EXHIBIT 1

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RESEARCH ARTICLE

Population structure and conservation genetics of the Oregon

spotted frog, Rana pretiosa

Michael S. Blouin • Ivan C. Phillipsen •

Kirsten J. Monsen

Received: 5 January 2010 / Accepted: 22 June 2010 / Published online: 6 July 2010

Springer Science+Business Media B.V. 2010

Abstract The Oregon spotted frog (Rana pretiosa) is one

of the most threatened amphibians in the Pacific Northwest.

Here we analyzed data from 13 microsatellite loci and

298 bp of mitochondrial DNA in frogs collected from 23 of

the remaining R. pretiosa populations in order to (1) assess

levels of genetic diversity within populations of R. pretiosa, (2) identify the major genetic groups in the species, (3)

estimate levels of genetic differentiation and gene flow

among populations within each major group, and (4)

compare the pattern of differentiation among R. pretiosa

populations with that among populations of R. cascadae, a

non-endangered congener that also occurs in Oregon and

Washington. There is a strong, hierarchical genetic structure in R. pretiosa. That structure includes six major

genetic groups, one of which is represented by a single remaining population. R. pretiosa populations have

low genetic diversity (average He = 0.31) compared to

R. cascadae (average He = 0.54) and to other ranid frogs.

Genetic subdivision among populations is much higher in

R. pretiosa than in R. cascadae, particularly over the

largest geographic distances (hundreds of kilometers). A

joint analysis of migration rates among populations and of

effective sizes within populations (using MIGRATE) suggests that both species have extremely low migration rates,

and that R. pretiosa have slightly smaller effective sizes.

However, the slight difference in effective sizes between

species appears insufficient to explain the large difference

in genetic diversity and in large-scale genetic structure. We

therefore hypothesize that low connectivity among the

more widely-spaced R. pretiosa populations (owing to their

patchier habitat), is the main cause of their lower genetic

diversity and higher among-population differentiation.

Conservation recommendations for R. pretiosa include

maintaining habitat connectivity to facilitate gene flow

among populations that are still potentially connected, and

either expanding habitat or founding additional ‘backup’

populations to maintain diversity in the isolated populations. We recommend that special consideration be given

to conservation of the Camas Prairie population in Northern Oregon. It is the most geographically isolated population, has the lowest genetic diversity (He = 0.14) and

appears to be the only remaining representative of a major

genetic group that is now almost extinct. Finally, because

the six major groups within R. pretiosa are strongly

differentiated, occupy different habitat types, and are

geographically separate, they should be recognized as

evolutionarily significant units for purposes of conservation

planning.

Keywords Ranidae Evolutionarily significant unit 

Pacific Northwest Amphibian declines 

Genetic diversity Gene flow

Introduction

Frog species in the genus Rana often show low genetic

diversity within populations (Garner et al. 2003, 2004;

Martı ́nez-Solano et al. 2005; Ficetola et al. 2007) and high

genetic differentiation among populations (Monsen and

Blouin 2003; Palo et al. 2004a; Funk et al. 2005). This

population structuring is usually attributed to behavioral

M. S. Blouin (&) I. C. Phillipsen

Department of Zoology, Oregon State University, 3029 Cordley

Hall, Corvallis, OR 97331-2914, USA

e-mail: blouinm@science.oregonstate.edu

K. J. Monsen

Department of Biology and Molecular Biology, Montclair State

University, Montclair, NJ 07043, USA

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DOI 10.1007/s10592-010-0104-x

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philopatry and to a limited ability to disperse long distances

between aquatic habitats (Seppa and Laurila 1999; Palo

et al. 2004b). Dispersal appears to be most strongly

restricted in high elevation or other harsh habitats (Monsen

and Blouin 2003; Funk et al. 2005). However, some ranid

frogs have high genetic diversity within populations (Arens

et al. 2007; Crosby et al. 2008) and little genetic differentiation among populations over appreciable distances

(Newman and Squire 2001). Therefore, it cannot be

assumed that all ranid frogs have genetically impoverished

populations that are highly differentiated—these genetic

characteristics must be assessed on a case by case basis,

particularly in the context of conservation. Characterizing

patterns of genetic diversity within populations and genetic

differentiation among populations has become an important first step in conservation status assessments for

endangered species (Allendorf and Luikart 2007). Towards

that end, we here describe the species-wide genetic structure of the Oregon spotted frog (Rana pretiosa), one of the

most threatened amphibians in the Pacific Northwest

(Cushman and Pearl 2007). We also compare this genetic

structure to that of a more common and non-threatened

congener, Rana cascadae, whose range overlaps substantially with that of R. pretiosa. We conclude that R. pretiosa

is subdivided into six major genetic groups, and that

R. pretiosa populations have unusually low genetic diversity and connectivity for a ranid frog.

R. pretiosa currently occupies only 10–30% of its original

range (Hayes 1997; McAllister et al. 1993), which historically spanned northeastern California, western and central

Oregon and Washington in the United States, and southern

British Columbia in Canada (Stebbins 2003). The species is

believed to be now extinct in California and western Oregon,

and it persists in only a few scattered locales in Washington,

and British Columbia (Fig. 1). R. pretiosa is a candidate for

federal listing as endangered by the U.S. Fish and Wildlife

Service (2005), is listed as ‘‘sensitive-critical’’ by the Oregon Department of Fish and Wildlife and ‘‘endangered’’ in

the state of Washington. It is an endangered species in

Canada where it is known from only three tiny populations

(Seburn and Seburn 2000). Functioning metapopulations

(groups of populations connected by gene flow) probably

still exist only in the central Cascades region of central

Oregon, in the Klamath Basin of southern Oregon (Fig. 1),

and in the Chehalis drainage south of Seattle, Washington

(populations DC and BC; Fig. 1). With the exception of the

Chehalis drainage pair, the few populations remaining in

northern Oregon, Washington, and British Columbia are

geographically isolated and almost certainly on independent

evolutionary trajectories. Therefore, for the purposes of

prioritizing conservation efforts in this species (e.g. delineating management units, identifying particularly at-risk or

genetically unique populations) it is vital to understand the

historical genetic relationships among populations, the

current patterns of genetic diversity within populations, and

the degree of connectivity among populations. This information would be very timely given the species is under

review for listing as endangered in the US, and given captive

breeding and reintroduction programs are already underway

in the US and in Canada (Chelgren et al. 2008; MP Hayes,

pers. comm.; P Govindarajulu, pers. comm).

Here we analyzed data from microsatellite and mitochondrial loci collected from most of the remaining

R. pretiosa populations. The goals of this study were to

(1) assess levels of genetic diversity within populations of

R. pretiosa across its range, (2) identify the major historical

genetic groups in the species, (3) estimate levels of genetic

Fig. 1 Map of sampling localities for R. pretiosa in the Pacific

Northwest. Localities are shown as black circles and are labeled with

their respective two-letter codes (Table 1). The six major genetic

groups of populations identified in this study are outlined and labeled.

Sampling localities of R. cascadae from Monsen and Blouin (2004)

are shown as small ‘x’ symbols

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differentiation and gene flow among populations within

each of the remaining groups, and (4) compare the pattern

of differentiation among R. pretiosa populations with that

among populations of R. cascadae, a more continuouslydistributed and non-endangered congener that also occurs

in Oregon and Washington.

Methods

Sampling

23 R. pretiosa populations were sampled from across the

species’ range (Fig. 1; Table 1). These represent most of

the known populations of the species, which still persists in

approximately 33 known sites (Hayes 1997; Cushman and

Pearl 2007). Sampling took place in 1999 and 2000, with

the exception of the three Canadian populations, one of

which was sampled in 1999, and all three of which were

sampled in 2007. For the US populations, we took toe-clip

samples from adults (samples stored in Drierite dessicant;

W.A. Hammond Drierite Co., Xenia, OH). We released all

sampled frogs at the site of capture. Sample sizes ranged

from 26 to 63 (mean n = 30, median = 31; Table 1).

Because of the endangered status of the Canadian populations, we could obtain only samples from egg masses in

2007 (n = 7, 7 and 15 egg masses for populations AG, MO

and MA, respectively; Table 1). We had previously

obtained toe clips from 5 adults and eggs from 8 masses

from population AG in 1999. For the egg mass samples we

analyzed only one egg per mass in order to avoid including

siblings in the population sample. Nonetheless, due to the

sampling of eggs and to the small sample sizes for the

Canadian populations, we do not include these populations

when comparing genetic diversity among populations.

Also, we recognize that genetic distances involving these

populations may be slightly inflated relative to values one

would obtain using randomly-sampled adults.

Microsatellite loci—amplification and genotyping

Each population except the 1999 British Columbia sample

was assayed for allelic variation at thirteen microsatellite loci (n = 26–44 individuals scored per population,

excluding the British Columbia egg samples; Table 1).

These loci were developed for R. pretiosa (loci with ‘‘RP’’

prefix) or for R. luteiventris (loci with ‘‘SFC’’ prefix),

which is the sister species of R. pretiosa (Green et al. 1997;

Hillis and Wilcox 2005; Funk et al. 2008). PCR amplifications (25 ll) were carried out with the components and

conditions as described in Monsen and Blouin (2004), but

with locus-specific annealing temperatures and fluorescently-labeled forward primers (see Appendix A for primer

sequences). One locus (RP385) would not amplify cleanly

in the samples from the Klamath Basin (JC, KW, KE, AR,

WR, and BL populations), but gave clean, scorable bands

from all other populations. Therefore, we removed this

locus before estimating genetic structure among the entire

set of 23 populations. We used all 13 loci to examine

genetic structure within groups of populations other than

the Klamath Basin. Another locus (RP15) is a polymorphic

dinucleotide (AT)n repeat locus in the Klamath Basin, but

is monomorphic for a single allele in all other populations.

Sequencing revealed that most of the repeats were lost to a

large deletion in those other populations. Therefore, we

excluded this locus as well as RP385 when comparing

genetic diversities among all populations.

Microsatellite loci analysis

We tested for deviations from Hardy–Weinberg and genotypic equilibrium within populations using GENEPOP

version 3.2a (Raymond and Rousset 1995). To estimate

genetic diversity within populations, we calculated average

heterozygosity (He) and allelic richness (AR). These measures were calculated using data from the 11 loci that were

homologous in all populations. Allelic richness for each

population was estimated at a common sample size of 15 via

rarefaction (hereafter AR15) using the program POPULATIONS (Langella 1999). This sample size reflects the

minimum number of individuals in a population that were

scored for all 11 loci (i.e. had no missing data). Genetic

diversity was not estimated for the three Canadian populations (AG, MO, MA) or for the KW population due to small

sample sizes and/or missing data for these populations.

We used information from three methods to identify the

hierarchical pattern of genetic structure in this species: (1)

a STRUCTURE analysis (Pritchard et al. 2000), (2) a

bootstrapped neighbor-joining tree based on Nei’s unbiased

genetic distance (Nei 1978) between pairs of populations

(POPULATIONS software, Langella 1999), and (3) a

principle coordinates analysis (PCoA) of genetic distances

between individuals (Smouse and Peakall 1999) using

GenAlEx (Peakall and Smouse 2006).

Assuming that we would find a hierarchical pattern of

genetic structure, we first performed the STRUCTURE

analysis using the entire dataset of 685 individuals with K

(the hypothesized number of distinct genetic groups) from 1

to 15. We then carried out separate analyses on each major

group identified by the first analysis (K from 1 to 10). For

each value of K we carried out 20 independent runs under

the correlated allele frequencies model allowing admixture.

Each run had a total of 1 9 106 iterations with a burn-in of

5 9 104 iterations. For each value of K, we calculated the

mean and standard deviation of ln Pr(X|K) (the estimated

likelihood of K) across the 20 runs. We also used the DK

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method of Evanno et al. (2005) as an aid in identifying the

most likely number of genetic groups at each level of the

hierarchy.

To quantify the overall degree of genetic differentiation

among populations in a way that is comparable with that in

other studies, we estimated FST (Weir and Cockerham

1984) and Hedrick’s (2005) standardized G0

ST within each

of the major genetic groups that were identified by the

STRUCTURE analysis. For each group of populations that

we considered to be still connected by gene flow (Klamath

Basin group and Central Cascades group), we performed

Mantel tests (with 1,000 permutations) between the matrix

Table 1 Locality information for the sampled R. pretiosa populations

Locality Code Lat. Long. Elev.

(m)

n

(mtDNA)

SSCP results n (MS) He AR AR15 Genetic group

British Columbia, CA

Aldergrove (1999) AG 49.072 -122.473 116 13 13 type Ja – – – – British Columbia

Aldergrove (2007) AG 49.072 -122.473 116 – 7

Mountain Slough MO 49.265 -121.691 17 – 7 – – – British Columbia

Maria Slough MA 49.238 -121.848 18 – 15 – – – British Columbia

Washington, USA

Dempsey Creek DC 46.967 -123.000 42 34 34 type Ab 30 0.47 3.15 3.07 Chehalis Drainage

Beaver Creek BC 46.883 -122.917 77 26 26 type Ac 26 0.48 3.38 3.27 Chehalis Drainage

Trout Lake TL 46.017 -121.533 596 37 37 type C 36 0.17 2.15 1.91 Columbia

Drainage

Conboy Wildlife

Refuge

CB 45.950 -121.317 555 48 48 type C, 1 type Ad 39 0.50 4.38 3.98 Columbia

Drainage

Oregon, USA

Camas Prairie CP 45.137 -121.569 962 25 25 type D 29 0.14 1.69 1.64 Camas Prairie

Hosmer Lake HL 43.970 -121.773 1518 30 26 type A, 4 type G 31 0.24 1.92 1.87 Central Cascades

Little Cultus Lake LC 43.803 -121.874 1451 – – 34 0.25 2.08 2.02 Central Cascades

Little Lava Lake LL 43.911 -121.757 1445 43 34 type A, 9 type G 37 0.22 1.85 1.78 Central Cascades

Wickiup Reservoir WI 43.700 -121.771 1325 30 30 type A 32 0.24 1.85 1.85 Central Cascades

Sunriver SR 43.880 -121.446 1267 63 40 type A, 23 type B 38 0.29 2.31 2.28 Central Cascades

La Pine LP 43.683 -121.516 1282 – – 32 0.28 2.54 2.36 Central Cascades

Davis Lake DL 43.636 -121.857 1346 30 30 type A 31 0.32 2.38 2.38 Central Cascades

Big Marsh BM 43.392 -121.954 1443 63 63 type A 29 0.22 2.31 2.29 Central Cascades

Gold Lake GL 43.633 -122.043 1466 32 32 type A 31 0.20 1.85 1.75 Central Cascades

Jack Creek JC 43.151 -121.537 1497 30 30 type A 30 0.38 2.75 2.54 Klamath Basin

Klamath Marsh East KE 42.963 -121.586 1381 – – 34 0.46 3.08 2.92 Klamath Basin

Klamath Marsh West KW 42.946 -121.748 1379 – – 27 0.48 3.25 – Klamath Basin

Aspen Ridge AR 42.933 -121.483 1387 – – 31 0.36 2.75 2.61 Klamath Basin

Wood River WR 42.623 -121.971 1263 25 20 type A, 5 type E 40 0.38 4.08 3.44 Klamath Basin

Buck Lake BL 42.252 -122.204 1506 15 12 type A, 3 type I 44 0.30 2.92 2.83 Klamath Basin

Sample sizes (n) for each locality are listed for SSCP (mtDNA) and for microsatellite loci (MS). Microsatellite genetic diversity measures for

each locality are given as expected heterozygosity (He), unrarified allelic richness (AR), and allelic richness rarified to a common sample size of

15 (AR15). The major genetic group assignment is based on the results of the microsatellite-based STRUCTURE analysis and NJ tree

a Two individuals were sequenced for the full mtDNA amplicon (see Online Resource 6) in this population. 1 had an additional substitution in

the right hand region (the J* haplotype; see Fig. 7)

b Three individuals were sequenced for the full mtDNA amplicon (see Online Resource 6) in this population. All three had an additional

substitution in the right hand region (the A* haplotype; see Fig. 7)

c Four individuals were sequenced for the full mtDNA amplicon (see Online Resource 6) in this population. All four had an additional

substitution in the right hand region (the A* haplotype; see Fig. 7)

d Two individuals were sequenced for the full mtDNA amplicon (see Online Resource 6) in this population, one with haplotype C and one with

haplotype A (identified via SSCP). The ‘‘A’’ individual had an additional substitution in the right hand region (the A** haplotype; see Fig. 7)

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of pairwise genetic distances (FST/(FST - 1)) and the

matrix of pairwise geographic distances (ln-transformed

Euclidean distance between pairs of populations) using

GenAlEx (Peakall and Smouse 2006).

Comparison with R. cascadae

R. cascadae is a close congener of R. pretiosa (Hillis and

Wilcox 2005). The two species have similar ranges that run

North–South through Central Oregon and Washington,

although R. cascadae occur mostly on the wetter Western

slopes of the Cascades Mountains, and R. pretiosa on the

drier Eastern slopes (Corkran and Thoms 2006; Fig. 1).

R. cascadae populations appear to be relatively healthy

and abundant in the Cascades Mountains of Oregon and

Washington, and their populations are distributed continuously in this region (Pearl and Adams 2005). We previously showed that R. cascadae displays a strong pattern of

isolation by distance (IBD) among populations that is

apparent at all geographic scales (Monsen and Blouin

2003, 2004). Thus, R. cascadae appears to exist as a continuously-distributed network of small populations that are

connected by migration that probably approximates a

stepping-stone pattern (Kimura and Weiss 1964; Monsen

and Blouin 2004). R. pretiosa are much more patchily

distributed than R. cascadae, presumably owing to their

more aquatic habit and requirement for permanent bodies

of water with both summer feeding zones and thermal

refugia for overwintering (Pearl and Hayes 2004). Here we

compared patterns of isolation by distance (G0

ST vs. geographic distance) in R. pretiosa and in R. cascadae over

the same geographic scale (Fig. 1). The question was

whether R. pretiosa shows a different pattern of IBD than

R. cascadae, as one might expect given the patchier distribution of R. pretiosa populations. For this comparison

we used the 18 R. cascadae populations assayed for

microsatellite variation in Monsen and Blouin (2004).

These R. cascadae samples were scored at 6 microsatellite

loci, 4 of which were also used in this study on R. pretiosa.

Sample sizes were slightly smaller in the R. cascadae

samples (n between 20 and 30 for most of the R. cascadae

populations vs. between 30 and 40 for most of the

R. pretiosa populations). We excluded the three Canadian

populations of R. pretiosa for the purposes of this comparison. We used the t test to test for differences in mean

He, AR15, and h (see below) between the two species, after

using the Shapiro–Wilks test to confirm that the data did

not deviate from normally-distributed.

R. cascadae showed much less genetic differentiation

over any given distance than R. pretiosa (see results

below). To investigate whether this difference results from

differences in migration rates or in effective sizes we

used MIGRATE 3.0 (Beerli and Felsenstein 2001) to

simultaneously estimate interpopulation migration rates

(M = m/l, where m is the migration rate and l is the

mutation rate) and population effective sizes (h = 4Nel,

where Ne = effective size) for clusters of populations of

each species on a small scale (pairwise distances less than

40 km). Here we used the Klamath Basin and the Central

Cascades clusters of populations for R. pretiosa, and three

clusters of R. cascadae populations in which populations

are separated by \25 km. Separate MIGRATE analyses

were performed for each cluster within each species. See

Online Resource 1 for details of the MIGRATE analyses.

Mitochondrial DNA

We also assayed mtDNA variation in individuals from

most of the populations collected in 1999/2000. These

included all four Washington populations, 12 out of the 16

Oregon populations, and the 1999 sample from Aldergrove

(AG) in Canada (Table 1). We assayed for sequence variation in a 298 bp fragment of mtDNA that includes 257 bp

of the 50 end of the ND1 gene, and 41 bp of the 30 end of

the flanking tRNA-leu (positions 4731 to 5291 in the

Xenopus complete mtDNA sequence; Roe et al. 1985).

We used single strand conformation polymorphism

analysis (SSCP; Orita et al. 1989) to identify new variants.

PCR and SSCP protocols followed Monsen and Blouin

(2003; primers in Appendix A). We sequenced at least two

individuals of every putative unique haplotype in both

directions. Homologous sequences from 12 individuals of

R. luteiventris (the sister species of R. pretiosa) were also

included in the mtDNA analysis in order to verify that none

of our samples were actually R. luteiventris (see Online

Resource 2). All R. pretiosa and R. luteiventris haplotypes

are accessioned in GenBank (GU056784–GU056800).

Results

Microsatellite loci analysis

Genetic diversity

With the exception of the 2007 sample from AG (one egg

from each of 7 egg masses), all populations were in Hardy–

Weinberg Equilibrium and showed little genotypic disequilibrium between loci (Online Resource 3). The few

pairs of loci showing significant genotypic disequilibrium

were not consistent across populations. The average

expected heterozygosity (He), allelic richness (AR), and

allelic richness rarified to n = 15 (AR15) of each population

are listed in Table 1. Genetic diversity measures for the

R. cascadae populations are given in Online Resource

4. Levels of genetic variation in populations of R. pretiosa

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are low for a frog (Ficetola et al. 2007), with mean

He = 0.31 (range 0.14–0.50) versus mean He = 0.54 in

R. cascadae (range 0.33–0.74). Similarly, mean AR15 = 2.46

in R. pretiosa (range 1.64–3.98) while mean AR15 = 4.22

in R. cascadae (range 2.17–5.90). In fact, every R. pretiosa

population has at least one locus that was fixed for a single

allele (range 1–7 loci per population fixed for one allele).

The differences in He and AR15 between the two species

were statistically significant (t test; P\0.01). Note that all

but one of the loci used with R. cascadae were actually

cloned from spotted frog (R. pretiosa or R. luteiventris)

libraries, and that four loci overlap between the two datasets. Thus, the difference in genetic diversity between the

two species is unlikely to reflect a bias in choice of loci (if

anything, there would be an ascertainment bias towards

observing lower diversity in R. cascadae). These results

suggest that R. pretiosa has populations that are very small

and/or very isolated, even for a ranid frog.

Major genetic groups

When data from all individuals was analyzed with

STRUCTURE, the highest ln Pr(X|K) was associated with

K = 9 (Online Resource 5). However, the ln Pr(X|K) values increased only incrementally above K = 3. Applying

the DK method of Evanno et al. (2005), K = 3 had the

strongest signature, with K = 9 also showing some weak

support (Online Resource 5). All individuals were consistently assigned to the same populations across the 20 runs

with K = 3, and there was very little inferred admixture.

Based on this analysis, there appear to be three genetic

groups within R. pretiosa at the highest hierarchical level

of population structure (Fig. 2): (1) a northern group

including all populations from Canada south to Camas

Prairie (CP) in northern Oregon, (2) the Central Cascades

group in central Oregon, and (3) the Klamath Basin group

in southern Oregon. These three major groups are particularly obvious in the PCoA plot (Fig. 3).

There is additional substructure within each of the three

main groups (Fig. 2). By far the strongest hierarchical

substructure occurs within the northern group, in which

STRUCTURE revealed three, four or five very distinct

groups, depending on what level of the nested hierarchy is

being examined (Fig. 2). This nested hierarchy is also

clearly evident in the NJ tree (Fig. 4). At K = 5 the two

Columbia Basin populations (TL and CB) are separated,

but we suspect this may reflect, in part, recent genetic drift

in the TL population, which has much lower genetic

diversity than other Northern populations (Table 1).

Therefore, there appear to be four natural subgroups within

the Northern clade that also reflect geographic proximities:

the British Columbia populations (AG, MO, and MA), the

two populations in the Chehalis drainage in WA (DC and

BC), the two populations in the Columbia drainage in WA

(TL and CB), and the Camas Prairie (CP) population in

Oregon. Within this hierarchy the Chehalis and British

Columbia groups form the next natural grouping (e.g. see

Fig. 3 and the K = 3 panel in the Northern group from

Fig. 2 Plots of population assignment of R. pretiosa individuals

based on the STRUCTURE analysis. Each sampling locality is

designated by its two-letter code (Table 1). Results for the analysis

using the entire dataset (n = 685 frogs) is shown in the top bar. When

K was set to 3, all individuals were unambiguously assigned to 3

genetic groups: a Northern group (populations in British Columbia

and WA plus the Camas Prairie population), a Central Cascades

group, and a Klamath Basin group. Subsequent analyses were run on

each of these three major groups. A strong hierarchical structure is

evident in the Northern group, which gives strong support for K = 3,

4 or 5 populations (left side panels). Much weaker structure is evident

in the Cascades Lakes group and in the Klamath Basin group

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Fig. 2), which is again consistent with their geographic

proximity to each other.

STRUCTURE revealed further substructure within the

Central Cascades and within the Klamath Basin groups.

Here there was support for K = 2 or 6 in the Central

Cascades and for K = 2 or 5 in the Klamath Basin, with

the larger numbers mostly identifying individual populations (Fig. 2). However, the structuring within these two

major groups is minor relative to that observed further

North (also evident in the NJ tree, Fig. 4). Thus, we recommend that the Central Cascades group and the Klamath

Basin group each be considered a single group.

The distinctness of the CP population (e.g. Fig. 2) within

the Northern group results in part because it carries several

alleles in high frequency that are absent or rare in other

populations. However, it is also the least genetically diverse

population in the study (He = 0.14, AR = 1.64). Thus,

recent genetic drift may have inflated genetic distances

Fig. 3 Results of principal coordinates analysis on genetic distances

between all R. pretiosa individuals. Top Coordinate 1 vs. 2. Bottom

Coordinate 1 vs. 3. Individuals in each of the major genetic groups

identified in the STRUCTURE analysis and NJ tree form distinct

clusters when plotted for the first three principal coordinates

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between it and the other groups. Recent drift may also

explain the long branch connecting CB to TL (Columbia

drainage) in the NJ tree, as TL has very low diversity

relative to the other Washington populations (Table 1).

The three Canadian populations also appear to be well

differentiated from each other in the NJ tree. However,

the genetic distances among these populations are almost

certainly inflated by the small sample sizes and the fact

that we sampled egg masses (gametes from one season’s

breeders), rather than frogs from the entire population’s

age structure.

Genetic differentiation among populations within major

groups

There is substantial allele frequency variation among populations on a local scale (Appendix B). Considering each of

the three major groups at the highest hierarchical level,

global FST is 0.23 among the Central Cascades populations,

0.21 among the Klamath Basin populations, and 0.53

among the northern populations (or FST = 0.45 if we

exclude the CP population). The corresponding G0

ST values

for these groups were 0.31, 0.34, and 0.77, respectively.

Fig. 4 Bootstrapped neighborjoining tree based on pairwise

genetic distances among

R. pretiosa populations. The

distance measure is Nei’s

unbiased genetic distance

(Nei 1978). The six major

genetic groups are labeled.

Bootstrap values are shown on

branches

Fig. 5 Plots of pairwise genetic versus geographic distance for

R. pretiosa (left) and R. cascadae (right). Genetic distances between

population pairs are measured as G0

ST (Hedrick 2005). R. pretiosa

shows very high differentiation between populations at all but the

shortest geographic distances, suggesting that gene flow is very

limited between most populations. R. cascadae, on the other hand,

more closely fits an isolation-by-distance pattern at both small and

large geographic scales. Populations of R. cascadae are probably

interconnected in a stepping-stone pattern of gene flow, whereas

R. pretiosa populations are mostly isolated

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Pairwise tests for differences in global FST between the

three groups were highly significant for comparisons

between the Northern group and each of the other two

groups (P\0.01; based on 5,000 permutations performed

in FSTAT), but not between the Central Cascades and

Klamath Basin groups (P = 0.742; FSTAT permutation

test). Mantel tests showed a significant correlation between

genetic (FST/(FST - 1)) and geographic distances for populations in the Klamath Basin (r = 0.703, P = 0.008), but

not for populations in the Central Cascades (r = 0.102,

P = 0.293). This latter result is in sharp contrast to the

pattern in R. cascadae, in which there is strong IBD over

similar geographic scales (Monsen and Blouin 2004). Plots

of G0

ST versus geographic distance for each species over

their entire ranges also show very different patterns of IBD

(Fig. 5; similar pattern obtained using FST, not shown).

R. cascadae shows a more continuous pattern of IBD over

all geographic scales. Although R. pretiosa populations are

not obviously less connected at the smallest geographic

scales (e.g.\80 km), at larger scales they show the signature of complete isolation (Hutchison and Templeton 1999).

Comparison of gene flow and effective sizes between

R. pretiosa and R. cascadae

The MIGRATE analysis revealed two interesting patterns.

First, R. pretiosa populations have slightly lower long-term

effective sizes (t test, P\0.01). Average h = 0.87 in

R. pretiosa, versus average h = 1.04 in R. cascadae, a 1.2-

fold difference (Fig. 6). These values of h would correspond

to long-term effective sizes of 435 and 525, given a typical

vertebrate microsatellite mutation rate of l & 5 9 10-4

(Goldstein and Schlo ̈tterer 1999). However, this difference

in h is not sufficient to explain the difference between

species in observed genetic diversity (assuming the two

species have similar mutation rates). A 2.6-fold difference

in effective sizes would be required to explain the difference in average heterozygosities of 0.31 and 0.54, assuming

mutation-drift equilibrium in closed populations and an

infinite alleles model of mutation (Nei 1987, p. 375; e.g.

Ne’s of 112.3 and 587.0 if l & 5 9 10-4

). An approximately 3.4-fold difference in effective sizes would be

required under a stepwise mutation model (Nei 1987,

p. 379; e.g. Ne’s of 68.8 and 232.8). That the MIGRATE

results are reasonable is suggested by a comparative study

of short-term Ne estimated via the linkage disequilibrium

and temporal methods in populations of four species of

ranid frog (Phillipsen, unpub. data). In that dataset

R. pretiosa effective sizes were again only slightly smaller

than those of R. cascadae. Thus, the higher heterozygosities

in R. cascadae populations probably result more from

higher gene flow than from larger effective sizes.

The second interesting result from the MIGRATE analysis is that for populations of both species separated by

greater than about 10 km, the estimated migration rates are

remarkably low—on the order of the mutation rate (Fig. 6).

Gene flow appears to be much higher between populations

separated by less than *10 km (see also Monsen and Blouin

2004), so gene flow over larger distances probably occurs by

a stepping stone process. But most R. pretiosa populations

are farther apart than 10 km, and they are much less continuously distributed over the species-wide landscape than

are populations of R. cascadae. So again, we hypothesize

that low connectivity owing to more widely spaced populations is the key factor causing R. pretiosa to have less diverse

populations and greater genetic structure than R. cascadae.

Fig. 6 MIGRATE results: oneway migration rates (M = m/l)

plotted against pairwise

geographic distances on a small

geographic scale for

R. cascadae populations (filled

circles) and for R. pretiosa

populations (open circles).

Notice that migration rates are

similar for the common pairwise

geographic range over which

the two species were both

sampled (*5 to 25 km), and

that migration rate is extremely

small in both species beyond

about 10 km pairwise distance

(i.e. m & l). Inset panel shows

estimated h (4Ne l) for each

population. Average. h = 0.87

in R. pretiosa and 1.05 in

R. cascadae

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Mitochondrial DNA analysis

All R. pretiosa sequences were more closely related to each

other than to the sequences from R. luteiventris (Fig. 7; see

also Funk et al. 2008). We found 8 unique R. pretiosa

haplotypes via SSCP. One haplotype, ‘‘A’’, was found in

Washington, the central Cascades and Klamath Basin. To

explore whether this was really a single, wide-spread allele,

we sequenced an additional 263 bp of ND1 30 of the SSCP

fragment in representative ‘‘A’’ individuals from throughout Washington and Oregon and in representatives of each

of the other 7 haplotypes (see Online Resource 6 for more

information). We found three new haplotypes (indicated by

asterisks in Fig. 7A). In particular, the ‘‘A’’ allele in

Washington does indeed appear to be distinct from the ‘‘A’’

allele in Oregon (Fig. 7B; Online Resource 6).

We observed a single haplotype in most populations, and

at most two haplotypes, despite large sample sizes assayed

per population (n of up to 63 individuals in some samples,

with most sample sizes [30; Table 1). Also, several

private alleles (i.e. found in only a single population) were

found in the Central Cascades and Klamath Basin metapopulations, and these were at very high frequencies

(20–36%; Fig. 7A). These private haplotypes all appear to

be single-step mutations derived from the ancestral ‘‘A’’

haplotype (Fig. 7B). Haplotypes that originate in a population and drift to high frequency without spreading to nearby

populations indicate that these populations are connected by

very small numbers of migrants per generation (Slatkin and

Barton 1989). This result reinforces the microsatellite-based

conclusion that gene flow among populations has been very

low, even on a small geographic scale.

Fig. 7 A Relative frequencies of mtDNA alleles identified by SSCP

and sequencing (sample sizes and allele counts in Table 1). Note the

high frequencies of the private alleles. Asterisked alleles were

identified only by sequencing (e.g. ‘‘A*’’ and ‘‘A**’’ appear to be

‘‘A’’ alleles by SSCP; see Online Resource 6). All 7 sequenced alleles

from DC and BC were ‘‘A*’’, so we assume that all the ‘‘A’’ alleles

were ‘‘A*’’, but cannot be sure. The relative frequency of ‘‘J’’ and

‘‘J*’’ in AG was not determined. B Parsimony network of all mtDNA

alleles identified by sequencing in this study. Color codes match the

alleles in panel A

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Discussion

Major genetic groups

Synthesizing the results of the STRUCTURE analyses, the

neighbor-joining tree, and the PCoA, we conclude that

there are six major population groups within R. pretiosa.

Four of these groups—British Columbia, Chehalis drainage, Columbia drainage, and Camas Prairie—are related

and form the larger ‘‘northern’’ group. Within this Northern

hierarchy, the Chehalis and British Columbia populations

form the next natural grouping, which seems reasonable

given their proximity and use of similar lowland marsh

habitat. The uniqueness of the Camas Prairie population

(e.g. Fig. 2) is supported not only by its very different

allele frequencies, but also by its unique mtDNA haplotype

and by the three microsatellite alleles (one at each of three

loci) that were found only in the Camas Prairie population.

This population thus appears to be the sole remaining

representative of a distinct genetic group that once existed

in northern Oregon.

Genetic diversity within populations

It is not unusual for an amphibian to exhibit low withinpopulation genetic diversity, relative to other vertebrates

(Sjo ̈gren 1991; Rowe et al. 1999; Spear et al. 2005;

Allentoft et al. 2009). However, our comparison between

R. cascadae and R. pretiosa populations highlights that

R. pretiosa populations have low diversity even for a ranid

frog (e.g. average He was 0.31 for R. pretiosa in our study,

whereas most microsatellite-based studies of ranid frogs

have reported values higher than 0.40; Newman and Squire

2001; Zeisset and Beebee 2003; Brede and Beebee 2004;

Hoffman and Blouin 2004; Funk et al. 2005). Whether low

diversity results mostly from isolation or from small population sizes, or both, can be difficult to disentangle. In this

case, however, the results from MIGRATE show that

individual populations of R. pretiosa have only slightly

smaller estimates of long-term effective size than populations of R. cascadae. Furthermore, gene flow is extremely

small beyond about 10 km, a distance much smaller than

that which separates most R. pretiosa populations. Therefore, we hypothesize that lower connectivity is an important cause of the lower genetic diversity within R. pretiosa

populations. In other words, because R. cascadae are much

more continuously distributed in their habitat than are

R. pretiosa, unsampled R. cascadae populations between

our sampled sites probably keep those sites linked by a

constant trickle of stepping stone migration that maintains

genetic diversity. Such intervening stepping stone populations do not exist for R. pretiosa.

There is a range in diversities among the extant

R. pretiosa populations (Table 1). It is interesting to note

that, despite their greater isolation, three of the Washington

populations (DC, BC, and CB) have higher genetic diversity than populations from the Central Cascades or Klamath Basin groups. One explanation is that the Washington

populations exist at much lower elevations, and so may

maintain larger population sizes than the montane populations (indeed, the CB population has yearly egg mass

counts in the thousands; M. Hayes, pers. comm.). A similar

difference in diversity between high and low elevation

populations was also observed in R. luteiventris (Funk et al.

2005).

Gene flow and genetic differentiation

among populations

Genetic subdivision is very high in R. pretiosa relative to

that in R. cascadae. We observed five very distinct, historical groups within R. pretiosa over the same geographic

range from Oregon to Washington in which R. cascadae

shows a classic pattern of isolation by distance (Monsen

and Blouin 2003). We see the same pattern of higher differentiation in R. pretiosa on a smaller scale. For example,

we obtained low estimates of gene flow, and no pattern of

IBD within the Central Cascades metapopulation, even

though these populations still inhabit relatively pristine

habitat in national forests. R. cascadae, in contrast, shows

strong IBD at this geographic scale (Monsen and Blouin

2004). The high frequency of mtDNA private alleles in the

two R. pretiosa metapopulations also suggests a history

of historical (rather than recent) isolation among individual populations. Thus, something about the biology of

R. pretiosa predisposes it to substantial fragmentation,

even in relatively undisturbed habitat. Again, we hypothesize that low connectivity, rather than small effective

sizes, is the primary explanation. R. pretiosa’s highly

aquatic habit seems like the obvious cause of this low

connectivity. For example, the distinctness of the Klamath

Basin group from populations to the North is particularly

striking when one considers that the southernmost Central

Cascades population (BM) and the northernmost Klamath

Basin population (JC) are only 43 km apart. A low-gradient divide between the Deschutes River and Klamath River

watersheds is all that separates these two populations

topographically. Note also that all the major groups identified in this study correspond to distinct watersheds. Thus,

connectivity between R. pretiosa populations probably

depends more on the local distribution and connectivity of

streams, rivers, and lakes, than on distance per se (Hayes

1997; Cushman and Pearl 2007).

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Conservation Implications

Our data suggest that the six major genetic groups within

R. pretiosa have been reproductively isolated for many

generations, certainly pre-dating European influences.

These groups also occupy different habitats (Pearl and

Hayes 2004). For example, the Northernmost populations

are found in lowland marshes, and the Central Cascades

populations are found in high elevation lakes. These six

groups thus seem like obvious candidates for designation as

evolutionarily significant units (ESUs; Crandall et al. 2000)

or distinct population segments (DPSs; Federal Register

1996) for the purposes of conservation management, Thus it

would be prudent to keep track of the group membership of

any frogs used in captive breeding programs, reintroductions, or translocations. Knowing the collection locality of a

frog will in most cases be enough to determine its group

membership. However, if uncertainties arise, the molecular

markers used in this study could be used to unambiguously

assign individuals to their respective groups.

We observed a wide range of genetic diversities among

R. pretiosa populations. Whether low diversity per se is a

threat to any of these populations has not been demonstrated. Indeed, many of these populations persist despite

what appear to be remarkably low levels of microsatellite

DNA (and presumably genome-wide) diversity. Nevertheless, a general correlation between population fitness and

genetic marker diversity has been demonstrated in a

number of species (Reed and Frankham 2003), and studies

suggest that this correlation is true for amphibians as well

(Lesbarre`res et al. 2005; Rowe et al. 1999; Andersen et al.

2004). So given the isolation of many R. pretiosa populations, it would seem reasonable to focus some management

effort on minimizing future genetic drift and inbreeding by

expanding the sizes of key populations. This is probably

best accomplished by improving, or expanding the available wetland habitats at each site. We recommend against

supplementation of extant populations with captive reared

individuals, as this strategy can actually harm wild populations (Araki et al. 2007, 2009; MciGinnity et al. 2009).

One could also found additional populations in suitable

habitat near their source populations. This strategy was

used to successfully found a new R. pretiosa population in

artificially-constructed ponds in the central Cascades, using

a handful of founders (20 adults and 9 egg masses; Chelgren et al. 2008). Despite the obvious founder effect and

subsequent inbreeding that must have taken place, this

population appears to be thriving.

Low connectivity among populations, in addition to

naturally small population sizes, appears to be an important

cause of low diversity within populations and high differentiation among populations in R. pretiosa. Thus, preserving habitat connectivity among populations to maintain

natural patterns of gene flow should be a high priority in

the management of this species. This recommendation is

most relevant to populations in the Central Cascades and

Klamath Basin, where some gene flow among populations

is likely to still occur. Research to identify what specific

habitat or landscape features enhance or discourage

movement among populations is critically needed.

Finally, special consideration should be given to the

Camas Prairie population. This population appears to be

the only remaining representative of a major genetic group

that is now almost extinct. CP also has the lowest genetic

diversity and is the most geographically isolated population

of R. pretiosa. Egg mass surveys and mark-recapture of

adults in 2001 and 2002 revealed about 30 breeding

females per year and an estimated adult size of\100

(Blouin, unpub. data). Thus, measures to expand the size of

this population or to found additional populations nearby

should be considered.

Acknowledgments We are indebted to the many colleagues who

helped us collect tissue samples. These include M. Hayes, K. McAllister, B. Leonard, J. Bowerman, R. Diehl, P. Brewer, C. Corkran,

C. Bishop, R. Haycock, T. Forbes, S. Reid, D. Ross, P. Govindarajulu,

C. Funk, J. James, M. Gilmore, and G. Knight, A. Bauman, and L.

Granum. Apologies to anyone we omitted. Mark Christie provided

useful comments on early drafts. Thanks also to A. Bauman, and L.

Granum for laboratory work, and to the OSU Center for Genome

Research and Biocomputing for help with genotyping.

Appendix

See Tables 2, 3

Table 2 PCR primers for mtDNA and microsatellite loci

Locus Primers Ta (C) No. of alleles

ND1 (mtDNA) 50

F: MB77 TGGCAGAGCTTGGTTATGCAAAAGA

R: MB129 GGAAATGGGGTTCATATGATTATG

R: MB130 GAAATGGTAAAAGAAGAGAGGGT

Microsatellite loci

RP3 45 12

F: MB165 CCTGAGAGCCATCCAATAAGTGCCA

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Table 2 continued

Locus Primers Ta (C) No. of alleles

R: MB166 GAAAGCAAAACTGGGAAAGTACATA

RP15 50 5

F: MB146 CTTGATACAGTGTGCAAGAGGC

R: MB147 ATACTCGTGATAGGGAGTTT

RP17 50 4

F: MB149 GTCTCTACTTCCATCCAAC

R: MB171 GTGTAGACAAACAAATGAAAGTCAG

RP22c 55 18

F: MB150 ACCCCACCAGCAGAATACAATGA

R: MB198 AGACCAGAGCCAGAGCAACC

RP23 45 11

F: MB152 ACATAGATACAATAGATAGATAGAC

R: MB153 CACAGGAATGTAAAATCTGGCTTTC

RP26 58 10

F: MB154 TAGATAGTAACTCCATACAGTGACA

R: MB155 TAGTCTCCTTACAAAATATAACTGC

RP193 50 9

F: MB224 CCATTTTCTCTCTGATGTGTGT

R: MB225 TGAAGCAGATCACTGGCAAAGC

RP385 50 10

F: MB232 ATTGAAACTTGCGGCTCTCT

R: MB233 GGCATGTGTCCACAATGTAA

RP415 45 9

F: MB234 AAGTTTCATTAAAGCAGATT

R: MB235 GGTATATCTTAGGGTTACCT

RP461 45 7

F: MB236 AAGGGTTTGAGTACAACTAC

R: MB237 GTAACACATGAACTGTCACG

SFC104 58 6

F: MB256 CCCTGTTAAACCACTGGCTAT

R: MB257 GTCATTTCAGAAGTCCGCTTTCAG

SFC120 56 26

F: MB262 AACCCTGGTAGTATGACCAAC

R: MB263 GTGGAACTCCAGTTATGATCC

SFC134 57 21

F: MB276 TGGGAAAAGACTCTGTGGT

R: MB277 AGGAAATGTGTGGAAGCAT

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Table 3 Matrix of pairwise genetic differentiation between R. pretiosa populations

AG MO MA DC BC TL CB CP HL LC LL WI SR LP DL BM GL JC KE KW AR WR BL

AG – 0.33 0.42 0.32 0.30 0.69 0.43 0.73 0.71 0.66 0.71 0.69 0.64 0.62 0.60 0.70 0.71 0.58 0.49 0.49 0.59 0.54 0.61

MO 0.45 – 0.33 0.40 0.39 0.73 0.49 0.78 0.76 0.72 0.76 0.75 0.70 0.69 0.66 0.76 0.77 0.63 0.53 0.53 0.63 0.58 0.64

MA 0.55 0.40 – 0.49 0.48 0.74 0.52 0.78 0.77 0.73 0.77 0.76 0.71 0.71 0.69 0.76 0.77 0.66 0.57 0.58 0.65 0.61 0.66DC 0.56 0.64 0.75 – 0.03 0.53 0.38 0.62 0.56 0.53 0.57 0.55 0.51 0.50 0.48 0.55 0.56 0.55 0.48 0.47 0.55 0.51 0.57BC 0.56 0.63 0.74 0.06 – 0.51 0.37 0.60 0.56 0.53 0.57 0.56 0.52 0.51 0.48 0.55 0.56 0.54 0.46 0.45 0.53 0.49 0.55

TL 0.90 0.90 0.91 0.78 0.75 – 0.35 0.77 0.72 0.69 0.72 0.71 0.67 0.66 0.62 0.70 0.72 0.69 0.61 0.62 0.69 0.64 0.67CB 0.75 0.77 0.79 0.68 0.67 0.51 – 0.57 0.53 0.49 0.54 0.53 0.48 0.46 0.41 0.49 0.51 0.49 0.39 0.37 0.47 0.42 0.48CP 0.92 0.92 0.92 0.88 0.87 0.93 0.81 – 0.74 0.70 0.75 0.75 0.70 0.70 0.69 0.75 0.76 0.68 0.59 0.62 0.68 0.64 0.67HL 0.94 0.95 0.95 0.83 0.84 0.90 0.78 0.90 – 0.32 0.28 0.21 0.25 0.31 0.24 0.45 0.47 0.64 0.58 0.60 0.65 0.62 0.66LC 0.92 0.94 0.94 0.81 0.82 0.88 0.74 0.89 0.41 – 0.21 0.20 0.11 0.19 0.23 0.19 0.30 0.62 0.56 0.58 0.64 0.61 0.64LL 0.94 0.95 0.95 0.83 0.85 0.90 0.79 0.91 0.36 0.28 – 0.13 0.14 0.24 0.25 0.33 0.40 0.64 0.59 0.61 0.66 0.63 0.67WI 0.93 0.95 0.95 0.83 0.84 0.90 0.79 0.91 0.27 0.26 0.17 – 0.09 0.17 0.24 0.25 0.30 0.61 0.56 0.58 0.64 0.61 0.64SR 0.91 0.93 0.93 0.80 0.82 0.87 0.74 0.89 0.32 0.16 0.18 0.12 – 0.10 0.21 0.18 0.29 0.59 0.53 0.54 0.61 0.58 0.62LP 0.90 0.93 0.93 0.80 0.82 0.86 0.72 0.90 0.42 0.26 0.31 0.22 0.13 – 0.20 0.09 0.14 0.59 0.53 0.54 0.61 0.58 0.62DL 0.90 0.91 0.92 0.78 0.79 0.82 0.65 0.89 0.32 0.31 0.34 0.33 0.29 0.28 – 0.25 0.31 0.59 0.53 0.54 0.61 0.58 0.62BM 0.93 0.95 0.95 0.83 0.84 0.88 0.73 0.91 0.56 0.25 0.41 0.31 0.24 0.12 0.34 – 0.13 0.60 0.54 0.56 0.62 0.59 0.64GL 0.94 0.95 0.95 0.83 0.85 0.89 0.75 0.91 0.59 0.39 0.50 0.38 0.38 0.17 0.41 0.16 – 0.63 0.56 0.59 0.65 0.62 0.67JC 0.95 0.96 0.96 0.94 0.93 0.95 0.86 0.92 0.91 0.89 0.90 0.88 0.88 0.88 0.90 0.86 0.88 – 0.18 0.21 0.26 0.34 0.40KE 0.88 0.88 0.90 0.88 0.85 0.89 0.73 0.86 0.89 0.87 0.88 0.85 0.85 0.84 0.87 0.83 0.84 0.31 – 0.02 0.07 0.12 0.23

KW 0.89 0.88 0.90 0.88 0.86 0.89 0.71 0.87 0.89 0.87 0.89 0.87 0.86 0.85 0.87 0.85 0.86 0.38 0.03 – 0.11 0.09 0.23

AR 0.93 0.92 0.94 0.91 0.90 0.93 0.80 0.90 0.92 0.90 0.92 0.90 0.89 0.88 0.91 0.88 0.90 0.42 0.13 0.19 – 0.19 0.28WR 0.88 0.87 0.89 0.87 0.85 0.88 0.73 0.87 0.90 0.88 0.90 0.87 0.86 0.86 0.89 0.86 0.88 0.57 0.21 0.17 0.30 – 0.12BL 0.90 0.89 0.90 0.89 0.87 0.88 0.76 0.87 0.90 0.88 0.90 0.87 0.87 0.88 0.88 0.86 0.89 0.62 0.38 0.37 0.42 0.19 –Two-letter population codes are as given in Table 1. Pairwise

FST are above the diagonal and

G0ST are below the diagonal. Non-significant values are in bold

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