Landscape Features Shape Genetic Structure in 

Threatened Northern Spotted Owls

Open-File Report 2008–1240

U.S. Department of the Interior

U.S. Geological Survey

Landscape Features Shape Genetic Structure in 

Threatened Northern Spotted Owls 

By W. Chris Funk, U.S. Geological Survey, Eric D. Forsman, U.S. Forest Service, and 

Thomas D. Mullins,

 and Susan M. Haig, U.S. Geological Survey

Open-File Report 2008-1240

U.S. Department of the Interior

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

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey

Mark D. Myers, Director

U.S. Geological Survey, Reston, Virginia: 2008

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Suggested citation:

Funk, C.W., Forsman, E.D., Mullins,T.D., and Haig, S.M., 2008, Landscape features shape genetic structure in 

threatened northern spotted owls: U.S. Geological Survey Open-File Report 2008-1240, 12 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Materials and Methods.................................................................................................................................3

Sampling.................................................................................................................................................3

Microsatellite Genotyping...................................................................................................................3

Standard Population Genetic Analyses ............................................................................................3

Results .............................................................................................................................................................4

Standard Population Genetic Analyses ............................................................................................4

Landscape Genetics.............................................................................................................................4

Discussion.......................................................................................................................................................6

Landscape Genetics of Northern Spotted Owls..............................................................................6

Conservation Implications...................................................................................................................7

Conclusions.....................................................................................................................................................7

Acknowledgments.........................................................................................................................................7

References......................................................................................................................................................7

Figures

Figure 1. Map illustrating the distribution of northern spotted owl samples included in

landscape genetic analyses ............................................................... 2

Figure 2. Results of genetic landscape shape interpolation analysis for northern

spotted owls using a distance weighting parameter (a) of 0.25 and residual

genetic distances ........................................................................... 5

Tables

Table 1. Results from analysis of molecular variance (AMOVA) for northern spotted

owls breeding north versus south of the Columbia River .............................. 5
iv

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Landscape Features Shape Genetic Structure in 

Threatened Northern Spotted Owls 

W. Chris Funk1, Eric D. Forsman2, Thomas D. Mullins1 and Susan M. Haig1

1

U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, 

3200 SW Jefferson Way, Corvallis, Oregon 97331, USA 

2

USDA Forest Service, Pacific Northwest Research Station, 3200 SW 

Jefferson Way, Corvallis, Oregon 97331, USA

Abstract

Several recent studies have shown that landscape features 

can strongly affect spatial patterns of gene flow and genetic 

variation. Understanding landscape effects on genetic variation 

is important in conservation for defining management 

units and understanding movement patterns. The landscape 

may have little effect on gene flow, however, in highly 

mobile species such as birds. We tested for genetic breaks 

associated with landscape features in the northern spotted 

owl (Strix occidentalis caurina), a threatened subspecies 

associated with old forests in the U.S. Pacific Northwest 

and extreme southwestern Canada. We found little evidence 

for distinct genetic breaks in northern spotted owls using a 

large microsatellite dataset (352 individuals from across the 

subspecies’ range genotyped at 10 loci). Nonetheless, dry 

low-elevation valleys and the Cascade and Olympic Mountains 

restrict gene flow, while the Oregon Coast Range facilitates 

it. The wide Columbia River is not a barrier to gene flow. In 

addition, inter-individual genetic distance and latitude were 

negatively related, likely reflecting northward colonization 

following Pleistocene glacial recession. Our study shows that 

landscape features may play an important role in shaping 

patterns of genetic variation in highly vagile taxa such as 

birds.

Introduction

A fundamental question in evolutionary biology is 

how landscape features such as mountains, rivers, and 

environmental heterogeneity affect spatial patterns of 

genetic variation (Manel and others, 2003; Storfer and 

others, 2007). Understanding how landscapes mold genetic 

variation is also important in conservation genetics for 

defining demographically independent management units 

(Moritz, 1994; Palsbøll and others, 2007) and understanding 

movement patterns. The simultaneous development of highly 

variable molecular markers, increasing computer power for 

computationally intensive genetic analyses, and Geographic 

Information System (GIS) technology has improved the 

potential to address these questions. As a result, the field 

of landscape genetics has emerged, providing a research 

framework for investigating the influence of landscape 

and environmental features on genetic structure, genetic 

discontinuities, and gene flow (Manel and others, 2003; 

Holderegger and Wagner, 2006; Storfer and others, 2007). 

Landscape genetics is currently in an exponential growth 

phase (Holderegger and Wagner, 2006). Only four years 

after the term ‘landscape genetics’ was first coined (Manel 

and others, 2003), several studies have demonstrated the 

importance of landscape and environmental features in 

structuring genetic variation in many taxa, including mammals 

(Geffen and others, 2004; Coulon and others, 2006; Cushman 

and others, 2006; Miller and others, 2006), amphibians (Funk 

and others, 2005; Spear and others, 2005; Giordano and 

others, 2007; Marsh and others, 2007), reptiles (Manier and 

Arnold, 2006), fish (Neraas and Spruell, 2001; Crispo and 

others, 2006), and insects (Finn and others, 2006).

Although landscape features may have a strong influence 

on gene flow in terrestrial or aquatic animals, it is less 

clear if gene flow in highly mobile animals such as birds is 

similarly influenced. Some studies of birds that are strong 

fliers indicate high levels of gene flow over large areas (for 

example, Koopman and others, 2007; Funk and others, 

2007b; Draheim and Haig, unpub. data, reviewed in Avise, 

2000), whereas other studies of less-mobile birds suggest 

that landscape features such as water bodies (Broderick and 

others, 2003), habitat breaks (Piertney and others, 1998), 

mountains (Caizergues and others, 2003), and anthropogenic 

habitat fragmentation (Bouzat, 2001; Veit and others, 2005) 

can restrict gene flow. These results suggest that the influence 

of landscape features on gene flow in birds varies, depending 

on a number of factors, including flying ability, whether or 

not the species is migratory, degree of habitat specialization, 

home-range size, mating system, and latitude (Martin and 

McKay, 2004). In addition, repeated population isolation and 

expansion associated with Pleistocene climatic fluctuations 

has played an important role in structuring intraspecific 

genetic variation in northern temperate birds (Avise and 

Walker, 1998; Klicka and Zink, 1999; Milá and others, 2000).
2 Landscape Features Shape Genetic Structure in Threatened Northern Spotted Owls

The northern spotted owl (Strix occidentalis caurina) 

is a threatened subspecies associated with old forests in the 

U.S. Pacific Northwest and southwestern British Columbia, 

Canada. The subspecies was listed as threatened under the 

U.S. Endangered Species Act (ESA) in 1990, primarily 

because of declining habitat and populations (U.S. Fish 

and Wildlife Service [USFWS], 1990), and is considered 

endangered in Canada (Committee on the Status of 

Endangered Wildlife in Canada [COSEWIC], 2000). Despite 

ESA-listing and dramatic reductions in forest harvest under 

the Northwest Forest Plan (Noon and Blakesley, 2006), the 

species is still experiencing declines in demographic (Anthony 

and others, 2006) and genetic effective population sizes (Funk 

and others, 2008a).

There are several landscape features in the range of 

northern spotted owls that could affect their movements and 

gene flow. These include the large, non-forested valleys of 

western Oregon (Willamette, Rogue, and Umpqua Valleys); 

the Yakima Valley in central Washington; high mountain 

ridges formed by the Cascade and Olympic Mountains; and 

wide water bodies such as the Columbia River and Puget 

Sound (fig. 1). Although northern spotted owls can disperse 

long distances, most birds disperse only a few territories from 

natal sites (Forsman and others, 2002) with lowland valleys 

such as the Willamette Valley, Oregon acting as barriers to 

dispersal. In addition, Pleistocene glaciations are thought to 

have isolated some forest species into multiple forest refugia 

in the Pacific Northwest, resulting in independent lineages 

(Soltis and others, 1997; Brunsfeld and others, 2001; Carstens 

and others, 2005; Steele and Storfer, 2006). Thus, current 

landscape features and historic isolation may have resulted 

in distinct genetic groups, or management units, of northern 

spotted owls. Identification of any such management units 

is important for conservation of this threatened subspecies. 

Previous genetic analyses of northern spotted owls using 

random amplified polymorphic DNA (Haig and others, 2001) 

Figure 1. Map illustrating the distribution of northern spotted owl samples included in landscape genetic analyses. 

(A) Ranges of northern (gray) and California (black) spotted owls and boundaries of 15 study areas (dashed lines). Subspecies ranges 

are based on Gutiérrez and others (1995). Study area locations (and sample sizes) are: A. Olympic Peninsula, WA (n = 22); B. Western 

Washington Cascades (13); C. Cle Elum (eastern Cascades), WA (51); D. Yakima (eastern Cascades), WA (18); E. Northern Oregon Coast 

Range (12); F. Middle Oregon Coast Range (47); G. Southern Oregon Coast Range (31); H. Northwestern Oregon Cascades (15); I. Warm 

Springs (eastern Cascades), OR; J. Western Oregon Cascades (28); K. Siskiyou Mountains, OR and CA (17); L. South Umpqua River area, 

OR (10); M. Southern Oregon Cascades (32); N. Klamath National Forest, CA (14); O. Humboldt, CA (28). (B) Prominent mountain ranges 

(capital letters) and valleys (italicized) in the range of northern spotted owls.
Materials and Methods 3

and mitochondrial DNA (mtDNA, Barrowclough and others, 

1999; Haig and others, 2004; Barrowclough and Gutiérrez, 

2005) did not uncover such discontinuities, but these markers 

are likely not as powerful as hypervariable microsatellite 

markers coupled with larger sample sizes for identifying 

genetic discontinuities.

The goal of the present study was to investigate 

the landscape genetics of northern spotted owls using 

microsatellite loci. Our two specific questions were: (1) Are 

there any genetic discontinuities in northern spotted owls; 

and (2) Do mountains, non-forested valleys, or water bodies 

restrict gene flow?

Materials and Methods

Sampling

We collected blood samples from 352 northern spotted 

owls from 15 study areas from across the subspecies’ range 

in Washington, Oregon, and California from 1990 to 2006 

(fig. 1). Study areas were bounded by landscape features 

such as mountain ridges, rivers, and non-forested habitat. Our 

choice of study area boundaries, however, does not affect our 

conclusions because most analyses were individual-based 

and, thus, do not require a priori definition of sampling units. 

No known close relatives (parent-offspring or siblings) were 

included. Samples were collected following the protocol of 

the American Ornithologists’ Union (Gaunt and Oring, 1997). 

Blood was stored in cryogenic tubes containing a buffer 

solution (100 mM Tris—HCl, pH 8.0; 100 mM EDTA, pH 8.0; 

10 mM NaCl; 0.5% SDS) and frozen at -800 C until analyses.

Microsatellite Genotyping

DNA extraction, PCR, and fragment analysis were 

performed as described previously (Funk and others, 2007a). 

All owls were genotyped at 10 variable microsatellite loci 

developed for Mexican spotted owls (loci: 6H8, 15A6, 13D8, 

and 4E10.2; Thode and others, 2002), Lanyu scops owls (Otus 

elegans botelensis, loci: Oe37, Oe53, Oe128, Oe129, and 

Oe149, Hsu and others, 2003, 2006), and ferruginous pygmyowls (Glaucidium brasilianum; locus: FEPO5, Proudfoot and 

others, 2005). One of these loci (Oe128) and an additional 

microsatellite marker (Bb126; Isaksson and Tegelström, 

2002) are diagnostic of spotted versus barred owls (Funk and 

others, 2007a) and were genotyped to assure that no spotted 

owl-barred owl hybrids were included in the analysis. PCR 

conditions and annealing temperatures were the same as those 

described in the original primer notes.

Standard Population Genetic Analyses

We calculated exact probabilities for Hardy-Weinberg 

proportions, genotypic disequilibrium, genic differentiation, 

and Weir and Cockerham’s (1984) FST values among study 

areas and subspecies using GENEPOP 3.4 (Raymond and 

Rousset, 1995). Expected heterozygosities were calculated 

with MICROSATELLITE ANALYZER 2.39 (Dieringer and 

Schlötterer, 2003). Critical a values for pairwise tests of 

allelic differentiation were determined using the sequential 

Bonferroni adjustment (Rice, 1989). We also tested for 

isolation-by-distance among individuals using a Mantel test 

implemented in ALLELES IN SPACE (AIS, Miller, 2005). 

Landscape genetic analyses

In a previous analysis including all three spotted owl 

subspecies (Funk and others, 2008a), we found evidence for 

introgression between California and northern spotted owls 

(primarily in southern Oregon and northern California) and 

between Mexican and northern spotted owls (centered in 

Washington) using a clustering algorithm implemented in 

STRUCTURE (Pritchard and others, 2000). Because the goal 

of the current study was to investigate the genetic structure 

of northern spotted owls alone, we excluded individuals 

estimated by STRUCTURE to have greater than 50 percent 

membership in California or Mexican spotted owl genetic 

clusters (clusters 3 and 4, respectively, in Funk and others, 

2008a) from all subsequent analyses. This left 304 northern 

spotted owls for analysis.

The first method we used to test for genetic 

discontinuities in northern spotted owls is a Bayesian 

clustering method implemented in GENELAND 2.0.0 

(Guillot and others, 2005a). This method is similar to that 

of STRUCTURE, except that GENELAND explicitly 

incorporates geographic coordinates of animals in the analysis 

and generates maps of population ranges. Populations are 

assumed to conform to Hardy-Weinberg proportions and to 

be spatially organized through the so-called colored PoissonVoronoi tessellation (Lantuéjoul, 2002). GENELAND is 

designed to identify genetic breaks in populations that are 

spatially contiguous, and therefore is well suited for analysis 

of northern spotted owls, which have a fairly contiguous 

distribution (fig. 1A). The method has previously been tested 

using simulated datasets and has shown to perform well at 

estimating the number of populations, assigning individuals 

to populations, and drawing borders between populations 

(Guillot and others, 2005b). For inferring the number of 

populations (K), we used 100,000 Markov chain Monte Carlo 

MCMC) iterations, thinning interval of 100, minimum K at 1, 

and maximum K at 15 (equivalent to the total number of study 

areas, fig. 1A). We ran the MCMC five times to verify the 

consistency of results.
4 Landscape Features Shape Genetic Structure in Threatened Northern Spotted Owls

The next method we used to test for genetic barriers in 

northern spotted owls was Monmonier’s maximum difference 

algorithm (Monmonier, 1973) using AIS. This procedure 

locates barriers to gene flow by iteratively identifying sets 

of contiguous, large genetic distances along a connectivity 

network. The steps of this analysis consist of: (1) connecting 

adjacent geographical positions of individuals using Delaunay 

triangulation (Brassel and Reif, 1979; Watson and others, 

1992), resulting in a connectivity network; (2) calculating 

genetic distances between neighboring samples and 

associating these distances to each edge of the network; 

and (3) using Monmonier’s maximum difference algorithm 

to identify boundaries, as described in detail elsewhere 

(Monmonier, 1973; Manel and others, 2003). Because there 

was substantial variation in geographic distances between 

sampled individuals, we followed the recommendation of 

Manni and others (2004) and performed this analysis using 

residual genetic distances derived from the linear regression of 

genetic versus geographic distances.

 We then used AIS to generate a three-dimensional 

surface plot of genetic distance patterns across the range 

of northern spotted owls, termed a ‘genetic landscape 

shape’ (Miller, 2005). Unlike the GENELAND clustering 

algorithm or Monmonier’s algorithm, this method provides a 

visual representation of genetic divergence across the entire 

landscape as opposed to only identifying distinct genetic 

breaks or barriers. This procedure involves three main 

steps: (1) construction of a connectivity network of sampled 

individuals and assignment of calculated interindividual 

genetic distances to landscape coordinates at midpoints of 

the connectivity edges; (2) use of an interpolation procedure 

(inverse distance-weighted interpolation, Watson, 1992; 

Watson and Philips, 1985) to infer genetic distances at 

locations on a uniformly spaced grid overlaid on the sampled 

landscape; and (3) generation of a three-dimensional surface 

plot where X and Y coordinates correspond to geographic 

locations on a rectangular grid and surface plot heights (Z) 

reflect genetic distance. Details of the procedure are provided 

in Miller (2005). We used several different distance weighting 

values (a = 0.25–2), grid sizes, and raw and residual genetic 

distances to make sure that interpretations were not sensitive 

to these parameters.

Our previous STRUCTURE analysis (Funk and 

others, 2008a) indicated that two genetic clusters (clusters 

1 and 2) were unevenly represented on either side of the 

Columbia River, suggesting that this river might restrict gene 

flow. Therefore, we also performed a post hoc analysis of 

molecular variance (AMOVA, Excoffier and others, 1992) in 

ARLEQUIN 3.01 (Excoffier and others, 2005) to test whether 

separating northern spotted owl study areas into groups north 

versus south of the Columbia River explained a significant 

portion of the variance. Ten thousand permutations were used 

to determine significance of variance components.

Results

Standard Population Genetic Analyses

Genotypic frequencies within study areas generally 

corresponded to expected Hardy-Weinberg proportions. 

Only 7 out of 150 tests for deviations from Hardy-Weinberg 

proportions were significant at the a = 0.05 level, which 

is less than the 7.5 (= 0.05 × 150) expected to deviate by 

chance. No loci had a consistent excess of homozygotes, 

suggesting no null alleles were present. Similarly, 31 out of 

665 possible tests for departure from linkage equilibrium were 

significant, less than the 33.25 tests expected to be significant 

by chance. No pairs of loci consistently departed from linkage 

equilibrium across study areas, thus loci were independent. 

Expected heterozygosity (HE) was similar across study areas, 

ranging from 0.685–0.764 (mean = 0.736). 

Pairwise FST values among northern spotted owl study 

areas ranged from 0.001–0.061. Allelic differentiation was 

significant at the a = 0.05 level for 61 out of 105 (58.1%) of 

these pairwise comparisons after correcting for multiple tests. 

The correlation coefficient of the Mantel test of isolation-bydistance was only r = 0.089, but still statistically significant 

(P = 0.001).

Landscape Genetics

Within northern spotted owls, only one population was 

inferred using the clustering method implemented in 

GENELAND. In all five MCMC runs, support was higher for 

a single cluster (mean posterior probability = 65.0%) than two 

or more clusters (mean = 35.0%), suggesting an absence of 

distinct genetic discontinuities in the range of northern spotted 

owls.

Monmonier’s maximum difference algorithm identified 

a circular area in the center of the southern Oregon Cascades 

(fig. 1A) as the strongest genetic barrier in the range of 

northern spotted owls. This area corresponds to the eastern 

portion of the Rogue River Valley (fig. 1B). The genetic 

landscape shape generated in AIS also revealed that a number 

of landscape features were associated with increased or 

decreased inter-individual genetic distance (fig. 2), suggesting 

that these features restrict or facilitate gene flow, respectively. 

First, genetic distance peaked in the centers of three dry, lowelevation valleys, namely the Rogue River and Umpqua River 

Valleys in southern Oregon (south section of fig. 2) and the 

Yakima River Valley near Cle Elum, Washington (northeast 

section of fig. 2). Second, genetic distances were high in the 

Cascade and Olympic Mountains (eastern edge and northwest 

corner of fig. 2, respectively), but low in the Oregon Coast 

Range (in northwest Oregon). Finally, there was an overall 

negative relationship between genetic distance and latitude 

(that is, higher genetic distances towards the south). The same 

general patterns were observed regardless of whether raw or 
Results 5

residual genetic distances were used and regardless of the 

choice of grid size and distance weighting parameter.

Grouping northern spotted owl study areas based on 

which side of the Columbia River they were on explained 

less than 1% of the variation in the AMOVA, although this 

grouping was still significant at the a = 0.05 level (table 1). 

The small proportion of variation explained by this grouping 

suggests that the Columbia River is not an effective barrier to 

gene flow.

Figure 2. Results of genetic landscape shape interpolation analysis for northern spotted owls using a distance weighting parameter 

(a) of 0.25 and residual genetic distances. A 29 × 80 grid was used so that each square in the genetic landscape shape is equivalent to 

approximately 10 × 10 km (and each square in the base is equivalent to 20 × 20 km). X and Y axes correspond to geographic locations 

and surface plot heights reflect genetic distances. Higher genetic distances have lighter shading, and lower genetic distances darker 

shading. Qualitatively similar results were obtained using raw genetic distances, different grid sizes, and a range of distance weighting 

parameters (a = 0.25–2). Mountains (capital letters) and valleys (italicized) associated with specific genetic peaks and troughs are 

shown.

Table 1. Results from analysis of molecular variance (AMOVA) for northern spotted owls breeding north versus 

south of the Columbia River.

Grouping Number

of groups Variance components Percent of variation P-value

North versus south of 

Columbia River

2 Among groups 0.62 0.018

Among study areas 1.83 <0.001

Within study areas 97.55 <0.001
6 Landscape Features Shape Genetic Structure in Threatened Northern Spotted Owls

Discussion

Landscape Genetics of Northern Spotted Owls

We found no evidence for distinct genetic breaks 

in northern spotted owls, but several landscape features 

influenced patterns of gene flow and genetic variation. 

This demonstrates that even in vagile bird species such as 

the northern spotted owl, topography and environmental 

heterogeneity can shape genetic variation. The field of 

landscape genetics is thus relevant to a wide variety of taxa, 

not only dispersal-limited species such as small mammals 

(Miller and others, 2006); amphibians (Funk and others, 2005; 

Spear and others, 2005; Giordano and others, 2007; Marsh and 

others, 2007); reptiles (Manier and Arnold, 2006); and fish 

(Neraas and Spruell, 2001; Crispo and others, 2006).

Low-elevation valleys were particularly important in 

structuring genetic variation, especially the Rogue River and 

Umpqua River Valleys in southern Oregon and the Yakima 

River Valley on the east side of the Washington Cascades 

(fig. 2). Interestingly, the largest valley in the range of northern 

spotted owls, the Willamette Valley sandwiched between the 

Oregon Coast Range and Cascade Mountains in northwestern 

Oregon, did not have any obvious effects on genetic variation. 

One difference between the Willamette Valley and the other 

three valleys is that it receives more precipitation. The 

Willamette Valley receives approximately 75–200 cm of 

precipitation (Daly and Taylor, 1998), primarily in the form of 

rain, whereas the Rogue, Umpqua, and Yakima Valleys only 

receive 38–100 cm, 75–100 cm, and 50–75 cm, respectively. 

These drier valleys may act as greater physiological or 

behavioral barriers to northern spotted owls, which are adapted 

to cool, mesic forest conditions, than the relatively wet 

Willamette Valley. In contrast, the Willamette Valley has been 

shown to reduce gene flow in red tree voles (Miller and others, 

2006), a prey species of northern spotted owls. 

A large-scale dispersal study of northern spotted owls 

conducted from 1985 to 1996 found that all of these valleys, 

the Willamette, Rogue, Umpqua, and Yakima Valleys, acted 

as important barriers to dispersal (Forsman and others, 2002). 

This effect was most pronounced for the Willamette Valley. 

It is not clear why the Willamette Valley was a barrier to 

dispersal in the Forsman and others (2002) study, but was not 

a barrier to gene flow in our genetic analysis. One possible 

explanation is that since genetic structure is influenced by 

current and historical gene flow, restricted dispersal across 

the Willamette Valley is a new phenomenon caused by the 

dramatic anthropogenic alteration of the valley. Approximately 

70 percent of Oregon’s human population resides in the 

Willamette Valley (Willamette Valley Livability Forum, 1999) 

and the valley has been extensively modified for agriculture 

and urbanization since European settlement in the past century 

and a half (Gibson, 1985; Robbins, 1997; Taft and Haig, 

2003). Thus, lack of a genetic barrier effect of the Willamette 

Valley may reflect high levels of gene flow prior to European 

settlement. Alternatively, lack of a barrier effect may be due to 

owls flying around the southern end of the Willamette Valley, 

as observed by Forsman and others (2002), resulting in high 

gene flow between the Cascade Mountains and Oregon Coast 

Range. This seems like an unlikely explanation for lack of a 

barrier effect of the Willamette Valley, however, because owls 

are also capable of flying around the other three valleys that 

act as barriers (Forsman and others, 2002).

Mountains also influenced the genetic structure of 

northern spotted owls, but interestingly, different mountains 

affected genetic structure in opposite directions (fig. 2). 

The Cascade and Olympic Mountains were associated with 

increased genetic distance, thus apparently restricting gene 

flow. The Oregon Coast Range, on the other hand, was 

associated with genetic-distance troughs, indicating high levels 

of gene flow. We suspect that the reason that these mountains 

have different effects on gene flow is due to the fact that the 

Oregon Coast Range consists of low-elevation mountains 

(maximum elevation = 1,249 m) covered by forests, whereas 

the Cascade and Olympic Mountains are dissected by high 

alpine ridges and peaks (maximum elevations of 4,392 m and 

2,428 m, respectively) that probably represent formidable 

barriers to movement. Mountains have also been shown to 

act as barriers to gene flow in black grouse (Caizergues and 

others, 2003), as well as several other vertebrate taxa (Funk 

and others, 2005; Cushman and others, 2006; Giordano and 

others, 2007).

The Columbia River did not act as a barrier to gene flow 

for northern spotted owls. Although the two northern spotted 

owl clusters previously identified by STRUCTURE were 

unevenly distributed across the Columbia (Funk and others, 

2008a), neither the GENELAND nor AMOVA analyses here 

supported a barrier effect of this river. We are unaware of any 

study showing restricted gene flow across rivers in a temperate 

bird species, although there are several such examples 

for tropical birds (Aleixo, 2004; Hayes and Sewlal, 2004; 

Höglund and Shorey, 2004; Cheviron and others, 2005).

A final pattern observed in the genetic landscape shape 

was increasing genetic distance towards the south (fig. 2). This 

pattern is consistent with northward colonization following 

Pleistocene glacial recession, a phenomenon that is supported 

by previous phylogenetic studies in the Pacific Northwest 

(Matocq 2002, Carstens and others, 2004; Spinks and 

Shaffer, 2005; Steele and Storfer, 2006). This interpretation 

is also consistent with genetic tests for changes in effective 

population size in northern spotted owls, which show a strong 

signal of Pleistocene population expansion in Washington 

and a weaker signal of expansion in Oregon (Funk and others, 

2008b). The negative relationship between inter-individual 
References 7

genetic distance and latitude seen here in northern spotted 

owls mirrors a similar negative relationship between interpopulation genetic distance and latitude observed among many 

taxa on a global scale (Martin and McKay, 2004).

A limitation of the genetic landscape shape analysis 

conducted in AIS is that it is largely descriptive and only 

identifies geographic areas of low and high inter-individual 

genetic divergence. It does not explicitly test the effects of 

specific landscape features on genetic structure. Thus, the 

current study should be treated as a coarse-scale foundation 

for future, finer-scale research including additional samples. 

Sampling focused around specific landscape features (e.g., 

valleys and mountains) would allow dissection of the effects 

of specific landscape layers (e.g., forest cover, rivers, and 

topography) on genetic structure using an explicit hypothesistesting framework (Cushman and others, 2006).

Conservation Implications

Lack of evidence for genetic discontinuities in the 

range of northern spotted owls suggests that the subspecies 

represents one genetic population or deme. Nonetheless, 

geographically separated parts of the subspecies’ range 

may function as demographically independent populations, 

because only a few dispersers per generation are sufficient to 

homogenize gene pools (Wright, 1931; Slatkin, 1985; Mills 

and Allendorf,1996). In addition, minimal genetic structure 

in neutral markers such as microsatellites does not indicate 

lack of adaptive genetic differences across the subspecies’ 

range. Adaptive differences can be maintained with ongoing 

gene flow (Haldane, 1930, Lewontin and Krakauer, 1975). 

Substantial variation in life histories and ecology has been 

documented across the range of northern spotted owls 

(Gutiérrez and others, 1995), and it is likely that at least some 

of this variation is genetically based and adaptive. Maintaining 

this variation is important for persistence in the face of 

environmental change.

The finding that high-elevation mountains and dry 

valleys restrict gene flow is also relevant for conservation. 

In particular, this result suggests that dispersal is similarly 

restricted by these landscape features. Thus, northern spotted 

owls separated by mountain ridges and valleys may be less 

demographically connected, have fewer opportunities for gene 

flow from other populations, and consequently be at higher 

risk of local extinction. For northern spotted owls, areas that 

appear to be isolated from the rest of the subspecies’ range 

by mountains or valleys include the eastern Cascades, the 

Olympic Peninsula, and the eastern edge of the Rogue River 

Valley. Finer-scale genetic analyses with additional samples 

will help determine the degree to which these and other areas 

are isolated by topography and habitat breaks.

Conclusions

Our analysis of the genetic structure of northern spotted 

owls across most of the range of the subspecies allowed us to 

test for genetic discontinuities and identify landscape features 

that influence the subspecies’ genetic structure. Although no 

distinct genetic breaks were found in northern spotted owls, 

several landscape features were important in structuring 

genetic variation. Dry, low elevation valleys and the high 

elevation Cascade and Olympic Mountains restricted gene 

flow, while the lower Oregon Coast Range facilitated gene 

flow, acting as a “genetic corridor.” The Columbia River did 

not act as a barrier, suggesting owls readily fly over this large 

river. Thus, even in taxa such as northern spotted owls with 

potential for long-distance dispersal, landscape features can 

have an important impact on gene flow and genetic structure.

Acknowledgments

We are indebted to the many people who helped with 

sample collection and logistics, particularly P. Loschl, R. 

Anthony, S. Ackers, R. Claremont, D. Herter, S. Hopkins, J. 

Reid, and S. Sovern. We also thank R. Anthony, J. Matthews, 

and M. Miller for comments on the manuscript. Funding 

was provided by the USGS Forest and Rangeland Ecosystem 

Science Center and the USDA Forest Service Pacific 

Northwest Research Station. Samples were collected under 

U.S. Fish and Wildlife Service threatened species permit 

number TE026280-11 and Oregon State University Animal 

Care and Use permit number 3091.

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Director, Forest and Rangeland Ecosystem Science Center 

U.S. Geological Survey, 777 NW 9th Street, Suite 400

Corvalis, OR 97330

http://fresc.usgs.gov/
Funk and others— Landscape Features Shape Genetic Structure in Threatened Northern Spotted Owls—OFR 2008–1240