Genetic diversity of core and peripheral Sitka spruce (Picea sitchensis (Bong.) Carr) populations: implications for conservation of widespread species

BIOLOGICAL CONSERVATION

Biological Conservation 123 (2005) 113–123 www.elsevier.com/locate/biocon

Genetic diversity of core and peripheral Sitka spruce (Picea sitchensis (Bong.) Carr) populations: implications for conservation of widespread species Washington J. Gapare *, Sally N. Aitken, Carol E. Ritland Centre for Forest Gene Conservation, University of British Columbia, 3041-2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 Received 18 June 2004

Abstract This study investigated levels of genetic diversity and population differentiation among Sitka spruce (Picea sitchensis) populations classified as core or peripheral based on ecological niche, and continuous or disjunct based on species distribution. Large numbers of trees (N = 200) were sampled from each of eight populations to evaluate the distribution of rare as well as common alleles across the species range. Codominant alleles for eight sequence-tagged site loci were classified based on frequency and geographic distribution in order to develop appropriate sampling strategies to target specific classes of alleles. An important finding of this study is the similarity in genetic diversity as measured by expected heterozygosity between core populations (mean HE = 0.58) and peripheral populations (mean HE = 0.56). However, there was significant inbreeding in peripheral (FIS = 0.17) but not in core (FIS = 0.03) populations. Large differences in gene flow estimates were observed between core (Nm = 9.0) and peripheral populations (Nm = 3.5). Irrespective of population classification, over 75% of the alleles were common and widespread. Only one allele was classified as rare and localized, and this allele was limited to one core, disjunct and two peripheral, disjunct populations. There was stronger evidence of past bottlenecks in peripheral, disjunct populations than in core, continuous populations. Results are used to suggest sampling strategies for capture of maximum level of genetic diversity and conservation of rare alleles. The conservation of peripheral, particularly disjunct, populations as well as populations in putative glacial refugia may present the best opportunity for conserving rare alleles.
2004 Elsevier Ltd. All rights reserved. Keywords: Genetic diversity; Sitka spruce; Peripheral populations; Disjunct populations; Sampling strategies; Rare alleles; Ex situ gene conservation

1. Introduction Most widespread species of conifers comprise many individuals in many populations occupying wide geographic and ecological niches. Levels of genetic variation

*

Corresponding author. Present address: CSIRO Forestry and Forest Products, Bank Street, Yarralumla, P.O. Box E4008, Canberra, Kingston ACT 2604, Australia. Tel.: +61 2 6281 8327; fax: +61 2 6281 8312. E-mail address: [email protected] (W.J. Gapare). 0006-3207/$ - see front matter
2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2004.11.002

are high, on average, and populations show little genetic differentiation for selectively neutral markers (Hamrick and Godt, 1992; Hamrick and Godt, 1996) conforming to expectations under models of mutation, genetic drift and migration. However, neutral genetic variation is expected to be lower in peripheral and disjunct populations than in core and continuous ones (e.g., Aitken and Libby, 1994; Ledig, 2000). The most obvious reasons are the greater influence of genetic drift and lower levels of gene flow in these typically smaller populations and perhaps a lack of gene flow between core and peripheral populations (Nei et al., 1975; Hartl and Clark, 1997). While

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peripheral, particularly disjunct, populations may have lower levels of genetic diversity, they may also harbour rare alleles or phenotypes of future adaptive value (Lesica and Allendorf, 1995). Peripheral populations experience different abiotic and biotic environments than those occupying environments in the core of the species ecological niche, while disjunct populations are physically separated from continuous populations but may or may not experience similar environments. Geographically peripheral populations often differ significantly from core populations and contribute substantially to geographic variation (Lesica and Allendorf, 1995). Biologists disagree concerning the importance of peripheral populations for evolution of a species and their value for conservation. Peripheral populations have been viewed as unimportant for overall species and range persistence because of their small size, isolated locations and frequent occurrence in suboptimal habitat and the consequent increased likelihood of extinction (Hoffman and Blows, 1994). As such, the relative genetic composition and importance for conservation of core versus peripheral populations have long been a subject of debate (Lesica and Allendorf, 1995). Peripheral, disjunct populations may be particularly important to conservation as they are often genetically divergent and may harbour distinct genotypes or phenotypes important for adaptation to local or new conditions (e.g., Garcia-Ramos and Kirkpatrick, 1997). Range peripheries are thought to be some of the most important areas for speciation, and at least some species have shown range collapse towards the periphery rather than the central portion of their ranges in response to environmental change (Lesica and Allendorf, 1995; Cassel and Tammaru, 2003). A more thorough understanding of how genetic variation in nature is partitioned among peripheral and core populations would contribute much to this debate. Many studies of plants have compared levels of heterozygosity between core and peripheral populations using molecular markers. Peripheral populations often have reduced genetic variation compared to core populations in conifers including Pinus rigida Mill (Guries and Ledig, 1982), Pinus contorta Douglas ex Loudon (Aitken and Libby, 1994), and Pseudotsuga menziesii (Mirb.) Franco (Li and Adams, 1989). However, in some cases, peripheral populations receive sufficient gene flow to have as much variation as core populations; for example, in Picea abies (L.) Karst (Muona et al., 1990), Alnus rubra Bong (Hamann et al., 1998), Pinus strobus L (Beaulieu and Simon, 1994) and Picea mariana [Mill.] (BSP.) (Gamache et al., 2003). Other species have low levels of genetic diversity rangewide, possibly due to bottlenecks in glacial refugia during the Pleistocene, followed by range expansion. Possible examples are red pine (Pinus resinosa Ait; Fowler and Morris, 1977; Walter and Epperson, 2001), eastern white pine (Pinus stro-

bus L.; Rajora et al., 1998) and western redcedar (Thuja plicata D. Don; OÕConnell, 2003). Sitka spruce (Picea sitchensis (Bong.) Carr.) is a suitable model for numerous common, wind-pollinated trees and possibly other plants that have colonized their habitats since the last glaciation. It is an economically and ecologically important native tree species in North America and an important exotic in parts of Europe (Lines, 1987; Harris, 1990; Peterson et al., 1997). It occurs naturally throughout a narrow belt along the Pacific coast of North America over 3000 km from northwest California through Oregon, Washington, British Columbia and up to Alaska (Fig. 1; Harris, 1990). The species is bounded to the west by the Pacific Ocean. In British Columbia and Alaska, the species has mainland as well as large and small island populations. In northern California, the range is more attenuated, then becomes discontinuous. A disjunct population near Fort Bragg, California, marks the southern tip of the speciesÕ current range. On the other hand, Kodiak Island, Alaska, marks the northwestern, advancing front of migration with a population established within the past few centuries. This species has been studied extensively for among and within-population quantitative variation in economic and adaptive traits (summarized

Fig. 1. Native range of Sitka spruce and locations of sampled populations.

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in Peterson et al., 1997); for variation in isozymes (Chaisurisri and El-Kassaby, 1994); and for introgression with Picea glauca in northwestern British Columbia and western Alaska (Yeh and Arnott, 1986; Bennuah et al., 2004). In this study, we address the following questions: (i) how is genetic diversity distributed within and among populations classified as core or peripheral, based on ecological conditions, and continuous or disjunct populations based on geographic distribution of Sitka spruce? (ii) to what extent has the evolutionary history of the species shaped the present genetic diversity and population structure, and (iii) based on (i) and (ii), how can widespread species be sampled to capture allelic diversity for in situ and ex situ conservation and base breeding populations?

2. Materials and methods 2.1. Sampling locations and technique Populations of Sitka spruce were classified as either core or peripheral based on location of the population within the species geographic range (Fig. 1), with core populations found in the center of the range in terms of climatic conditions, and peripheral populations near the current climatic limits of the species to the north or south. The eastern species margin was not sampled as introgression with white spruce is extensive in British Columbia and Alaska. Populations were also classified as continuous or disjunct based on proximity of nearest populations, for a total of four classes (Table 1; Fig. 1). Sites of collections used in this study are indicated in Fig. 1. For each population at each sampling site, several East–West transects 100 m wide were established. Fresh needle tissue (current yearÕs growth) was collected from 200 mature trees that were at least 30 m apart in each population. The overall area sampled for each population was 550 ha (3200 m · 1700 m) except for the Fort Bragg and Qualicum populations, where sampling in each covered well over 800 ha due to the lower density and clustered distribution of Sitka spruce in those locations. The location of each tree sampled was determined

Table 1 Two-way classification of Sitka spruce populations according to ecological and geographic distribution within its range and sampling sites Core populations

Peripheral populations

Continuous

Port McNeill, BC Prince Rupert, BC

Brookings, OR Seward, AL

Disjunct

Qualicum, BC Queen Charlotte Islands, BC

Fort Bragg, CA Kodiak Island, AL

BC, British Columbia; OR, Oregon; AL, Alaska; CA, California.

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on a coordinate grid system using a hand-held Global Positioning System instrument (GPS Garmin Model 12XL). After collection, fresh needles were frozen in liquid nitrogen for transport, then stored at 80 C. 2.2. DNA isolation and PCR amplification Total genomic DNA was extracted from 0.3 to 0.5 g of fresh frozen needle tissue following a modified CTAB procedure (Doyle and Doyle, 1990). DNA samples were amplified using polymerase chain reactions (PCR) with specific primers revealing codominant alleles for eight polymorphic sequence-tagged site loci (Sb16, Sb17, Sb21, Sb29, Sb32, Sb49, Sb60 and Sb62) developed for P. mariana and previously characterized in P. glauca and P. sitchensis (Perry and Bousquet, 1998a,b; Bennuah et al., 2004). Seven of these primers amplify intron-length polymorphisms while Sb29 reveals an exon-length polymorphism (Perry and Bousquet, 1998a,b). PCR products were resolved by electrophoresis on 2% agarose gel in 1· TBE buffer at 140 V for 4–7 h, depending on product length. DNA gels were stained in ethidium bromide, then photographed under UV light and recorded on thermal paper. Allele sizes were estimated by comparison with 100-bp and 1-kb DNA ladders (Invitrogen, Canada). Alleles were numbered in decreasing order from anode to cathode. Alleles were consistently scored when reactions were repeated. 2.3. Data analysis Standard genetic diversity parameters (allele frequencies), average number of alleles per locus (also called allelic richness, AR), observed heterozygosity (HO), and expected heterozygosity (HE) were estimated for each population using GDA version 1.0 (Lewis and Zaykin, 2001). Deviations in genotype frequencies from Hardy–Weinberg expectations were examined at each of the variable loci, and linkage disequilibrium between pairs of loci tested using the v2-test of GDA. GENEPOP (Raymond and Rousset, 1995) was also used to estimate the p-values from exact tests of departure from Hardy–Weinberg equilibrium using the Markov chain method with 1000 iterations (Guo and Thompson, 1992). Neutrality of the STS markers was tested with the Ewen–Watterson test (Manly, 1985) using an empirical distribution of homozygosities for 1000 random neutral samples with a fixed number of alleles and sample size (e.g., Jaramillo-Correa et al., 2001). Measures of fixation indices were calculated using GDA for each allele and locus following methods of Weir and Cockerham (1984), where f, F and h correspond to FIS, FIT and FST, respectively. GENEPOP (Raymond and Rousset, 1995) was used to estimate gene flow (Nm) using the frequency and

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distribution of rare alleles (Slatkin, 1985). Nm values were estimated for core and peripheral populations separately. The unbiased genetic distance (D) among populations according to Nei (1978) was generated from allele frequency data. From the genetic distances, a dendrogram was created using RitlandÕs (1989) approach, where the unweighted pair group method (UPGMA) (Sneath and Sokal, 1973) is used for clustering, then the standard error of branch length at each step is estimated. To test for isolation-by-distance (divergence due to drift and mutation), relationships between unbiased genetic distance measures (Nei, 1978) and geographical distances (estimated from latitudes and longitudes converted to kilometers among sampled populations using the R-software version 4 (Casgrain and Legendre, 2001)) was estimated using MantelÕs tests on matrices of genetic and geographical distances. Results of these tests were standardized to obtain a correlation coefficient whose significance was tested using a Monte-Carlo simulation (1000 permutations). We classified alleles by both frequency (common p P 0.05 or rare p < 0.05) and distribution (widespread over many populations, or localized to just a few) (Marshall and Brown, 1975). Marshall and Brown (1975), Adams (1981) and Brown and Hardner (2000) defined any allele occurring in P0.25 of populations as a widespread allele; otherwise it is a localized allele (in only one or few (<0.25) adjacent populations). However, given that we sampled only eight populations, we defined any allele occurring in P0.50 of populations as a widespread allele; otherwise it was localized. Four classes of alleles emerge from this classification: common, widespread, common, localized; rare and widespread, and rare, localized (Table 2). The program BOTTLENECK (Cornuet and Luikart, 1996) tests for a genetic signature of historical population decline by using mutation-drift equilibrium to compute the distribution of gene diversity expected from the observed number of alleles. Populations without a recent change in size on an evolutionary timescale will be in mutation-drift equilibrium where the expected hetTable 2 The modified Marshall and Brown (1975) two-way classification of allele distribution Allele occurrencea

Widespread (W) P 50% Localized (L) < 50%

Allele frequencyb Common (C) P 0.05

Rare (R) < 0.05

(CW) (CL)

(RW) (RL)

CW: common, widespread; CL: common, localized; RW: rare, widespread; RL: rare, localized. a Allele occurrence refers to the percentage of populations that have the allele in question and proximity of populations. b Allele frequency refers to the frequency of an allele in a population.

erozygosity based on the number of alleles (Heq) will equal the Hardy–Weinberg heterozygosity (HE). This expected heterozygosity (Heq) can be calculated through simulation under either the infinite-alleles model or the stepwise mutation model. We tested populations for a bottleneck signature under both the infinite-alleles model and stepwise mutation model as these two models span the range of possible conditions for sequencetagged site markers. We used the Wilcoxon sign-rank test to test the significance of heterozygosity excess (Cornuet and Luikart, 1996).

3. Results 3.1. Allele frequency distribution All eight loci genotyped were polymorphic and variable in all eight populations of Sitka spruce. Two to six alleles were detected per locus, with a total of 26 alleles across all populations and loci (Table 3). The Ewen–Watterson test for neutrality revealed that no case departed significantly from neutral expectations once the Bonferroni correction for multiple tests was applied. Linkage disequilibrium was not significant among any of the loci. Most of the alleles were widely distributed over the populations, with only two alleles not found in all populations (Table 3). Only one allele (Sb62-4) was classified as rare and localized, detected in only three widely separated, disjunct populations (Fort Bragg, Queen Charlotte Islands and Kodiak) (Table 3; Fig. 1). Certain alleles were rare in certain populations, particularly disjunct populations, but common (frequency P 0.05) in others, for example, alleles Sb16-5, Sb17-3 and -4, Sb32-3 and -4 and Sb62-3 (Table 3). These loci all had at least three alleles per locus. At locus Sb16, allele 6 was rare and widespread, rare in all populations and missing in Qualicum (Table 3). In most cases, alleles 4, 5 and 6 for loci Sb16, Sb17, Sb32 and Sb62 were rare, irrespective of the population classification. In all, over 75% of the alleles were common, widespread (Table 4). No common, localized alleles were detected. Rare, widespread alleles were detected in all population classes and averaged 9% of all alleles (see Table 4). 3.2. Genetic diversity and population genetic structure Allelic richness (AR), observed heterozygosity (HO) and expected heterozygosity (HE) averaged 3.3 ± 0.05, 0.51 ± 0.03, and 0.58 ± 0.04, respectively, over all populations and loci (Tables 5 and 6). Hardy–Weinberg equilibrium was rejected for five of the eight populations (p < 0.05), which showed a deficiency of heterozygotes. FIS values for core, continuous populations (mean FIS = 0.03 averaged over two populations) were not sig-

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Table 3 Allele frequencies for eight loci studied in eight range-wide natural populations of Sitka spruce Populations Locus

Allele

FBRG

BRKS

QUAL

PMCNL

QCI

PRUPT

KDIAK

SEWD

SB16

1 2 3 4 5 6

0.195 0.551 0.191 0.050 0.010 0.003

0.166 0.456 0.155 0.101 0.074 0.048

0.493 0.364 0.063 0.060 0.020 –

0.518 0.229 0.063 0.109 0.058 0.023

0.328 0.232 0.114 0.207 0.100 0.019

0.298 0.344 0.124 0.115 0.109 0.010

0.269 0.479 0.100 0.152 0.031 0.033

0.329 0.421 0.149 0.038 0.018 0.045

SB17

1 2 3 4

0.373 0.569 0.028 0.030

0.518 0.367 0.044 0.071

0.505 0.275 0.110 0.110

0.460 0.278 0.237 0.025

0.477 0.337 0.146 0.040

0.495 0.295 0.152 0.058

0.376 0.325 0.170 0.129

0.358 0.425 0.153 0.065

SB21

1 2

0.943 0.157

0.877 0.123

0.662 0.338

0.812 0.188

0.468 0.532

0.958 0.142

0.827 0.173

0.785 0.215

SB29

1 2

0.793 0.207

0.883 0.117

0.430 0.570

0.625 0.375

0.530 0.470

0.460 0.540

0.441 0.559

0.171 0.829

SB32

1 2 3 4

0.169 0.767 0.028 0.036

0.339 0.270 0.184 0.206

0.439 0.426 0.084 0.051

0.230 0.525 0.153 0.092

0.084 0.661 0.142 0.113

0.100 0.791 0.088 0.021

0.091 0.711 0.091 0.107

0.105 0.708 0.079 0.108

SB49

1 2

0.640 0.360

0.676 0.324

0.687 0.313

0.788 0.212

0.785 0.215

0.554 0.446

0.545 0.455

0.505 0.495

SB60

1 2

0.655 0.345

0.593 0.407

0.596 0.404

0.508 0.492

0.563 0.437

0.598 0.402

0.316 0.684

0.543 0.457

SB62

1 2 3 4

0.501 0.421 0.038 0.040

0.276 0.563 0.161 –

0.238 0.630 0.132 –

0.398 0.375 0.227 –

0.508 0.312 0.170 0.010

0.455 0.368 0.177 –

0.480 0.388 0.107 0.025

0.458 0.449 0.093 –

BRKS, Brookings; FBRG, Fort Bragg; KDIAK, Kodiak; PMCNL, Port McNeill; QCI, Queen Charlotte Islands; QUAL, Qualicum; PRUPT, Prince Rupert; SEWD, Seward. Rare alleles are marked in bold.

Table 4 Classification of alleles based on frequency and geographic distribution in four population classes defined by ecological and geographical distribution of Sitka spruce Class

Source

# Loci

CW (%)

CL (%)

CC

Port McNeill Prince Rupert

8 8

92 92

0 0

RW (%) 8 8

RL (%) 0 0

PC

Brookings Seward

8 8

92 96

0 0

8 4

0 0

CD

Qualicum Queen Charlotte Islands

8 8

96 89

0 0

4 7

0 4

PD

Fort Bragg Kodiak Island

8 8

69 88

0 0

27 7

4 5

Overall mean

8

89

0

9

2

Common, widespread, CW; common, localized, CL; rare, widespread, RW; and rare, localized, RL in eight range-wide natural populations of Sitka spruce by population class: CC = core, continuous; PC = peripheral, continuous; CD = core, disjunct; PD = peripheral, disjunct. Numbers indicate percent alleles by class.

nificant (p = 0.065) but were positive and significant for peripheral populations, both continuous and disjunct (mean FIS = 0.17 over four populations; p < 0.05) (Table 6). Differentiation among populations was low (mean

single-locus FST = 0.03; Table 5). Gene flow (Nm) among populations appears to be moderately high, with estimates of 9.0 migrants per generation in core populations and 3.5 migrants per generation in peripheral

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Table 5 Gene diversity and population structure at eight sequence-tagged-site (STS) polymorphic loci in Sitka spruce populations Locus

na

HO

HS

HT

FIS

FIT

FST

SB16 SB17 SB21 SB29 SB32 SB49 SB60 SB62 Overall SE

6 4 2 2 4 2 2 4 3.3 ±0.05

0.560 0.552 0.483 0.510 0.533 0.450 0.455 0.560 0.510 ±0.030

0.697 0.634 0.464 0.598 0.661 0.438 0.479 0.597 0.558 ±0.040

0.712 0.645 0.481 0.600 0.689 0.457 0.496 0.607 0.580 ±0.040

0.196 0.129 0.040 0.090 0.206 0.028 0.049 0.063 0.097 (0.032; 0.152)a

0.216 0.147 0.003 0.100 0.230 0.021 0.086 0.082 0.122 (0.065; 0.174)a

0.024 0.021 0.042 0.011 0.030 0.048 0.040 0.020 0.030 (0.022; 0.036)a

na, observed number of alleles; HO, observed heterozygosity; HS, expected heterozygosity within populations; HT, total expected heterozygosity; FIS, fixation index over the total populations; FIT, fixation index within population; FST, reduction in fixation index due to differences among populations; GST, gene differentiation among populations (according to Nei, 1978). a Lower and upper limits of bootstrap 95% confidence intervals for the fixation indices and GST values based on 1000 bootstrap resampling over loci.

Table 6 Estimates of within-population genetic diversity parameters for eight natural populations of Sitka spruce Population

AR

R

HO

HE

Port McNeill (CC) Prince Rupert (CC) Mean CC

3.3 ± 0.5 3.3 ± 0.5 3.3 ± 0.5

2 2 2

0.52 ± 0.03 0.57 ± 0.03 0.54 ± 0.03

0.56 ± 0.05 0.56 ± 0.03 0.56 ± 0.04

0.07NS 0.02NS 0.03NS

Brookings (PC) Seward (PC) Mean PC

3.3 ± 0.5 3.3 ± 0.5 3.3 ± 0.5

2 1 2

0.48 ± 0.03 0.49 ± 0.03 0.49 ± 0.03

0.580.04 0.59 ± 0.04 0.59 ± 0.04

0.17* 0.17* 0.17*

Qualicum (CD) Queen Charlotte Islands (CD) Mean CD

3.1 ± 0.4 3.4 ± 0.5 3.3 ± 0.5

1 3 2

0.54 ± 0.03 0.55 ± 0.04 0.55 ± 0.04

0.54 ± 0.03 0.60 ± 0.05 0.57 ± 0.04

0NS 0.08* 0.04NS

Fort Bragg (PD) Kodiak Island (PD) Mean PD

3.4 ± 0.5 3.4 ± 0.5 3.4 ± 0.5

8 3 5

0.48 ± 0.02 0.45 ± 0.03 0.47 ± 0.03

0.53 ± 0.03 0.59 ± 0.04 0.55 ± 0.04

0.09* 0.24* 0.17*

Overall mean ± s.e.

3.3 ± 0.5

3

0.51 ± 0.03

0.58 ± 0.04

0.09*

FIS

CC, core and continuous population; PC, peripheral and continuous population; CD, core and disjunct population; PD, peripheral and disjunct population; AR, mean number of alleles per locus; R, number of rare alleles in a population; HO, observed heterozygosity; HE, expected heterozygosity; FIS, average inbreeding coefficient. NS Not significant after sequential Bonferroni correction (Rice, 1989). Exact test of departure from Hardy–Weinberg equilibrium. * p < 0.05.

populations resulting from SlatkinÕs (1985) rare alleles method. 3.3. Genetic distances and relationships among populations Genetic distances (Nei, 1972) among populations were generally small, averaging 0.03 and ranging from 0.013 between Fort Bragg and Brookings to 0.075 between the Queen Charlotte Islands and Kodiak Island. Fig. 2 depicts the hierarchical structure of genetic relatedness among populations. Pairs of populations sampled from geographically proximal locations (Seward and Kodiak, Fort Bragg and Brookings, Prince Rupert and Queen Charlotte Islands) generally clustered together. Significant clusters included the most northern

populations; Seward and Kodiak Island are the most genetically different from all other populations while the most southern populations, Fort Bragg and Brookings are most similar. Although Prince Rupert and the Queen Charlotte Islands clustered together, this grouping is not significant. The lack of strong geographic patterns was supported by the absence of a significant relationship between pairwise population multilocus FST values and geographical distances between populations (Mantel test r = 0.245, P = 0.09) (Gapare, 2003). 3.4. Test for bottleneck signature In the BOTTLENECK test (Cornuet and Luikart, 1996) under the infinite alleles model, the Wilcoxon sign-rank test indicated an excess of heterozygotes in

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Fig. 2. UPGMA-derived dendrogram showing the clustering of the eight natural populations of Sitka spruce based on the genetic distance of Nei (1978). Thicker line indicates standard error and clusters of populations are significant when branch length is at least twice the error bar. Table 7 Cornuet and Luikart (1996) test for recent bottlenecks in Sitka spruce populations under both the infinite alleles model (IAM) and stepwise mutation model (SMM) Population

IAM

SMM

Port McNeill (CC) Prince Rupert (CC) Brookings (PC) Seward (PC) Qualicum (CD) Queen Charlotte Islands (CD) Fort Bragg (PD) Kodiak Island (PD)

0.001 0.002 0.009 0.005 0.009 0.003 0.022 0.001

0.578 0.230 0.027 0.003 0.027 0.125 0.019 0.001

Significance of heterozygote excess according to the Wilcox sign-rank test under infinite alleles model and stepwise mutation model for each population. Non-significant p-values are indicated in italics.

all population classes over that expected at mutationdrift equilibrium (Table 7), suggesting that the populations went through a bottleneck in the past. However, under the stepwise mutation model the Wilcoxon signrank test did not detect a significant bottleneck signature in either of the core, continuous populations (Port McNeill and Prince Rupert) or in one of the core, disjunct populations (Queen Charlotte Islands). The remaining five populations did, however, show a significant bottleneck pattern under both the stepwise mutation model and the infinite alleles model.

4. Discussion 4.1. Genetic diversity and allele distribution in core and peripheral populations The large population samples used in this study allowed for the assessment of distribution of both common and rare alleles for inferring population history and relatedness, and for designing sampling strategies for conservation. Most alleles, whether locally common or rare, were distributed throughout the range of the species. Over 75% of all alleles were common and widespread in all population classes, while rare, widespread alleles averaged just 9% of all alleles. The single rare,

localized allele represented only 2% of all alleles and was restricted to one core, disjunct and two peripheral, disjunct populations. Sampling more populations, i.e., sampling more than 2 populations in each class could have possibly revealed geographic differentiation among population classes and distribution of rare alleles. However, sampling more populations would have been at the cost of sampling fewer individuals per population class, thus limiting the ability to detect rare alleles within a population and power of analyses on spatial structure (Gapare, 2003). No common, localized alleles were detected in any of the populations. Such alleles are unlikely to occur for selectively neutral markers in the presence of high gene flow. Alleles conferring adaptation to local specific conditions or new alleles with a strong selective advantage would be expected in this class, but the sequence-tagged site markers utilized are mostly the result of indel polymorphisms located in transcribed but untranslated regions of arbitrary genes (Perry and Bousquet, 1998a) and are therefore likely to be essentially selectively neutral (e.g., Jaramillo-Correa et al., 2001). Only the Sb29 polymorphism is located in an exon, with length differences observed in P. sitchensis, P. abies, P. glauca and P. mariana caused by indels in a protein coding region (Perry and Bousquet, 1998a,b; Perry et al., 1999). However, this locus did not differ significantly from neutral expectations in the Ewen–Watterson test. In addition, based upon amplification trials using reference genomic DNA, the loci investigated in this study revealed no null alleles (Perry and Bousquet, 1998a). Gene flow is an important force for the maintenance of genetic diversity. For example, our finding that most of the genetic diversity is within populations is supported by evidence of an appreciable amount of interpopulation gene flow (Nm = 9.0 in core populations) suggesting either that periodic gene exchange among sampled populations is high or that little genetic change has occurred since populations were restricted to Pleistocene glacial refugia. In addition, the presence of the same, rare allele (Sb62-4) in widely separated populations (Fort Bragg, Queen Charlotte Islands and Kodiak), might reflect occasional long-distance dispersal

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by pollen or possibly seeds (e.g., Slatkin, 1985). The gene flow estimates indicate that pollen (and to a lesser extent, seed) dispersal is substantial in core populations. The apparent high levels of gene flow inferred from genetic structure in the core populations are likely to reduce inbreeding. In contrast, gene flow in peripheral populations is three times lower than in core populations. Restricted dispersal may result in offspring establishment near maternal parents in seed shadows and subsequent biparental inbreeding. Population structuring consistent with this phenomenon was observed in peripheral but not core populations, with significant, relatively high coancestry coefficients in peripheral populations between pairs of trees up to 500 m apart (Gapare, 2003). Core and peripheral populations had similar expected heterozygosities. However, there were marked differences in observed heterozygosity (HO) and inbreeding coefficients between core and peripheral populations. Observed heterozygosity was highest in core populations, both continuous and disjunct (mean HO = 0.55 ± 0.03) and lowest in peripheral, disjunct populations (mean HO = 0.47 ± 0.03). This reflects higher inbreeding in peripheral than core populations. The significant inbreeding coefficients in peripheral populations, both continuous and disjunct, but not in core populations (Table 6) are somewhat surprising, given that all populations have similar levels of expected heterozygosity. Heterozygote deficiencies can be caused by: (1) selection against heterozygotes; (2) selectioninduced micro-scale differentiation; (3) inbreeding (selfing), or (4) the Wahlund effect, due to the presence of breeding subunits inside the studied populations (Bush and Smouse, 1992; Sproule and Dancik, 1996). Conifer seeds and seedlings often show a deficiency of heterozygotes, but mature trees (the subjects of the current study) typically either do not deviate significantly from Hardy– Weinberg-equilibrium or show an excess of heterozygotes (e.g., Plessas and Strauss, 1986; Ledig et al., 2000). The high levels of inbreeding in these adult populations may have also resulted from less selection pressure against homozygotes than in core populations such that inbreeding levels are equally reflected in the adult and seedling stages. Gapare (2003) observed a highly structured distribution of alleles and genotypes in peripheral populations and attributed this to offspring establishment near maternal trees and subsequent biparental inbreeding in these typically lower density stands. 4.2. Population structure and differentiation The proportion of total genetic diversity attributable to population differentiation (GST = 0.03) is lower than in many widespread temperate conifers that are windpollinated and primarily outcrossed (Hamrick and Godt, 1996). It is also lower than previous estimates

for Sitka spruce based on isozymes: e.g., Yeh and ElKassaby (1980) reported GST = 0.082 ± 0.016, using eight populations and 10 loci and Chaisurisri and ElKassaby (1994) reported GST = 0.079 ± 0.011 using 10 populations and 13 loci. However, our results are generally consistent with the typical lack of strong population differentiation for selectively neutral, nuclear markers in temperate forest trees. Some usual explanations for this pattern include large population sizes, mating systems close to strict allogamy and pollen or seed dispersion over great distances (Hamrick and Godt, 1992; Le Corre and Kremer, 1998). Consistent with the low GST estimate, genetic distances among populations were relatively low. The UPGMA dendrogram based on NeiÕs unbiased genetic distances (D) between populations partially reflects the geographic relationships between them. For example, one significant cluster included the most northern populations with Seward and Kodiak being the most genetically different from all others. The other significant cluster comprised Fort Bragg and Brookings, the most southerly populations. The clustering of populations may reflect shared ancestral populations or substantial gene flow among populations during the last glacial period. Yeh and El-Kassaby (1980) reiterated that the narrow, attenuated distribution of Sitka spruce and its confinement to maritime habitats leads to the expectation that geographic variation exhibited by molecular markers would predominantly be clinal with respect to latitude. However, this is not the case as demonstrated by the lack of significant relationship between pairwise population multilocus FST values and geographic distance between populations through the Mantel test (Mantel test r = 0.245, P = 0.09). For example, the Fort Bragg and Kodiak populations are clearly geographically separated from the majority of the current species distribution at opposite ends of the range, yet the Mantel test found no significant correlation between the two (r = 0.020) (Gapare, 2003). 4.3. Population history The ability to detect a recent bottleneck depends on factors including mutation rate and predominant mutation type for the loci sampled, in addition to effective population size (Ne) (Cornuet and Luikart, 1996). A reduction in genetic diversity can result from a reduction in Ne, either through a large bottleneck or from successive small events, e.g., during colonization of new areas. We found evidence of bottleneck events in all eight populations under the infinite alleles model, whereas under the stepwise mutation model, three of four core populations (Port McNeill, Prince Rupert and Queen Charlotte Islands) did not show a significant bottleneck signature. A higher heterozygosity level at mutation-drift equilibrium (Heq) is expected under the stepwise mutation

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model than for the infinite alleles model (e.g., Cornuet and Luikart, 1996). Di Rienzo et al. (1994) and Luikart and Cornuet (1998) suggest that most loci probably evolve according to a model intermediate between the extremes of infinite alleles model and stepwise mutation model, hence the actual expected equilibrium heterozygosity (Heq) for a given locus probably lies between the Heq values calculated by these two models. Our results indicate that peripheral populations may have gone through bottlenecks in the past. However, bottleneck-induced heterozygosity excess is transient and likely to be detectable for 0.2–4.0 Ne generations until a new equilibrium is reached at the new Ne (Luikart and Cornuet, 1998). The last glacial period lasted 100,000 years. Sitka spruce reaches reproductive maturity around 25 or 30 years, but generation lengths are typically centuries. Refugial populations of Sitka spruce may have been separated from each other for a maximum of about 100–200 generations, and even fewer in recently colonized areas like Kodiak or for populations with longer generation lengths. It is likely that Sitka spruce has experienced periodic expansions and contractions in both range and effective population sizes. In addition, most conifers, including Sitka spruce harbors overlapping generations, which may increase effective population size. Therefore, it seems unlikely that the bottleneck signature detected under the infinite-allelesmodel is recent, but more likely reflects a bottleneck prior to the last glaciation. The bottleneck signature under the stepwise mutation model in peripheral but not core populations may be evidence of populations that have not yet reached equilibrium since the last bottleneck prior to the last glaciation. The lack of a significant bottleneck signature in the Queen Charlotte Islands and nearby core populations Prince Rupert and Port McNeill is in agreement with the suggestion that Queen Charlotte Islands were glacial refugium for Sitka spruce as well as other plants, animals, and insects (e.g., Soltis et al., 1997; Stone et al., 2002). It is possible that present-day Sitka spruce populations in the northern portion of the range descended largely from refugial populations in the Queen Charlotte Islands (Fig. 1). The similarity in patterns of genetic diversity of co-occurring species can help identify the location of glacial refugia and post-colonization routes (Brunsfield et al., 2001). Isozyme variation in Alnus rubra (red alder, Hamann et al., 1998) and Chamaecyparis nootkatensis (Alaska yellow-cedar, Ritland et al., 2001), and microsatellite variation in Thuja plicata (western redcedar, OÕConnell, 2003) all suggest multiple glacial refugia along the Pacific coast. The evidence of multiple refugia in several species co-occurring with Sitka spruce during the last glaciation, combined with relatively high levels of genetic variation and a lack of much population differentiation over the range of Sitka spruce suggest that if bottlenecks occurred, they were relatively minor,

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and likely predate the last glaciation. Subsequently, populations have been able to rebound to large effective population sizes.

4.4. Conservation genetic implications Results from this study have several implications for the conservation of allelic diversity. While core, continuous populations had comparable levels of genetic diversity to peripheral and disjunct populations based on either expected heterozygosity or allelic richness, there were striking differences among these population classes in spatial genetic structure (Gapare, 2003). This has important implications for size and location of in situ reserves and sampling strategies for ex situ conservation. Small reserves or collections made over a small area are likely to capture a higher portion of current standing genetic variation in core, continuous populations than in peripheral populations due to strong spatial structuring in the latter. For this set of genetic markers, 97% of the total genetic variation is within populations and 3% among populations. This might be interpreted to mean that large numbers of samples from few populations would capture a sufficient amount of the speciesÕ standing genetic variability. However, such a practice would increase the chance of missing rare alleles, particularly in disjunct populations. These populations also express extreme phenotypes for phenological and growth traits related to climatic adaptation (M. Mimura and S.N. Aitken, University of British Columbia, unpublished data). Therefore, samples for establishment of ex situ gene conservation populations should be made from different geographic areas to maximize genetic diversity for ex situ collection and probability of conserving rare alleles despite a low GST estimate. This suggests that these types of populations may be best served by different conservation strategies (e.g., Gapare, 2003). In sampling populations for capture of different classes of alleles, common, widespread alleles are likely to be captured irrespective of the sampling strategy employed; however, sampling strategy is critical for the capture of common, localized alleles in ex situ collections. The capture of rare, widespread alleles will depend on the total collecting effort and not on how samples are allocated among versus within populations. Rare and localized alleles are much more difficult to sample than their more common, more widely distributed counterparts. Sampling in a single population, irrespective of its geographic or ecological distribution will capture common, widespread alleles. No common, localized alleles were detected for the eight loci included in this study. However, such alleles for adaptive loci are of particular interest since these may be responsible for adaptation to local conditions (Brown and Hardner, 2000),

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and patterns of variation detected for neutral genetic markers would not reveal these. The population classification scheme used in this study may be useful for prioritizing populations for conservation. The conservation of disjunct, peripheral populations like Fort Bragg and Kodiak Island may present the best possible solution for preserving rare alleles and unique phenotypes in Sitka spruce, thus should also be a high priority for in situ protection or extensive sampling for ex situ conservation of outlying populations.

Acknowledgements We acknowledge, with great appreciation, funding through the Centre for Forest Gene Conservation, which made this study possible, initially from the Forest Renewal British Columbia and subsequently from the Forest Investment Account, British Columbia, Canada. We thank Don Pigott, Jim Herbers, and Lynn Norton for assistance in the field and Joanne Tuytel with laboratory assistance. We thank two anonymous reviewers whose insightful comments helped clarify this manuscript.

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