E. t. extim

In cooperation with Northern Arizona University 

Using Molecular Genetic Markers to Resolve a 

Subspecies Boundary: the Northern Boundary 

of the Southwestern Willow Flycatcher 

in the Four-corner States 

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y#

Y# Y#

Y#

ALPI

CAVE

GILA

LOCO

ROOS

SAPE

ARAP

CCCO

DELT

MCSP

RHEA

RICO

SILT

AZUL

GICL

SHIP

ZUNI

EACA

FICR

FILA

LIBE

SEEG

STLA STRI

TOPO

E. t. adastus 

us 

E. t. traillii 

Open-File Report 2008–1117
 

U.S. Department of the Interior 

U.S. Geological Survey 
This page intentionally left blank
In cooperation with Northern Arizona University 

Using Molecular Genetic Markers to Resolve a 

Subspecies Boundary: the Northern Boundary 

of the Southwestern Willow Flycatcher in 

the Four-corner States 

By Eben H. Paxton1 2, Mark K. Sogge2, Tad C. Theimer1, Jessica Girard1, and Paul Keim1

1 Northern Arizona University, Department of Biological Sciences, Flagstaff, Ariz. 

2

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

Open-File Report 2008–1117 

U.S. Department of the Interior 

U.S. Geological Survey 
 ii

U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2008 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Paxton, E.H., Sogge, M.K., Theimer, T.C., Girard, J., and Keim, P., 2008, Using molecular genetic markers to 

resolve a subspecies boundary: the northern boundary of the Southwestern Willow Flycatcher in the fourcorner states: U.S. Geological Survey Open-File Report 2007–1117, 20 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 
 iii

Contents 

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

Introduction .........................................................................................................................................................................2

Methods ...............................................................................................................................................................................4

Results ..................................................................................................................................................................................7

Discussion..........................................................................................................................................................................13

Acknowledgements .........................................................................................................................................................17

References.........................................................................................................................................................................18

Figures 

1. Location of Willow Flycatcher breeding sites sampled for this study..................................................................3 

2. Willow Flycatcher breeding sites clustered out into two groups using a UPGMA analysis of genetic 

distances, based on mitochondrial DNA and nuclear DNA........................................................................10

3. Frequency of C-group haplotypes observed at breeding sites decreases with increasing latitude ............12 

4. Contours indicating the predicted probability of a breeding site having a C-group haplotype based on 

latitude and elevation.........................................................................................................................................14 

5. The amount of genetic information that each C-group probability line separates is highest at the 30% 

contour line. .........................................................................................................................................................15 

Tables 

1. Willow Flycatcher breeding sites sampled in Arizona, Colorado, New Mexico, and Utah, 1996–2004..........5 

2. Cytochrome-b sequence haplotype polymorphic positions and their frequency encountered at sampled 

breeding sites ........................................................................................................................................................8 

3. Analysis of molecular variance results indicate that a significant amount of variation in mtDNA and 

nDNA frequencies is explained by grouping breeding sites into the two UPGMA clades ...................11
Using Molecular Genetic Markers to Resolve a 

Subspecies Boundary: the Northern Boundary 

of the Southwestern Willow Flycatcher 

in the Four-corner States 

By Eben H. Paxton, Mark K. Sogge, Tad C. Theimer, Jessica Girard, and Paul Keim 

Executive Summary 

The northern boundary of the endangered Southwestern Willow Flycatcher 

(Empidonax traillii extimus) is currently approximated as running through southern 

Colorado and Utah, but the exact placement is uncertain because this subspecies shares a 

border with the more northern and non-endangered E. t. adastus. To help resolve this 

issue, we evaluated the geographic distribution of mitochondrial and nuclear DNA by 

sampling breeding sites across the four-corner states (Arizona, Colorado, New Mexico, 

and Utah). We found that breeding sites clustered into two major groups generally consistent with the currently designated boundary, with the exception of three sites situated 

along the current boundary. However, delineating a precise boundary that would separate 

the two subspecies is made difficult because (1) we found evidence for a region of intergradation along the boundary area, suggesting the boundary is not discreet, and (2) the 

boundary region is sparsely populated, with too few extant breeding populations to 

precisely locate a boundary. The boundary region encompasses an area where elevation 

changes markedly over relatively short distances, with low elevation deserts to the south 

and more mesic, higher elevation habitats to the north. We hypothesized that latitudinal 

and elevational differences and their concomitant ecological effects could form an ecological barrier that inhibited gene flow between the subspecies, forming the basis for the 

subspecies boundary. We modeled changes in geographic patterns of genetic markers as a 

function of latitude and elevation finding significant support for this relationship. The 

model was brought into a GIS environment to create multiple subspecies boundaries, 

with the strength of each predicted boundary evaluated on the basis of how much genetic 

variation it explained. The candidate boundary that accounted for the most genetic variation was situated generally near the currently recognized subspecies boundary, but should 

be more biologically meaningful because it incorporates the landscape features that may 

be driving separation of the subspecies. Even so, we caution that using any narrow 

boundary line as an indicator of subspecies identity could be misleading because biologically the boundary is a region of intergradation rather than a discrete line. Designating, a 

boundary ultimately becomes a regulatory and management decision based on how much 

of the genetic variation unique to a subspecies should be protected. We discuss how the 

results of this study can help guide this decision process by wildlife policy makers. 

 1
Introduction

A wide array of national and international conservation laws and treaties, 

including the U.S. Endangered Species Act, provide for the recognition and protection of 

distinct portions of a species (e.g., subspecies, distinct populations); however, subspecific 

protection can raise challenging scientific, management, and conservation issues (Haig et 

al. 2006). One such issue is the difficulty of delineating the intraspecific range boundary 

between endangered and non-endangered subspecies or populations because of the 

management implications inherent to being on one side of the boundary line or the other. 

Delineating range boundaries is particularly difficult for intraspecific groups with 

contiguous, shared boundaries where a zone of introgression commonly occurs, 

increasing the difficulty in defining a distinct boundary (Avise 1994, Barrowclough et al. 

2005). 

The Willow Flycatcher (Empidonax traillii) is a neotropical migratory passerine 

that breeds across most of the United States and southern Canada, and winters from 

southern Mexico through Central America into northwestern South America (DeGraaf 

and Rappole 1995). The Willow Flycatcher is a polytypic species composed of four 

morphologically similar subspecies (Unitt 1987). In 1995, the USFWS declared the 

southwestern subspecies (E. t. extimus) endangered due to the extirpation from many 

historical breeding sites and a marked decrease in rangewide population size (USFWS 

1995). Although there is general agreement on the approximate ranges of the subspecies 

among studies based on geographic patterns of morphological (Unitt 1987, Browning 

1993), molecular genetic (Paxton 2000) and song (Sedgwick 2001) variation, range 

boundaries were for the most part approximated. Most of the boundary of E. t. extimus is 

discontinuous with other Willow Flycatcher subspecies: E. t. extimus occupies the 

southern most extent of the species range west to the Pacific Ocean, while the range of E. 

t. extimus to the east (Texas) and northwest (California and Nevada) is separated from 

other subspecies’ populations by large geographic regions that are unoccupied due to 

either inhospitable terrain or historical extirpations (Unitt 1987). However, in Utah and 

Colorado the northern boundary of E. t. extimus is adjacent to the southern boundary of 

E. t. adastus (Fig. 1). Within this region, where the ranges of E. t. extimus and E. t. 

adastus meet and individuals of different subspecies may interbreed, the subspecies 

boundary is in need of resolution for legal and management purposes (USFWS 2002). 

 2
Figure 1. Location of Willow Flycatcher breeding sites sampled for this study. The four 

letter alpha code associated with each location is associated to the name of the site (see 

Table 1). The solid-dashed lines represent the approximate location of the currently 

managed subspecies boundaries (USFWS 2002). 

Multiple studies have evaluated the geographic distribution of the two subspecies 

within this region, while only indirectly considering the issue of where the boundary 

between E. t. extimus and E. t. adastus lies. Virtually all studies found evidence of a 

region of intergradation separating the subspecies, though the proposed location of this 

region varied among studies. Based on morphological characteristics of museum 

specimens, Behle (1985) argued that flycatchers in northernmost Utah were E. t. adastus, 

those in the extreme southern part of the state were E. t. extimus, while most of Utah 

represented a gradual cline of morphological variation from one subspecies to the other. 

In contrast, Burleigh (1972) placed the location of E. t. adastus and E. t. extimus

intergrades farther north into northern Utah, while Unitt (1987) and Browning (1993) 

believed the best boundary was through southern Utah, although Unitt (1987) noted 

 3
evidence for intergradation northward. In western Colorado, where very few museum 

specimens were collected, Unitt (1987) stated that there was not enough evidence to 

determine subspecies status, while Browning (1993) believed the region to be an 

intergrade zone. Based on geographic distribution of cytochrome-b sequences from 

flycatchers across their breeding range, Paxton (2000) found the strongest support for a 

southern Colorado/Utah boundary, but recognized that more sampling of the region was 

needed. Sedgwick (2001) analyzed geographic variation in Willow Flycatcher song 

patterns, which are innate (Kroodsma 1984), and found evidence for intergradation 

further south than other studies, into high elevation areas of Arizona and New Mexico. 

Based on the available evidence, the Southwestern Willow Flycatcher Recovery Team 

approximated the boundary as running along the southern portion of Utah and Colorado 

(Fig. 1), although it clearly acknowledged that more work is needed on exactly where to 

place the boundary (USFWS 2002). 

The purpose of this study was to examine geographic patterns of Willow 

Flycatcher mitochondrial (mtDNA) and nuclear (nDNA) molecular genetic markers to 

determine the most appropriate location of the Southwestern Willow Flycatcher’s 

northern range boundary in the four-corner states of Arizona, Colorado, New Mexico, 

and Utah. Mitochondrial DNA is ideal for evaluating genetic patterns across geographic 

space (Avise 1994), and the inclusion of nDNA provides additional independent 

information to help support or contrast patterns in the mitochondrial DNA (Moritz 1994). 

We assessed genetic relationships among flycatcher breeding sites distributed across the 

boundary area to understand where genetic structuring occurred. The boundary region is 

characterized by rapid changes in biotic communities, with desert lowlands to the south, 

and more mesic, high elevation habitats to the north. Because these environmental and 

biotic changes may inhibit gene flow between the two subspecies, and thus could help 

inform where a biologically meaningful boundary may lie, we built a statistical model 

associating the geographic distribution of the molecular markers with latitude and 

elevation as informative factors. Results from the model formed the basis of a GIS model 

that produced multiple candidate boundaries. Herein we evaluate the strength of each 

candidate boundary and discuss potential approaches to choosing one for management 

and conservation purposes. 

Methods 

Study Sites and Genetic Sampling 

We sampled 145 individual flycatchers from 25 sites in Arizona, Colorado, New 

Mexico, and Utah (Fig. 1, Table 1). Sample sizes for sites varied from 2 to 16 individuals 

per site, with a mean of 6 per site (Table 1). Most known currently-occupied breeding 

sites in this region were sampled (Durst et al. 2007). Our objective was to sample at least 

five individuals from each site, but this was not always possible due to small breeding 

population sizes at many of the breeding locations, and nDNA markers were only 

generated for the larger breeding sites. 

 4
Table 1. Willow Flycatcher breeding sites sampled in Arizona, Colorado, New Mexico, 

and Utah, 1996–2004. For each breeding site there is a four letter site code (SITE), sample 

sizes for cytochrome-b sequences and AFLP analysis information, and information on the 

latitude, elevation. C-group refers to the predicted probability of finding a C-group 

haplotype at a particular breeding site, given latitude and elevation (see text). 

Site Name 

State Sample 

size 

mtDNA 

Sample 

size 

nDNA 

Latitude Elevation C-group

ALPI Alpine Horse Pasture AZ 9 0 33.830 2414 61%

CAVE Camp Verde AZ 3 0 34.558 942 76%

GILA Solomon Bridge, Gila River AZ 2 0 32.825 899 88%

LOCO Lower Colorado River AZ 7 9 36.050 200 73%

ROOS Roosevelt Lake AZ 12 8 33.767 640 85%

SAPE San Pedro River AZ 7 8 32.975 634 89%

TOPO Topock Marsh AZ 3 7 34.792 139 83%

ARAP Arapahoe National Wildlife 

Refuge 

CO 5 5 40.617 2470 7%

CCCO Clear Creek CO 9 6 37.792 2900 16%

DELT Escalante State Wildlife Area CO 6 0 38.756 1700 22%

MCSP McIntire Springs CO 16 6 37.283 2300 27%

RHEA Gothic CO 2 0 38.946 2830 11%

RICO Rio Blanco Lake CO 7 6 40.088 1830 12%

SILT Colorado River at Silt CO 4 0 39.541 1650 17%

AZUL Tierra Azul NM 5 0 36.304 2179 38%

GICL West Fort Ditch NM 5 9 33.039 1402 82%

SHIP Shiprock NM 2 0 36.867 1463 44%

ZUNI Zuni/Nutria Diversion Reservoir NM 4 0 35.235 2081 52%

EACA East Canyon Reservoir UT 4 11 40.874 1798 9%

FICR Fish Creek UT 5 6 39.775 2560 9%

FILA Fish Lake UT 4 5 38.693 3018 10%

LIBE Little Bear River UT 6 11 41.719 1372 9%

SEEG St. George UT 3 4 37.108 732 54%

STLA Stewart Lake UT 8 5 40.346 1158 17%

STRI Strawberry River UT 7 9 40.3468 2621 7%

We detected flycatchers via standard play-back surveys (Sogge et al. 1997), 

capturing them using mist nets (Sogge et al. 2001) for banding and genetic sampling. All 

captured flycatchers were banded with a Federal bird band imprinted with a unique 

number to assure we sampled each individual only once. To assure that no migrants were 

used in this study, we included only adults that were territorial at a site during the nonmigrant period (15 June–20 July; Unitt 1987), or were known by field observations to be 

resident breeders (e.g., observed breeding activities). DNA samples were collected from 

1996 to 2004, and included samples used in previous work (Busch et al. 2000; Paxton 

2000). 

Blood samples were taken by clipping a toenail to the vascularized tissue, and 

rinsing a drop of blood into a 1.5 mL tube with approximately 40 μL of collection buffer 

(1xSSC, 50mM EDTA). Samples were stored on ice until frozen, with DNA 

subsequently isolated from the blood following the protocol of Mullenbach et al. (1989). 

Blood was digested overnight at 55o C in lysis buffer (10mM Tris, 1nM EDTA, 1 SDS, 

 5
100mM NaCl, pH 8) with 200 μg/mL proteinase K and 2 ηM DTT (final concentration 

of both). This lysate was extracted with chloroform and followed by an isopropanol 

precipitation. An aliquot of each DNA extraction was then run on a 0.7 agarose gel to 

assess DNA quality and quantity. 

DNA Sequencing 

We sequenced 1,063 nucleotides of the cytochrome-b beginning 80 bases 

downstream from the start codon of the gene. All sequences for this region were 

confirmed with at least two aligned sequence reads. DNA was sequenced with primers 

obtained from Helm-Bychowski and Cracraft (1993; L14827: 5' CCACACTCCACACA 

GGCCTAATTAA 3', H16065: 5' GGAGTCTTCAGTCTCTGGTTTACAAGAC 3'). 

PCR reactions consisted of 50 ng of DNA, 1x PCR buffer, 3mM MgCl2, 200μM dNTPs, 

1μM of each primer, and 1 U of Taq DNA Polymerase. Cycling parameters were 35 

cycles of 30 seconds at 94o

 C, 30 seconds at 55o C, and 2 minutes at 72o C. PCR products 

were concentrated using a QIAquick PCR purification kit (Qiagen), then sequenced on an 

ABI 377 DNA sequencer. We aligned the sequences manually and edited them using 

Sequence Navigator version 1.0.1 (Applied Biosystems). All unique sequences were 

deposited in Genbank (acquisition numbers: AF297237 to AF297276). 

AFLP Analysis

AFLP markers were generated using the procedure of Vos et al. (1995) with 

modifications as described in Busch et al. (2000). Adenine was used as the selective 

nucleotide in the first amplification; while ACG/AGG, ACG/AGC, ACG/ACC, 

ACG/ACA, ACG/ACG, ACG/AAG (EcoRI/MseI primers, respectively) were used for 

the second selective amplification. Polymorphic AFLP markers were manually scored, 

with only distinct and unambiguous polymorphic markers that conformed to the 95% 

polymorphic rule being used for this study (Hartl and Clark 1997). A set of 20 individuals 

was blindly replicated and indicated 0% error in scoring and marker consistency of those 

loci used in this study. 

Statistical Analysis 

Individuals were grouped by their respective breeding site. Due to small number 

of individuals sampled at some sites, we grouped some breeding locations if they were 

within 30 km of one another and greater than 100 km from any other site. Determination 

of distances to group samples was based on banding studies in central Arizona which 

documented that flycatcher movements between breeding sites were frequent within 30–

40 km but became increasingly rarer with increasing distance (Paxton et al. 2007). 

To understand the genetic structure among breeding sites, we calculated pairwise 

FST genetic distance measures using Nei’s (1972) minimum distance algorithm in TFPGA 

(Miller 1997), and graphically represented them using a UPGMA cluster analysis. To 

compare the degree of genetic separation between the two subspecies based on the 

currently recognized boundary and the one suggested from the genetic distances, we used 

an analysis of molecular variance (AMOVA) to obtain estimates of genetic variation 

explained and ΘST estimates (analogous to FST values, measuring genetic structure among 

groups; Excoffier et al. 1992), calculated using Arlequin v 2.0 (Schneider et al. 1997). 

 6
 7

Mantel tests were run in TFPGA using genetic distances from TFPGA, geographic 

distances from the Distance Matrix Extension in ArcView (v. 3.2, ESRI, Inc.), and 

Euclidean distances for elevation and latitude. 

We modeled the frequency of C-group haplotypes (a group of cytochrome-b 

haplotypes closely associated with E. t. extimus; Paxton 2000, this study) observed at 

each breeding site as a function of the breeding site’s latitude (decimal degrees) and 

elevation (m) using a binary logistic regression in SAS (v 9.1, SAS, Inc.). The goodnessof-fit of the logistic regression was evaluated by a ratio of the deviation to the degrees of 

freedom, with a ratio of 1.5 or less taken as assurance of good fit (Agresti 2002). We 

tested latitude, elevation, and an interaction of the two factors. Those variables with a 

WALD test significance of P≤0.05 were included in the model. To evaluate how well the 

breeding site frequency of C-group haplotypes reflects overall genetic differences among 

sites, we created in Arlequin vs 2.0 a pairwise genetic distance matrix of all sites 

compared to one another which considered all the cytochrome-b haplotypes and AFLP 

information. Using the genetic distance of each breeding site from the Roosevelt Lake 

breeding site (chosen to represent a core E. t. extimus breeding population with a 

relatively large sample size), we evaluated the correlation between the genetic distance 

from Roosevelt Lake and the predicted occurrence of a C-group haplotype for each site 

using a Pearson’s Correlation in JMP vs 6.0 (SAS, Inc.). 

The logistic regression model was brought into a GIS environment to create a 

“landscape” of C-group haplotype probability estimates across the four-corner states. 

Using Spatial Analysis’ Raster Calculator in ArcGIS (v 9.0, ESRI, Inc.), we modified 

USGS DEM grids and a simple grid with latitude values (created by authors) to create the 

probability grid using the following equation derived from the logistic regression: 

{e17.4399-0.0007(elevation)-0.4519(latitude)/(1+e17.4399-0.0007(elevation)-0.4519(latitude))}. 

Using ArcView Spatial Analysis Contour function, we created 10% increment 

contour lines (10–90%) to visually simplify the image, and to create lines that represent 

candidate subspecies boundaries. To help determine which line or lines were the most 

appropriate for designating a subspecies boundary, we quantify how much genetic 

variation was separated by each probability contour line. We ran multiple AMOVAs in 

Arlequin for each grouping of sites (e.g., all sites north of the 10% line versus everything 

south of that line) to obtain FST estimates, and graphed their values by the probability 

contour in SigmaPlot 8.0 (SPSS, Inc.). Statistical significance was assumed at the P<0.05 

level. 

Results 

Genetic Structuring Between Subspecies 

We found no fixed differences between the two subspecies from either 

cytochrome-b haplotypes (Table 2) or AFLP polymorphic loci that would allow us to 

unambiguously distinguish individuals of one subspecies from the other; however, we did 

observe strong frequency differences between the subspecies. Based on genetic 

relationships among the breeding sites, a UPGMA dendrogram suggested two major 

groups of breeding sites for both mtDNA and nDNA markers (Fig. 2). 
Table 2. Cytochrome-b sequence haplotype polymorphic positions (left side of table) and their frequency encou

breeding sites. 

Arizona New Mexico Colorado 

1 1 1 1 A C G L R S T A G S Z A C D M R 

1 2 3 3 3 4 4 4 5 5 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 9 9 9 9 0 0 1 1 L A I O O A O

P

Z I H U R C E C H 

5 9 7 2 2 8 2 4 8 5 7 0 5 8 8 9 0 0 0 2 8 8 0 2 4 5 9 0 1 6 7 1 2 0 1 P V L C O P U C I N A C L S E 

6 2 9 1 9 4 9 4 6 8 4 9 4 3 9 0 2 4 8 1 0 6 1 8 6 2 5 6 8 9 3 7 0 4 6 I E A O S E O L L P I P O T P A 

T T C T A T T C A C G G A A T C C T A A C G T T A T G A A A C T C T A D1 - 1 - - 3 - 1 - 1 1 - - 2 1 4 1 

. . T . . . . . . . . . . . . . . . . . . . . C . . . . . . . . . . . A1 - - - - - - - - - - - 1 - - - 1 

. . T . . . . T . . . . . . . . . . . . . . . C . . . . . . . . . . . A2 - - - - - - - - - - - - - - - - 

. . T . . . . . . . . . . . . . . . . . . . . C . C . . . . . . . . . A5 - - - - - - - - - - - - - - - - 

. 

C 

. T 

T 

. . . . . . T . . . . . . . . . . . . . C

C

. . . . . . . . 

C

. . . A7 - 

- 

- - 

- - 

- - 

- - 

- - 

- - 

- - 

- - 

- - 

- 

1

- - 

- - 

- - 

1

- 

- - 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A8 

. . T . . . . . . . . . . . . . . . . . . . . C . . . . . . . . A . . A9 - - - - - - - - - - - - - - - - 

. . T . . . . T . . . . . . . . . . . . . . . C . . . . . . . . . . G A11 - - - - - - - - - - - - - - - - 

. . T . . . . T . . . . . . . . . C . . . . . C . . . . . . . . . . . A12 - - - - - - - - - 1 - - - - - - 

. . . . . . . . . . . C . . . . . . . . . . . . . . . . . . . . . . . B1 1 - - - - - - - - - - 2 3 - 4 - 

. . . . . . . . . . . C . . . . . . . . . . . . . . . . . . . . . . G B2 2 - - 1 - - - - - - - - - - 1 - . C . . . . . . . . . C . . . . . . . . . . . . . . . . . . . . . . . B4 - - - - - - - - - - - - 1 - - - 

. . . . . . . . . . . C G . . . . . . . . . . . . . A . . . . . . . . B5 - - - 1 - - - - - - - - - - - - 

. . . . . . . . . . . C . . . . . . . . . . . . T . . . . . . . . . . B6 - - - - - - - - - - - - - - - - 

. . . . . T . . . . . C . . . . . . . . . . . . . . . . . . . . . . . B7 - - - 1 - 1 - 1 - - - - 1 - - - 

. . . . C . . . . . . C . . . . . . . . . . . . . . . . . . . . . . . B9 1 - - - - - - - - - - - - - - - 

. . . . . . T . . . . C . . . . . . . . . . . . . . . . . . . . . . . B11 - - - 1 - - - - - - - - - - - - 

. . . . . . . . . . . C . . . . . . . . . . C . . . . . . . . . . . . B12 - - - - - - - - - - - - - - - - 

. . . . . . . . . . . C . . . T . . . . . . . . . . . . . . . . . . . B14 - - - - - - - - - - - - - - 4 - 

. . . . . . . . . . A C . . . . . . . . . . . . . . . . . . . . . . . B15 - - - - - - - - - - - - - - - - 

. . . . . . . . . . . C . . . . . . . . . . . . . . . . . G . . . . . E1 - - - - - - - - - - - - 1 - - - 

. . . . . . . . . . . C . . . . T . . . . . . . . . . . . G . . . . . E2 - - - - - - - - - - - - 1 - - - 

. . . . . . . . . . . C . . . . . . . . . . . . . . . . . G . . . A . E3 - 

5

- 

1

- 

2

- - - - 

2

- 

4

- 

3

- - - - 1 - 

2

- 

. . . . . . . . . . . . . . . . . . . . T . . . . . . . . . . . . . . C1 3 7 5 - 3 2 - 1 - 

. . . . . . . . . . . . . . . . . . G . T

T

. . . . . . . . . . 

G

. . . . C2 - 

- 

1

- 

- - 

- - 

2 

- 

- 

1 

- - 

- - 

1

- 

- - 

- - 

- - 

- - 

- - 

- - 

- - 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C3 

. . . C . . . . . . . . . . . . . . . . T . . . . . . . . . . . . . . C6 - - - - - - - - - - - - - - - - 

. . . . . . . . . . . . . . C . . . . . . A . . . . . . . . . . . . . D6 - - - - - - - - - - - - - - - - 

. . . . . . . . G . . . . . . . . . . . . . . . . . . . . . . . . . . D7 - - - - - - - - - - - - - - - - 

. . . . . . . . . . . . . . . . . . . T . . . . . . . . . . . . . . . D10 - - - - - - - - - - - - - - - - 

. . . . . . . . . . . . . G . . . . . . . . . . . . . . . . . . . . . D13 - - - - - - - - - - - - - 2 - - 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . T . . . . . . D14 - - - - - - - - - - - - - 1 - - 

. . . . . . . . . . A . . . . . . . . . . . . . . . . T . . . . . . . D15 - - - - - - - - - - - - - - - - 

 8
With mtDNA, which provided genetic information for all breeding sites sampled, the two 

dendrogram groups are generally consistent with the currently accepted boundary between the 

subspecies, with all the core E. t. extimus sites clustering together and all the core E. t. adastus sites 

clustering together. However, three sites that are generally considered to be within the range of E. 

t. extimus, but located along the currently accepted boundary, were clustered with the “E. t. 

adastus” group (Fig. 2). Two of these sites (SEEG and SHIP; Table 1), were breeding sites 

consisting of a small number of individuals relatively isolated from other known sites. Therefore, 

the clustering of these latter two sites with core E. t. adastus breeding locations may be a function 

of small samples sizes (n=2 and 3 individuals at each site), and their affiliation with one subspecies 

or the other should be viewed with some caution. However, the third site (MCSP) had a relatively 

large sample size analyzed (n=16; Table 1), and is rooted deep in the E. t. adastus-associated group. 

The nDNA dendrogram provided generally similar results, with core E. t. adastus sites 

grouping together, and core E. t. extimus sites grouping together (Fig. 2). Similar to the mtDNA 

dendrogram, the MSCP site was deeply rooted in the “E. t. adastus” group, while SEEG and two 

other sites which were located in northern Colorado and Utah (ARAP and STLA) were unresolved 

(basal to the two groups; Fig. 2). No nDNA information was available for the SHIP breeding site. 

Because the two dendrograms generally agreed, but only the mtDNA dendrogram included all 

breeding sites sampled, we used the mtDNA UPGMA as the best genetic grouping of breeding 

sites. 

We used an AMOVA to compare the amount of genetic variation accounted for if we 

grouped breeding sites into either the currently recognized boundary or the dendrogram grouping. 

The UPGMA clustering of breeding locations received more support, with 19% of the variation in 

the mtDNA markers explained by the dendrogram grouping versus 11% for the traditional 

boundary, and 7% of nDNA variation explained for the dendrogram grouping versus 4% for the 

traditional boundary (Table 3). All four values were significantly different from 0% (p<0.001). 

Although both the UPGMA clustering and AMOVA analysis indicated strong genetic 

frequency differences between the two subspecies, the geographic distribution of haplotypes 

suggested a region of introgression in the boundary region. For example, the C-group haplotypes 

(C1, C2, C3, C6) are dominate within the range of E. t. extimus (Table 2) but the proportion of 

individuals possessing a C-group haplotype changed from high frequencies at low latitudes within 

the core breeding range of E. t. extimus to low frequencies at the northern latitudes of the study area 

(Fig. 3). No C-group haplotypes were detected in previously sampled breeding sites further north in 

Idaho, Wyoming, and Montana (Paxton 2000). A Mantel test indicated a significant relationship 

between genetic and geographic distance when all sites were considered simultaneously, (mtDNA, 

r=0.23, p=0.002; nDNA, r=0.30, p=0.007); however, Mantel tests of genetic distance versus 

geographic distance indicated no relationship among breeding sites within the range of E. t. extimus

(mtDNA, r=0.05, p=0.32; nDNA, r=0.24, p=0.19) or E. t. adastus (mtDNA, r=0.0, p=0.78; nDNA, 

r=0.03, p=0.39). These results suggest that the relationship between genetic and geographic 

distance is not due to a gradual frequency cline but rather a rapid change in frequencies at the 

boundary. 

 9
nDNA 

mtDNA 

Figure 2. Willow Flycatcher breeding sites clustered out into two groups using a UPGMA analysis 

of genetic distances, based on mitochondrial DNA (cytochrome-b sequences; top) and nuclear DNA 

(AFLP; bottom). Four letter alpha codes designate breeding sites (see Table 1), with sites currently 

considered E. t. extimus followed by an asterisk. 

10
 
Table 3. Analysis of molecular variance (AMOVA) results indicate that a significant amount of 

variation in mtDNA and nDNA frequencies is explained by grouping breeding sites into the two 

UPGMA clades (Fig. 2). An AMOVA partitions genetic variation among individuals, breeding sites, by 

grouping the individuals into the three categories and subspecies, with those percentages 

interpreted as the percent of genetic variation explained. 

Current boundary UPGMA cluster 

Source of 

variation d.f. Sum of squares 

Variance 

components 

Explained 

variation (%) 

Sum of 

squares 

Variance 

components 

Explained 

variation (%) 

Mitochondrial DNA 

Subspecies 1 9.894 0.115 10.63 15.966 0.211 18.63 

Breeding sites 23 31.909 0.089 8.24 25.837 0.043 3.79 

Individuals 121 106.368 0.879 81.13 106.368 0.879 77.58 

Total 145 148.171 1.084 148.171 1.133 

Nuclear DNA 

Subspecies 1 51.426 0.583 4.36 68.616 0.975 7.18 

Breeding sites 14 248.994 0.821 6.14 231.805 0.649 4.78 

Individuals 99 1183.736 11.957 89.49 1183.736 11.957 88.04 

Total 114 1484.157 13.361 1484.157 13.581 

 11
Latitude

33 34 35 36 37 38 39 40 41 42

Frequency of C-group haplotypes

0.0

0.2

0.4

0.6

0.8

1.0

Figure 3. Frequency of C-group haplotypes (see Table 2) observed at breeding sites decreases 

with increasing latitude. Frequencies are high at low latitudes within the core range of E. t. extimus, 

and occur in low frequencies within the core range of E. t. adastus at high latitudes, with 

intermediate frequencies within the region between the subspecies. 

Effects of Latitude and Elevation 

The putative divide between the two subspecies lies within a geographic region of relative 

rapid transition from low elevation sites in the south, to higher elevation breeding sites in the north. 

To assess whether these landscape features (latitude and elevation) might influence genetic 

distances among sites, we ran Mantel tests of the pairwise genetic distances among sites and 

latitude and elevation. Latitude was significantly related to genetic distance for mtDNA (r=0.43, 

p<0.001), but only marginally with nDNA (r=0.15, p=0.1). Elevation was strongly associated with 

nDNA genetic distances (r=0.35, p=0.002), but weakly with mtDNA (r=0.09, p=0.14). Although 

the strength of the relationships varied between the variables and mtDNA and nDNA, we 

concluded that a relationship existed between latitude, elevation and geographic patterns of 

molecular markers, and that these landscape features could be informative for predicting a 

boundary. 

Using the strong association of C-group haplotypes with the E. t. extimus subspecies, we 

built a logistic model evaluating the frequency of C-group haplotypes occurring at breeding sites 

with latitude and elevation as predictive factors. The model indicated that both latitude and 

 12
elevation were important explanatory factors, with no support for an interaction between the two 

(p=0.78). Latitude had the strongest effect (beta=-0.4519, p <0.001), with the odds of finding a Cgroup haplotype decreasing 36% with each increase in a degree of latitude. Elevation also 

contributed (beta=-0.0007, p=0.029), with the odds of finding a C-group haplotype decreasing 50% 

with every thousand meter increase in elevation. The model fit the data well, with a goodness-of-fit 

ratio of 1.27. The logistic model predicted the probability of a C-group haplotype occurring at a 

particular site, given a specific latitude and elevation, and should reflect gene flow as influenced by 

latitude and elevation. To evaluate this basic assumption, we evaluated the correlation between the 

predicted probability of C-group occurrence for each breeding location (based on the logistic 

model; Table 1) with each location’s genetic distance from Roosevelt Lake, a breeding site chosen 

for comparison because it is within the core-range of E. t. extimus and was well sampled. The 

resulting correlations (mtDNA: -0.80, P<0.001; nDNA: -0.71, P=0.003) supported this general 

relationship, indicating that as the predicted probability of a C-group haplotype occurring at a site 

decreases, its observed genetic distance from a core E. t. extimus site (Roosevelt Lake) increases. 

We incorporated the logistic model into a GIS environment, projecting a complex 

“landscape” of C-group probability values across this topographically rich region (graphically 

shown in 10% probability contours; Fig. 4). The currently recognized boundary lies primarily 

between the 20% and 40% probability contour lines, while the genetically-derived dendrogram 

groups were between the 30% and 40% contour lines. Each 10% probability contour divides 

different groups of breeding sites; to evaluate how much genetic variability is explained by 

different groupings of breeding sites, we grouped sites north and south of each probability line (10–

80%) and obtained measures of genetic distance (FST) for both mtDNA and nDNA per grouping. 

Genetic distance, based on mitochondrial DNA, had a strong inflection point at the 30% contour 

line (Fig. 5), suggesting that the line of greatest geographic-genetic separation lies at or near this 

probability contour. Partitioning nuclear DNA information in a similar way was less informative 

because multiple breeding sites from the greater boundary region were not represented, but genetic 

structuring appears to increase moving toward the boundary region (Fig. 5). 

Discussion 

The geographic distribution of Willow Flycatcher mitochondrial and nuclear molecular 

genetic markers within the study region suggests two distinct groups (subspecies) based on strong 

frequency differences, with the two groups geographically separated by a region roughly 

overlapping the currently recognized boundary. However, the region that separates the two 

subspecies shows evidence of intergradation, such as the frequency change in cytochrome-b 

haplotypes moving south to north across the boundary region. Thus, the subspecies boundary does 

not appear to be distinct, but rather a region of intergradation approximately centered along the 

southern areas of Utah and Colorado, and the northern areas of Arizona and New Mexico. These 

findings, based on molecular genetic information, are consistent with morphological (e.g., Unitt 

1987, Browning 1993) and song-based studies (Sedgwick 2001) which also found evidence for an 

intergradation zone. It is not uncommon or unexpected to find some degree of introgression along 

the shared boundary of two subspecies (Mayer 1982, O’Brien and Mayr 1991, Crandall et al. 

2000), and different genes may show different patterns of geographic distribution in such an 

intergrade region (Latta 2004). 

 13
Figure 4. Contours indicating the predicted probability of a breeding site having a C-group 

haplotype based on latitude and elevation (shown in 10% increment contours). Each contour line 

represents 10% increment probabilities of encountering a C-group haplotype from 10–90%. The 

exact probabilities associated with each site are given in Table 1. 

 14
Latitude/elevation contour lines

10% 20% 30% 40% 50% 60% 70% 80%

Genetic distance between groups (FST)

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Cytochrome-b

AFLP

Figure 5. The amount of genetic information that each C-group probability line separates is highest 

at the 30% contour line. For each 10%-probability contour line (see Fig. 4), sites were grouped by 

their geographic placement on either side of the dividing line, and a measure of genetic distance 

(FST) was calculated for each grouping and plotted, for both cytochrome-b sequences (mtDNA) and 

AFLP data (nDNA). AFLP contour values from 30–60% are the same due to no change in the division 

of sites.

Effects of Latitude and Elevation on Distribution of Genetic Markers 

Landscape features such as elevation and latitude have long been recognized as shaping the 

distribution of bird communities (Morton 2002, Hawkins and Diniz-Filho 2006). In particular, 

landscape features can influence gene flow between populations, thus influencing the location of a 

boundary between groups such as subspecies (Manel et al. 2003, Storfer et al. 2007). Landscapescale habitat differences are believed to be important drivers of speciation within the genus 

Empidonax, with subspecies of multiple Empidonax species showing strong habitat differences 

(Johnson and Cicero 2002). For the Willow Flycatcher specifically, mountain ranges (Cascade, 

Sierra Nevada, and Rocky Mountains) form the boundaries between the other Willow Flycatcher 

subspecies, and the biotic changes due to latitude and elevation at the northern range of E. t. 

extimus potentially form an ecological barrier that inhibits geneflow between the two subspecies. 

Much of the breeding range of E. t. extimus is characterized by riparian habitat within low elevation 

desert habitats, whereas the habitat of E. t. adastus is characterized primarily by riparian habitat in 

more mesic, higher altitude locations. The Willow Flycatcher breeding sites formed two genetic 

 15
groups separated geographically within the region of change between these two types of habitat. 

Additionally, the relationship between genetic distance among sites and differences in latitude and 

elevation also suggest that latitude and elevation co-vary with genetic patterns across this area, 

suggesting these geographic features either directly influence, or are associated with other factors 

that influence, gene flow in Willow Flycatchers. This pattern is consistent with the idea that animal 

populations spanning altitudinal extremes may show genetically-based physiological adaptations to 

elevation that could lead to selection for locally adapted subpopulations (Storz and Dubach 2004), 

as well as influence of habitat selection on dispersal patterns (Kisdi 2002). 

Inclusion of landscape features such as latitude and elevation in determining the location of 

the northern boundary of E. t. extimus helps to overcome two difficulties of this study: (1) the 

boundary is not discrete, but rather a region of intergradation, and (2) the boundary region is 

sparsely populated providing minimal information that would help narrow down the location of the 

boundary. If the landscape features form an ecological barrier across which gene flow is inhibited, 

then identifying those features that are driving the ecological barrier can help inform where a divide 

between the subspecies occurs. Additionally, landscape-informed boundaries can be used to predict 

genetic patterns that might be expected in areas where there are no longer extant populations. For 

example, the Colorado River running through southeastern Utah represents a low elevation corridor 

extending northward that once supported breeding populations of flycatchers, from which at least 

one study skin collected in Glen Canyon (now under Lake Powell) was classified as E. t. extimus

based on morphological characters (Unitt 1987). However, despite extensive surveys, no breeding 

populations have been identified from this area in recent times (USFWS 2002), making decisions 

about where a boundary line may lie in this area exceedingly difficult. Our model suggests that low 

elevation sites in this area would have been more likely to have been occupied by E. t. extimus. 

Where Should the Northern Boundary of E. t. extimus be Placed? 

While both mtDNA and nDNA distinguished between two genetic lineages that roughly 

reflected current designations of E. t. extimus versus E. t. adastus, with the greatest amount of 

genetic differentiation near the 30% contour line, the evidence for a region of intergradation 

suggests there is little support biologically for the legal necessity of designating a boundary line. As 

a result, designating the location of a discrete line separating the two subspecies cannot be made 

unambiguously based on genetic data and thus becomes a value judgment dependent on the 

philosophy of policy makers. Yet resolution of this issue is important for protection and 

conservation of E. t. extimus, because its listing was based on subspecies status. We describe three 

alternative approaches that could be taken in making such a decision based on the findings of this 

study: 

1. Place the boundary in the zone of intergradation at a point that maximizes the degree of genetic 

differentiation based on our analysis of genetic markers. This line would best correspond with 

the 30% probability contour (Fig. 4) in the case of the GIS model used in this study. 

2. If management priorities instead favor protecting as much of the E. t. extimus genetic stock as 

possible, then the boundary line should be drawn farther north, and at higher elevations (i.e., 

contour lines <30%). How much further north would be determined based on the degree of 

priority for protecting E. t. extimus genes in areas that become increasingly dominated by E. t. 

adastus genes. 

3. Alternatively, management priorities may favor protecting only those breeding sites that are 

dominated by E. t. extimus genes, and therefore would place the range boundary further south 

 16
and at lower elevations than the 30% probability line (i.e., contour lines >30%). Again, the 

distance beyond this line would be related to the level of stringency one placed on designating a 

population as E. t. extimus. 

Because there is no biological basis for a distinct boundary line, the location of a boundary 

line ultimately becomes a policy and regulatory decision, ideally one that integrates the best 

available science, taxonomic principles, policy objectives of the responsible agencies, and an 

examination of the intent of the Endangered Species Act (Haig et al. 2006). 

An alternative to designating a specific geographic line would be to manage the boundary 

region as a special status area. Biologically, the boundary region is a dynamic area that is unlikely 

to be static over time with respect to the distribution of genetic lineages (Rohwer et al. 2000) and in 

face of distributional changes of many bird species brought on by climatic changes (Johnson 1994). 

Through the process of geneflow, especially driven by the extirpation and colonization of new 

breeding sites, it is likely that the affinity of breeding sites to one or the other of the two subspecies 

may change over time, with the region likely widening and contracting over time. The boundary 

region is an area where there is the exchange of genes between the two subspecies, and may be an 

important area of evolution (Barrowclough 1980, Crandall et al. 2000, Haig et al. 2006). 

Conclusions 

This study examined the possible location of the northern boundary of the Southwestern 

Willow Flycatcher (E. t. extimus) in the four-corner states. The genetic markers indicated strong 

mitochondrial and nuclear DNA frequency differences among subspecies, with breeding sites 

clustering into two groups seperated approximately along the currently recognized boundary; 

however, the geographic pattern of the molecular markers indicated that a distinct genetic boundary 

line between the subspecies does not exist. Thus, the boundary between the two subspecies should 

be thought of as a region of genetic overlap as previous work based on museum skins and song 

variation also suggested. Our GIS model, which used latitude and elevation to produce a number of 

potential boundary lines based on the geographic distribution of mtDNA haplotypes, indicated that 

the line that separates the greatest amount of genetic information among our sample sites is the 

30% probability contour. Although this line approximates the currently recognized management 

boundary, policy and management goals may favor designation of a boundary line farther north or 

south of this approximate mid-point. Alternatively, management of the region of subspecies 

intergradation as a special management unit in itself may more closely reflect the biological reality 

of the subspecies distributions. Given that the molecular genetic data suggests that there is no 

biological basis for a distinct boundary, the final decision of where to place the boundary, for the 

purpose of the Endangered Species Act management, will ultimately be a policy-based choice.

Acknowledgements 

We thank the many persons, agencies, and private organizations and companies that helped 

in coordination, sharing of information, and land access permission. Funding was provided by the 

U.S. Fish and Wildlife Service, U.S. Geological Survey, and Northern Arizona University. We 

greatly appreciate the hard work of the USGS field biologists who collected samples over the years. 

This manuscript has been greatly improved through reviews of earlier drafts by Karen Mock, 

Kristina Paxton, and Dustin Wood. 

 17
References 

Agresti, A. 2002. Categorical data analysis, second edition. Wiley-Interscience, John Wiley and 

Sons, Hoboken, New Jersey. 

Avise, J. C. 1994. Molecular Markers, Natural History and Evolution. Chapman and Hall, New 

York, NY. 

Barrowclough, G. F. 1980. Genetic and phenotypic differentiation in wood warbler (genus 

Dendroica) hybrid zone. Auk 97:655–668. 

Barrowclough, G. F., J. G. Groth, L. A. Mertz, and J. Gutierrez. 2005. Genetic structure, 

introgression, and a narrow hybrid zone between northern and California spotted owls (Strix 

occidentalis). Molecular Ecology 14:1109–1120. 

Behle, W. H. 1985. Utah birds: Geographic distributions and systematics. Utah Museum of Natural 

History Occasional Publications 5. 147 pp. 

Browning, M. R. 1993. Comments on the taxonomy of Empidonax traillii (Willow Flycatcher). 

Western Birds 24:241–257. 

Burleigh, T. D. 1972. The birds of Idaho. Caxton, Caldwell, Idaho. 

Busch, J. D., M. P. Miller, E. H. Paxton, M. K. Sogge, and P. Keim. 2000. Genetic variation in the 

endangered southwestern Willow Flycatcher. Auk 117:586–595. 

Crandall, K. A., O. R. P. Bininda-Emonds, G. M. Mace, and R. K. Wayne. 2000. Considering 

evolutionary processes in conservation biology. Trends in Evolution and Ecology 15:290–295. 

DeGraaf, R. M. and J. H. Rappole. 1995. Neotropical migratory birds: Natural history, distribution, 

and population change. Comstock Publishing Associates, Ithaca, New York. 

Durst, Scott L., Sogge, Mark K., Stump, Shay D., Williams, Sartor O., Kus, Barbara E., and Sferra, 

Susan J., 2007, Southwestern Willow Flycatcher breeding site and territory summary; 2006: U.S. 

Geological Survey Open-File Report 2007–1391, 28 p. [http://pubs.usgs.gov/of/2007/1391/]. 

Excoffier, L., P. E. Smouse, and J. M. Quattro. 1992. Analysis of molecular variance inferred from 

metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction 

data. Genetics 131:479–491. 

Haig, S. M., E. A. Beever, S. M. Chambers, H. M. Draheim, B. D. Dugger, S. Dunham, E. ElliotSmith, J. B. Fontaine, D. C. Kesler, B. J. Knaus, I. F. Lopes, P. Locshl, T. D. Mullins, and L. M. 

Sheffield. 2006. Taxonomic considerations in listing subspecies under the U.S. Endangered 

Species Act. Conservation Biology 20:1584–1594. 

Hartl, D. L., A. G. Clark. 1997. Principles of population genetics. 3rd ed. Sinauer Associates, 

Massachusetts. 

Hawkins, B. A., and J. A. F. Diniz-Filho. 2006. Beyond Rapoport’s rule: evaluating range size 

patterns of New World birds in a two-dimensional framework. Global Ecology and 

Biogeography 15:461–469. 

Helm-Bychowski, K. and J. Cracraft. 1993. Recovering phylogenetic signal from DNA sequences: 

Relationships within the Corvine assemblages (Class Aves) as inferred from complete sequences 

of the mitochondrial DNA cytochrome-b gene. Molecular Biology and Evolution 10:1196–1214. 

 18
Johnson, N. K. 1994. Pioneering and natural expansion of breeding distributions in western North 

American birds. Studies in Avian Biology 15:27–44. 

Johnson, N. K. and C. Cicero. 2002. The role of ecological diversification in sibling speciation of 

Empidonax flycatchers (Tyrannidae): multigene evidence from mtDNA. Molecular Ecology 

11:2065–2081. 

Latta, R. G. 2004. Relating process to patterns of genetic variation across landscapes. Forest 

Ecology and Management 197:91–102. 

Kisdi, E. 2002. Dispersal: Risk spreading versus local adaptation. American Naturalist 159:579–

596. 

Kroodsma, D. E. 1984. Songs of the Alder Flycatcher (Empidonac alnorum) and Willow 

Flycatcher (Empidonax traillii) are innate. Auk 101:13–24. 

Manel, S., M. K. Schwartz, G. Luikart, and P. Taberlet. 2003. Landscape genetics: combining 

landscape ecology and population genetics. Trends in Ecology and Evolution 18:189–197. 

Mayr, E. 1982. Of what use are subspecies? Auk 99:593–595. 

Miller, M. P. 1997. Tools for Population Genetic Analyses (TFPGA) version 1.3: A windows 

program for the analysis of allozyme and molecular population genetic data. Computer software 

distributed by author. 

Moritz, C. 1994. Applications of mitochondrial DNA analysis in conservation: A critical Review. 

Molecular Ecology 3:401–411. 

Morton, M. 2002. The Mountain White-crowned Sparrow: migration and reproduction at high 

altitude. Studies in Avian Biology 24:1–236. 

Mullenbach, R., J. P. L. Lagoda, and C. Welter. 1989. An efficient salt-chloroform extraction of 

DNA from blood and tissue. Trends in Genetics 5:391. 

Nei, M. 1972. Genetic distance between populations. American Naturalist 106:283–292. 

O’Brien, S. J. and E. Mayr. 1991. Bureaucratic mischief: recognizing endangered species and 

subspecies. Science 251:1187–1188. 

Owen, J. C. and M. K. Sogge. 1997. Banding and genetic sampling of Willow Flycatchers in 

Colorado – 1996 and 1997 summary report. USGS Colorado Plateau Field Station Report. 

Paxton, E. H. 2000. Molecular genetic structuring and demographic history of the Willow 

Flycatcher (Empidonax traillii). MS thesis, Northern Arizona University, Flagstaff, Arizona. 

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

Southwestern Willow Flycatcher in central Arizona: A 10-year Synthesis Report. USGS Open 

File Report 2007–1381. 

Rohwer, S., E. Bermingham, and C. Wood. 2001. Plumage and mitochondrial DNA haplotype 

variation across a moving hybrid zone. Evolution 55:405–422. 

Schneider, S., J. M. Kueffer, D. Rosessli, and L. Excoffier. 1997. Arlequin. 

Http://anthropologie.unige.ch/arlequin, University of Geneva, Geneva, Switzerland. 

Sedgwick, J. A. 2001. Geographic variation in the song of Willow Flycatchers: Differentiation 

between Empidonax traillii adastus and E. t. extimus. Auk 118:366–379. 

 19
 20

Sogge, M. K., R. M. Marshall, T. J. Tibbitts and S. J. Sferra. 1997. A Southwestern Willow 

Flycatcher Natural History Summary and Survey Protocol. National Park Service Technical 

Report NPS/NAUCPRS/NRTR-97/12. 

Sogge, M. K., J. C. Owen, E. H. Paxton, S. M. Langridge and T. J. Koronkiewicz. 2001. A 

Targeted Mist Net Capture Technique for the Willow Flycatcher. Western Birds 32:167–172. 

Storfer, A., M. A. Murphy, J. S. Evans, C. S. Goldberg, S. Robinson, S. F. Spear, R. Dezzani, E. 

Delmelle, L. Vierling, and L. P. Waits. 2007. Putting the ‘landscape’ in landscape genetics. 

Heredity 98:128–142. 

Storz, J. F., and J. M. Dubach. 2004. Natural selection drives altitudinal divergence at the albumin 

locus in deer mice, Peromyscus maniculatus. Evolution 58:1342–1352. 

United States Fish and Wildlife Service. 1995. Final rule determining endangered status for the 

southwestern Willow Flycatcher. Federal Register 60:10694–10715. 

United States Fish and Wildlife Service. 2002. Southwestern Willow Flycatcher Recovery Plan. 

U.S. Fish and Wildlife Service Region 2, Albuquerque, NM. 

Unitt, P. 1987. Empidonax traillii extimus: an endangered subspecies. Western Birds 18:137–162. 

Vos, P., R. Hogers, M. Bleeker, M. Reijans, T. van de Lee, M. Hornes, A. Frijters, J. Pot, J. 

Peleman, M. Kuiper, and M. Zabeau. 1995. AFLP: a new technique for DNA fingerprinting. 

Nucleic Acids Res. 23:4407–4414. 
This page intentionally left blank
This page intentionally left blank
Paxton and others—Using Molecular Genetic Markers to Resolve a Subspecies Boundary: the Northern Boundary of the Southwestern Willow Flycatcher in 

the Four-corner States—Open-File Report 2008–1117