diamond) S.E. of the mean values. Population 

census size in 1996 was used to start the projections at Roosevelt Lake (n=64) and San Pedro 

(n=73).

Roosevelt Lake

San Pedro/Gila

Year

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Number of individuals

0

100

200

300

400

500

600

700

800

Observed

Average observed (10-year)

Average observed (8-year)

Average observed (5-year)

Predicted growth (O = 1.01)

Pred. growth + 1 S.E. (O = 1.14)

Pred. growth + 2 S.E. (O = 1.28)

Year

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Number of individuals

0

100

200

300

400

500

600

Observed

Average observed (10-year)

Average observed (8-year)

Average observed (5-year)

Predicted growth (O = 0.94)

Pred. growth + 1 S.E. (O = 1.06)

Pred. growth + 2 S.E. (O = 1.19)

Figure 3. Projections of 

population growth at the 

Roosevelt Lake and San Pedro/

Gila study sites, 1996–2005. 

Projections include actual 

observed census numbers 

(closed circle), population 

change expected at average 

observed change for 10-year 

average (1996–2005; open 

circle), 8-year average (1998–

2005; closed triangle), and 

5-year average (2001–05; open 

triangle), predicted growth 

at observed lambda (closed 

square), predicted population 

growth with vital rates 

increased by 1 (open square) 

and 2 (closed diamond) S.E. of 

the mean values. Population 

census size in 1996 was used 

to start the projections at 

Roosevelt Lake (n=64) and San 

Pedro/Gila (n=73).
90 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

expected (in 0.2 increments) given a particular combination of vital rates. For example, at the 

average adult survivorship of 64%, a seasonal fecundity of 3.5 and juvenile survivorship of 20% 

would result in a lambda of approximately 1.0, whereas the same fecundity but a juvenile 

survivorship of 70% would result in a lambda of approximately 1.9.

Adult survivorship = 58% Adult Survivorship = 64%

1.0

1.5

2.0

2.5

3.0

3.5

1.0

1.5

2.0

2.5

3.0

3.5

Juvenile survivorship

20% 30% 40% 50% 60% 70%

Fecundity

1.6

1.4

1.4

1.4

1.2

1.2

1.2

1.2

1.0

1.0

1.0

1.0

1.0

0.8

0.8

0.8

Adult survivorship = 70%

Juvenile survivorship

20% 30% 40% 50% 60% 70%

Fecundity

1.0

1.5

2.0

2.5

3.0

3.5

1.8

1.6

1.6

1.4

1.4

1.4

1.2

1.2

1.2

1.2

1.0

1.0

1.0

1.0

Juvenile survivorship

1.8 1.6 1.4

1.2

1.0

1.6

1.4

1.2

1.0

Fecundity

1.4

1.2

1.0

1.2

1.0

0.8

1.0

20% 30% 40% 50% 60% 70%

Figure 4. Isobar graphs showing the relationship of adult and juvenile survivorship and seasonal fecundity on estimates of lambda. 

Each panel represents a separate adult survivorship value, with the x-axis containing a range of possible juvenile survivorship 

estimates, and the y-axis containing a range of possible seasonal fecundity values. Isobars represent the lambda values expected (in 

0.2 increments) given a particular combination of vital rates. For example, at the average adult survivorship of 64%, a seasonal fecundity 

of 3.5 and juvenile survivorship of 20% would result in a lambda of approximately 1.0, whereas the same fecundity but a juvenile 

survivorship of 70% would result in a lambda of approximately 1.9.
 91

suggests the models we used are underestimating each population’s growth potential, with the magnitude of the discrepancy 

dependent on the time frame over which we consider the 

observed population change. The observed lambda, based 

on annual population censuses for both study sites, averaged 1.25 for 1996–2005 and 1.08 for 2001–05. However, 

the calculated lambdas were lower: 0.94 for Roosevelt Lake 

and 1.01 for the San Pedro/Gila study sites. A difference 

between estimated lambda and observed population trends 

is not uncommon (Beissinger and Westphal 1998, Akcakaya 

et al. 2003), although most studies do not have a good sense 

of their actual population growth trajectory. In cases where 

estimates of lambda suggested a decline when the population 

under study was believed to be stable or growing (Akcakaya 

et al. 2003, Blakesley et al. 2001, Budnik et al. 2000, Jones 

et al. 2004, Keyser et al. 2004), it is often assumed that one 

or more of the vital rates were poorly measured (Akcakaya 

et al. 2003). The discrepancy between observed population 

growth and estimated lambda in our study could arise from 

one or more factors: the census numbers were inaccurate, 

one or more of our vital rates are inaccurate, or the models 

used to describe potential population growth are inappropriate. We explore these possibilities below.

Census Numbers

One of the major objectives of this 10-year study was 

to produce highly accurate counts of all territories within the 

defined study sites. Standardized surveys supplemented by 

intensive area searches gave us high confidence that our census estimates were robust, at least for the number of territories 

and territorial individuals within the areas searched. Additionally, all areas of suitable habitat that could be reached and 

were contiguous or adjacent to the study area were surveyed. 

Although census efforts were extensive, several factors 

could influence the accuracy of the population estimates. 

Land ownership is one such factor. Within the Roosevelt Lake 

study area, most sites were on public lands and accessible to 

surveyors; portions of private land on Tonto Creek constituted 

a very small portion of the breeding habitat. However, much of 

the land on the San Pedro/Gila is privately owned, and permission to access lands increased over time such that more sites 

were surveyed and new areas were contributing to the census 

numbers (although permission to access some lands were 

lost over time as well). Delays between habitat development 

and surveys at a particular site could be another factor. Over 

much of the study period, the amount of breeding habitat at 

Roosevelt Lake increased substantially, and it is possible that 

some young habitat supported breeding flycatchers for a year 

or two before we discovered them. A final consideration is that 

rates of population increase (based on surveys) were highest 

in the early years of the study. While there may be biological 

reasons for this, it may also be due in part to refinement and 

improvement of survey techniques and skills over time, including a greater emphasis on surveying potential breeding habitat 

at Roosevelt Lake, and the access to more private lands on the 

San Pedro/Gila study site. 

Overall, despite some degree of uncertainty over which 

time frame of census numbers to consider, we believe the 

census numbers are a good approximation of the actual population size at the two study sites. Therefore, the true rate of 

increase probably lies between our highest (1996–2005) and 

lowest (2001–05) estimates, such that our observed population 

growth rate can be used to retrospectively assess the strength 

of the demographic models’ ability to describe population 

growth potential at the sites during the period surveyed. Thus, 

the discrepancy between the estimate growth potential and the 

observed population size change is 12%–31% average yearly 

change at Roosevelt Lake, and 9%–24% at San Pedro/Gila.

Adult Survivorship

Adult survivorship is the vital rate typically believed to 

have the greatest impact on the population growth models 

(based on matrix elasticity tests; Stoleson et al. 2000). We 

believe our adult survivorship estimates are robust because (1) 

they are based on the long-term tracking of over 1,000 individuals, (2) we used maximum likelihood methods to estimate 

survivorship, and (3) we had high detection rates for adults, 

suggesting most individuals were detected if alive (see chapter 

2). Additionally, we assessed the potential confounding effects 

of permanent emigration on survivorship estimates, by looking 

at how survivorship estimates changed with increasing spatial 

scale, and concluding that we were accurately tracking most 

adults (see chapter 2). Survivorship did not differ between 

sites, suggesting that the different levels of banding and resighting between the study sites in the last half of the study did not 

influence survivorship estimates; further, evidence suggested 

that most mortality occurred away from the breeding grounds. 

Adult survivorship did vary yearly, by as much as 20% (range 

= 53%–73%), and thus a 10-year mean value does not necessarily reflect the year-to-year variation. However, most yearly 

values were within 10% or less of the overall mean.

Juvenile Survivorship

Juvenile survivorship rates are also generally believed 

to have a great influence on population growth models. As 

with adults, our juvenile survivorship estimates benefited 

from the long-term tracking of many individuals, and the use 

of maximum likelihood estimation. However, although we 

banded nearly 500 nestlings over the course of this study, and 

employed extensive efforts to detect those that survived and 

returned, our detection rate for second-year (SY) adults was 

lower than that of adults (51%; see chapter 2). Many nestlings 

that were banded were not detected again until after their 

second year, and 41% of those that were detected as SY were 

not territorial (see chapter 2). Thus, detection of second-year 

adults was difficult. Additionally, our juvenile survivorship 
92 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

estimates are based on Roosevelt Lake, and the San Pedro/Gila 

may have higher (or lower) survivorship estimates. Finally, the 

longest movements we detected, especially to other drainages, 

were by juveniles, reinforcing the idea that a greater proportion of juveniles could be leaving the site than we detected 

doing so. 

However, while there are several possible reasons why 

our juvenile survivorship estimates may not be as robust as 

those for adults, it is not necessarily a major contributor to the 

discrepancy between the estimated growth potential and the 

observed population size changes. For example, when juveniles survive and return to the study sites but are not detected, 

they do not contribute to either the population census or the 

observed population growth. Once they become territorial 

and attempt to breed, they have a high probability of being 

detected and therefore are accounted for in both the estimated 

lambda and the census numbers. Non-detected permanent 

emigration of juveniles is incorporated into the survivorship 

estimates, but immigration of young adults into the study sites 

is difficult to detect. However, our juvenile survivorship probabilities are equal to or higher than other comparable passerine 

studies (see chapter 2), so there is no evidence to suggest we are 

substantially underestimating survivorship. Finally, our juvenile 

survivorship rates would have to increase by approximately 

22%–30% to produce the observed lambda of 1.08–1.25; 

while juvenile survivorship probability may reach this level 

some years, our data suggest that in most years it does not.

Fecundity

Given the large number of nests monitored each year, 

and the intensive efforts to track individuals throughout the 

season, our estimates of seasonal fecundity at the two study 

sites are also believed to be robust. The only apparent source 

of bias in our estimates is that banded females are more 

likely to be older adults, which would bias the estimate of 

seasonal fecundity toward higher, rather than lower values. 

The isobar graphs (fig. 4) suggest that seasonal fecundity 

would have to be approximately 2.4–3.4 young fledged per 

season, 0.4 to 1.8 more young fledged than average, to obtain 

a lambda of 1.08–1.25 (assuming average adult and juvenile 

survivorship rates). While some years approached this higher 

rate, others were much lower. Similarly, the average minimum lifetime productivity (see chapter 3) was 3.3 offspring 

per individual (1.7 daughters per female). Based on our 

average juvenile survivorship (34%), this level of lifetime 

productivity would mean that one adult produces on average 

only 0.56 “replacement” flycatchers in its lifetime—a rate 

that is unsustainable. Nonetheless, the seasonal fecundity 

rates recorded in this study were at or above most estimates 

from other Willow Flycatcher populations, suggesting the 

rates are not outside the norm.

The models we used assumed an equal sex ratio of young 

produced. Earlier studies documented that while the overall 

sex ratio of young from four Arizona study sites was 1:1 

male to female, it varied from site to site (Paxton et al. 2002). 

Evaluating the sex ratio from 1996 to 2000, Roosevelt Lake 

was biased toward females while San Pedro/Gila was biased 

toward males. While modeling a female bias at the Roosevelt 

Lake study site would increase the lambda estimates, the 

male bias at the San Pedro/Gila would reduce the estimated 

lambda. More work is needed to explore the full implications 

of nestling sex ratios on the growth potential at both of these 

study sites.

Immigration and Emigration

Immigration and emigration can potentially have profound effects on population growth. Most studies have no 

information on rates of immigration and emigration, assume 

the rates to be equal (canceling each other out in terms of 

population growth), and therefore do not consider immigration and emigration in their demographic models. Pulliam 

(1988) pointed out that in habitats with different quality and 

productivity levels, this assumption may not be accurate. For 

example, populations with high productivity can have estimates of population growth potential far exceeding what is 

observed (source populations), while others that are stable 

have lambda estimates that could not sustain the populations 

(sinks). Pulliam (1988) suggested that the source populations 

may be sustaining the sink populations, and cautioned that 

immigration and emigration can be important to understanding 

the population dynamics. 

Therefore, one explanation for the discrepancy between 

the census numbers and estimated growth potential at the 

Roosevelt Lake and San Pedro/Gila study sites is that they are 

population sinks being supported by an influx of individuals 

emigrating from other breeding sites. There is certainly some 

level of undetected immigration into the study sites, but it is 

unfortunately impossible to distinguish between immigration 

and the local recruitment of unbanded individuals. Further, 

the maximum-likelihood survivorship estimates incorporate 

permanent emigration into their estimates (though not distinguishable from mortality), so in running our model we 

attempted to provide some estimate of immigration as well. 

The correction that we made (3% per capita immigration) 

is based on the observed rate of between-site movement of 

banded birds, and may be an underestimate. 

Overall, though, with the location and size of many 

breeding populations of the Southwestern Willow Flycatcher 

known (Durst et al. 2006), we think it unlikely that growth of 

the Roosevelt Lake and the San Pedro/Gila sites, two of the 

largest populations known, was primarily due to immigration 

from other breeding populations. For this to be the case, substantial numbers of individuals would need to have moved into 

the two study sites from elsewhere in most years to account for 

the discrepancies in population change. Although we believe 

that our estimated low rates of immigration and emigration 

reflect the typical situation, it is possible that other sites occasionally contribute an unusual number of emigrating individu-
 93

als that could bolster the population size of our study areas. 

However, although immigration is one plausible explanation 

for why the model results differed from observed growth, and 

may have helped to increase the population growth at the study 

sites, we feel it is unlikely to have been the primary driver of 

the observed population change. 

Clearly, the discrepancies between the observed population growth and the estimated lambda are difficult to explain. 

Stoleson et al. (2000) made one of the first attempts to model 

the demographic growth potential of the Southwestern Willow 

Flycatcher. Using available data from a variety of sources and 

sites, they estimated population-wide lambdas under conservative, intermediate, and optimistic vital rate scenarios and 

arrived at values of 0.46, 0.78, and 1.11, respectively. This 

large range reflects considerable variability in the data available to them, and they concluded their study with a call for 

more research to generate better vital rates. Vital rates from 

our two study sites are at the upper end of the range presented 

by Stoleson et al. (2000), primarily reflecting better estimates 

of survivorship, and thus both Stoleson et al. (2000) and our 

work indicated population growth potential substantially lower 

than the documented rate of population change at our two 

study sites. A demographic study of the Kern River Preserve 

population in California (Noon and Farnsworth 2000) found 

similar results. Overall lambda estimates of 0.89 were estimated, even though the population was not correspondingly 

declining. These modeling attempts suggest that different types 

of demographic models will need to be employed for flycatchers to provide a better fit with their actual demographic dynamics. For example, if in fact movement is largely contributing to 

the growth of these populations, then a meta-population spatial 

approach could be used to model where the birds are moving 

from and patterns of movement across the landscape. However, 

it may be that new classes of models will need to be developed 

that can better mimic flycatcher population dynamics, and 

future efforts in this area may yield useful results. 

At this point, the most parsimonious explanation for the 

discrepancy between actual and model-predicted population 

growth may be a consistent underestimation of all vital rates. 

For example, if we increased our fecundity, juvenile survival, 

and adult survival by 10% (assuming they were underestimated by that amount), the model-generated lambda would 

approach 1.2 without requiring any one of the vital rates to 

change dramatically (e.g. 38% to 42% juvenile survival, 64% 

to 70% adult survival, and 2.0 to 2.2 fecundity). Additionally, 

a higher rate of immigration than estimated would close the 

gap between the observed and estimated growth rates, especially if the 5-year (2001–05) estimates of observed population 

change (8% annual growth) were used. 

Considering both year-to-year estimates of population 

growth potential and the actual observed population change, 

breeding flycatcher populations at our study sites appear to 

have a lambda somewhere near 1.0. This suggests that in good 

years they will exceed 1.0, and in bad years they may fall 

short of that threshold. If Roosevelt Lake and San Pedro/Gila 

are dependent on emigrants from other sites, then their fate is 

tied with breeding populations at a regional level. However, 

periods of drought resulted in lower productivity (see chapter 

3), and estimates of lambda for those years dipped to their 

lowest levels. Climate models projecting changes in climate 

due to global warming generally agree that droughts in the 

southwestern U.S. will become more frequent and more severe 

in the coming centuries (Seager et al. 2007). Additionally, 

population growth in the Southwest has been increasing such 

that water demands are generally rising, and stressors on riparian habitat may increase in the future. Thus, long-term predictions of population viability must be taken cautiously given the 

many uncertainties regarding future events.

Management and Research 

Considerations 

Population Dynamics

The lambda estimates for our two study sites were just 

above and a little lower than 1.0, and well below the observed 

census-based population growth. This suggests that either the 

two study sites are population sinks, sustained and growing via 

emigrants from other sites, or that the model (or the vital rate 

estimates it is based on) are poorly reflecting the population 

dynamics of the study sites. More research may help clarify 

which of these two scenarios is the true cause of this discrepancy. Regardless, the observed population growth was real, 

suggesting that these two populations can sustain themselves 

given adequate breeding habitat, and no significant change in 

the vital rates of the population and/or in the rate of emigrants 

from other sites. 

Population Monitoring

While vital rate values and the lambda values generated 

via demographic modeling can inform us about the health of 

a population during the period that we studied it, projecting 

these models far into the future can be misleading. The farther 

forward that population growth is projected, the lower the 

accuracy and greater the uncertainty in the projections. For 

instance, vital rates may change over time, the amount of suitable breeding habitat could vary, and future changes in climate 

and human land practices all could affect future flycatcher 

population trends in unpredictable ways. Relying solely on 

modeling estimates to direct future management would be 

problematic at best, especially with regard to population trends 

over the long term. Therefore, at least periodic population 

monitoring is needed if managers seek to track the progress 

of populations or to verify whether vital rates have remained 

stable.
94 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Demographic Modeling

Given the discrepancies between the observed changes 

in population size at our two study sites versus what was 

predicted via demographic models, we believe that further 

work is warranted to develop new models to better describe 

flycatcher population dynamics. Documenting the vital rates 

of a population of interest is an important step for informed 

management, but to fully understand whether those vital rates 

are sufficient to sustain a population requires consideration of 

the synergistic contribution that all vital rates have to overall 

population health. The models we employed are used widely 

in most other comparable studies, yet may not be able to fully 

capture the dynamic nature of flycatcher population demography. While current models can act as a starting point, new 

modeling directions may be necessary before demographic 

models can be used with confidence in the future for managing the Southwestern Willow Flycatcher. The long-term and 

multiscale demographic data collected during our study could 

be serve as the foundation for testing new model approaches.

Limiting Factors for Flycatcher Populations

Ultimately, a key question to ask with regard to the 

eventual recovery of the flycatcher in the Southwest is whether 

they are habitat limited or demographically limited. If limited by suitable breeding habitat, then the key to recovery is 

promoting an increase in the amount of riparian habitat across 

the flycatcher’s range. Based on the observed population 

growth during this study, flycatchers (at least at our two study 

sites) would have the potential to expand into new habitats if 

it were suitable and available. However, this growth may be 

sustain by immigration, a situation that may not occur in other, 

more isolated populations. Thus, any increase in the occupation of suitable breeding habitat may be slow or fast and may 

vary by region or among different populations. There may 

be areas with suitable breeding habitat that are unoccupied 

because there are not enough flycatchers to occupy all available habitat, creating a lag time between creation of the suitable habitat and full occupancy. Therefore, flycatchers may 

be limited by both breeding habitat and demographics, and 

effective future management will likely need to address both 

potential limiting factors.

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1997. A Southwestern Willow Flycatcher Natural History 

Summary and Survey Protocol. National Park Service Technical Report NPS/NAUCPRS/NRTR-97/12. 

Stoleson, S.H., M.J. Whitfield, and M.K. Sogge. 2000. Demographic Characteristics and Population Modeling. Pages 

83–93 in Status, Ecology, and Conservation of the Southwestern Willow Flycatcher. Finch, D.M. and S.H. Stoleson 

(eds). USDA Forest Service Rocky Mountain Research 

Station General Technical Report RMRS-GTR-60.

Unitt, P. 1987. Empidonax traillii extimus: An endangered 

subspecies. Western Birds 18:137–162.

U.S. Fish and Wildlife Service. 1993. Proposal to list the 

Southwestern Willow Flycatcher as an endangered species 

and to designate critical habitat. Federal Register 58:39495–

39522.

U.S. Fish and Wildlife Service. 1995. Final rule determining 

endangered status for the Southwestern Willow Flycatcher. 

Federal Register 60:10694.

White, G.C., and K.P. Burnham. 1999. Program MARK: Survival estimation from populations of marked animals. Bird 

Study 46:s120-s139.
96 The Ecology of the Southwestern Willow Flycatcher in Central Arizona
 97

Introduction

Habitat is essential for the presence, persistence, and long 

term viability of any wildlife species and thus understanding the relationship between wildlife populations and habitat 

characteristics is crucial for the long term management of 

species and communities (Morrison et al. 1998). Habitats for 

avian species provide basic resources including food, shelter, and protection from predators. Habitat for breeding must 

provide additional resources including nest structure support, 

extra food resources for raising young, and protection from 

nest predators. Thus, species need to be well adapted to their 

habitat, and understanding their habitat needs is important for 

management and conservation.

However, habitat relationships are complex, involving 

many factors, and as such quantifying habitat relationships 

is difficult (Cody 1985). Additionally, habitat may vary in its 

level of quality (Pulliam 1988), and linking habitat quality 

to lifetime fitness of species is the ultimate goal of wildlifehabitat relationship research (Wiens 1989). An important 

first step to understanding these relationships is to be able to 

predict the occurrence and estimate the amount of available 

breeding habitat for a species in a given area. This can be done 

at multiple scales, from the nest site to the full geographic 

range of a species, but as the geographic scale increases, the 

difficulty in accurately identifying potential suitable habitat 

increases (Cody 1985). One approach to quantifying habitat 

use over a large area is by developing Geographic Information Systems (GIS) based models. GIS modeling allows the 

characterization of habitat and the ability to predict suitable 

breeding habitat over a large area. In addition to issues of 

scale, temporal change in habitat adds an additional level of 

complexity to the problem. In habitats that are dynamic over 

short time scales, models can be improved by incorporating 

variables or factors that reflect temporal change (Reino 2005). 

Although using these approaches requires detailed information 

about the presence/absence or abundance of a species, it can 

ultimately provide an important tool that can be applied over a 

large geographic area.

The Southwestern Willow Flycatcher (Empidonax traillii 

extimus) is a small, migratory passerine that breeds exclusively in riparian habitats scattered throughout portions of the 

southwestern U.S. (Unitt 1987, Marshall 2000 ), and winters 

from central Mexico south to northern South America (Sedgwick 2000). Flycatcher numbers have declined precipitously 

as riparian habitats on the breeding grounds have been lost or 

modified (USFWS 1993, Marshall and Stoleson 2000), and E. 

t. extimus was listed as a federally endangered species in 1995 

(USFWS 1995). Because of the perceived link of declining 

numbers and loss of breeding habitat, substantial research has 

been directed toward determining flycatcher habitat requirements, especially at the landscape level. Yet there is still much 

that is unknown about why flycatchers occur in some areas but 

not others, and an exact understanding of the habitat requirements of the flycatcher is still elusive. Additionally, the riparian habitats upon which the flycatcher depends are spatially 

and temporally dynamic. Because of this, flycatcher breeding 

sites are constantly shifting across the landscape, making 

determination of suitable breeding habitat and protection of 

such habitat all the more difficult.

In 1999 the Arizona Game and Fish Department (AGFD) 

developed a GIS-based model (i.e., spatially explicit) of 

flycatcher breeding habitat in south-central Arizona (Hatten 

and Paradzick 2003). It was a generalist model designed to 

predict both riverine and reservoir habitats, and has been used 

in a statewide mapping effort (Dockens et al. 2004), and along 

the Rio Grande in New Mexico (Hatten and Sogge 2007). The 

AGFD model has been successful at identifying riparian areas 

where flycatcher nests are most likely to be located, but it is 

not as specific as desired for some management applications. 

That is, the AGFD model is good at finding patches of riparian 

vegetation that contain flycatcher nests, but it is less good at 

eliminating areas where nests do not occur. 

We used the AGFD model as our starting point in this 

modeling exercise, expanding upon it in several important 

ways. First, we increased the number of spatial variables 

examined to determine if the AGFD model could perform 

better. Second, we included data from all territories, not just 

those with nest locations, to obtain a broader representation of occupied habitat, rather than basing the model only 

on habitat in which actual nesting occurred. Third, because 

riverine and reservoir/lake systems differ in the amounts 

and types of riparian habitat, we modeled them separately 

to increase model accuracy within each specific system. 

Fourth, because riparian habitat is dynamic and experiences repeated cycles of habitat creation and destruction, 

we incorporated temporal variables into some of our models 

that characterized habitat stability and variability. Lastly, 

we evaluated the relationships between spatial attributes and 

demographic characteristics (e.g., productivity, mating status) in order to explore whether spatial characteristics that 

help predict suitable habitat could also distinguish between 

different levels of habitat quality (e.g., nests in gher quality 

habitat have higher productivity).

Chapter 6—Spatial Modeling
98 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Methods

Study Site 

Our two study sites were breeding populations at 

Roosevelt Lake (33o39’N, 110o58’W) and the San Pedro/

Gila River confluence (hereafter San Pedro/Gila; 32o59’N, 

110o46’W) from 1996 to 2005 (fig. 1), where we conducted 

demographic research in cooperation with AGFD (Causey 

et al. 2006, English et al. 2006). These two sites supported 

among the largest known Southwestern Willow Flycatcher 

breeding populations throughout the period of this study 

(Durst et al. 2006), with mean population sizes of 201 and 239 

individuals at Roosevelt Lake and the San Pedro/Gila, respectively. At each site, extensive survey efforts were conducted 

throughout the breeding season to detect all territorial flycatchers within the immediate and surrounding areas. 

The breeding habitat at Roosevelt Lake and the San 

Pedro/Gila consisted of a heterogeneous mosaic of discrete 

riparian forest patches of varying ages and vegetation composition, ranging from 0.2 to 43 ha in size. At both study 

sites, vegetation patches were composed of native, exotic, or 

a mixture of native and exotic tree species. Native habitat was 

characterized by Goodding’s willow (Salix gooddingii) and 

Fremont cottonwood (Populus fremontii). Exotic habitat was 

dominated by tamarisk (saltcedar; Tamarix spp.). The understory vegetation consisted of a variety of grasses, forbs, shrubs 

(mesquite [Prosopis spp.], coyote willow [S. exigua], tamarisk, Baccharis spp., and cocklebur [Xanthium strumarium]). 

Adjacent and surrounding non-riparian habitats were primarily 

composed of Sonoran Desert Uplands (Brown 1994), and in 

some cases agricultural lands. 

Although vegetation and general habitat structure was the 

same for both study sites, each site was distinct in geomorphology, extent of water regulation, and history.

Roosevelt Lake Study Site

Roosevelt Lake is a large reservoir fed by the Salt River 

and Tonto Creek. It is a major source of water for the city of 

Phoenix, and is allowed to fluctuate freely depending on precipitation and water demand. Breeding locations were found 

within the reservoir bed, at the confluence of the reservoir and 

the inflows of the Salt River and Tonto Creek, extending as 

far as 12 km upstream. Although lake levels have fluctuated 

annually, the overall trend for the reservoir from 1995 to 2004 

was a decline due to long term drought conditions (fig. 2). 

Because of lowering reservoir levels, the lakebed was exposed 

and colonized by riparian vegetation. This new habitat was 

subsequently occupied by breeding flycatchers, such that in 

2004 most breeding flycatchers were in lakebed habitat with 

the lowest territory being well below the maximum reservoir 

level. In 2005, following unusually high winter precipitation, 

Roosevelt Lake filled to near capacity, inundating much of the 

breeding habitat occupied in 2004. Breeding was documented 

in up to 23 patches comprising 242 ha of riparian habitat, 

although the number of patches and amount of habitat changed 

over the course of the study. 

San Pedro/Gila Study Site

The San Pedro/Gila study site consisted of 101 km of 

riverine habitat, centered at the confluence of the free-flowing 

San Pedro River and regulated Gila River. The number of habitat patches varied over the years, but we documented breeding 

in as many as 29 distinct habitat patches comprising approximately 222 ha of riparian forest. The San Pedro/Gila study site 

also changed over the period of this study, though for different 

reasons than the Roosevelt Lake reservoir. In particular, high 

water flows (such as measured along the Gila River downstream from the San Pedro confluence; fig. 3) create scouring 

floods that can remove large tracts of riparian vegetation. As 

the riverine riparian vegetation recovered, it created multiple 

vegetation patches of differing successional stages. Additionally, some flycatcher breeding patches, especially along the 

San Pedro River, receive substantial water input from agriculture runoff.

Surveys and Monitoring

Southwestern Willow Flycatcher breeding surveys were 

conducted from 1995 to 2005 along Tonto Creek and the Salt 

River at Roosevelt Lake, and along the Gila and San Pedro 

Rivers (fig. 4), following a standardized presence/absence survey protocol (Sogge et al. 1997). Potential flycatcher breeding 

habitat was surveyed by AGFD using tape playback at least 

three times a season; in addition, sites were searched for flycatchers multiple times between the standard survey periods. 

Due to intensive survey efforts each year, little potential breeding habitat was left unsurveyed and we believe no breeding 

patches went undetected within the defined study area (fig. 4). 

Once a flycatcher was detected, intensive searches were conducted to document breeding status (monogamy, polygamy, 

or unpaired), locate nests, and monitor productivity. For each 

territory detected a specific spatial location was recorded in 

Universal Transverse Mercator (UTM) coordinates. The exact 

spatial location was based on one of the following criteria: (1) 

the location of the first nest; (2) the next subsequent nest location; or (3) the center of the male’s defended territory when 

nest was located or for unpaired males. 

Nests were monitored following the Southwestern Willow Flycatcher Nest Protocol (Rourke et al. 1999). After 

incubation was confirmed, monitored nests were visited every 

2–4 days until the nest fledged young (presumed) or failed 

due to predation, parasitism, desertion, abandonment, weather, 

infertility, or other causes. Most re-nests followed a failed 

nesting attempt; however, double brooding, a re-nest following a successful nesting attempt, was encountered occasion-
 99

Figure 1. Location of the Roosevelt Lake and San Pedro/Gila study areas in central Arizona. 
100 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 2. Lake level fluctuations at Roosevelt Lake reservoir from 1985 to 2005, with 1995-2004 regression line of 

general trend toward lower lake levels. Each solid circle is a mean monthly value of lake level elevation. 

Year

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 

Roosevelt Lake elevation (feet)

2020

2040

2060

2080

2100

2120

2140

2160

Figure 3. Peak discharge (cfs) at the Kelvin gage, Gila River, between 1981 and 2005. Gage station was located 

downstream of the San Pedro/Gila River confluence, and corresponds to flows from both the regulated Gila and 

unregulated San Pedro rivers. 

100

1000

10000

100000

1000000

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Water year (Oct - Sept)

Log Peak discharge (cfs)

Figure 2. Lake level fluctuations at Roosevelt Lake reservoir from 1985 to 2005, with 1995–2004 regression line of general trend toward 

lower lake levels. Each solid circle is a mean monthly value of lake level elevation.

Figure 3. Peak discharge (cfs) at the Kelvin gage, Gila River, between 1981 and 2005. Gage station was located downstream of the San 

Pedro/Gila River confluence, and corresponds to flows from both the regulated Gila River and unregulated San Pedro River.
 101

Figure 4. Areas in which Southwestern Willow Flycatcher surveys were conducted and habitat modeled (area contained within the 

red lines) at the Roosevelt Lake (top) and San Pedro/Gila (bottom) study sites. 
102 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

ally. A nest was considered successful if it fledged at least one 

flycatcher nestling.

Spatially Explicit Modeling 

Spatial Variables

We developed a set of GIS predictor variables with fine 

resolution and broad scope to characterize vegetation and 

floodplain features at multiple scales (table 1). Vegetation 

and floodplain features were characterized in discrete 30x30 

m (0.09 ha) cells obtained from Landsat Thematic Mapper 

(TM) imagery and a USGS Digital Elevation Model (DEM), 

respectively. We focused upon predictor variables extractable 

from TM or DEM data because they could be created for any 

part of the project area, and throughout the state. We examined 

vegetation density, amount of edge habitat, and proximity 

of territories to patch boundaries because these are thought 

to be important to flycatchers (Sogge et al. 1997, Sogge and 

Marshall 2000), and width of floodplain because it can influence riparian plant community establishment and persistence 

(Szaro 1990, Stromberg 1993). We did not examine vegetation 

species or successional stage, two variables which may influence habitat selection (Sogge et al. 1997, Sogge and Marshall 

2000), because they could not be accurately extracted from 

TM imagery. We also explored for the first time the spatialtemporal component of habitat because it has been found 

to be an important factor in the development of bird habitat 

models (Reino 2005).

We created riparian-vegetation density grids (0.09-ha 

cells) for the project area with TM imagery and ERDAS Imagine software (Atlanta, GA). We selected TM images (path 36, 

row 37) that were acquired during cloud-free periods inside 

the months of June or July, 1985–2005. We created riparianvegetation density grids in a manner similar to Hatten and 

Paradzick (2003): (1) we calculated the Normalized Difference 

Vegetation Index (NDVI), which correlates with relative density and biomass of green vegetation (Avery and Berlin 1992), 

considering only an area within 1.6 km of perennial/intermittent waters, and (2) we distinguished riparian vegetation from 

non-riparian (Sonoran Upland) vegetation by selecting NDVI 

values >0.126. We created two additional riparian-vegetation 

density grid spatial layers that were found to be important 

(Hatten and Paradzick 2003) by selecting NDVI values >0.336 

(ND_TOP3) and NDVI >0.413 (ND_BEST; table 1). 

 We used ArcInfo GRID focal functions (ESRI 1992) to 

explore different spatial scales by characterizing vegetation 

and floodplain features within 0.3 to 72 ha circular neighborhoods, then stored results from each operation in a separate 

grid. We calculated the proportion of neighborhood covered in 

dense vegetation (NDVI >0.413) with a FOCALSUM function, with the percentage relative to the neighborhood size. We 

characterized floodplain size (ha) with SLOPE and FOCALSUM functions from the DEM because the floodplain was 

incised and flatter (slope <2.5 degrees) than its surroundings. 

We identified the distance between riparian and non-riparian 

features of the riparian-vegetation density grid with the 

EUCDISTANCE function. We characterized heterogeneity in 

vegetation density with a FOCALSTD function by calculating 

the standard deviation among 12 NDVI classes. 

In addition to the spatial variables that were created from 

a single year of vegetation data, we created a class of temporal variables that characterized the variability, stability and 

changes in predicted flycatcher breeding habitat over a range 

of years. We accomplished this by running the recalibrated 

spatial models (LakeModel_1 or RiverModel_1) in successive years, populating them with TM imagery data specific 

to each year modeled (i.e., vegetation characteristics), and 

then subtracting the five probability classes (e.g., 0%–20%, 

21%–40%, etc.) that each model produced (with probability 

classes derived from the logistic modeling, see below). This 

technique resulted in an interval-scaled change detection variable grid that contained values from -5 through 5. If a change 

variable’s class value was negative, the quality of predicted 

habitat declined over the time interval modeled. If the change 

variable’s class value was positive, the quality in predicted 

habitat increased. We also characterized the density and stability in predicted breeding habitat by calculating the mean and 

standard deviation (SD), respectively, over 5-, 10-, and 15-year 

time intervals. Characterizing the vegetation temporally 

required that we overlay the model probability grids that were 

output for each year and calculating the descriptive statistics 

with GIS GRID functions (ESRI 1992). 

Response Variables

We used the presence or absence of flycatcher territories 

to relate the spatial variables with the occurrence of flycatcher 

territories. To develop a dataset for characterizing and modeling flycatcher breeding habitat, we employed a case-control 

sampling design (Keating and Cherry 2004) at Roosevelt 

Lake in 2004 (n = 215) and San Pedro/Gila in 2000 (n = 125). 

Because the two study sites have different histories, we used 

a different survey year for the two study sites, with the year 

selected offering the best survey coverage and capturing a 

period of high flycatcher numbers within both young and 

old vegetation. We complimented the location of territories 

(presence) by creating an equal number of nonuse (absence) 

locations that were randomly generated in areas that had been 

searched and found to be void of flycatcher territories during 

the breeding season of the year modeled. We used one half of 

the territory and nonuse locations for model construction and 

the remaining half to challenge (i.e., validate) the models. To 

create the most specific models possible, we excluded nonriparian and marginal riparian vegetation from our modeling 

by only sampling and modeling riparian areas where NDVI 

cell values exceeded 0.126 (Hatten and Paradzick 2003). Retrospective sampling provided a practical way to examine our 
 103

Variable Study sitea Description

CH_1Y 1,2 Change (magnitude and direction) in probability class (1-yr interval)

CH_1YR 2 Absolute change in probability class (1-yr interval)

CH_2Y 1,2 Change (magnitude and direction) in probability class (2-yr interval)

CH_2YR 2 Absolute change in probability class (2-yr interval)

CH_3Y 1,2 Change (magnitude and direction) in probability class (3-yr interval)

CH_3YR 2 Absolute change in probability class (3-yr interval)

CH_4Y 1,2 Change (magnitude and direction) in probability class (4-yr interval)

CH_4YR 2 Absolute change in probability class (4-yr interval)

CH_5Y 1,2 Change (magnitude and direction) in probability class (5-yr interval)

CH_5YR 2 Absolute change in probability class (5-yr interval)

CREEKDIST2K 2 Distance to perennial/intermittent stream in 2000

distndbest 1,2 Distance from interior of patch to patch edge (NDVI > 0.41)

Disttop3 1,2 Distance from interior of patch to patch edge (NDVI > 0.33)

Ed120 2 Edge density in 120-m radius

Ed180 1,2 Edge density in 180-m radius

Ed240 2 Edge density in 240-m radius

Ed300 2 Edge density in 300-m radius

Ed360 2 Edge density in 360-m radius

FlOOD10 1,2 Amount of floodplain in 41-ha circle (from 10-m DEM)

FlOOD30 1,2 Amount of floodplain in 41-ha circle (from 30-m DEM)

LAKEDIST 1 Distance from lake surface 

MOD_CV5Y 1,2 Variability (CV) in probability classes (5-yr interval)

MOD_CV10YR 2 Variability (CV) in probability classes (10-yr interval)

MOD_CV15Y 2 Variability (CV) in probability classes (15-yr interval)

MOD_MN5Y 1,2 Mean probability class (5-yr interval)

MOD_MN10Y 2 Mean probability class (10-yr interval)

MOD_MN15Y 2 Mean probability class (15-yr interval)

MOD_SD5Y 1,2 Variability (SD) in probability classes (5-yr interval)

MOD_SD10Y 2 Variability (SD) in probability classes (10-yr interval)

MOD_SD15Y 2 Variability (SD) in probability classes (15-yr interval)

Nd_12 1,2 Interval-scaled NDVI variable (12 classes)

nd_best 1,2 Binary - densest vegetation (NDVI values > 0.41) 

Nd_best4 1,2 Amount of ND_BEST in 120-m radius

nd_rap 1,2 Riparian density and biomass (NDVI value)

Nd_sd4 1,2 Variability (SD) in NDVI in 120-m radius

Nd_top3 1,2 Binary – NDVI > 0.33 inside 30-m cell

patchbest 1,2 Size of patch (NDVI values > 0.41)

Patchtop3 1,2 Size of patch (NDVI values > 0.33)

Perint_sp 1,2 Distance to perennial/intermittent stream in 1992

Table 1. We created 39 predictor variables to characterize vegetation or floodplain features at Southwestern Willow Flycatcher 

territories and random nonuse locations in south-central Arizona (Roosevelt Lake and San Pedro/Gila). Use sites contained a flycatcher 

breeding territory (0.09 ha) and nonuse sites (0.09 ha) did not. We extracted vegetation variables from Landsat Thematic Mapper 

imagery (30-m resolution; 0.09 ha), and floodplain characteristics from digital elevation models. We also created change (temporal) grids 

for modeling by running lake and riverine models in two different years and then subtracting the probability classes output by the model. 

Variables with a prefix of ND were derived from NDVI; variables with a prefix of MOD are probability classes that were derived from the 

refit Model_99 (lake or riverine); and variables with a prefix of CH are change detection variables that were derived from running the 

refit Model_99 (lake or riverine) at 2 or more time intervals. Distances are from the 30 m grid that a territory was found in to the nearest 

point of the feature of interest. (a

 1 = Roosevelt Lake study site; 2 = San Pedro/Gila study site)
104 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

survey data and is well suited for animals that exhibit preferences for rare habitat types (Ramsey et al. 1994). 

Comparing vegetation and floodplain characteristics 

around the breeding territory locations with the randomly 

selected nonuse locations, we examined habitat association 

at multiple spatial scales (Ripple et al. 1991) by characterizing vegetation and floodplain features within different-sized 

neighborhoods of territory and nonuse locations. We characterized neighborhood variables at multiple scales (0.3–72 ha) 

within concentric circles (e.g., amount of densest vegetation 

[ND_BEST4]; table 1). Other variables such as patch size (e.g., 

PATCHBEST) were characterized within irregularly shaped 

contiguous patches that could be any size. For clarification, we 

defined fine scales as 0.09–1.1 ha; the smaller value is the 30-m 

resolution limit of TM imagery, and the higher value corresponds to the upper end of flycatcher territory sizes (Cardinal 

and Paxton 2005). We selected intermediate (2.5–28 ha) and 

coarse scales (>41 ha) to characterize riparian forest patches 

and floodplains, respectively (Hatten and Paradzick 2003). 

We adjusted our database to minimize the effects of 

temporal and spatial autocorrelation (Legendre 1993) because 

breeding flycatchers have high territory fidelity between years 

(see chapter 4) and are tightly clustered (Hatten and Paradzick 2003). To identify patterns in dispersion, we used nearest 

neighbor index (Boots and Getis 1988, Chou 1997). We found 

territories were significantly clumped at Roosevelt Lake in 

2004 (Z = -26.0, p <0.001) and San Pedro/Gila in 2000 (Z 

= -20.5, p <0.001), with a mean nearest neighbor (MNN) 

distance of 59 m and 50 m, respectively. Thus we minimized 

clustering in the data by randomly removing 50% of territories, producing a MNN of 103 m for Roosevelt Lake and 156 

m for San Pedro/Gila, with all territories that we removed 

from the model development stage later used during the model 

challenge (i.e., validation) stage.

Model Building and Selection

We used binary logistic regression to identify habitat 

associations and to develop equations useful for spatially 

explicit GIS habitat models, considering both univariate and 

multivariate models. Logistic regression is ideal for evaluating 

relationships between predictor variables and flycatcher occurrence because presence/absence data are binary. We evaluated more than 20 multivariate models, but for brevity’s sake 

discuss only 11 in this report: 6 models for Roosevelt Lake 

(LakeModels: 3 spatial and 3 spatial-temporal) and 5 models 

for San Pedro/Gila (RiverModels: 3 spatial and 2 spatial-temporal models). The first model that we built for each system 

(LakeModel_1 and RiverModel_1) was obtained by fitting the 

four covariates in the AGFD model to annual-specific data at 

San Pedro/Gila and Roosevelt Lake. This approach resulted 

in recalibrated SWFL breeding habitat models for Roosevelt 

Lake and San Pedro/Gila (i.e., lake or riverine, respectively). 

All of the other models we considered contained additional 

or fewer variables. To reduce the chance of producing overfit models that result from too many predictor variables and 

too few events per variable (EPV), we maintained an EPV ≥

10 (Peduzzi et al. 1996). Thus, for each covariate in a given 

model, we ensured that there were at least 10 flycatcher territories. This restricted the maximum number of variables per 

model subset to 10 at Roosevelt Lake and 6 at San Pedro/Gila. 

We used ArcInfo (GRID) to calculate and map the relative quality of breeding habitat within 0.09-ha (30x30 m) cells. 

We calculated the relative quality of breeding habitat (P) with 

the following equation: Pi

 = eg(x) / 1 + eg(x), where g(x) is the 

linear combination of parameter estimates obtained from the 

logistic regression (Hosmer and Lemeshow 1989; Keating 

and Cherry 2004). Relative habitat quality is based upon the 

probability of a flycatcher territory occurring within a given 

cell based on the logistic regression under consideration. 

Each model assigned cells a probability between 1% and 

98%, depending on the logistic regression equation employed, 

which we reclassified into 1 of 5 possible probability classes: 

1 = 1%–20% 2 = 21%–40%; 3 = 41%–60%; 4 = 61%–80%; 

and 5 = 81%–98%.

Because the models and the systems modeled are complex, we used a number of methods to judge how well the 

models fit the data. Each method can tell us different aspects 

of the model’s fit, and some models may have good results 

with one method but poor results with another. Thus, it is 

important to look at multiple methods to fully understand how 

well the model performs. To assess the accuracy of the models 

in a spatially explicit manner, we assessed model fit and accuracy with a validation dataset (territories not used in model 

development) and a classification table (Norusis 1999) using 

108 control locations at Roosevelt Lake from 2004, and 67 

control locations at San Pedro/Gila from 2000. Control locations were breeding territories not used in model development 

and were between 1 and 5 cells (30–150 m) from the territory 

locations used in model development. 

We used density of territories within the five probability 

classes as our measure of fit, reasoning that higher-probability 

habitat should contain more territories (Hatten and Paradzick 

2003). Model accuracies depended upon a probability cutpoint 

that we used to extract suitable versus unsuitable breeding vegetation (i.e., cells) from the model probability value assigned 

to each grid. For this report, we set the probability cutpoint at 

50%, with all cells ≤50% probability considered unsuitable 

and cells with a probability >50% suitable. Breeding territories that fell outside of predicted habitat were counted as an 

omission error (Story and Congalton 1986), whereas cells that 

were predicted suitable but found to be empty (contained no 

breeding territory) were counted as a commission error. We 

examined model accuracies at this 50% probability cutpoint 

by calculating (1) model sensitivity (1 – omission error), (2) 

specificity (1 – commission), and (3) overall model accuracy 

(sensitivity + specificity / 2). 

Additional methods used to evaluate the strength of our 

models in explaining the presence or absence of flycatcher territories included the G statistic (Hosmer and Lemeshow 1989), 
 105

Nagelkerke’s pseudo r2 (Nagelkerke 1991), and a receiver 

operating characteristic (ROC) statistic. The G statistic and 

Nagelkerke r2 are roughly equivalent to the F and r2 statistics, respectively, in linear regression. ROC summarizes the 

tradeoffs between model sensitivity and commission error 

(Fawcett 2004). The closer ROC is to 1, the better the overall 

fit of the model at all possible cutpoints. Additionally, we 

checked for linearity between the logit and the continuous 

variables with the Box-Tidwell test (Box and Tidwell 1962). If 

nonlinearity was observed, we examined model fit by transforming the covariate (e.g., squared, categorical, exponential). 

We examined the temporal accuracy of the models 

between 1995 and 2005 with linear regression (decadeR2), 

overlaying predicted breeding habitat (number of cells 

predicted as suitable habitat) and territory numbers (number 

of cells containing territories) specific to each year. We had 

to restrict the temporal regression analysis to 1995 and later 

because accurate flycatcher survey data did not exist prior 

to 1995. However, we used the predicted habitat between 

1985 and 1994 to create some of the temporal variables and 

to examine trends in flycatcher breeding habitat. The temporal analysis required that we populate each spatial model 

with annual-specific vegetation characteristics derived from 

a unique TM scene for that year. In contrast, the floodplain 

variables were treated as a geomorphic constant in our models 

(calculated only once from a single DEM). Finally, to decide 

between models with similar degrees of fit, we qualitatively 

assessed the biological plausibility of each model. For this 

criterion, models with a more understandable biological 

mechanism were deemed preferable to models without clear 

biological mechanisms. After selecting the best spatial and 

spatial-temporal models for each area, we summarized the 

amount of predicted breeding habitat for each year between 

1985 and 2005, generating histograms to display the variability in predicted habitat over this 20-year period. 

Demographic Analysis

In addition to territory-habitat associations, we evaluated the utility of the spatial models to predict two important 

demographic indicators, productivity and territory status. To 

accomplish this, we created a georeferenced table that contained all the territory locations, per year, spatial layers, and 

demographic attributes. While the demographic information 

was collected from the entire breeding season in each year, 

the spatial values collected from the TM images (e.g., NDVI) 

were measured during the mid-June period of each year (see 

above). All measures of productivity were at the territory level. 

For all territories without missing data, we calculated the total 

number of young fledged (presumed; see chapter 3). Productivity estimates were made irrespective of territory holders, 

and zero nesting attempts (e.g., unpaired males) were recorded 

as zero productivity. Territory status (monogamous, polygamous, and unpaired) was recorded for those territories where 

there was sufficient information. To evaluate correlations 

between the seasonal number of young fledged per territory 

and spatial information, we used Spearman’s nonparametric 

correlation rho. ANOVAs were used to test differences in spatial values by territory status, with mean values tested using a 

Shapiro-Wilks goodness-of-fit test for normality before being 

used. All statistical analyses were conducted in the statistical 

software JMP vs. 6.0 (SAS, Inc.), and all graphs were created 

in Sigma Plot 2000 (SAS, Inc.). Statistical significance was 

accepted at p<0.05.

Results

Roosevelt Lake Models

Univariate Analysis

The majority (78%) of the spatial variables we evaluated 

were significantly associated with the presence of flycatcher 

territories in 2004 (table 2). Ranked by G-statistic values, 

some of the most informative variables were distance from 

lake or river, vegetation density characteristics (NDVI), and 

temporal characteristics (e.g., mean model probability, change 

detection; table 2). Most coefficients were positively associated with the presence of flycatcher territories. For example, 

the odds of a flycatcher territory being present in a given 

area with the highest NDVI values (ND_TOP3, NDVI >0.33) 

was 13.8 times that of an area with NDVI ≤0.33 (obtained by 

exponentiating the coefficient). Negative relationships (i.e., 

a decreased probability of a territory being present) were 

distance from lake or creek, positive changes in a probability 

class within the first or second year of the change detection, 

and variation (CV) in NDVI within a 4.5-ha neighborhood 

(1999–2004).

Multivariate Models

All three spatial lake models (LakeModels 1–3, table 

3) improved classification accuracy when compared with 

the original AGFD model at Roosevelt Lake. LakeModel_1 

improved overall classification by 9.5%, LakeModel_2 by 

11.1%, and LakeModel_3 by 13.2%. The improvement in 

overall accuracy was obtained through an increase in model 

specificity (i.e., 1 - commission error), which increased 

progressively from 42.1% (Model_99) up to 72% (LakeModel_3). Conversely, at a 50% cutpoint Model_99 had the 

highest model sensitivity (93.5%), indicating it performs well 

for identifying potential habitat, but does so at a cost of poor 

specificity (table 3). LakeModel_3 had the best overall fit 

when challenged with the validation set, but its decadeR2

 was 

the lowest of the spatial models, suggesting that it was overfit 

for the year 2004. LakeModel_2 had the second highest accu-
106 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Table 2. Univariate logistic regression results for Roosevelt Lake (n = 215). Variables are presented in order of descending G values. 

Larger G values indicate a stronger association between the predictor and response variable (presence of territories), while the sign 

of the coefficient (β) indicates the direction of the association. All change detection variables (grids) were created by running the lake 

model between two or more time intervals and describing the changes (i.e., mean, SD, etc.). If the change variable’s class value was 

positive, the relative quality of predicted habitat increased in the time interval modeled, while a negative value indicates that the relative 

quality of predicted habitat declined in the time interval modeled. 

Variable β G p-value Description

LAKEDIST -0.001 59.6 <0.001 Distance from lake surface (NDVI < -0.2)

MOD_MN5Y 0.918 59.1 <0.001 Mean probability class between 1999 and 2004

ND_TOP3 2.622 53.2 <0.001 Vegetation density at site (0.09 ha) [NDVI > 0.33 ]

CH_BEST5Y 0.059 43.3 <0.001 Change in densest vegetation in 4.5-ha circle (1999–2004)

ND_SD5Y 0.145 38.45 <0.001 SD in vegetation density in 4.5-ha circle (1999–2004)

CH_5Y 0.477 34.85 <0.001 5-yr change in probability class 2001–2004

MOD_SD5Y 1.306 33.89 <0.001 5-yr SD in probability classes 1999–2004

ND_BEST4 0.048 33.2 <0.001 Amount (%) of densest vegetation in 4.5-ha circle

PATCHBEST 0.015 24.78 <0.001 NDVI > 0.43 (continguous cells)

ND_MN5Y 0.053 22.24 <0.001 Mean density of vegetation in 4.5-ha circle (1999–2004)

CH_BEST4Y 0.039 21.5 <0.001 Change in densest vegetation in 4.5-ha circle (2000–2004)

CH_BEST3Y 0.063 19.1 <0.001 Change in densest vegetation in 4.5-ha circle (2001–2004)

PATCHSIZE 0.006 18.97 <0.001 Size of patch (NDVI > 0.43)

PATCHSIZEB 0.004 15.89 <0.001 Size of patch (NDVI > 0.33)

CH_1Y -0.593 15.38 0.001 1-yr change in breeding habitat 2003–2004

DISTTOP3 0.006 13.28 <0.001 Distance from patch edge (NDVI > 0.33)

FLOOD30 0.046 12.4 0.002 Amount of floodplain in 41-ha circle (30-m DEM)

CH_4Y 0.288 11.32 0.001 4-yr change in probability classes (2000–2004)

CREEKDIST02 -0.002 7.71 0.005 Distance from 2002 creek channel (Tonto or Salt R.)

CH_3Y 0.257 7.22 0.01 3-yr change in probability classes (2001–2004)

FLOOD10 2.861 6.5 0.014 amount of floodplain or flat in 41-ha circle (10-m DEM)

CH_2Y -0.204 3.16 0.079 2-yr change in probability classes (2002–2004)

CH_BEST2Y 0.014 0.9 0.344 Change in densest vegetation in 4.5-ha circle (2002–2004)

CH_BEST1Y -0.015 0.88 0.354 Change in densest vegetation in 4.5-ha circle (2003–2004)

ND_SD4 64 0.7 0.38 SD in NDVI (vegetation heterogeneity) in 4.5-ha circle

ND_CV5Y -0.05 0.04 0.849 Variation (CV) in NDVI in 4.5-ha circle (1999–2004)

MOD_CV5Y 0.001 0 0.998 Variation (CV) in probability classes 1999–2004

racy, as judged by the validation set and the highest decadeR2; 

therefore, we believe it is the best spatial lake model.

Two spatial-temporal models (LakeModels 4 and 6) 

made significant improvements in the Nagelkerke r2 values 

compared to the spatial models, indicating improvement in 

their ability to predict where flycatcher territories would be in 

2004 (table 3). However, while LakeModel_6 had the highest Nagelkerke r2

, its decade r2 was almost zero, suggesting it 

was overfit for the 2004 data. In contrast, LakeModel_5 had a 

lower Nagelkerke r2 but its decadeR2 was the best of all models considered (table 3). Therefore, we believe LakeModel_5 

is the best model for evaluating multiple years.

LakeModel_2, which provided the best spatial accuracy 

and DecadeR2

 score of the spatial models, contained the four 

covariates found to be important in Model_99, plus a distance 

to creek variable (CREEKDIS; table 4). The WALD statistic 

indicates that vegetation density at the site (ND_TOP3) was 

the most influential covariate, followed by floodplain width 

(FLOOD30), distance to creek (CREEKDIS), and amount and 

variability in dense vegetation in a 120-m radius (ND_BEST4 

and ND_SD4, respectively). LakeModel_2 also produced 

an excellent fit between its 5 probability classes (i.e., model 

probabilities divided into 20% intervals) and territory density 

(fig. 5), explaining 98% of the variability in territory density 
 107

LakeModela r

2 b Presc Abd alle ROC f DecadeR2 g Densityh Predictedi

Spatial models

Model_99 NA 93.5 42.1 67.8 NA 0.65 0.12 845.6

LakeModel_1 0.43 88.9 54.2 71.6 0.83 0.68 0.14 671.6

LakeModel_2 0.47 80.6 72.0 76.3 0.85 0.74 0.19 462.9

LakeModel_3 0.53 84.2 72.0 78.1 0.87 0.31 0.23 403.6

Spatial-temporal models

LakeModel_4 0.62 77.8 70.1 74.0 0.91 0.41 0.14 608.7

LakeModel_5 0.51 65.7 71.0 68.4 0.86 0.82 0.13 551.2

LakeModel_6 0.68 75.0 59.8 67.4 0.93 0.03 0.17 483.3

a Refer to table 4 for model parameters

b

Nagelkerke pseudo r2 statistic

c

Percent of presence sites correctly classified (sensitivity)

d

Percent of absence sites correctly classified (specificity)

e

Overall model accuracy (sensitivity + specificity) / 2 as determined from validation dataset (n = 107)

f Receiver operating characteristic (ROC) test (area under the curve)`

g

Explained variability in the inter-annual fluctuations in # of flycatcher territories between 1995 and 2004

h

Territory density at a 50% cutpoint

i

Amount of predicted breeding habitat at a 50% cutpoint 

NA = stat not applicable for the original model because dataset contained lake and riverine data

Table 3. Spatial and spatial-temporal models resulting from the multivariate logistic regression analysis at Roosevelt Lake (n = 215). 

Model_1 was obtained by fitting a set of covariates found to be important in 1999 (Hatten and Paradzick 2003; Model_99) to 215 use/

nonuse locations from Roosevelt Lake in 2004. To calculate accuracy statistics for these models, we set the probability cutpoint for all 

models at 50% (cells with a probability value ≤50% were considered unsuitable for breeding, values >50% were considered suitable). 

Highlighted models are the best for each category.

with the following polynomial: territory density = 0.008(X2

)+ 0.011(X) – 0.009, where X is the model probability class. 

In contrast, the best fitting spatial-temporal model (LakeModel_5) produced a threshold-like relationship between the 

five probability classes and territory density (fig. 5), with 6.7% 

found within classes 1 and 2, 18.7% within class 3, and 78.4% 

within classes 4 and 5. LakeModel_5 was comprised of two 

spatial variables that quantified the density of vegetation at 

the site (ND_TOP3) and the variability in vegetation density 

within a 120-m radius (ND_SD4), plus two temporal variables that characterized the stability and variability of habitat 

(MOD_MN5Y and MOD_SD5Y, respectively) over a 5-year 

period. Both models did well in many of the measures, and 

while we believe LakeModel_5 is superior, it requires multiple 

years of TM imagery; in the absence of such temporal information, LakeModel_2 is an acceptable substitute. 

San Pedro/Gila Models

Univariate Analysis

The 10 top scoring predictor variables at San Pedro/Gila 

(as determined by the G statistic) were associated with either 

riparian vegetation density (NDVI values), proximity to patch 

edges, or temporal changes in habitat quality (table 5). Five of 

the top ten predictor variables characterized the relative density and biomass of green riparian vegetation at the location 

of the territory, or the amount of densest riparian vegetation 

(NDVI > 0.41) within a 120-m radius (ND_BEST4; table 5). 

Another three of the top ten predictor variables characterized 

the temporal variability and stability in breeding habitat quality between 1990 and 1995, and 1990–2000 (MOD_MN5Y, 

MOD_MN10Y, MOD_SD5Y). The final two best predictors 

were measures of distance from patch edge (the distance of a 

territory to the outer edge at two different riparian-vegetation 

density thresholds [DISTTOP3 and DISTNDBEST]). 
108 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Table 4. Model parameters of multivariate logistic regression models developed and tested for Roosevelt Lake ( n = 215). Accuracy 

and model fit statistics are displayed in table 3. LakeModel_1 was created by fitting a set of covariates that were found to be important 

in a previous study of flycatcher habitat (Hatten and Paradzick 2003) to use/nonuse locations and vegetation characteristics collected 

in 2004 at Roosevelt Lake. LakeModel_2 is the same as Lakemodel_1 except that it contains a proximity variable (distance to river). 

LakeModels 5 and 6 are spatial-temporal models that incorporate changes in predicted habitat (change-detection grids) observed 

between 1999 and 2004, at different time intervals. 

Variable β S.E. Wald df Sig. Odds 95% C.I. for Odds

Lower Upper

LakeModel_1 (constant = -7.288)

ND_TOP3 2.035 0.59 11.901 1 0.001 7.649 2.408 24.3

ND_SD4 0.547 0.144 14.516 1 0 1.729 1.304 2.291

FLOOD30 0.06 0.018 11.448 1 0.001 1.062 1.025 1.099

ND_BEST4 0.056 0.021 7.213 1 0.007 1.057 1.015 1.101

LakeModel_2 (constant = -6.046)

ND_TOP3 2.032 0.594 11.723 1 0.001 7.631 2.384 24.424

ND_SD4 0.387 0.154 6.276 1 0.012 1.472 1.088 1.992

FLOOD30 0.057 0.018 10.583 1 0.001 1.059 1.023 1.096

ND_BEST4 0.058 0.021 7.539 1 0.006 1.060 1.017 1.104

CREEKDIS -0.003 0.001 8.627 1 0.003 0.997 0.996 0.999

LakeModel_3 (constant = -2.348)

ND_TOP3 2.608 0.508 26.320 1 0.000 13.575 5.012 36.768

FLOOD30 0.055 0.018 8.913 1 0.003 1.056 1.019 1.095

CREEKDIST -3.235 0.809 16.010 1 0.000 0.039 0.008 0.192

LAKEDIST -0.402 0.091 19.589 1 0.000 0.669 0.560 0.799

LakeModel_4 (constant = -8.769)

ND_TOP3 2.756 0.738 13.940 1 0.000 15.739 3.704 66.884

ND_SD4 0.758 0.183 17.145 1 0.000 2.134 1.491 3.056

FLOOD30 0.073 0.021 12.529 1 0.000 1.075 1.033 1.120

ND_BEST4 0.060 0.025 5.610 1 0.018 1.062 1.010 1.116

MOD_CH3Y 0.991 0.251 15.628 1 0.000 2.693 1.648 4.401

MOD_CH2Y -1.106 0.294 14.195 1 0.000 0.331 0.186 0.588

MOD_CH1Y -0.634 0.213 8.829 1 0.003 0.531 0.349 0.806

LakeModel_5 (constant = -5.386)

ND_SD4 0.235 0.097 5.906 1 0.015 1.265 1.046 1.528

ND_TOP3 1.327 0.568 5.460 1 0.019 3.769 1.238 11.472

MOD_MN5Y 0.893 0.182 24.104 1 0.000 2.443 1.710 3.489

MOD_SD5Y 1.512 0.327 21.388 1 0.000 4.534 2.389 8.605

LakeModel_6 (constant = -4.732)

ND_TOP3 3.628 0.674 28.946 1 0.000 37.653 10.040 141.209

ND_SD4 0.339 0.153 4.882 1 0.027 1.403 1.039 1.895

FLOOD30 0.078 0.023 11.995 1 0.001 1.082 1.035 1.131

CREEKDIST -3.277 1.070 9.374 1 0.002 0.038 0.005 0.308

LAKEDIST -0.362 0.105 11.909 1 0.001 0.696 0.567 0.855

CH_3Y 0.766 0.260 8.658 1 0.003 2.152 1.292 3.585

CH_2Y -0.943 0.299 9.954 1 0.002 0.390 0.217 0.700

CH_1Y -0.749 0.245 9.322 1 0.002 0.473 0.292 0.765
 109

 Figure 5. The density of flycatcher breeding territories (n  107) increased across five modelgenerated probability 

classes at Roosevelt Lake, based on both spatial (LakeModel_2) and spatial-temporal modes (lLakeModel_5). 

Densities were obtained by dividing the number of flycatcher breeding territories (from a validation dataset) by the 

area contained within each probability class. Actual flycatcher densities would have been 2x higher if we had used 

the full dataset instead of the validation dataset (a random subset of the total dataset). 

LakeModel_2

Probability class

12345

Territory density (territories/ha)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

LakeModel_5

Probability class

12345

Territory density (territories/ha)

0.00

0.05

0.10

0.15

0.20

0.25

 Figure 5. The density of flycatcher breeding territories (n = 107) increased across five model-generated probability classes at 

Roosevelt Lake, based on both spatial (LakeModel_2) and spatial-temporal modes (LakeModel_5). Densities were obtained by dividing 

the number of flycatcher breeding territories (from a validation dataset) by the area contained within each probability class. Actual 

flycatcher densities would have been 2x higher if we had used the full dataset instead of the validation dataset (a random subset of the 

total dataset). 
110 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Variable β G p-value Description

nd_rap 14.82 64.07 <0.001 Riparian density and biomass (NDVI value)

MOD_MN5Y 1.01 50.48 <0.001 Mean probability class 1995–2000

Nd_12 0.48 49.75 <0.001 Interval-scaled NDVI variable (12 classes)

nd_best 2.78 48.31 <0.001 Binary - densest veg (NDVI > 0.41)

Nd_best4 0.11 45.58 <0.001 Amount of veg (NDVI > 0.41) in 120-m radius

Nd_top3 2.92 43.68 <0.001 Binary – NDVI > 0.33

Disttop3 0.05 40.01 <0.001 Distance from patch edge (NDVI > 0.33)

distndbest 0.06 37.79 <0.001 Distance from patch edge (NDVI > 0.41)

MOD_MN10Y 0.95 37.12 <0.001 Mean probability class (1990–2000)

MOD_SD10Y 1.88 32.05 <0.001 SD in probability classes (1990–2000)

Ed120 0.014 31.54 <0.001 Edge density in 120-m radius

MOD_SD15Y 1.78 28.28 <0.001 SD in probability classes (1985–2000)

FlOOD30 0.058 27.7 <0.001 Amount of floodplain in 41-ha circle (30-m DEM)

Ed180 0.016 22.36 <0.001 Edge density in 180-m radius

FlOOD10 4.46 21.98 <0.001 Amount of floodplain in 41-ha circle (10-m DEM)

MOD_MN15Y 0.69 21.54 <0.001 Mean probability class 1955–2000

Ed300 0.021 17.08 <0.001 Edge density in 300-m radius

patchbest 0 14.68 <0.001 Size of patch (NDVI > 0.41)

Ed360 0.022 14.39 0 Edge density in 360-m radius

CH_5 0.47 13.78 0 Absolute change in probability class (5-yr interval)

MOD_SD5Y 0.83 7.66 0.006 SD in probability classes (1995–2000)

CH_2Y 0.37 6.63 0.01 Change in prob class (+/-) between 1998 and 2000

CH_5Y 0.24 6.3 0.012 Change in prob class (+/-) between 1995 and 2000

CH_4Y 0.33 4.79 0.03 Absolute change in probability class (4-yr interval)

CH_3Y 0.26 4.37 0.04 Change in prob class (+/-) between 1997 and 2000

Nd_sd4 0.31 4.08 0.04 SD in NDVI in 120-m radius

Perint_93 0.003 3.8 0.05 Distance to perennial/intermittent stream (alris layer)

Patchtop3 0 3.32 0.07 Size of patch (NDVI > 0.33)

CH_4y 0.2 3.05 0.08 Change in prob class (+/-) between 1996 and 2000

Perint_2k -0.002 1.85 0.17 Distance to perennial/intermittent stream in 2000

CH_1Y 0.2 1.53 0.22 Change in prob class (+/-) between 1999 and 2000

MOD_CV5Y -0.57 0.96 0.33 CV in probability classes (1955–2000)

CH_1 0.17 0.89 0.34 Absolute change in probability class (1-yr interval)

MOD_CV15Y 0.48 0.78 0.39 CV in probability classes (1985–2000)

CH_3 0.12 0.71 0.4 Absolute change in probability class (3-yr interval)

CH_2 0.09 0.31 0.57 Absolute change in probability class (2-yr interval)

MOD_CV10Y 0.27 0.21 0.65 CV in probability classes (1990–2000)

Ed240 0.003 0.001 0.98 Edge density in 240-m radius

Table 5. Univariate logistic regression results for the San Pedro/Gila study site (n = 125). The test statistic (G) is calculated as 

-2(change in log likelihood) for the constant only model versus the full model (constant and predictor variable). Variables are presented 

in order of descending G values. Larger G values indicate a stronger association between the predictor and response variable 

(presence of territories). If the change variable’s class value was positive, the relative quality of predicted habitat increased in the time 

interval modeled, while a negative value indicated that the relative quality of predicted habitat declined in the time interval modeled.
 111

The remaining 33 predictor variables characterized four 

functional groups: habitat fragmentation, floodplain size, patch 

size, and heterogeneity in vegetation density. The top scoring 

variable in each functional group, in order of occurrence, was 

edge density within a 120-m radius (ED120), amount of floodplain within a 360-m radius (41-ha circle; FLOOD30), patch 

size (NDVI > 0.413), and heterogeneity in vegetation density 

within a 120-m radius (4.5-ha circle; ND_SD4). Distance to 

the pre-flood 1993 stream channel (PERINT_93) was significant, but no significant difference was detected for distance to 

the 2000 channel (table 5).

Multivariate Models

The three spatial riverine models made minimal improvements in overall classification accuracy (<6%) when compared 

to Model_99 (table 6). The refit Model_99 (RiverModel_1) 

improved overall accuracy by 1%. RiverModel_2, which 

included a distance to creek variable (CREEKDIS), decreased 

overall model accuracy by 7%. In contrast, RiverModel_3, 

which contained only a riparian density variable (ND_RAP) 

and a floodplain width variable (FLOOD30), increased overall 

accuracy by 5.2%, but achieved a poor decadeR2, suggesting it 

was overfit (table 6). 

The amount of predicted habitat and territory density 

varied by model at a 50% cutpoint (table 6). When compared 

to Model_99, RiverModel_1 reduced the amount of predicted 

habitat by 22.1%, while increasing territory density by 16.7% 

at a 50% cutpoint. Thus, RiverModel_1 was more specific and 

sensitive than Model_99. RiverModel_2 was not as sensitive 

or accurate as RiverModel_1, while RiverModel_3 had the 

greatest density of the four spatial RiverModels and the least 

amount of predicted habitat. Given the poor decadeR2

 of RiverModel_3, we believe RiverModel_1 is the best spatial model 

for San Pedro/Gila. 

The two spatial-temporal RiverModels (10 and 14, table 

6) outperformed the spatial RiverModels in most model 

accuracy and fit categories, with 6% and 13% improvements 

in Nagekerke r2 values and the highest overall accuracy from 

the validation set; this increased overall classification accuracy by 6% compared to Model_99. Both spatial-temporal 

RiverModels performed similarly across all categories, which 

is not surprising since they shared two of the same variables 

(ND_RAP and MOD_SD10Y). However, RiverModel_10 

RiverModela r

2 b Presc Abd Alle ROCf DecadeR2 g Densityh Predictedi

Spatial models

Model_99 NA 98.4 59.7 79.1 NA 0.48 0.030 2035

RiverModel_1 0.61 87.3 72.6 80.0 0.90 0.5 0.035 1586

RiverModel_2 0.64 84.1 62.9 73.5 0.91 0.52 0.031 1704

RiverModel_3 0.63 88.9 77.4 83.2 0.92 0.18 0.037 1527

Spatial-temporal models

RiverModel_10 0.68 88.9 79.0 84.0 0.93 0.42 0.038 1482

RiverModel_14 0.69 90.5 77.4 84.0 0.93 0.52 0.037 1523

a Refer to table 7 for model parameters

b

Nagelkerke pseudo r2 statistic

c

Percent of presence sites correctly classified (sensitivity)

d

Percent of absence sites correctly classified (specificity)

e

Overall model accuracy (sensitivity + specificity) / 2 as determined from validation dataset (n = 107)

f Receiver operating characteristic (ROC) test (area under the curve)`

g

Explained variability in the inter-annual fluctuations in # of flycatcher territories between 1996 and 2005

h

Territory density at a 50% cutpoint

i

Amount of predicted breeding habitat at a 50% cutpoint

NA = stats not applicable for Model_99 because dataset contained lake and riverine data

Table 6. Spatial and spatial-temporal models resulting from the multivariate logistic regression analysis at the San Pedro/Gila study 

site (n = 125). RiverModel_1 was obtained by fitting a set of covariates found to be important in 1999 (Hatten and Paradzick 2003) to 125 

use/nonuse sites from the San Pedro/Gila in 2000. To calculate accuracy statistics for these models, we set the probability cutpoint for 

all models at 50% (cells with a probability value ≤50% were considered unsuitable for breeding, values >50% were considered suitable). 

Highlighted models are the best for each category.
112 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Variable β S.E. Wald df Sig. Odds 95% C.I. for Odds

Lower Upper

RiverModel_1 (constant = -7.46)

ND_BEST4 0.09 0.02 14.37 1 0.00 1.10 1.05 1.15

ND_SD4 0.63 0.24 6.67 1 0.01 1.87 1.16 3.02

ND_TOP3 2.24 0.65 11.70 1 0.00 9.37 2.60 33.80

FLOOD30 0.04 0.02 6.66 1 0.01 1.05 1.01 1.08

RiverModel_2 (constant = -8.66)

FLOOD30 0.04 0.02 4.98 1 0.03 1.04 1.01 1.08

ND_BEST4 0.11 0.03 14.90 1 0.00 1.12 1.06 1.19

ND_SD4 0.74 0.26 8.29 1 0.00 2.10 1.27 3.47

ND_TOP3 2.10 0.68 9.52 1 0.00 8.13 2.15 30.75

STREAMDIS 0.00 0.00 4.38 1 0.04 1.00 1.00 1.01

RiverModel_3 (constant = -8.37)

ND_RAPa 0.15 0.03 28.20 1 0.00 1.16 1.10 1.23

FLOOD30 0.06 0.02 11.77 1 0.00 1.06 1.03 1.10

RiverModel_10 (constant = -10.6)

MOD_SD10Y 2.55 0.67 14.68 1 0.00 12.81 3.48 47.24

MOD_MN10Y 0.78 0.29 6.97 1 0.01 2.17 1.22 3.87

ND_RAPa 0.134 0.032 17.96 1 0.00 1.143 1.075 1.216

RiverModel_14 (constant = -8.10)

ND_RAP 14.34 3.23 19.73 1 0.00 169162 3021 9471007

MOD_CV10Y -5.66 2.09 7.30 1 0.01 0.003 0.001 0.21

MOD_SD10Y 4.34 1.12 14.91 1 0.00 76.96 8.49 697.70

a

Coefficient multiplied by 100 to correct for non-normality

Table 7. Model parameters of multivariate logistic regression models that we developed and tested for the San Pedro/Gila study site 

(n = 125) in 2000. RiverModel_ 1 was created by fitting four covariates that were found to be important in a previous study of flycatcher 

(Hatten and Paradzick 2003) to 125 use/nonuse sites and vegetation characteristics in June 2000. 

contained a 10-yr mean (MOD_MEAN10Y) and RiverModel_14 contained a 10-yr CV (MOD_CV10Y), both of 

which were output by RiverModel_1. RiverModel_14 did better 

for the decadeR2 and we believe it is the best spatial-temporal 

RiverModel.

RiverModel_1, which provided the best accuracy 

from both a spatial and DecadeR2 perspective, contained 

the same four covariates as Model_99, but fit specifically 

to San Pedro/Gila (table 7). The WALD statistic showed 

that the amount of dense vegetation within a 120-m radius 

(ND_BEST4) was the most influential covariate, followed 

by vegetation density at the site (ND_TOP3), variability in 

dense vegetation in a 120-m radius (ND_SD4), and amount 

of floodplain within a 360-m radius (FLOOD30). RiverModel_1 also produced an excellent fit between its five probability classes and territory density (fig. 6), explaining 95% 

of the variability in nest density with the following polynomial: territory density = 0.0052x2

 - 0.0176x + 0.0151, where 

X is the model probability class. Both spatial-temporal RiverModels (10 and 14) obtained 23%–27% greater territory 

densities and 25%–27% less predicted habitat than Model_99 

(at a 50% cutpoint; table 6). From a temporal accuracy 

assessment, though, there was little observed difference 

between the spatial (0.18–0.52) and spatial-temporal RiverModels (0.42–0.52). Both spatial-temporal RiverModels 

contained change-detection variables that spanned a 10-year 

time frame (MOD_CV10Y, MOD_SD10Y), plus a vegetation 

density variable (ND_RAP; table 7). While not dramatic, 

RiverModel_10 did achieve an 8.6% increase in territory 

density over RiverModel_1, while decreasing predicted 

habitat by 6.6%. Thus, the temporal dimension of breeding 

habitat did modestly increase the sensitivity and specificity 

compared to the best spatial model. 
 113

Figure 6. The density of flycatcher territories (n = 62) found within five probability classes at San Pedro, produced 

from spatial model RiverModel_1. Densities were obtained by dividing the number of flycatcher breeding 

territories (from a validation dataset) by the area contained within each probability class. Actual flycatcher densities 

would have been 2x higher if we had used the full dataset instead of the validation dataset (a random subset of the 

total dataset). 

RiverModel_1

Probability class

12345

Territory density (territories/ha)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Figure 6. The density of 

flycatcher territories (n = 62) 

found within five probability 

classes at San Pedro/Gila, 

produced from spatial model 

RiverModel_1. Densities 

were obtained by dividing the 

number of flycatcher breeding 

territories (from a validation 

dataset) by the area contained 

within each probability class. 

Actual flycatcher densities 

would have been 2x higher if 

we had used the full dataset 

instead of the validation 

dataset (a random subset of 

the total dataset). 

Flycatcher Habitat: 1985–2005 

Amount of Predicted Habitat Over Time

The amount of predicted habitat between 1985 and 2005 

changed markedly, though not necessarily progressively, at 

both Roosevelt Lake and San Pedro/Gila (fig. 7, table 8). At 

Roosevelt Lake, there was a general increase in the amount 

of habitat from 1985 to 2004, particularly in the 1995–2004 

period (fig. 7), but the amount of habitat fluctuated along with 

lake levels. Most noticeable is the drop in predicted habitat in 

2005, when the reservoir rose to near capacity; reduction in 

habitat was due to both the inundation of suitable and occupied habitat, as well as the model having difficulty predicting 

the semi-inundated habitat that was occupied by flycatchers in 

2005. At the San Pedro/Gila, the amount of predicted habitat 

also fluctuated over the 20-year period (fig. 7, table 8). The 

least amount of habitat occurred in years of flood (1985, 1993) 

and severe drought year (2002). Habitat was greatest in the 

periods between these roughly 10-year events. 

The amount of estimated habitat varied based upon which 

model was used and what cutpoint we selected (table 9) (fig. 

8, table 8). Changes in the cutpoint for determining suitable 

habitat are trade-offs between specificity and sensitivity, as 

increasing specificity decreases sensitivity, and vice versa 

(table 9); thus the best cutpoint varies by model, and which 

cutpoint is best should be evaluated with specific management 

objectives in mind. Additionally, the model used can change 

the amount of predicted habitat. Generally, the spatial-temporal model at Roosevelt Lake predicted more habitat, but the 

proportion of additional habitat predicted varied by year. At 

the San Pedro/Gila study site, the spatial-only model tended to 

predict more habitat, but not in all years (fig. 8). 

Factors Influencing Changing Amounts of 

Habitat

At Roosevelt Lake, the strongest factor influencing the 

amount of predicted habitat was the lake elevation. Roosevelt 

Lake’s water level hovered between 2,120 to 2,140 ft elevation from 1985 to 1989, then fluctuated greatly between 1989 

and 2006 (2,035–2,150 ft; fig. 2). In spite of the fluctuations, 

there was a general downward pattern in water surface elevations between 1992 and 2003, before rising again in 2005. 

The amount of predicted habitat at Roosevelt Lake inversely 

mirrored the water level, with the least predicted habitat in 

the highest water years of 1985 and 2005, and an irregular 

increase in the predicted habitat between 1991 and 2004. 

From 1985 to 2005, the correlation between water level and 

predicted habitat was strong (-0.86, p = 0.004), indicating that 

low reservoir levels provide more habitat for flycatchers than 

high reservoir levels. While rainfall may contribute to overall 

amounts of predicted habitat, the relationship between rainfall 

and amount of predicted habitat was confounded by the strong 

relationship of the lake level; years with heavy precipitation 

saw increasing lake levels and reduction in the amount of 

predicted habitat. 
114 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Figure 7. Amount of predicted habitat, 1985-2005, at the Roosevelt Lake (top) and San Pedro (bottom) study sites. 

Roosevelt Lake predicted habitat was from LakeModel_2, using a 50% cutpoint, while San Pedro predicted habitat 

was from RiverModel_1, using a 50% cutpoint. See Table 9 for the exact amount of hectares per model per year.

Year

198519861987

19881989

19901991

19921993199419951996

19971998

199920002001

20022003

20042005

Predicted habitat (ha)

0

200

400

600

800

1000

1200

1400

1600

Year

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

0

100

200

300

400

500

600

Roosevelt Lake study site 

Predicted habitat (ha)

San Pedro/Gila study site 

Figure 7. Amount of predicted habitat, 1985–2005, at the Roosevelt Lake (top) and San Pedro/Gila (bottom) study sites. Roosevelt Lake 

predicted habitat was from LakeModel_2, using a 50% cutpoint, while San Pedro/Gila predicted habitat was from RiverModel_1, using a 

50% cutpoint. See table 9 for the exact amount of hectares per model per year.
 115

Roosevelt Lake study site San Pedro/Gila study site

Year LakeModel_2 LakeModel_5 RiverModel_1 RiverModel_14

1985 53 NA 719 NA

1986 146 NA 1339 NA

1987 159 NA 1393 NA

1988 160 NA 1158 NA

1989 125 NA 989 NA

1990 290 NA 1070 NA

1991 155 NA 1331 NA

1992 152 NA 992 NA

1993 171 NA 746 NA

1994 251 NA 1157 NA

1995 143 213 887 797

1996 268 294 1255 1082

1997 314 347 1366 1217

1998 303 376 1392 1240

1999 280 393 1303 1187

2000 280 376 946 910

2001 386 448 864 845

2002 375 458 406 685

2003 475 553 732 819

2004 483 556 851 864

2005 41 55 1085 1021

Table 8. Amount of predicted habitat (in ha) at the Roosevelt Lake and San Pedro/Gila study sites using the best spatial and spatialtemporal models from each site. Spatial models were run from 1985 to 2005, while the spatial-temporal models (which include information 

from earlier years) were run from 1995 to 2005. For all models, we used a 50% cutpoint to determine the amount of predicted habitat.

At the San Pedro/Gila, we found a strong quadratic relationship (r2

 = 0.51) between winter rains (October – March) 

and mean density of riparian vegetation (NDVI > 0.41) (fig. 

9; Mean NDVI = 0.01(RAIN) – 0.001(RAIN2) + 0.458). 

Additionally, there was a weaker but significant quadratic 

relationship between fall/winter rains and predicted flycatcher 

habitat at San Pedro/Gila (r2

 = 0.37, p-value = 0.017; predicted 

habitat = 1007.04 + 262.8 (RAIN) – 17.999 (RAIN2)). The 

years with the lowest mean NDVI values corresponded to 

years with the heaviest fall/winter rains (1985, 1993, 1995) or 

extreme droughts (2002). Given the importance of vegetation 

density in our models, it is not surprisingly that the amount of 

predicted habitat was lowest during the drought year in 2002. 

Perhaps correspondingly, the years with the greatest amount of 

predicted habitat (1987 and 1998) followed major flood events 

by 4 to 5 years. 

Over a 20-year interval at San Pedro/Gila, patch numbers 

were lowest in 1985 and highest in 1994. An examination of 

the satellite imagery revealed that the scouring flood of 1982 

appeared to have removed huge swaths of the riparian vegetation, greatly reducing the number of patches; thus, 1985 had 

relatively few patches. As the riparian vegetation increased 

from 1985 to 1992, there was a corresponding increase in the 

number of patches. In 1993 another large flood fragmented 

the riparian corridor again. However, unlike the 1982 flood 

that was much larger, the 1993 flood apparently fractured 

but did not remove much of the riparian habitat, leading to 

an increased number of patches. From 1994 until 2005 there 

appeared to have been a slow revegetation process during 

which the number of distinct patches decreased due to consolidation, resulting in fewer but larger patches. 

Relationship of the Amount of Habitat and 

Flycatcher Numbers

At Roosevelt Lake, there was a strong positive relationship between the increase in predicted habitat and the increasing number of territories (1995–2004), with a correlation of 

0.90 (p<0.001) using the amount of predicted habitat (50% 

cutpoint) for LakeModel_5 (fig. 8). Fitting a regression line to 

the relationship suggests (if the relationship were causal) that 
116 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Pedro (bottom) study sites, 19952005. For each site, the best spatial (LakeModel_2 and RiverModel_1) and 

spatial-temporal (LakeModel_5 and RiverModel_14) models were used at a 50% cutpoint to predict the amount of 

habitat (Roosevelt Lake and San Pedro, respectively). The number of detected flycatcher territories were overlayed 

to show relationship with the amount of changing habitat and changes in the number of territories. See Table 9 for 

exact amounts of predicted habitat. 

Year

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Predicted habitat (ha)

0

200

400

600

800

1000

1200

1400

1600

Number of territories

0

20

40

60

80

100

120

140

160

180

RiverModel_1 200

RiverModel_14 

# territories

0

50

100

150

200

250

0

100

200

300

400

500

600

Year

LakeMode Roosevelt Lake l_2

LakeModel_5

# territories

Predicted habitat (ha)

Number of territories

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

San Pedro/Gila 

Figure 8. The amount of predicted habitat and number of flycatcher territories at the Roosevelt Lake (top) and San Pedro/Gila (bottom) 

study sites, 1995–2005. For each site, the best spatial (LakeModel_2 and RiverModel_1) and spatial-temporal (LakeModel_5 and 

RiverModel_14) models were used at a 50% cutpoint to predict the amount of habitat (Roosevelt Lake and San Pedro/Gila, respectively). 

The number of detected flycatcher territories were overlayed to show relationship with the amount of changing habitat and changes in 

the number of territories. See table 9 for exact amounts of predicted habitat.
50% cutpoint 60% cutpoint 70% cutpoint 

Pres Abs All Pres Abs All Pres Abs All Pres 

Roosevelt Lake

Model_ 99 93.5 42.1 67.8 93.5 49.5 71.5 91.7 51.4 71.55 83.3 

Habitat (ha) 845.6 762.1 680.9 549.2Nests 101 101 99 90

Density 0.12 0.13 0.15 0.16

LakeModel_2 80.6 72 76.3 75 77.6 76.3 64.8 82.2 73.5 52.8 

Habitat (ha) 462.9 385.1 312.8 234.2Nests 87 81 70 57

Density 0.19 0.21 0.22 0.24

LakeModel_5 65.7 71 68.35 61.1 76.6 68.85 49.1 40.6 44.85 43.5 

Habitat (ha) 551.2 465.0 386.3 311.2

Nests 71 66 53 47Density 0.13 0.14 0.14 0.15

San Pedro/Gila

Model_ 99 98.4 59.7 79.1 90.5 64.5 77.5 80.9 79.0 80.0 76.2 

Habitat (ha) 2035 1684 1327 910

Nests 61 57 51 48Density 0.03 0.03 0.04 0.05RiverModel_1 87.3 72.6 80.0 82.5 80.6 81.5 73.0 85.5 79.2 62.9 

Habitat (ha) 1586 1309 1002 644Nests 55 52 46 39Density 0.03 0.04 0.05 0.06RiverModel_14 90.5 77.4 84.0 84.1 79.0 81.6 81 85.5 83.2 69.8 

Habitat (ha) 1523 1234 938 634Nests 57 53 51 44Density .04 .04 .05 .07

Table 9. Accuracy results for the best models at the Roosevelt Lake and San Pedro/Gila study sites were obtained by overlaying predicted breeding modeled validation use/nonuse locations. If a cutpoint was 50%, all cells with a probability ≤ 50% were coded unsuitable, and cells with a probability > Habitat is the amount of vegetation considered suitable for flycatcher breeding at a given cutpoint. The density of territories (territories / ha) was detenumber of territories by the area considered suitable at each cutpoint. Presence (Pres) refers to the percent of territories correctly classified (model srefers to the percent of nonuse sites correctly classified (model specificity), and the overall accuracy (All) refers to the combined accuracy: (sensitivi
118 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

 Figure 9. The quadratic relationship between fall/winter rains and mean riparian vegetation density (considering 

NDVI values of 0.41 or greater) at the San Pedro study site. Those years with the lowest mean NDVI values 

corresponded to years with the heaviest Fall/Winter rains (1985, 1992, 1993, 1995), presumably due to floods, or 

extreme drought (2002), presumably due to water-stressed vegetation, with years of intermediate rainfall having the 

highest NDVI values. 

0.5200

0.5100

1997

Mean ndvi value (NDVI > 0.41)

1998 1987 0.5000 1996

1999 1991 1990 2005

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

Fall/winter rain (Oct - May) - inches

0.4900

1994 2004

0.4800

0.4700

0.4600

0.4500

0.4400

2003

2002

2001 2000

1995

1993

1992

1986 1988

1989

R Sq Cubic =0.507

Figure 9. The quadratic 

relationship between fall/

winter rains and mean riparian 

vegetation density (considering 

NDVI values of 0.41 or greater) 

at the San Pedro/Gila study 

site. Those years with the 

lowest mean NDVI values 

corresponded to years with 

the heaviest Fall/Winter rains 

(1992, 1993, 1995), presumably 

due to floods, or extreme 

drought (2002), presumably due 

to water-stressed vegetation, 

with years of intermediate 

rainfall having the highest 

NDVI values. 

one flycatcher territory is supported for every 2 ha of predicted 

habitat. However, the pattern at San Pedro/Gila was markedly different. The correlation between the number of flycatcher territories at San Pedro/Gila and the amount of predicted 

habitat from RiverModel_14 (at a 50% cutpoint) was negative 

and weak (-0.33, p = 0.31). For example, the amount of predicted 

habitat declined at the San Pedro/Gila from 2000 to 2004, yet the 

number of flycatcher territories continued to increase (fig. 8). 

Demographic Relationships to Spatial 

Characteristics

In general, the spatial variables that provided the strongest relationships in predicting territories had much weaker 

relationships with productivity variables (table 10). Although 

there were a number of correlations that were statistically 

significant, the relationships were generally weak, with all 

correlation coefficients under 0.2. NDVI values did not differ 

based on flycatcher territory status (unpaired, monogamous 

breeding, or polygamous; F2,1826 = 0.05, p = 0.96) nor 

did NDVI neighborhood values (F2,1853 = 0.85, p = 0.43). 

However, model probability did differ between territory status 

categories (F2,1825 = 3.76, p = 0.02), with unpaired territories 

having a lower mean model probability (0.61) versus monogamous (0.66) and polygamous (0.66) territories; this pattern 

was similar between sites. However, as with the models that 

predicted territory occurrence, the Roosevelt Lake and San 

Pedro/Gila study sites had different relationships between the 

spatial information and several of the demographic variables 

(table 10). For example, the neighborhood average of NDVI 

values (ND_BEST4) had a stronger relationship to total young 

fledged at San Pedro/Gila (rho = 0.1859) than at Roosevelt 

Lake (rho = 0.1047), while the NDVI value at the specific territory (ND_RAP) provided a stronger relationship at Roosevelt 

Lake (rho = 0.1713) than the San Pedro/Gila (rho = 0.0901). 

Discussion

The starting point for increasing our understanding of the 

relationships between flycatcher breeding site selection and 

spatial characteristics was to build upon the GIS-based habitat 

model developed in 1999 by AGFD (Hatten and Paradzick 

2003). The AGFD model (Model_99) is a generalized habitat 

model, designed to predict flycatcher habitat in both reservoir and riverine systems. Model_99 has been used to predict 

flycatcher breeding habitat in riverine and reservoir systems 

statewide (Dockens et al. 2004), assess changes in breeding 

habitat at Roosevelt Lake between 1985 and 2001 (Paradzick 

and Hatten 2004), and assist in the definition of critical habitat 

(USFWS 2005). However, fitting spatial models separately 

to each specific system (i.e., lake vs river) led to improved 

models for both systems because each have different under-
 119

Roosevelt Lake San Pedro/Gila

Variable n rho n rho

Distance from creek (CREEKDIST2K) 762 0.0212 894 -0.0095

Model probability value 746 0.0725 885 0.0695

Distance from patch edge (DISTTOP3) 762 0.1076 894 0.0936

Neighborhood NDVI value (ND_BEST4) 762 0.1047 894 0.1859

Neighborhood NDVI standard deviation (ND_SD4) 762 0.003 894 -0.0423

NDVI value (ND_RAP) 738 0.1713 894 0.0901

Patch size (PATCHTOP3) 623 0.0884 729 0.1537

Table 10. Spearman’s correlation (rho) results of productivity (number of young fledged) and spatial characteristics for the Roosevelt 

Lake and San Pedro/Gila study sites (Bold = p < 0.05)

lying drivers that influence habitat establishment, development, and persistence. Overall, the efforts presented here 

greatly improved our ability to predict flycatcher habitat, 

increased our insight into flycatcher habitat needs and the 

dynamics of their habitat, and led to the development of 

more powerful tools. 

Spatial Models

Reservoir and riverine systems are fundamentally different in many characteristics, and our data supported our prediction that developing separate spatially-explicit models for 

these two types of riparian habitat could result in significantly 

better model fit. Modeling specifically at Roosevelt Lake produced the greatest improvements observed over the Model_99. 

In particular, the distance to river increased model specificity, 

as it decreased the probability of including habitat patches that 

were farther from the rivers, which earlier models could not 

exclude. The importance of distance to water (both river and 

lake) may be related to the age of habitat, with younger habitat 

developing closer to water due to fluctuations in lake levels 

leaving bare ground suitable for vegetation establishment 

and development. Based on movement patterns, it appears 

flycatchers prefer younger habitat when it is available (see 

chapter 4). Floodplain width was also important because it 

relates to several key parameters that affect vegetation development and growth, including sediment/substrate availability 

and water table depth. Another distinction between the lake 

models versus Model_99 was the increased importance of 

NDVI values at the territory location versus in a surrounding 

area (neighborhood). This may reflect the fact that patches 

tend to be much larger at Roosevelt Lake compared to the San 

Pedro/Gila, and therefore the vegetation may be more uniform 

around each territory than at the San Pedro/Gila where there 

are smaller patches with more adjacent edge. 

The river-specific models for the San Pedro/Gila were 

much closer to the generalized Model_99, which suggests that 

Model_99 was more closely fitted for a riverine system than a 

reservoir system. Therefore, improvements in fit were negligible for the spatial only considerations. Unlike Roosevelt Lake, 

distance to water had little influence on the performance of the 

models. This may be due to a number of reasons that are possibly related and not mutually exclusive. For example, a high 

water table (which promotes riparian vegetation density and 

vigor) may occur away from the active channel due to uneven 

topography within the floodplain and subsurface water flow. 

Occupied patches that were away from the river may also have 

been supported by water from adjacent agriculture or wastewater. It may also be that a patch has better long-term persistence 

probability if it is not within the major scour zone of the active 

channel. Additionally, heterogeneity in NDVI (SD or CV) 

was an important factor in the river models, perhaps relating 

to stability versus dynamics of riparian habitat along the river, 

flooding, degree of edge, development of new/young habitat at 

periphery of older vegetation, etc. 

Spatial-temporal Models

The greatest increase in accuracy came from applying 

a temporal component to both the Roosevelt Lake and San 

Pedro/Gila models. The importance of the temporal component within the model demonstrates that flycatcher habitat is 

a dynamic rather than static environment, and including this 

in models predicting suitable riparian habitat is important. 

The dynamic nature of riparian habitat has ramifications for 

flycatcher movement patterns and metapopulation dynamics. 

Although there is evidence that flycatchers will visit young 

habitat in the year or two preceding occupation, it is likely that 

flycatchers determine suitability in the year that they settle 

into a territory. Therefore, the temporal aspects of the model 

may be a surrogate for features that the spatial-only model has 

not directly measured but that flycatchers use to determine age 

and stability. For example, vegetation structure and density 

can be partially inferred from NDVI, but multiple years of 

high NDVI values in the same place may reflect a more static, 

homogeneous, or older vegetation structure that is not favored 
120 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

by flycatchers. More study is needed to understand these links 

between the temporal modeling of habitat and what a flycatcher perceives in the breeding season.

For Roosevelt Lake, the best spatial-temporal model 

included a 5-year mean of NDVI values, which may help the 

model identify (as low probability) the very young vegetation that has a high NDVI value but has not reached an age 

to provide the structural density and complexity required by 

nesting flycatchers. Many of the temporal models fitted to 

Roosevelt Lake had unexpected or contrary relationships, and 

we believe that the stochastic nature of the reservoir fluctuations confounded modeling efforts. For example, from 1995 

to 2005, the reservoir water level generally declined, but it 

did increase in some years—most dramatically in 2005. Thus, 

while the general trend was for more habitat to emerge on the 

exposed lakebed, there were several times during this period 

when habitat was destroyed. Nonetheless, fluctuating reservoirs can provide suitable habitats for flycatchers, and in some 

ways mimic the cycle of habitat creation and destruction that 

historically occurred in large river systems. Thus, although 

we might consider a reservoir system to be more stable than 

a riverine one, fluctuating lake levels may in fact create many 

of the dynamic disturbance events and responses that riverine 

habitats typically experience on less regulated rivers.

At the San Pedro/Gila, temporal consideration was also 

important, with the best temporal scale being 10 years. This 

temporal period may correspond with the roughly 10-year 

cycles of flood and drought that had such a marked effect on 

the amount of predicted habitat. Thus, approximately every 10 

years in this 20-year period we saw a decrease in predicted flycatcher habitat, followed generally by an increase in predicted 

habitat peaking at 5 years post-event, then declining until 

another event restarted the cycle. One important consideration is that the year in which the model is built (2000 for the 

case of the San Pedro/Gila) will influence the exact statistical 

relationships of the model. Building the same model in other 

years may change some of those relationships. One approach 

for future efforts would be to build models in multiple years 

and average the resulting models for an even more temporallyrobust model.

Reservoir Versus Riverine Systems

Both differences and similarities in the model structures 

for the two systems can help elucidate habitat characteristics 

correlated with flycatcher presence. Overriding all other considerations is the importance of NDVI-based variables, both 

for the lake and river system breeding habitats, which underscores the importance of dense, vigorous riparian vegetation as 

a major selection criteria for breeding flycatchers. Additionally, heterogeneity in NDVI is important, reflecting factors 

such as canopy roughness, patchiness, habitat mosaic, openings, and different vegetation types. This suggests that beyond 

dense riparian vegetation, flycatchers chose habitat that is 

heterogeneous in structure. This use of heterogeneous structure may be related to foraging behavior and diet, by providing 

open spaces to hawk insects as well as increased arthropod 

biomass and diversity associated with higher environmental 

heterogeneity. 

One of the primary differences between our reservoir 

and river models was that distance to water was important at 

Roosevelt Lake but not at the San Pedro/Gila. At Roosevelt 

Lake, flycatchers quickly established territories in developing 

habitat that was closer to the receding lake, and used refuge habitat near rising water levels during inundation years; 

these two characteristics may explain the strong relationship 

between territories and distance to water. At San Pedro/Gila, 

some models showed that habitat farther from the river was 

more likely to be occupied, perhaps due to scouring flood 

impacts, water table influences, or importance of agriculture 

runoff at some breeding locations. Scouring floods in 1985 

and 1993 may have destroyed much of the habitat directly 

adjacent to the river, forcing flycatchers into riparian woodlands farther from the river. 

Another difference between the two systems was that 

patch size was more important for the San Pedro/Gila models 

than the Roosevelt Lake models. Patch sizes at Roosevelt Lake 

were typically larger, which may reduce the importance of 

patch size as a predictive factor there compared to the more 

fragmented San Pedro/Gila study site. Probably related, the 

scale of NDVI differed between the two systems, with the lake 

models favoring a local scale (ND_RAP) of NDVI compared 

to Model_99 (120m radius scale), whereas the river model 

also favored the neighborhood estimate. The stronger effect of 

a neighborhood NDVI value at the San Pedro/Gila may reflect 

the typically smaller habitat patches.

Predicted Habitat

Predicting an exact amount of suitable habitat for breeding flycatchers is difficult, and the amount of predicted habitat 

can vary for two important model-based reasons. First, all of 

the spatial and spatial-temporal models produce suitability 

probabilities per spatial cell between 0% and 100%. While 

flycatchers are far more likely to be found in high probability 

areas, some are found in low probability areas. However, to 

predict the amount of habitat a cutpoint, or probability threshold, needs to be chosen at which “suitable” (i.e., high model 

probability) habitat is dichotomously distinguished from 

“unsuitable” (i.e., low model probability) habitat. For all models, lower cutpoint will lead to greater amounts of predicted 

habitat and higher model sensitivity, but lower model specificity. Inversely, as the cutpoint is increased, the model’s specificity increases, but the amount of predicted habitat and the sensitivity decrease. This is an issue inherent with all models of 

this kind, and there is no perfect cutpoint. Overall, the spatial 

models built for this study were better in both sensitivity and 

specificity than the more generalized model (Model_99). The 

new models predict less high-probability habitat compared to 

Model_99, while improving classification accuracy. The large 
 121

improvement provided by LakeModel_2 shows that the first 

modeling effort (Model_99) did not evaluate a crucial variable for understanding a reservoir system compared to a river 

system—distance from water. 

Another source of variation in the amount of predicted 

habitat is the choice of which model is used. At Roosevelt 

Lake, the amount of habitat predicted per year differs among 

the top spatial (LakeModel_2) and spatial-temporal model 

(LakeModel_5) at a 50% cutpoint. This is also true for the San 

Pedro/Gila study site for RiverModel_1 and RiverModel_14. 

In general, the models for each system show similar trends, 

varying only in the exact amount of habitat. Thus, it is important to keep in mind that determining the amount of predicted 

habitat is a much more challenging prospect than the already 

difficult task of identifying breeding habitat. Nonetheless, 

the differences between the estimates of habitat are relatively 

small, suggesting that the models are converging on a true, but 

undeterminable amount of suitable breeding habitat.

The amount of predicted habitat changed over time, as 

highlighted by the better performance of the spatial-temporal 

models. At Roosevelt Lake, the maximum amount of predicted 

habitat was in 2004, while the least amount of habitat was in 

the following year (2005) when the lake rose to inundate most 

historical breeding habitat (fig. 7). The amount of predicted 

habitat also fluctuated at the San Pedro/Gila study site, being 

greatest in 1987 and 1998, and lowest amount during the 

severe drought in 2002, with nearly a 1,000-ha difference 

between the year with the most habitat and that with the least.

The changes in the amount of predicted habitat at our 

two study sites are ultimately linked to water and time. At 

Roosevelt Lake, the fluctuating reservoir levels had an overwhelming effect on the amount of habitat, as very little of the 

total habitat was above the reservoir’s maximum level between 

1995 and 2005. At the San Pedro/Gila, rainfall and its influence on river flows had a dominant effect. In general, too little 

rainfall (such as in 2002) or too much (e.g., scouring floods) 

decreased the amount of predicted habitat at the San Pedro/

Gila site, with the highest levels of predicted habitat occurring 

in the middle-range of rainfall for this period. 

Linking Flycatcher Numbers to the Amount of 

Habitat

At Roosevelt Lake, the changes in the number of 

breeding flycatchers appeared to be related to the amount 

of predicted habitat. As the amount of predicted suitable 

habitat increased, the breeding population at Roosevelt Lake 

increased. The availability of this new habitat, coupled with 

successful productivity of flycatchers, may have led to the 

observed parallel expansion of habitat and the flycatcher 

population at Roosevelt Lake. However, at the San Pedro/

Gila there was no apparent relationship between the amount 

of predicted habitat and the increasing population size. For 

example, the amount of predicted habitat declined at the San 

Pedro/Gila from 2000 to 2004, yet the number of flycatcher 

territories continued to increase. It may be that flycatcher 

populations along the San Pedro, which has 2–3 times more 

predicted habitat than Roosevelt Lake for a similar population 

of flycatchers, may not have been limited by breeding habitat 

over the course of our analysis (1995–2005); there may never 

have been enough flycatchers to occupy all suitable habitat. 

Instead, San Pedro numbers may have been more influenced 

by other factors such as survivorship, predation, productivity, 

or dispersal outside the drainage. Because the two study sites 

show different relationships between predicted habitat and the 

number of territories, linking the amount of habitat needed to 

support each territory remains elusive. 

Demographic Relationships with Spatial 

Characteristics

The spatial models were constructed to predict the 

occurrence of flycatcher territories, and the way in which 

they were developed did not take into account factors such as 

habitat quality (e.g., higher productivity, higher survivorship). 

Although habitat identified as having a higher probability of 

supporting a flycatcher territory is inferred to be higher quality habitat than locations with lower probabilities, density by 

itself does not necessarily equate to quality (van Horne 1983). 

At both sites, there may be occupied habitat of both high and 

low quality in terms of how habitat influences or mediates productivity, survivorship, and ultimately lifetime fitness. Thus, 

a direct link between probability of occurrence and habitat 

quality cannot be assumed.

Based on our expanded modeling, the relationship 

between productivity and measures of spatial habitat quality 

(as defined by our models) was weak, suggesting that factors 

not measured by the spatial variables are also influencing productivity, given a flycatcher territory location. Two such possible factors are predator density and arthropod biomass. Thus, 

we believe that there may be value in having future spatial modeling efforts investigate whether incorporating other measures 

of habitat quality, such as arthropod biomass and densities of 

nest predators, can improve spatial models of habitat quality.

Management and Research 

Considerations

Considering Temporial Changes

Both the strong support for the spatial-temporal models, 

and the changes in predicted habitat over a 20-year period 

at both Roosevelt Lake and San Pedro/Gila, demonstrate 

the dynamic nature of riparian habitat and the importance of 

considering temporal changes in management and modeling 

efforts. For example, safeguarding existing flycatcher breeding 

sites might provide sufficient protection in the short-term, but 

over a long period habitat at protected sites may be destroyed, 
122 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

degraded, or simply mature beyond optimal age and structure that the flycatchers will use. Thus, habitat management 

plans that focus at the level of a drainage or region, as recommended by the Southwestern Willow Flycatcher recovery plan 

(USFWS 2002), are more likely to succeed than those that 

target only specific, finite breeding locations.

Identifying, Quantifying, and Evaluating Habitat

As with earlier work by AGFD (Dockens et al. 2004), 

these GIS models can be used to identify potential flycatcher 

habitat across large regions for the purpose of prioritizing 

future survey locations and potential restoration or mitigation properties. At a more local level, land managers can use 

the models to identify the probability that flycatchers may be 

breeding on their land, although this approach is not a substitute for on-the-ground surveys where suitable habitat exists. 

Managers may be able to use the models to plan riparian restoration strategies; e.g., to choose areas that are already near 

the desired structure (based on probability class) and therefore 

more likely to reach target NDVI values more quickly. These 

models can be applied at a range of scales, from regions and 

drainages to a specific patch of riparian habitat. The GIS 

models could also be an effective tool for applying Adaptive Management principles to restoration efforts, by tracking 

and evaluating different restoration treatments over time, and 

adjusting on-the-ground activities or future restoration plans 

based on the response of riparian habitat.

Future Research Needs

We believe that an important next step in understanding 

the model performance and its relationship to habitat variables 

is to quantify how key physical factors drive the NDVI values 

within the riparian systems. Doing so would require a multidisciplinary and multiscale modeling effort that examines 

factors such climate patterns, groundwater levels, river flows, 

and geomorphology. If this can be successfully accomplished, 

a Decision Tool could be developed by which managers could 

test various “what if” scenarios to determine river or reservoir operations, or future climate scenarios, would influence 

amount, distribution, and suitability of flycatcher habitat (as 

modeled via NDVI). Another important extension of modeling 

would be to testing how these models, developed from riparian 

areas in the Sonoran Desert Uplands, perform when applied to 

riparian habitats in other areas (see Hatten and Sogge 2007). 

Additionally, newer satellite imagery with greater resolution 

may provide better detection of flycatcher habitat, and future 

modeling efforts should explore these next-generation satellite images. Finally, this research underscores the importance 

of understanding and incorporating the drivers for temporal 

change riparian habitat in riverine and reservoir systems, and 

how these may differ from drainage to drainage.

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 125

Introduction

Among bird species there are a wide variety of social 

systems, ranging from flocking groups to solitary individuals. 

These social systems can have major effects on the distribution of groups and individuals on the landscape and within a 

given habitat. A very common social system among birds and 

other vertebrate groups is territoriality (Maher and Lott 2000), 

wherein an individual (or group) defends and excludes others 

from a particular geographic area or other resource. Territoriality is generally considered to provide the dominant, territoryholding individual with benefits such as better survival and 

reproductive potential. However, defending a territory involves 

higher energy output due to advertising and aggression, and 

carries increased risk of injury (from interactions with other 

individuals) or predation (because advertising individuals are 

more easily detected). Therefore, individuals defend territories only when the benefits outweigh the costs, a concept that 

Brown (1969) termed “economic defendability.”

Territoriality among birds has been widely recognized for 

over a century, primarily because the territorial songs and displays of birds are generally conspicuous and often pleasing to 

human observers. As bird banding became a more widely used 

research tool in the early to mid-1900s, researchers became 

aware that not all individuals in a given area succeeded in 

establishing territories, though it was generally assumed that 

they would if the opportunity arose. In one of the first detailed 

syntheses of territorial behavior, Nice (1941) described how 

birds that fail to obtain territories form a “reserve supply” 

of individuals that will serve as replacements in the case of 

death of local territory holders. Her work stimulated additional 

attention to the phenomenon of territoriality, and a decade 

later Stewart and Aldrich (1951) published the first experiment 

to determine how quickly and to what degree territory holders would be replaced when removed (via shooting) during 

the breeding season. Over the course of roughly 4 weeks, 425 

adult birds (302 males) of more than 24 species were removed 

from an area that initially supported 148 territorial males. 

They reported a steady influx of new birds, with replacements 

occurring very quickly, typically the next morning. Based 

on these results, Stewart and Aldrich (1951) wrote that “... a 

large, surplus, “floating” population of unmated males must 

have been present...” This appears to be the origin of the term 

“floater”, which has since come to be used to describe a nonterritorial individual of a typically territorial species. 

Territorial behavior is therefore a mechanism by which 

local populations may be regulated, in terms of abundance 

Chapter 7—Non-territorial Floaters

and distribution, in part by forcing some individuals into 

a non-territorial lifestyle as a floater. This phenomenon of 

floating appears to be widespread and has now been documented among diverse bird taxa including songbirds, raptors, 

waterfowl, and shorebirds (Zack and Stuchbury 1992, Newton 

1998). Based on Newton’s synthesis of removal experiments 

and other studies, the percentage of non-territorial non-breeders in a population can be highly variable (range = 6%–52%), 

and even within a single study population, the relative percentage of non-territorial birds varies seasonally and annually 

(Newton 1998). Floaters probably occur within most territorial 

bird species, but because they live a relatively secretive life 

among the breeding individuals, they are much more difficult 

to detect and therefore comparatively understudied. 

Given the presence of floaters and territorial breeders 

within a species, what factors determine whether an individual 

is territorial or non-territorial at a given point in its life? Much 

research has shown that food supply strongly affects territorial 

behavior (Zach and Stuchbury 1992, Maher and Lott 2000); 

territory holders generally have more assured access to food 

resources due to familiarity with the location of resources or 

increased feeding rates. However, extreme food abundance 

or shortage can lead to a lower ratio of benefits to costs, such 

that individuals will abandon territory behavior. For example, 

resources may be so dispersed that the energetic costs of 

defending a large area outweigh the benefits; conversely, if 

resources are so abundant that they are not potentially limiting, there may be relatively little benefit gained from the 

energy required to exclude others (van Riper 1984). Another 

important factor is the amount of suitable breeding habitat 

in relation to the local abundance of the species. If there is 

too little habitat to support the entire population in an area, 

some individuals that are “potential settlers” will be excluded 

from being territory holders (Stewart and Aldrich 1951, Zach 

and Stutchbury 1992, Snetsinger et al. 2005). This scenario 

could occur with a stable population that experiences a loss of 

habitat, or as a result of a rising bird population in an area of 

stable habitat. The age and sex of an individual also seem to 

play a role; in most species male floaters outnumber females 

(presumably due to male-biased sex ratios), and younger birds 

are floaters more often than older birds (Zack and Stutchbury 

1992, Newton 1998). 

The Willow Flycatcher (Empidonax traillii) is a small 

migratory passerine that is considered to be aggressively 

territorial on the breeding grounds (Sedgwick 2000) and at 

wintering sites (Koronkiewicz et al. 2006). This strong territorial behavior and response to intruding conspecifics forms 
126 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

the basis for targeted survey protocols (Sogge et al. 1997) and 

capture techniques (Sogge et al. 2001). However, there has 

been surprisingly little published evidence of the existence 

of floater Willow Flycatchers. Stafford (1986) observed a 

supernumerary bird (i.e., a non-parent) feeding flycatcher 

fledglings of a pair he was studying in the Sierra Nevada 

mountains of California, and speculated that it may have been 

a floater. Sogge et al. (1997) described three non-territorial 

Southwestern Willow Flycatchers (Empidonax traillii extimus) 

that were present among a very small population of flycatchers 

(range of 2–11 territories per year) along the Colorado River 

in the Grand Canyon. At the Kern River in California, small 

numbers of floater Southwestern Willow Flycatchers were frequently present among the breeding population (M. Whitfield, 

personal commun.), and Pearson et al. (2006) determined 

through genetic studies that some of these non-territorial males 

fathered young in the nests of territorial males. On the wintering grounds, Koronkiewicz et al. (2006) documented floaters 

of both sexes at sites supporting male and female winter territory holders. Still, the overall prevalence of floating among 

Willow Flycatchers has remained unclear, possibly because of 

the intense efforts needed to detect and characterize floaters. 

Furthermore, the mechanisms that may influence whether a 

bird chooses territoriality or floating have not been previously 

studied for Willow Flycatchers.

From 1996 to 2005, the U.S. Geological Survey (USGS) 

collaborated with the Arizona Game and Fish Department 

(AGFD) to conduct a large-scale demographic study of 

Southwestern Willow Flycatchers at two large breeding sites 

in central Arizona. At one of our study sites, Roosevelt Lake, 

we attempted to determine the degree to which floaters were 

present and the factors that might affect whether a bird was 

territorial or non-territorial. We also investigated the possible 

consequences of being a floater, in terms of their lifetime 

contribution to the breeding population. These questions were 

of interest because floaters, if present in large numbers, could 

represent a substantial but generally “undercounted” part of 

the flycatcher population—both locally and rangewide. Furthermore, because of the possible link between food, habitat, 

territorial behavior, and local population growth, the prevalence of floaters might provide evidence of ecological factors 

that are currently affecting the distribution and demography of 

this flycatcher population.

Methods

Study Site 

To evaluate the issue of floaters, we focused on a population of birds at Roosevelt Lake (33o39’N, 110o58’W), where 

field work from 2001 to 2005 (see below) provided the extensive passive netting and resighting efforts needed to determine 

if non-territorial birds were present. This site supported one of 

the largest known Southwestern Willow Flycatcher breeding 

populations during our study period (Durst et al. 2006), with 

a mean population size of over 200 individuals per year from 

1996 to 2005. Extensive surveys were conducted at least three 

times in each breeding season to detect all territorial flycatchers within the immediate and surrounding areas. 

The breeding habitat at Roosevelt Lake consisted of a 

heterogeneous mosaic of discrete riparian forest patches of 

varying ages and vegetation composition, ranging from 0.2 

to 43 ha in size. Native habitat patches were characterized by 

Goodding’s willow (Salix gooddingii) and Fremont cottonwood (Populus fremontii). Exotic habitat was dominated by 

tamarisk (saltcedar; Tamarix spp.). The understory vegetation 

consisted of a variety of grasses, forbs, and shrubs (mesquite 

[Prosopis spp.], coyote willow [S. exigua], tamarisk, seep willow [Baccharis spp.], and cocklebur [Xanthium strumarium]). 

Adjacent and surrounding non-riparian habitats were primarily 

composed of Sonoran Desert Uplands (Brown 1994). 

The Roosevelt Lake site consisted of two sub-sites 

located at the inflows of the Salt River and Tonto Creek. The 

number of riparian patches in which breeding occured varied 

over time as a result of colonization or desertion of patches, 

in many cases driven by fluctuating lake levels. A long-term 

drought in the Southwest between 1996 and 2004 resulted in 

reservoir levels dropping to a low of 10% capacity in 2002; 

that year was classified as an extreme drought (McCabe et al. 

2004). In 2005, following unusually high winter precipitation 

and associated runoff, Roosevelt Lake filled to near capacity, 

inundating much of the breeding habitat that was occupied in 

2004 (see chapter 6). 

Banding and Resighting

Our objective was to band as many adults and nestlings 

as possible in order to detect and track individual birds over 

the period of this study. We banded nestlings from 1996 to 

2004 at 7–10 days of age (as determined via Paxton and Owen 

2002) with a color-anodized federal bird band; banded nestlings recaptured as adults in subsequent years were given an 

additional color band to produce a unique color combination 

(Koronkiewicz et al. 2005). Each adult captured through 2005 

was banded with a color-anodized Federal band and a second color band (Koronkiewicz et al. 2005) to create a unique 

color combination for each individual. Territorial adults were 

primarily target-netted (Sogge et al. 2001, Pollock and Paxton 

2006) to allow for efficient, focused capture effort. A drop of 

blood was taken from adults and nestlings at the time of banding for genetic gender determination (Paxton et al. 2002). 

From 1996 to 2000, we had occasional captures and 

resightings of birds that we could not assign to a territory, 

leading us to suspect the presence of floaters. Therefore, from 

2001 to 2005 we added extensive passive netting operations 

(see Ralph et al. 1993) in areas with occupied flycatcher territories, aimed at capturing non-territorial birds that might be 

present but unlikely to be detected with conventional survey 

techniques (i.e., tape-playback; Sogge et al. 1997). Overall, 
 127

the combination of target and passive netting resulted in an 

average of 74% (range: 68%–88%) of all detected flycatchers 

being banded in a given year. To determine gender of adult 

flycatchers, we used a combination of physical characteristics 

(presence of a cloacal protuberance for males or brood patch 

for females), behavioral cues, and/or genetic sexing methods 

(Paxton et al. 2002). Adult flycatchers were aged as “known” 

if the bird was first banded as either a nestling or a secondyear adults (SY: based on retained rectrices; Pyle 1998); otherwise, they were considered as after-hatch year (AHY), which 

was an unknown-age category for purposes of our analyses. 

We tracked banded territorial flycatchers primarily 

through resighting, supplemented by occasional recaptures via 

passive netting. Resighting consisted of using binoculars to 

determine the unique color band combination on a flycatcher’s 

legs. To ensure high accuracy in the identification of individuals, only high-confidence resights from at least two different 

observers on different days were used to confirm the identity 

and territory of individual banded flycatchers. Computer databases were updated and queried daily to prioritize resighting 

efforts, allowing us to track hundreds of uniquely color banded 

individuals in a given year. Large field crew sizes (USGS 

and AGFD combined) allowed for intensive detection and 

resighting of banded individuals, resulting in nearly all territorial flycatchers at the study sites being positively identified 

multiple times in each year. Recaptures, especially via passive 

netting, provided additional detection resolution, especially for 

non-territorial birds. 

Differentiating Territorial and Non-territorial 

Birds

In general terms, we define a floater as a bird that is 

present at a site but does not establish a breeding territory 

throughout the course of a given breeding season. Determining that a bird was a floater is challenging because it is based 

on “negative proof” (i.e., evidence that a bird was not territorial). It is easy to eliminate as floaters those birds that are 

clearly territorial (e.g., repeatedly detected as singing, paired, 

or nesting). However, there is a possibility of misclassifying 

a bird that was territorial but was simply not observed being 

so, which could lead to overestimating the number of floaters. 

Given this, we felt it was appropriate to use a systematic and 

conservative approach to determine which birds we considered 

floaters. Within this chapter, we use the terms non-territorial 

and floater interchangeably.

Our first step in determining which birds were nonterritorial was to create a large database of all flycatchers 

and every detection of them from 2001 to 2005 (termed the 

“Encounter History”). We conducted a series of step-wise 

selections to extract a subset of our initial 3,748 Encounter 

History records, to separate non-territorial birds from territorial and “unknown” birds. Note that our criteria for nonterritorial status were based on year-by-year behavior, so that 

a bird was considered a floater if it was non-territorial in any 

single year, but only for the year that the behavior was exhibited. Some birds in some years did not have enough detection 

information to classify as either territorial or non-territorial, 

and were excluded from the analysis. 

Territorial birds included all birds confirmed to an active 

territory (typically through repeated detection of singing or 

aggressive behavior), paired with a territorial bird, or documented as nesting. We eliminated from the potential floater 

database all individuals that were documented exhibiting territorial behavior in a given breeding season. 

Non-territorial birds are individuals that were seen (via 

color band resights) or recaptured one or more times in a given 

year, but were never observed engaging in territorial behaviors such as singing, territory defense, nesting, or feeding 

young. Birds classified as non-territorial were considered to 

be such for an entire breeding season. Many birds classified 

as non-territorial were detected via passive netting only one 

time and never seen again in that year; however, there were 

some instances of non-territorial birds being detected up to 

five times in a season. In some cases, such multiple detections spanned as long as one month. In these cases of multiple 

detections, the cumulative evidence indicated that those individuals were not territorial. 

To determine if any additional individuals should be 

excluded from the group of birds initially placed within the 

non-territorial group, we considered physiological evidence, 

flycatcher migration ecology, and patch-specific survey and 

capture effort. For example, based on breeding physiology, 

female flycatchers only exhibit a brood patch if they have 

initiated a nesting attempt. Therefore, we excluded from the 

non-territorial group 43 records of females that were not positively linked to a territory or nest during a season but that had 

a brood patch (possibly from nesting very early in the season). 

We also excluded four individuals that were detected in only 

a single encounter prior to 01 June or after 15 July, and might 

therefore have been migrants (Sogge et al. 1997, Sedgwick 

2000) rather than non-territorial. Finally, because flycatchers 

at the Bermuda Flat habitat patch were first discovered so late 

in the 2004 breeding season (07 June), our resight and capture efforts there may have been inadequate to confidently 

confirm all territorial and breeding activity of flycatchers 

within the patch. Thus, a high proportion of birds here could 

have been incorrectly classified as non-territorial. For this 

reason, we excluded all birds detected at Bermuda Flats from 

the floater database. 

Data Analysis

The final dataset included records for 134 floater events 

(133 separate individuals, with one bird non-territorial in 

2 separate years). We summarized basic information such 

as number of floaters per year by age class (for known age 

individuals) and sex, and percentage of floaters in the total

population. We used χ2 contingency tables to compare the 

population age structure each year of non-territorial and terri-
128 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

torial birds, and a Mann-Whitney U test to test for differences 

in the lifetime productivity (number of fledges per individual) 

of birds that were always territorial and those with at least one 

floater event.

In order to determine if floating was related to breeding success in years prior to or following a floater event, we 

compared the number of records, number of nests and successful nests, the percent nest success, the number and percent of 

polygamous nests, the number young fledged, and the number 

fledged per nest between non-territorial and territorial birds. 

We also used regression analysis to determine if the proportion of non-territorial birds in a given year was related to the 

overall population productivity during that year, or in the 

preceding year.

 Results

Prevalence and Characteristics

We documented 134 individual non-territorial events 

(i.e., a flycatcher being non-territorial for a season) between 

2001 and 2005. The number of floaters varied widely per year 

(range = 7–68; table 1). In all years except 2002, there were 

relatively few floaters (n<25); however, the number was more 

than double that in 2002 (n = 68). The relative percentage of 

floaters in the overall population was usually less than 10%, 

but rose to 25% in 2002. 

Among the 133 individuals that were classified as nonterritorial, all but one floated for only a single year. Of the 124 

known-sex floater events, 105 (85%) were by males and 19 

(15%) by females (table 1). A disproportionally large percentage of floater events for both sexes occurred in the drought 

year of 2002; 79% of female events and 46% of males events. 

Among known-age floaters (table 2), 88% were age secondyear adults (SY; the year following fledging). This held true 

for both sexes, with 75% of females and 91% of males being 

age SY. Overall, 30 (23%) floaters were returning nestlings.

As a group, floaters were generally younger than the territorial birds. Considering only known-age birds, there was a 

significantly greater proportion of older (ASY; after-second-year) 

territorial birds compared to non-territorial birds in 2001, 2002, 

and 2004. There were no known age non-territorial birds in 2003, 

and only 8 in 2005 (evenly split between the two age classes). 

Relationships with Productivity 

For flycatchers that attempted breeding before and after 

their non-territorial year, individual nest success and productivity did not differ substantially before and after a floating 

year (table 3). There was only a slight increase post-floating in 

the percent nesting success and percent of polygynous nests. 

There was no difference in the mean number of young fledged 

per nest, and the mean number of fledges per individual was 

not significantly different. 

There were significant relationships between some 

productivity measures and the percent of non-territorial birds 

in the population. The percentage of floaters in a year was 

negatively correlated with both the average number of fledglings per female (r2

 = 0.84, p = 0.03) and the total number of 

young fledged at Roosevelt (r2 = 0.80, p = 0.04) in that year. 

However, the percent of floaters in the population was not significantly correlated with average fledglings per nest nor total 

fledges in the previous year (r2 = 0.36, p = 0.20 for both). The

mean lifetime productivity of birds that had a non-territorial 

breeding season was lower than for birds that were territoTable 1. The number of non-territorial, territorial nesting, and total Southwestern Willow Flycatchers detected each year at Roosevelt 

Lake, 2001 through 2005. Roughly half of all non-territorial events occurred in 2002. 

Year Non-territorial1 Territorial birds Total 

Population2

Percent 

non-territorial3

Male Female Total

2001 17 2 19 164 245 8

2002 48 15 68 93 273 25

2003 7 0 7 172 229 3

2004 21 1 24 163 379 6

2005 12 1 16 164 291 6

1 Includes 133 individuals, one of which was non-territorial in 2 years

2

Estimates are from USGS annual reports for Roosevelt Lake. Total population differs from the sum of total non-territorial 

and territorial birds due to the presence of a class of unknown birds (excluded from our analyses). 3 Based on the percent of the total population.
 129

rial in every year detected (table 4); significantly so for males 

(U = -5.2, p<0.001) and for both sexes combined (U = -4.9, 

p<0.01), but not for females (U = -0.05, p = 0.60).

Discussion

In their summaries of territorial behavior literature, 

Newton (1998) and Maher and Lott (2000) noted that individuals will generally defend and monopolize any resources 

that they can, but defense can become increasingly impractical 

due to the influence of the environmental factors. The optimal 

behavior for any individual may be to accept the social status 

(territorial or non-territorial) with the greatest net benefit at 

that time. As a result, individuals may switch between floating 

and territoriality at different stages of life or under varying 

environmental conditions, such that adoption of the “inferior” 

non-territorial lifestyle may be a “conditional strategy” (Kamil 

and van Riper 1982, Zack and Stutchbury 1992, Newton 

1998). Floaters do not necessarily completely forfeit all opportunity for reproductive success (Barber and Robertson 1999, 

Pearson et al. 2006), as non-territorial males can potentially 

engage in extra-pair copulations (i.e., mate with a female of a 

territory holder). Floater females could lay eggs in the nest of 

a territorial pair, as has been shown in Herring Gulls (Larus 

argentatus; Shugart et al. 1987); however, this behavior is 

unrecorded in Willow Flycatchers. Still, territoriality appears 

in general to be the preferred state for most bird species and 

confers better survival and reproductive potential (Zack and 

Stutchbury 1992, Maher and Lott 2000). 

During the first 5 years of our Roosevelt Lake work 

(1996–2000), periodic captures and sightings of “unknown” 

flycatchers led us to believe that there were floater birds 

among the territorial breeders. Our efforts from 2001 to 2005 

confirmed that floaters were present each year, and in some 

years comprised a substantial part of the population. This 

is not surprising given that floating is now a widely known 

behavior among a large number of bird and other vertebrate 

species that are generally considered as territorial (Zack 

and Stutchbury 1992, Newton 1998, Maher and Lott 2000). 

Moreover, Stafford (1986), Sogge et al. (1997), Pearson et al. 

(2006) described observations that suggested the presence of 

non-territorial Willow Flycatchers. However, floating has not 

yet been well documented among North American Tyrannids 

or Empidonax flycatchers. We searched the on-line Birds of 

North America accounts (http://bna.birds.cornell.edu/BNA/; 

search terms “floater”, “non-territorial”, and “nonterritorial”), 

and found that breeding season floaters have been reported 

only among Black Phoebes (Sayornis nigricans; Wolf 1997), 

Eastern Phoebes (Sayornis phoebe; Weeks 1994), and Olivesided Flycatchers (Contopus cooperi; Altman and Sallabanks 

2000). Thus, our study provides the most complete assessment 

to date of non-territorial behavior in Willow Flycatchers of any 

subspecies, or in other Empidonax flycatchers. 

The presence of floaters in a population of an endangered 

species like the Southwestern Willow Flycatcher—during each 

year we investigated—suggests that there are more individuals 

present than the local habitat can support in terms of breeding. Snetsinger et al. (2005) described a similar presence of 

floaters among the critically endangered Puaiohi (Myadestes 

palmeri), and proposed that the population level was at the 

carrying capacity of its current habitat. Thus, even though 

available breeding habitat at Roosevelt Lake was generally 

increasing from 2001 to 2004, the local flycatcher population 

Sex Second-year 

(SY)

Third-year 

(TY) Fourth-year (4Y) Fifth-year (5Y) Total known age

Female 10 3 0 0 13

Male 50 4 0 1 55

Sexes Combined 60 7 0 1 68

Table 2. Age and sex of Southwestern Willow Flycatchers during floater events, based on known-age and known-sex individuals at 

Roosevelt Lake 2001–05. Most floaters were young (SY) and male.

Table 3. Productivity of non-territorial Southwestern Willow Flycatchers for which there were nesting attempts in the years preceding 

(Pre) and following (Post) a non-territorial year, at Roosevelt Lake 2001–04 (2005 is excluded because the study ended in that year and no 

post-year data were collected).

Year and 

status n Number of nests Percent nest success

Percent

polygynous

Number fledged 

per

nest

Mean fledges per 

individual (SE)

Pre 14 14 57 29 1.4 1.4 (0.4)

Post 55 62 60 32 1.4 1.5 (0.2)
130 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

was productive enough that available habitat did not keep pace 

with the increasing number of flycatchers. 

Floater Characteristics and Prevalence

More than 80% of the known-sex floater events that 

we detected (105 of 124) involved male flycatchers, which 

is a pattern consistent with most other studies (Newton 

1998, Maher and Lott 2000). Although less common than 

males, non-territorial females were also present each year at 

Roosevelt Lake. The predominance of younger (SY) birds 

as floaters (86% of known-age birds) is also consistent with 

the general pattern seen in most other studies (Newton 1998, 

Maher and Lott 2000). 

The prevalence of floating at Roosevelt Lake was highly 

variable by year. There appeared to be a relatively low “background” level (3%–8%) in most years, and a large increase 

in 2002 (25%; discussed below). The variability we observed 

falls within that described for an array of bird studies summarized by Newton (1998), who also noted that even among a 

single study population, the relative percentage of non-territorial birds varies both annually and seasonally.

In our study, fewer than half of non-territorial flycatchers 

returned and established territories in later years. This is as 

expected, given that the overall return rate for adult flycatchers 

in our population was 55% (see chapter 2). Thus, the proportion of non-territorial birds that return each year is similar to 

that for the overall population. 

The Costs of Non-territoriality

In a species such as the Willow Flycatcher, which has 

a short average lifespan (see chapter 2) and only moderate 

nesting success per year (see chapter 3), we would expect 

individuals to be territorial whenever possible. In addition, 

an individual that floats more than one year may be foregoing the majority of potential breeding opportunities during 

its lifetime. Thus, we expected that Southwestern Willow 

Flycatchers at Roosevelt Lake would generally not be nonterritorial for multiple years. This was indeed the case, in 

that all but one (>99%) of the floaters in our study did so 

for only a single year. 

Also because of their short average life span, we would 

expect that Southwestern Willow Flycatchers that spend a season as non-territorial will have lower lifetime productivity than 

flycatchers that breed every year. Engaging in a non-territorial 

year had a definite cost in productivity among male flycatchers at Roosevelt Lake, with an greater than 50% decrease in 

the number of fledged young over their lifetime (females and 

males, respectively). Female floaters also had lower mean 

lifetime productivity than territorial females, but the difference was not statistically significant. We believe that the lack 

of statistical significance in females is a function of the small 

sample size of non-territorial individuals (n = 19), and that 

females who float do incur a substantial lifetime reproductive cost.

If non-territorial behavior increased later breeding success, floating might be a suitable “strategy” to use for habitat 

prospecting (Zack and Stutchbury 1992). However, Southwestern Willow Flycatchers at Roosevelt Lake experienced much 

the same annual nest success and breeding productivity in the 

year before and after floating, so there was no annual productivity benefit to having been non-territorial in the previous year. 

It is important to note that non-territorial male Southwestern Willow Flycatchers still retain some chance of 

productivity during their floater year, as they can potentially 

infiltrate a territorial area and engage in extra-pair copulations 

with receptive females. Our estimates of lifetime reproductive success indicate that floaters would on average need to 

sire two successful young via extra-pair copulations to match 

the success of territorial males (table 4). In the only study that 

quantified extra-pair paternity by non-territorial Southwestern 

Willow Flycatchers, Pearson et al. (2006) documented that 

four nonterritorial males at the Kern River in California sired 

six offspring, for an average of 1.5 young per male. Thus, 

the cost of floating in terms of reduced lifetime reproductive 

success may not be as great as suggested by estimates based 

solely on nests when birds were known to be territorial. 

Why Be a Floater?

The factors that influence floating and territorial behavior 

among birds in general are not completely understood, and 

interpreting their relative roles is complicated by the fact that 

they may interact in complex ways and vary from year to year. 

Furthermore, this suite of factors may affect different sexes 

and age groups differently. Still, there is a general understanding of the major benefits and costs of territoriality and floating. 

Habitat availability and quality are key factors in territorial behavior of most birds. Given the potential costs of territorial behavior, especially for males, a shortage of high-quality 

breeding habitat could lead males to adopt a floater role (Zack 

and Stutchbury 1992, Maher and Lott 2000). For example, 

if there is insufficient suitable breeding habitat to support 

all potential territorial males, younger and less experienced 

males may be unsuccessful at supplanting another bird or may 

choose to be non-territorial rather than attempt to displace an 

already established male. However, floaters appear frequently 

to be relegated to lower-quality habitats (Newton 1998, 

Githiru et al. 2006), which may have physiological condition 

and survival costs, adding to the negative consequences of not 

establishing and holding a territory in good quality habitat. We 

do not suspect that there was an overall shortage of suitable 

breeding habitat at Roosevelt Lake; indeed, suitable habitat (as 

we perceived it) generally increased from 1996 to 2005 (see 

chapter 6). However, the extreme drought in 2002 decreased 

the vigor and vegetative cover of the riparian habitat patches 

at Roosevelt Lake, which may have substantially reduced the 

amount of suitable breeding habitat for the pool of potentially 
 131

Overall Males Females

Mean ± SE 95% CI Mean ± SE 95% CI Mean ± SE 95% CI

Always Territorial 2.84 ± 0.18

n = 406

2.48–3.20 3.49 ± 0.34

n = 171

2.83–4.16 2.40 ± 0.20

n = 231

2.01–2.79

At least one non-territorial year 1.46 ± 0.26

n = 133

0.95–1.97 1.49 ± 0.31

n = 104

0.87–2.11 2.05 ± 0.58

n = 19

0.83–3.27

Table 4. The number of fledglings produced per lifespan of Southwestern Willow Flycatchers classified as non-territorial in at least 

one year, and those that were territorial in every year detected. The difference in lifetime productivity between floaters and territorial 

birds was statistically significant for males (U = -5.2, p<0.001) and for both sexes combined (U = -4.9, p<0.01), but not for females (U = 

-0.05, p = 0.60). Overall sample size includes 14 unknown sex birds.

territorial males, and thereby brought about the very high level 

of floating we observed in that year. 

Male birds may also float rather than breed during a 

given year in order to assess various areas within a site, to 

guide selection of a future territory location (Zack and Stutchbury 1992, Newton 1998). At Roosevelt Lake, Cardinal (2005) 

and Cardinal et al. (2006) documented pre- and post-breeding 

movements by male Southwestern Willow Flycatchers that are 

consistent with this “prospecting” concept. However, because 

the individuals in these studies bred in the same year as these 

potential prospecting movements, they would not be classified 

as floaters. Still, the purpose and value of the movements may 

be analogous, though the relative value of prospecting for an 

entire breeding season (and foregoing reproduction) is uncertain compared to the lost productivity potential. 

It is more challenging to determine how female Willow 

Flycatchers would benefit from floating. Male flycatchers 

will frequently have multiple females in their territory and 

contribute relatively little to the care of nestlings (Davidson 

and Allison 2003; see chapter 3). Therefore, assuming no 

exclusion behavior from females already present (none has 

yet been reported), any female interested in breeding could 

hypothetically settle to nest within an existing territory and 

would not need to adopt a floater role. However, it is possible 

that floating may serve a similar habitat “assessment” function 

for females and males, allowing them to later choose among 

the different potential settlement areas. The generally young 

age of both male and female floaters at Roosevelt Lake is 

consistent with this scenario, but does not exclude other causes 

of non-territorial behavior.

Food availability is another major driver of territoriality 

(Maher and Lott 2000), and one which may affect both sexes 

similarly. Birds will generally defend food resources when 

they are abundant and defensible (Newton 1998). Although 

our data on productivity, survivorship, and population 

increases at Roosevelt Lake (see chapters 2, 3, and 5) suggest 

that food supply was not generally limiting in most years, we 

believe that the decreased food availability played a major 

role in the very high level of floating in 2002. In that breeding 

season, many birds failed to establish territories, pair, or initiate nests; nest success and productivity were very low among 

those pairs that did try to nest (Smith et al. 2003). Durst (2004) 

found that 2002 was a year of very low invertebrate abundance 

and biomass at Roosevelt Lake compared to 2003. Thus, the 

relative lack of food resources in the 2002 season may have (1) 

reduced the relative benefits of expending energy to defend a 

territory, (2) prevented birds from being in suitable physiological condition to initiate breeding, or (3) forced birds to range 

widely to acquire sufficient food for basic survival. All of these 

factors could potentially lead many birds to become floaters. 

The dramatically reduced food base in 2002 (Durst 2004) 

may have had a particularly telling influence on female floating. Body condition of females directly affects whether they 

attempt to breed; if energy reserves, physiological conditions, or food resources are not above the threshold needed 

to support breeding activities, the bird may not attempt to 

pair or nest (Sturkie 1986, Vezina and Williams 2003). The 

large number and comparatively high ratio of female floaters 

in 2002 suggests that their physiological/energetic condition 

may have been too poor to induce breeding in that year. Thus, 

the 2002 drought and its attendant lowered prey availability 

probably account for the majority of the female floaters events 

over the course of our entire study, as most were detected in 

2002 only. Note that this lack of food resources may have 

influenced floating and territoriality in males as well, given 

that male Southwestern Willow Flycatchers may be more 

energetically and physiologically stressed than females during 

the breeding season (Owen et al. 2005). 

An imbalanced sex ratio in which there are fewer females 

could also lead to floating by males (Marra and Holmes 1997), 

because defending a territory (even if in good habitat) will 

not necessarily result in procuring a mate. However, Sedwick (2000) reported an approximate 50:50 adult sex ratio in 

a study of Willow Flycatchers in Oregon, and Paxton et al. 

(2002) showed a female-biased sex ratio among Southwestern 

Willow Flycatchers at Roosevelt Lake from 1996 to 2000. So 

there is no evidence to suggest that a male sex bias is a major 

factor in floating at Roosevelt Lake or other breeding sites. 
132 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Overall, our data indicate that non-territorial behavior is 

triggered by local habitat conditions during a given breeding 

season, rather than by factors in the preceding year such as 

habitat quality or an individual’s reproductive success. The 

reasons for this conclusion are several. First, most non-territorial birds were floaters during their first potential breeding 

season, so there was no opportunity for previous breeding 

experience to be a factor. Second, among the group of older 

non-territorial birds that has previously bred, there was no 

significant difference in productivity pre- or post-floating; so 

floating did not appear to be triggered by poor reproductive 

success of that individual. Third, the proportion of nonterritorial flycatchers was lower in years with high population 

productivity (mean number of fledges per individual, and total 

fledges in the population). If habitat quality (especially food 

availability) is a major factor in determining Southwestern 

Willow Flycatcher productivity, as is often assumed for many 

bird species (Newton 1998), then floating at Roosevelt Lake 

was lowest in the years of highest habitat quality. The very 

high level of floating during the extreme drought year (2002) 

was a dramatic example of this relationship, but the overall 

pattern held true over the 2001–05 period.

Flycatchers Not Detected for an Entire Breeding 

Season

In defining our dataset of non-territorial birds, we 

excluded 97 individuals that were observed repeatedly at 

Roosevelt Lake over multiple years (2001–04), but were not 

detected in one or more years in which they were known to be 

alive because of subsequent detections. For purposes of this 

discussion we refer to them as “non-detected” birds. As was 

the case for non-territorial birds, the number of non-detected 

records varied per year (range = 8–39 individuals), as did the 

proportion within the total population (range = 3%–14%); both 

were greatest during the 2002 drought. However, unlike floaters, over half of non-detected birds (53 of 97) were female, 

and more non-detected individuals (54% of known age birds) 

were older than SY. Also, there was a higher percentage of 

returning nestlings among non-detected birds (34%) compared 

to non-territorial birds (23%). 

We do not know what these individuals were doing in 

the year that they were not detected. The most straightforward 

possibility is that they may simply have been at Roosevelt 

Lake but not detected. However, because of the generally high 

detectability of territorial flycatchers and the extensive field 

effort at the site, we do not think it likely that many of the nondetected birds were territorial. It is more likely that non-detected birds would be in the non-territorial group: present but 

unobserved because of their inconspicuous behavior. If this is 

the case, then the actual number of non-territorial flycatchers 

at Roosevelt Lake would total 231, and range from 16 to 107 

per year (almost double our current non-territorial estimate). 

However, the different age and sex characteristics of these 

non-detected birds compared to those we considered floaters 

suggest that they may have been exhibiting a fundamentally 

different behavior. For example, the non-detected birds may 

also have been present as undetected floaters or breeders at 

some other breeding site (especially at sites without intense 

monitoring), then returned to Roosevelt Lake in subsequent 

years. Although we have seen a few one-way movements of 

Southwestern Willow Flycatchers among distant breeding sites 

(see chapter 4), we do not know the probability of a bird from 

Roosevelt Lake moving to another site for a season and then 

returning. 

Other possibilities that might explain the missing year 

for non-detected birds are more intriguing, but their likelihood 

is unknown. For example, non-detected birds may have been 

traveling throughout the breeding season among widely dispersed riparian areas. If this type of behavior occurs, it would 

represent a previously undescribed “super floating” behavior 

by individuals that are prospecting for potential breeding 

habitats on a broad, landscape level. Another possibility is that 

some portion of the non-detected flycatchers may have stayed 

on the wintering grounds or undergone only a partial northward migration. This would be an example of “partial migration” (Dingle and Drake 2007). For partial migrant species, 

migration is dependent upon environmental conditions and 

individual behavior, such that not all individuals migrate in all 

years (Ramenofsky and Wingfield 2007). Relatively few bird 

species have shown evidence of partial migration (Nilsson et 

al. 2006), and the Willow Flycatcher and other North American Empidonax are considered to be completely migratory 

species (e.g., see Sedgwick 2000). However, given that “classic” examples of migration may be the exception rather than 

the rule (Dingle and Drake 2007), and that we know relatively 

little about the wintering and migration ecology of the Willow Flycatcher (USFWS 2002), the possibility of “atypical” 

migratory behavior in Southwestern Willow Flycatchers may 

warrant investigation.

Additional Research Opportunities

Although we have made substantial progress in documenting and understanding non-territorial flycatchers at 

Roosevelt Lake, there is still much we do not know. The 

degree of floating is variable over time, and possibly among 

sites. Coupled with changing environmental conditions over 

space and time, it is particularly challenging to document 

the underlying factors that drive territoriality versus floating behavior in a given year. From a practical standpoint, 

non-territorial birds are difficult to detect and therefore hard 

to study and document. As a result, research geared toward 

better understanding floaters is likely to be effort intensive and 

costly, at least within large sites. 

Our study was conducted at one of the largest known 

Southwestern Willow Flycatcher breeding sites, and we do 

not know to what degree our results are representative of what 

may be occurring at smaller sites. Similarly, we do not know 

if high-quality sites have larger floater Willow Flycatcher 
 133

populations than low quality sites, as was shown for some 

other species (Zack and Stuchbury 1992, Githiru et al. 2006). 

In terms of individual behavior, the term “floater” conjures 

images of random, wandering movements among the general 

population. However, Smith (1978) described well-defined 

home ranges and other social organization among floater 

Rufous-collared Sparrows (Zonotrichia capensis); we do not 

know to what degree there is such social structure among nonterritorial Southwestern Willow Flycatchers.

However, even with extended research, we may not be 

able to move far beyond describing the general patterns shown 

by most territorial birds. Individuals will generally be territorial when possible, but variations in available habitat, food 

resources, sex ratios, and other environmental parameters will 

interact in complex ways to produce the relative levels of territoriality and floating at any given time and place.

Management Considerations

Given the data from this study, combined with observations made at other sites (Stafford 1986, Sogge et al. 1997, 

Pearson et al. 2006), we suspect that floaters are present 

among many Southwestern Willow Flycatcher populations, 

especially the larger ones or those that are expanding rapidly 

beyond the availability of suitable habitat at a given breeding 

site. However, given the challenge of detecting non-territorial 

birds, it will be difficult to accurately determine the proportion 

of non-territorial birds in any particular site without extensive, 

targeted efforts to do so.

Because floaters are not readily detected by standard 

survey techniques, more flycatchers may be present at a 

site than would be indicated by simply counting territories, 

especially in years with extreme environmental conditions. 

However, although floater populations may be variable among 

sites and years, our data suggest that in most years a population estimate that fails to include floaters would still be within 

5%–10% of an estimate that did. This may be a reasonable 

level of accuracy when estimating the number of flycatchers at 

most sites.

Although in most years floaters comprise a relatively 

small proportion of Southwestern Willow Flycatchers at any 

one site, floaters in these areas could play several important 

demographic roles. First, because floaters likely indicate that 

habitat is saturated with territorial birds, floaters that remain 

in an area through the breeding season could act as rapid local 

replacements for any breeding birds that suffer mortality due 

to predation, and thereby maintain a stable breeding population. Second, floaters could help buffer between-year fluctuations. This may have occurred in 2003, when the population 

reduction following the previous year’s drought was not as 

great as expected based on the very low breeding productivity 

in 2002. Third, excess birds, those we would define as either 

floaters or non-detected birds, could act as longer distance 

dispersers that colonize unoccupied habitat or more distant 

occupied habitat, thereby playing an important role in regional 

metapopulation dynamics. Fourth, floaters and non-detected 

birds may help maintain genetic structure of populations, by 

contributing to local gene pools by siring extra-pair offspring 

or by maintaining gene flow when they move across larger 

regional areas. 

The presence of non-territorial birds each year at 

Roosevelt Lake, even in the presence of generally expanding 

riparian habitat, suggested that this population was expanding 

faster than new suitable breeding habitat. In other sites where 

the number of territorial Southwestern Willow Flycatchers 

remains relatively unchanged over time but reproductive success suggests the population should be expanding, the amount 

of suitable habitat could be limiting. Such sites could be candidate areas for potentially increasing the number of flycatchers by evaluating the local availability of breeding habitat and 

augmenting it where it appears to be a limiting factor.

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136 The Ecology of the Southwestern Willow Flycatcher in Central Arizona
 137

This study, which culminated in this report, was conceived 

and initiated over a decade ago, shortly after the Southwestern 

Willow Flycatcher was listed as endangered (USFWS 1995). 

Prior to its listing, the flycatcher had received relatively little 

research attention. At the time of listing there had been no 

long-term or large-scale ecological studies of the flycatcher 

within Arizona, and only one such study of this subspecies 

elsewhere—a long-term research project at the Kern River 

Preserve in California (Whitfield and Strong 1995). Thus, 

most facets of Southwestern Willow Flycatcher survivorship, 

productivity, movements, habitat use, and other aspects of 

breeding ecology were largely unknown; completely so for 

the populations at Roosevelt Lake and the San Pedro/Gila 

study sites. 

Our research was designed to provide flycatcher information that was needed for management and recovery (USFWS 

1996). Specifically, through surveys, nest monitoring, and 

intensive tracking of banded birds, this study was to generate 

information deemed to be important to managers, as described 

by Arizona Partners in Flight (Marshall 1995, Sferra et al. 1995) 

and the Roosevelt Lake Biological Opinion (USFWS 1996):

•	 Quantify (at multiple scales) habitat characteristics, 

in terms of patch area and shape, proximity to water, 

stand age, landscape matrix, and topography

•	 Develop and implement quantitative habitat descriptions of riparian patches, territories, and nest sites 

occupied by flycatchers

•	 Quantify site fidelity, natal dispersal, and adult dispersal within and among years

•	 Determine age structure of populations

•	 Estimate rates of survivorship for different age classes 

and among different populations

•	 Determine the effects of age on nesting success, and 

yearling and adult survival

•	 Characterize nesting success

•	 Determine the relation between nesting success and 

movements within and between years

•	 Determine the relationship between nesting success 

and habitat

Chapter 8—Conclusions

•	 Determine the presence, size, and age of floaters in 

relation to population size and quality of habitat

•	 Compare nesting success in exotic and native plant 

communities

This joint study by the U.S. Geological Survey (USGS) 

and the Arizona Game and Fish Department (AGFD) has 

provided large amounts of new data that directly address 

the research needs outlined above, and others as well. It is 

the most extensive research project conducted to date on the 

endangered Southwestern Willow Flycatcher in length of time, 

geographic scale, and complexity. Our goal was to gather data 

and glean insights that would be directly applicable to the 

conservation and management of the flycatcher, even beyond 

Roosevelt Lake and San Pedro/Gila study sites. Furthermore, 

we hoped to stimulate researchers to conduct similar studies 

elsewhere for comparative purposes, and to build upon our 

work to develop “next level” hypotheses and research projects. Our interim results, released as a series of USGS annual 

reports (e.g., Causey et al. 2005), have already been extensively used in recovery planning (USFWS 2002) and critical 

habitat designation (USFWS 2005), and have helped guide 

management activities such as habitat conservation plans and 

habitat assessments.

The purpose of this Conclusion chapter is to integrate 

and synthesize the major results from the earlier chapters of 

this report, and present a summary of their implications with 

regard to the following issues: 

•	 The role of reservoirs in the conservation of the flycatcher

•	 Flycatcher response to reservoir rise at Roosevelt Lake

•	 The importance of scale in management

•	 The dynamic nature of this species and its habitats

•	 Patterns of habitat use

•	 Implications for riparian restoration

•	 Implications for flycatcher monitoring

•	 Drought effects

•	 Future research directions
138 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

Role of Reservoirs

The development and operation of reservoirs were listed 

as contributing factors in the historical decline of Southwestern Willow Flycatchers (USFWS 1995), and reservoirs are 

often perceived as a continuing threat to the recovery of the 

species (Graf et al. 2002). While there is little direct documentation of the impacts of reservoirs on flycatchers, there 

are many places where reservoirs have inundated historically 

occupied habitat, as well as potential habitat, and dams have 

modified the downstream hydrology of many river systems 

such that suitable flycatcher habitat can no longer be supported (USFWS 2002, Graff 2006). However reservoirs 

can play a positive role by providing breeding habitat at the 

periphery of the reservoir that can help offset the lack of 

breeding habitat downstream. At Roosevelt Lake, the drawdown of the reservoir exposed extensive lakebed sediment 

that rapidly developed into apparently high-quality breeding 

habitat that subsequently supported a rapid increase in the 

population size of breeding flycatchers at that site. 

Elsewhere, other reservoirs have played similar roles in 

creating habitat for Southwestern Willow Flycatcher populations. In California, the broad floodplain at the inflow of the 

Kern River to Lake Isabella has supported breeding flycatchers for over 30 years (Whitfield and Strong 1995). In Arizona, 

reservoir drawdown zones have supported breeding flycatchers 

at Lake Mead (Colorado River), Alamo Lake (Bill Williams 

River), and Horseshoe Reservoir (Verde River; Graber et al. 

2007). In New Mexico, lower reservoir levels at Elephant 

Butte Reservoir (Rio Grande) allowed the development of 

new riparian habitat in which the local flycatcher population 

expanded from fewer than 10 territories in 1995 to over 175 by 

2006 (Moore and Ahlers 2006). 

To some degree, the cycle of riparian development and 

loss within a reservoir drawdown zone may be an analog for 

the natural pattern of riparian habitat dynamics in the Southwest. Along unregulated rivers, riparian vegetation within 

the hydrologically active channel is periodically scoured by 

high flows, and then establishes and regenerates over the next 

several years until the next scouring flood event. This often 

creates, at a drainage scale, riparian habitat patches of different age, structure, and composition; among these will be the 

younger habitats that are favored by Southwestern Willow 

Flycatchers. This mirrors what occurred at Roosevelt Lake 

during the course of our study and at Elephant Butte Reservoir 

during approximately the same period (Moore and Ahlers 

2006). However, it is important to note that reservoir geomorphology and the timing and nature of water operations will 

largely determine the degree to which the lakebed is exposed 

and new suitable riparian habitat develops, and ultimately 

how long the habitat will persist. Therefore, some reservoirs 

and operating criteria can be conducive to developing suitable 

flycatcher habitat, while others may not provide the right conditions. Still, the potential exists to manage reservoirs in ways 

that would create, enhance and maintain flycatcher habitat, 

which could be an important tool for maintaining populations 

in the face of future environmental challenges such as drought 

(see below). More study is needed to identify the features that 

promote riparian habitat creation, and which reservoirs have 

the potential for such benfits. 

Response to Reservoir Rise

This project was initiated as a result of a Biological 

Opinion associated with the increased water levels made possible by the modification of Roosevelt Dam (USFWS 1996). 

At the outset of this project, we expected that within the first 

few years Roosevelt Lake would rise and inundate the habitat 

that was used by breeding Southwestern Willow Flycatchers. 

Therefore, one of our primary goals was to determine how 

flycatchers would respond to this inundation; would they stay 

within flooded habitat at Roosevelt, move into adjacent riparian habitat above the high water level, or perhaps disperse to 

more distant breeding sites on the San Pedro River or elsewhere? Due to the long-term drought that began in 1996, lake 

levels did not rise to the new conservation pool level until the 

final year of our study in 2005. In the interim, riparian vegetation developed in the exposed lakebed and was colonized by 

breeding flycatchers. The Arizona Game and Fish Department 

has prepared a separate report that details the variety of flycatcher responses to the rise in lake levels (Ellis et al., 2008). 

Here we summarize a few general observations related to the 

major components of our study.

Most of the habitat in which flycatchers bred from 

1996 to 2004 was completely or partially inundated in 2005. 

The amount of remaining suitable breeding habitat (as we 

perceived it) in 2005 was substantially less than in the previous year, leading us to believe that only a small fraction of 

the 2004 population would return. Instead, there were 170 

territories in 2005, 79% of the number in 2004. However, 

many of the 2005 territories were found in riparian patches 

that flycatchers had either not bred in during the 10-years of 

the study, or had not bred in for many years. This suggests 

that these “novel” habitat patches, which were not initially 

utilized by a large and growing breeding population within the 

immediate area for multiple years, were either (1) rejuvenated 

by the high winter precipitation, (2) acted as refuge habitat 

by displaced flycatchers that would normally not use it, or (3) 

a combination of both. Irrespective of the reason, the novel 

patches provided important habitat for breeding flycatchers in 

2005.

Therefore, the inundation at Roosevelt Lake did not trigger the large emigration/dispersal event that we had expected. 

Rather, most flycatchers returned to the site and attempted to 

breed in the riparian patches that remained. Thus, even in the 

face of a large scale and dramatic habitat perturbation, most 

movement remained local, to nearby patches that remained 

relatively undisturbed farther upstream. This underscores 

the flycatcher’s high fidelity to previous breeding areas and 
 139

highlights the importance of refuge habitat to which flycatchers can move following loss of a breeding site. Based on the 

movements we observed, refuge habitat will be most effective 

if it is available in close proximity (within 30–40 km) to existing breeding sites. 

Scale of Management 

We observed a consistently high degree of betweenyear, within-drainage movement by the Southwestern Willow Flycatchers at both Roosevelt Lake and the San Pedro/

Gila study sites. Movements were common, resulting in high 

connectivity, between breeding patches that were less than 

30–40 km apart. This has changed our concept of the spatial 

scale at which to define a flycatcher breeding site. In the early 

years of the study, each individual breeding patch was considered a separate site. However, the high degree of connectivity 

between the breeding patches led us to conclude that, from a 

flycatcher’s perspective, breeding patches in close proximity to 

one another were all one functional site. As distance increased, 

movement decreased, and there was a low frequency of movement between drainages. Thus, there was a clear threshold at 

which frequent movement and connectivity among patches 

within 30–40 km could be readily distinguished from infrequent movement between areas that were farther apart. Thus, 

we believed that the most appropriate scale to delineate a 

site was the drainage level, recognizing that metapopulation 

connectivity (though infrequent movements) occurred at the 

region level. Therefore, management of flycatcher breeding 

sites will be most effective if done at a drainage scale, rather 

than focusing only on individual breeding patches, and emphasizing the management of patches of riparian vegetation with 

high connectivity (USFWS 2002). 

Dynamic Species and Dynamic Habitat

Because the Southwestern Willow Flycatcher is a riparian obligate, it is subject to highly variable environmental 

conditions over space and time. Accordingly, we found high 

variability in most life history traits; between years, among 

breeding sites and patches, and at different spatial and temporal scales. This has been shown to be true in other aspects of 

flycatcher ecology, such as diet (Drost et al. 2003) and prey 

base (Durst 2004). Because data from any single year or site 

cannot necessarily be extrapolated to others, there are risks 

to basing management decisions on short-term data, or in 

using data from sites that may be ecologically very different. 

Management actions need to recognize this inherent variability 

to be effective. 

In the Southwest, most riparian habitats are also dynamic 

and undergo repeated cycles of establishment, growth, and 

destruction. Under favorable conditions, riparian vegetation 

can grow or recover quickly, and become suitable Southwestern Willow Flycatcher breeding habitat in a relatively short 

time (USFWS 2002). For example, at Roosevelt Lake, flycatchers began breeding in some patches in which the vegetation was only 3 years old. On the other hand, our data suggest 

that occupancy of the habitat can start to decline within 5 

years of colonization, and many patches became unoccupied 

within 10-years or less. However, these older habitats may 

continue to support flycatchers if hydrological conditions 

change (e.g., non-scouring floods may reinvigorate older 

riparian vegetation), if adjacent habitat is suddenly lost and the 

older habitat becomes refuge habitat, or if alternative habitat 

is not available. Therefore, although potential breeding habitat 

can develop relatively quickly, the suitability of a given patch 

may be ephemeral. Accordingly, the Southwestern Willow 

Flycatcher recovery plan (USFWS 2002) emphasizes the need 

to maintain a mosaic of patch ages and structure within a given 

drainage, and notes the importance of managing for the processes that will create and maintain riparian habitat dynamics. 

In general terms, the dynamics of riparian habitat creation 

and loss cycles were similar at Roosevelt Lake and the San 

Pedro/Gila. In both cases, existing older habitat was periodically lost, and new habitat became established and grew rapidly. However, our spatial modeling showed that each system 

responded differently with regard to hydrology, climate, temporal scale, and spatial scale (especially patch size). These differences suggest that riparian habitat in reservoir and riverine 

systems may operate at different temporal scales that are not 

necessarily linked, leading to potentially very different population dynamics, even when those systems are relatively close 

geographically and support similar habitat matrices. Through 

time, the number of flycatchers breeding in a particular patch 

will change. Thus, focusing on achieving target population and 

habitat goals at the drainage or regional level, as recommended 

by the recovery plan (USFWS 2002), will be the most successful long-term management strategy. 

Habitat Use

Southwestern Willow Flycatchers typically breed 

in dense riparian habitat along streams or other wetlands 

(USFWS 2002). However, flycatchers showed a substantial 

degree of flexibility, using a range of different patch sizes and 

plant species composition. Although this is currently widely 

known and accepted (USFWS 2005), it was not well understood when this study began.

At both Roosevelt Lake and the San Pedro/Gila, flycatchers bred in patches that ranged from monotypic willow to 

monotypic saltcedar. This supports the view that Southwestern Willow Flycatchers respond more to habitat structure, 

microclimate, and patch-level characteristics than they do to 

plant species composition (USFWS 2002). This idea is further 

supported by the strong performance of our spatial habitat 

models that were good predictors of occupied habitat despite 

the fact that they did not include any plant species composition 
140 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

variables. Because flycatchers occupied both native and exotic 

habitats, we were able to determine whether flycatchers breeding in saltcedar experienced lower productivity or survivorship 

than those in willow or mixed willow/saltcedar patches. Our 

analyses showed that flycatchers did not suffer any detectable 

negative consequences from breeding in saltcedar. This is consistent with the findings of Owen et al. (2005) and Sogge et 

al. (2006, in press). However, throughout the Southwest there 

are many saltcedar-dominated and native-dominated habitats 

in which flycatchers do not breed (USWFS 2002, Sogge et 

al. in press). Therefore, the use of any riparian patch—native 

or exotic—as breeding habitat will be site specific and will 

depend on the spatial, structural, and ecological characteristics of that particular patch and the potential for flycatchers to 

colonize and maintain populations within it.

Based on patterns of flycatcher movement among riparian patches, breeding habitat may be most suitable or attractive when it is relatively young (at least 3 years of age, but 

less than 10–15 years). However, flycatchers will continue 

to occupy older habitat especially when patches of younger 

vegetation are lost or unavailable; thus, older patches and 

atypical habitat (e.g., narrow stringers of vegetation) may 

periodically serve as “refugia” for a local population until 

new patches develop. This illustrates the importance of 

creating and maintaining a mosaic of riparian habitat patches 

within a drainage (USFWS 2002), so that at least some of the 

patches will be suitable for breeding flycatchers at any given 

time, and flycatchers will have alternative patches to move 

to if some are lost to inundation, scouring, fire, drought, or 

other disturbance.

Restoration Implications

There are many challenges inherent in riparian restoration in the Southwest (USFWS 2002), especially with regard 

to providing the geomorphological and hydrological conditions necessary for riparian establishment and persistence. 

However, under the proper conditions, riparian vegetation can 

develop rapidly, and Southwestern Willow Flycatchers can 

subsequently respond by quickly colonizing sites once the 

habitat characteristics are suitable. We observed young habitat 

at Roosevelt Lake being occupied within 3 years after development, and similar settlement patterns have been documented at 

Elephant Butte Reservoir in New Mexico (Moore and Ahlers 

2006). Therefore, in some areas it is possible for managers 

to rapidly create or restore substantial amounts of riparian 

habitat, and for flycatcher populations to quickly react to and 

benefit from the new habitat.

The location of riparian restoration projects will affect 

the likelihood and speed at which Southwestern Willow Flycatchers find and colonize a site (USFWS 2002). Restoration 

projects near existing breeding sites will likely increase the 

probability of colonization; based on our movement data, 

distances up to 30–40 km appears best. However, more 

distant sites may still be colonized, especially if they are 

located within the same drainage as current sites. 

The spatial habitat models that we developed can 

serve as useful tools in riparian restoration efforts. For 

example, the models can be used to identify riparian 

patches that are close to desired conditions, thus requiring 

less active management to reach restoration goals. Alternatively, the model can be used to find “lower probability” 

patches where restoration may greatly increase the habitat value. Another potential use is in tracking restoration 

progress over time by monitoring the amount of habitat in 

the different model probability categories. Progress toward 

management goals could be measured periodically, and 

restoration activities modified accordingly. Finally, the 

model may allow managers to decide when a restoration 

site has developed sufficiently to warrant the beginning of 

flycatcher occupancy surveys. 

Monitoring Implications

It is clear from both Roosevelt Lake and the San Pedro/

Gila study sites that local Southwestern Willow Flycatcher 

populations can change in size quickly. This is true for both 

population growth and decline in a particular breeding patch or 

area. Therefore, it cannot be assumed that local or regional flycatcher populations will remain static over time, or even over 

the course of a few years. Demographic models are useful in 

simulating future population trajectories, but it does not appear 

that our model projections have closely matched observed 

trends. Therefore, periodic surveys and monitoring are important for accurately tracking the status and trends of flycatcher 

breeding sites and populations. Based on our data, monitoring 

efforts that follow the standard survey protocol (Sogge et al. 

1997) will likely fail to detect non-territorial floaters; however, 

there are relatively few floaters in most years, so population 

counts will not be greatly affected.

Flycatcher breeding habitat can develop or be lost over a 

period of only a few years, especially where there has been a 

change in human activities (grazing, fire, water management) 

or natural drivers such as climate (e.g., droughts, scouring 

floods). Therefore, the persistence of a currently occupied 

patch is not assured over the long term. Similarly, an area 

currently without suitable breeding habitat may develop suitable habitat in the future. In much the same way that periodic 

flycatcher surveys are necessary to track status of the birds, 

periodic riparian habitat surveys will be important in tracking 

habitat trends in a given area. The spatial habitat models that 

we developed could serve as a foundation for habitat monitoring efforts, by quantifying riparian habitat changes and drawing attention to where these have occurred.
 141

Drought Impacts

The Southwest experienced a long-term drought over 

the course of this study. In the year of most severe drought 

(2002), there was strong evidence that it affected virtually all 

aspects of Southwestern Willow Flycatcher ecology, especially 

at Roosevelt Lake. Nest success and productivity were all 

greatly reduced, and the degree of non-territorial behavior (and 

thus lost productivity) was dramatically higher. Although the 

Roosevelt and San Pedro/Gila flycatcher populations persisted 

through the drought, and eventually increased to above predrought levels, it is clear that the 2002 drought was a major 

disturbance event for the flycatchers.

This raises an important question as to whether the flycatcher population at Roosevelt Lake could sustain itself over 

multiple years of extreme drought. The near total collapse of 

reproductive success in a single drought year like 2002, coupled with the short average lifespan of Southwestern Willow 

Flycatchers, strongly suggests that several successive years 

of extreme drought could cause a major population crash and 

possible extirpation of flycatchers, the speed of which would 

depend on the starting population size. This is an important 

consideration with respect to forecasting the long-term persistence of flycatchers at our study sites, and possibly elsewhere. 

For example, most climate change models predict increased 

drought frequency and severity in the Southwest (Seager et 

al. 2007). Therefore, long-term management of Southwestern Willow Flycatchers will be more effective if it considers 

how flycatcher habitat and breeding populations may respond 

to changes in southwestern climate, and whether there are 

management actions that can ameliorate any negative effects. 

Reservoirs could potentially serve a unique role in adapting 

to changing hydrologic regimes through operations that favor 

riparian habitat development and persistence in drawdown 

zones or along regulated rivers downstream; however, such 

uses will need to be reconciled with the ongoing demand for 

water by cities and agriculture.

Future Research Needs and Directions

As noted above and in chapter 1, we have learned a great 

deal from this study of Southwestern Willow Flycatchers and 

their habitats. Still, there are a number of topics that warrant 

additional research attention, because they have direct implications for the conservation and management of the flycatcher.

•	 Are small populations different? Our study involved 

some of the largest known breeding populations; 

smaller and more isolated populations may function 

differently. However, few smaller sites have been studied in detail. This is an important issue because many 

Southwestern Willow Flycatcher breeding sites are 

comprised of fewer than 10 territories and geographically isolated from other known breeding sites (Durst 

et al. 2006). We do not know levels of productivity, 

movement, and survivorship of these small populations, or whether they act primarily as population 

sinks or sources (Kus et al. 2003). Directing additional 

research toward small populations rangewide can help 

us answer these key questions.

•	 Improved demographic modeling and forecasting. 

Despite the intense field efforts and extensive modeling that we conducted, our demographic models did 

not perform well in describing changes that occurred 

during our study. However, it is useful to understand 

the synergistic relationship among vital rates (e.g., 

survivorship, productivity), and using this to develop 

an accurate model of flycatcher populations would 

allow for evaluations of management actions, impacts 

of climate change, etc.. Therefore, we encourage the 

continued refinement and testing of the demographic 

models, using the Roosevelt Lake and San Pedro/Gila 

datasets as a foundation. We also believe that new 

models may be needed to more adequately take into 

account the dynamic nature of flycatcher populations 

and habitat, and the metapopulation dynamics of the 

subspecies.

•	 Spatial models as decision tools for managers. At 

present, the spatial habitat models that we developed 

can be used to look at current and past characteristics 

of Southwestern Willow Flycatcher habitat, which is 

useful in a variety of ways. However, the model cannot 

be extended into the future, nor used to test “what if” 

alternatives based on different future conditions (e.g., 

reservoir operations, flow regimes, changing climate, 

etc.). To do so would require a multidisciplinary modeling effort to quantitatively link NDVI values, upon 

which our model is based, and the factors that influence riparian habitat vigor (soils, geology, groundwater, hydrology, etc). We believe that developing an 

integrative model is an important next step, and would 

set the stage for forecasting capability. This would 

allow managers to evaluate how different scenarios 

affect riparian habitat, and to choose alternatives with 

the greatest probability of achieving their goals. These 

models can be applied to management of Southwestern Willow Flycatchers, as well as the wide array of 

wildlife dependent upon riparian systems, especially in 

the southwestern United States.

•	 Role and impact of diseases. One important aspect 

of flycatcher ecology that has received almost no 

attention is disease. There is virtually no information 

available on the prevalence and nature of disease in 

any of the Willow Flycatcher subspecies (Marshall and 

Stoleson 2000, Sedgwick 2000). Yet disease, especially 

new and highly pathogenic introduced forms, can have 

devastating effects on native bird populations, especially for endangered species. West Nile Virus (WNV) 

has recently become established in the Southwest and 
142 The Ecology of the Southwestern Willow Flycatcher in Central Arizona

is now widespread in Arizona, having been found in 

mosquito, bird, or human populations in almost every 

county (Arizona Department of Health Services). In 

the early 2000s, we collected mosquito samples and 

avian blood samples at Roosevelt Lake to determine 

if WNV was present. We found no evidence of the 

virus in mosquitoes, but confirmed it was present in at 

least two bird species (but not the Southwestern Willow Flycatcher; USGS unpublished data). Given that 

Tyrannid flycatchers, the bird family to which Willow 

Flycatchers belong, evolved in the New World, and 

that WNV is of Old World origin, flycatchers may be 

very susceptible to this disease. The same could be true 

of Highly Pathogenic Avian Influenza (HPAI), which 

has not yet been found in North America but which has 

the potential for eventual establishment through natural 

or human-assistance dispersal. We believe that future 

Southwestern Willow Flycatcher management and conservation would benefit from proactive research on the 

types and prevalence of diseases among flycatchers.

•	 Migration ecology. The overwhelming majority of 

research on the Southwestern Willow Flycatcher has 

been conducted on the breeding grounds (Sogge 2000, 

Stoleson et al. 2000). While this is a critically important period in the flycatcher’s life history, as a Neotropical migrant it spends more than two-thirds of its 

annual cycle outside of the breeding grounds. Although 

flycatcher surveys and research have increased on the 

wintering grounds (Koronkiewicz 2002, Lynn et al. 

2003, Schuetz et al. 2007), there is a dearth of information about the migration period (though see Yong and 

Finch 1997). This relative lack of data is problematic, 

given that migration habitat and behavior are key 

determinants of whether birds arrive on the breeding 

grounds in suitable condition to nest. Furthermore, 

most Willow Flycatcher mortality appears to occur 

during migration (see chapter 2). Therefore, we believe 

that effective long-term conservation of the Southwestern Willow Flycatcher requires additional research on 

migration, including stopover habitat selection and use, 

foraging ecology, and physiology.

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